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Tully NW, Chappell MC, Evans JK, Jensen ET, Shaltout HA, Washburn LK, South AM. The role of preterm birth in stress-induced sodium excretion in young adults. J Hypertens 2024; 42:1086-1093. [PMID: 38690907 PMCID: PMC11068094 DOI: 10.1097/hjh.0000000000003705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
BACKGROUND Early-life programming due to prematurity and very low birth weight (VLBW, <1500 g) is believed to contribute to development of hypertension, but the mechanisms remain unclear. Experimental data suggest that altered pressure natriuresis (increased renal perfusion pressure promoting sodium excretion) may be a contributing mechanism. We hypothesize that young adults born preterm will have a blunted pressure natriuresis response to mental stress compared with those born term. METHODS In this prospective cohort study of 190 individuals aged 18-23 years, 156 born preterm with VLBW and 34 controls born term with birth weight at least 2500 g, we measured urine sodium/creatinine before and after a mental stress test and continuous blood pressure before and during the stress test. Participants were stratified into groups by the trajectory at which mean arterial pressure (MAP) increased following the test. The group with the lowest MAP trajectory was the reference group. We used generalized linear models to assess poststress urine sodium/creatinine relative to the change in MAP trajectory and assessed the difference between groups by preterm birth status. RESULTS Participants' mean age was 19.8 years and 57% were women. Change in urine sodium/creatinine per unit increase in MAP when comparing middle trajectory group against the reference group was greater in those born preterm [β 5.4%, 95% confidence interval (95% CI) -11.4 to 5.3] than those born term (β 38.5%, 95% CI -0.04 to 92.0), interaction term P = 0.002. CONCLUSION We observed that, as blood pressure increased following mental stress, young adults born preterm exhibited decreased sodium excretion relative to term-born individuals.
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Affiliation(s)
| | - Mark C. Chappell
- Department of Surgery-Hypertension and Vascular Research, Wake Forest University School of Medicine
| | - Joni K. Evans
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine
| | - Elizabeth T. Jensen
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine
| | - Hossam A. Shaltout
- Department of Surgery-Hypertension and Vascular Research, Wake Forest University School of Medicine
- Department of Obstetrics and Gynecology, Wake Forest University School of Medicine
| | - Lisa K. Washburn
- Department of Pediatrics, Wake Forest University School of Medicine
| | - Andrew M. South
- Department of Surgery-Hypertension and Vascular Research, Wake Forest University School of Medicine
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine
- Section of Nephrology, Department of Pediatrics, Wake Forest University School of Medicine, Winston Salem, NC, USA
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Sanderson K, Griffin R, Anderson N, South AM, Swanson JR, Zappitelli M, Steflik HJ, DeFreitas MJ, Charlton J, Askenazi D. Perinatal risk factors associated with acute kidney injury severity and duration among infants born extremely preterm. Pediatr Res 2024:10.1038/s41390-024-03102-w. [PMID: 38438550 DOI: 10.1038/s41390-024-03102-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 01/29/2024] [Accepted: 02/03/2024] [Indexed: 03/06/2024]
Abstract
BACKGROUND We evaluated time-varying perinatal risk factors associated with early (≤7 post-natal days) and late (>7 post-natal days) severe acute kidney injury (AKI) occurrence and duration. METHODS A secondary analysis of Preterm Erythropoietin Neuroprotection Trial data. We defined severe AKI (stage 2 or 3) per neonatal modified Kidney Disease: Improving Global Outcomes criteria. Adjusted Cox proportional hazards models were conducted with exposures occurring at least 72 h before severe AKI. Adjusted negative binomial regression models were completed to evaluate risk factors for severe AKI duration. RESULTS Of 923 participants, 2% had early severe AKI. In the adjusted model, gestational diabetes (adjusted HR (aHR) 5.4, 95% CI 1.1-25.8), non-steroidal anti-inflammatory drugs (NSAIDs) (aHR 3.2, 95% CI 1.0-9.8), and vancomycin (aHR 13.9, 95% CI 2.3-45.1) were associated with early severe AKI. Late severe AKI occurred in 22% of participants. Early severe AKI (aHR 2.5, 95% CI 1.1-5.4), sepsis (aHR 2.5, 95% CI 1.4-4.4), vasopressors (aHR 2.9, 95% CI 1.8-4.6), and diuretics (aHR 2.6, 95% CI 1.9-3.6) were associated with late severe AKI. Participants who had necrotizing enterocolitis or received NSAIDs had longer severe AKI duration. CONCLUSION We identified major risk factors for severe AKI that can be the focus of future research. IMPACT STATEMENT Time-dependent risk factors for severe acute kidney injury (AKI) and its duration are not well defined among infants born <28 weeks' gestation. Over 1 in 5 infants born <28 weeks' gestation experienced severe AKI, and this study identified several major time-dependent perinatal risk factors occurring within 72 h prior to severe AKI. This study can support efforts to develop risk stratification and clinical decision support to help mitigate modifiable risk factors to reduce severe AKI occurrence and duration.
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Affiliation(s)
- Keia Sanderson
- University of North Carolina Department of Medicine-Nephrology, Chapel Hill, NC, USA.
| | - Russell Griffin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nekayla Anderson
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Andrew M South
- Department of Pediatrics, Section of Nephrology, Brenner Children's, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Jonathan R Swanson
- Division of Neonatology, Department of Pediatrics, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Michael Zappitelli
- Department of Pediatrics, Division of Nephrology, Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
- Montreal Children's Hospital, McGill University Health Centre, Montreal, QC, Canada
| | - Heidi J Steflik
- Division of Neonatal-Perinatal Medicine, Department of Pediatrics, Medical University of South Carolina, Charleston, SC, USA
| | - Marissa J DeFreitas
- Division of Pediatric Nephrology, Department of Pediatrics, University of Miami, Miami, FL, USA
| | - Jennifer Charlton
- University of Virginia, Department of Pediatrics, Division of Nephrology, Charlottesville, VA, USA
| | - David Askenazi
- Division of Nephrology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, AL, USA
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Brouwer ECJ, Floyd WN, Jensen ET, O'Connell N, Shaltout HA, Washburn LK, South AM. Risk of Obesity and Unhealthy Central Adiposity in Adolescents Born Preterm With Very Low Birthweight Compared to Term-Born Peers. Child Obes 2024. [PMID: 38387005 DOI: 10.1089/chi.2023.0115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Background: Early-life factors such as preterm birth or very low birthweight (VLBW) are associated with increased cardiovascular disease risk. However, it remains unknown whether this is due to an increased risk of obesity (unhealthy central adiposity) because studies have predominantly defined obesity based on BMI, an imprecise adiposity measure. Objective: Investigate if adolescents born preterm with VLBW have a higher risk of unhealthy central adiposity compared to term-born peers. Study Design: Cross-sectional analysis of data from a prospective cohort study of 177 individuals born preterm with VLBW (<1500 g) and 51 term-born peers (birthweight ≥2500 g). Individuals with congenital anomalies, genetic syndromes, or major health conditions were excluded. Height, weight, waist circumference, skin fold thickness, and dual energy X-ray absorptiometry body composition were measured at age 14 years. We calculated BMI percentiles and defined overweight/obesity as BMI ≥85th percentile for age and sex. We estimated the preterm-term differences in overweight/obesity prevalence and adiposity distribution with multivariable generalized linear models. Results: There was no difference in small for gestational age status or overweight/obesity prevalence. Compared to term, youth born preterm with VLBW had lower BMI z-score [β -0.38, 95% confidence limits (CL) -0.75 to -0.02] but no differences in adiposity apart from subscapular-to-triceps ratio (STR; β 0.18, 95% CL 0.08 to 0.28). Conclusions: Adolescents born preterm with VLBW had smaller body size than their term-born peers and had no differences in central adiposity except greater STR.
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Affiliation(s)
| | - Whitney N Floyd
- Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Elizabeth T Jensen
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Nathaniel O'Connell
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Hossam A Shaltout
- Department of Obstetrics and Gynecology and Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Lisa K Washburn
- Department of Pediatrics, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - Andrew M South
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA
- Section of Nephrology, Department of Pediatrics, Brenner Children's, Wake Forest University School of Medicine, Winston Salem, NC, USA
- Center on Diabetes, Obesity and Metabolism, Wake Forest University School of Medicine, Winston Salem, NC, USA
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South AM, Rigdon J, Voruganti S, Stafford JM, Dabelea D, Marcovina S, Mottl AK, Pihoker C, Urbina EM, Jensen ET. Uric Acid Is Not Associated With Cardiovascular Health in Youth With Type 1 Diabetes: SEARCH for Diabetes in Youth Study. J Clin Endocrinol Metab 2024; 109:e726-e734. [PMID: 37690117 PMCID: PMC10795892 DOI: 10.1210/clinem/dgad534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/17/2023] [Accepted: 09/05/2023] [Indexed: 09/12/2023]
Abstract
CONTEXT Uric acid's role in cardiovascular health in youth with type 1 diabetes is unknown. OBJECTIVE Investigate whether higher uric acid is associated with increased blood pressure (BP) and arterial stiffness over time in adolescents and young adults with type 1 diabetes and if overweight/obesity modifies this relationship. METHODS Longitudinal analysis of data from adolescents and young adults with type 1 diabetes from 2 visits (mean follow up 4.6 years) in the SEARCH for Diabetes in Youth multicenter prospective cohort study from 2007 to 2018. Our exposure was uric acid at the first visit and our outcome measures were the change in BP, pulse wave velocity (PWV), and augmentation index between visits. We used multivariable linear mixed-effects models and assessed for effect modification by overweight/obesity. RESULTS Of 1744 participants, mean age was 17.6 years, 49.4% were female, 75.9% non-Hispanic White, and 45.4% had a follow-up visit. Mean uric acid was 3.7 mg/dL (SD 1.0). Uric acid was not associated with increased BP, PWV-trunk, or augmentation index over time. Uric acid was marginally associated with PWV-upper extremity (β = .02 m/s/year, 95% CI 0.002 to 0.04). The magnitude of this association did not differ by overweight/obesity status. CONCLUSION Among adolescents and young adults with type 1 diabetes, uric acid was not consistently associated with increased BP or arterial stiffness over time. These results support findings from clinical trials in older adults with diabetes showing that lowering uric acid levels does not improve cardiovascular outcomes.
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Affiliation(s)
- Andrew M South
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, NC 27101, USA
- Section of Nephrology, Department of Pediatrics, Brenner Children's, Wake Forest University School of Medicine, Winston Salem, NC 27157, USA
- Cardiovascular Sciences Center, Wake Forest University School of Medicine, Winston Salem, NC 27101, USA
- Center on Diabetes, Obesity and Metabolism, Wake Forest University School of Medicine, Winston Salem, NC 27101, USA
| | - Joseph Rigdon
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, NC 27101, USA
| | - Saroja Voruganti
- Department of Nutrition, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC 27599, USA
| | - Jeanette M Stafford
- Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, NC 27101, USA
| | - Dana Dabelea
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Santica Marcovina
- Northwest Lipid Metabolism and Diabetes Research Laboratories, University of Washington, Seattle, WA 98109, USA
| | - Amy K Mottl
- Division of Nephrology and Hypertension, Department of Medicine, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Cate Pihoker
- Department of Pediatrics, University of Washington School of Medicine and Division of Endocrinology, Seattle Children's Hospital, University of Washington, Seattle, WA 98105, USA
| | - Elaine M Urbina
- The Heart Institute, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH 45229, USA
| | - Elizabeth T Jensen
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, NC 27101, USA
- Department of Medicine, Section of Gastroenterology, Wake Forest University School of Medicine, Winston Salem, NC 27101, USA
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Schiff AF, Deines D, Jensen ET, O'Connell N, Perry CJ, Shaltout HA, Washburn LK, South AM. Duration of Simultaneous Exposure to High-Risk and Lower-Risk Nephrotoxic Antimicrobials in the Neonatal Intensive Care Unit (NICU) and Future Adolescent Kidney Health. J Pediatr 2024; 264:113730. [PMID: 37722552 PMCID: PMC10873056 DOI: 10.1016/j.jpeds.2023.113730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 08/30/2023] [Accepted: 09/13/2023] [Indexed: 09/20/2023]
Abstract
OBJECTIVE To determine whether greater duration of simultaneous exposure to antimicrobials with high nephrotoxicity risk combined with lower-risk antimicrobials (simultaneous exposure) in the neonatal intensive care unit (NICU) is associated with worse later kidney health in adolescents born preterm with very low birth weight (VLBW). STUDY DESIGN Prospective cohort study of participants born preterm with VLBW (<1500 g) as singletons between January 1, 1992, and June 30, 1996. We defined simultaneous exposure as a high-risk antimicrobial, such as vancomycin, administered with a lower-risk antimicrobial on the same date in the NICU. Outcomes were serum creatinine, estimated glomerular filtration rate (eGFR), and first-morning urine albumin-creatinine ratio (ACR) at age 14 years. We fit multivariable linear regression models with days of simultaneous exposure and days of nonsimultaneous exposure as main effects, adjusting for gestational age, birth weight, and birth weight z-score. RESULTS Of the 147 out of 177 participants who had exposure data, 97% received simultaneous antimicrobials for mean duration 7.2 days (SD 5.6). No participant had eGFR <90 ml/min/1.73 m2. The mean ACR was 15.2 mg/g (SD 38.7) and 7% had albuminuria (ACR >30 mg/g). Each day of simultaneous exposure was associated only with a 1.04-mg/g higher ACR (95% CI 1.01 to 1.06). CONCLUSIONS Despite frequent simultaneous exposure to high-risk combined with lower-risk nephrotoxic antimicrobials in the NICU, there were no clinically relevant associations with worse kidney health identified in adolescence. Although future studies are needed, these findings may provide reassurance in a population thought to be at increased risk of chronic kidney disease.
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Affiliation(s)
- Andrew F Schiff
- Department of Pediatrics, Section of Neonatology, Wake Forest University School of Medicine, Winston Salem, NC
| | - Danielle Deines
- University of Otago School of Medicine, Dunedin, New Zealand
| | - Elizabeth T Jensen
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston Salem, NC
| | - Nathaniel O'Connell
- Division of Public Health Sciences, Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston Salem, NC
| | - Courtney J Perry
- Department of Physician Assistant Studies, Wake Forest University School of Medicine, Winston Salem, NC
| | - Hossam A Shaltout
- Department of Obstetrics and Gynecology, Wake Forest University School of Medicine, Winston Salem, NC; Department of Pharmacology and Toxicology, School of Pharmacy, University of Alexandria, Alexandria, Egypt
| | - Lisa K Washburn
- Department of Pediatrics, Section of Neonatology, Wake Forest University School of Medicine, Winston Salem, NC
| | - Andrew M South
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston Salem, NC; Department of Pediatrics, Section of Nephrology, Wake Forest University School of Medicine, Winston Salem, NC.
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Garg PM, Pittman IA, Ansari MAY, Yen CW, Riddick R, Jetton JG, South AM, Hillegass WB. Gestational age-specific clinical correlates of acute kidney injury in preterm infants with necrotizing enterocolitis. Pediatr Res 2023; 94:2016-2025. [PMID: 37454184 PMCID: PMC10937190 DOI: 10.1038/s41390-023-02736-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 06/02/2023] [Accepted: 06/26/2023] [Indexed: 07/18/2023]
Abstract
BACKGROUND To study the gestational age-specific risk factors and outcomes of severe acute kidney injury (AKI) in neonates with necrotizing enterocolitis (NEC). METHODS Retrospective cohort study comparing gestational age (GA)-specific clinical data between infants without severe AKI (stage 0/1 AKI) and those with severe AKI (stages 2 and 3 AKI) stratified by GA ≤27 and >27 weeks. RESULTS Infants with GA ≤27 weeks had double the rate of severe AKI (46.3% vs. 20%). In infants with GA >27 weeks, male sex, outborn, and nephrotoxic medication exposure were associated with severe AKI. On multivariable logistic regression, in infants with GA ≤27 weeks, surgical NEC (OR 35.08 (CI 5.05, 243.73), p < 0.001) and ostomy (OR 6.2(CI 1.29, 29.73), p = 0.027) were associated with significantly higher odds of severe AKI. Surgical NEC infants with GA >27 weeks and severe AKI were significantly more likely to be outborn, have later NEC onset, need dopamine, and have longer hospitalization (158 days [110; 220] vs.75.5 days [38.8; 105]; p = 0.007 than those with non-severe AKI. CONCLUSION In neonates with NEC, surgical intervention was associated with moderate-to-severe AKI in infants with GA ≤27 weeks and with longer hospitalization in infants with GA >27 weeks. IMPACT In both cohorts need for surgery, stoma, cholestasis, and mechanical ventilation were associated with severe AKI; however, the infants with GA <27 weeks had twice the risk of severe AKI than GA >27 weeks group. The longer exposure to nephrotoxic medication and referral need were significant risk factors for AKI in GA >27 weeks group. GA-specific kidney protective and monitoring strategies to prevent AKI and its consequences are needed to improve the clinical outcomes in neonates with NEC. Understanding the risk factors and short- and long-term outcomes unique to different GA groups will help inform those strategies.
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Affiliation(s)
- Parvesh Mohan Garg
- Department of Pediatrics/Neonatology, Atrium Health Wake Forest Baptist Hospital, Wake Forest University School of Medicine, Winston Salem, NC, USA.
- Department of Pediatrics/Neonatology, University of Mississippi Medical Center, Jackson, MS, USA.
| | - Isabella A Pittman
- Department of Pediatrics/Neonatology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Md Abu Yusuf Ansari
- Department of Data Sciences, University of Mississippi Medical Center, Jackson, MS, USA
| | - Chin Wen Yen
- Department of Pediatrics/Neonatology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Robbin Riddick
- Department of Pediatrics/Neonatology, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jennifer G Jetton
- Section of Pediatric Nephrology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest University School of Medicine, Winston Salem, NC, USA
| | - William B Hillegass
- Department of Data Sciences, University of Mississippi Medical Center, Jackson, MS, USA
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
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Perrin EC, Ravi HL, Borra GS, South AM. Prevalence and risk factors of disordered eating behavior in youth with hypertension disorders. Pediatr Nephrol 2023; 38:3779-3789. [PMID: 37195544 PMCID: PMC10189692 DOI: 10.1007/s00467-023-05921-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/08/2023] [Accepted: 02/13/2023] [Indexed: 05/18/2023]
Abstract
BACKGROUND Adolescents with certain health conditions requiring lifestyle management, such as diabetes mellitus, have higher disordered eating behavior (DEB) risk than the general adolescent population, but DEB is underdiagnosed and can lead to adverse health consequences. In youth with other conditions requiring lifestyle counseling such as hypertension (HTN), DEB prevalence and associated risk factors are unknown. We hypothesized that youth with HTN disorders would have higher DEB prevalence than the general adolescent population, and that obesity, chronic kidney disease (CKD), and less specialized lifestyle counseling would be associated with higher DEB risk. METHODS Prospective cross-sectional study of youth aged 11-18 years with HTN disorders. We excluded patients with diabetes mellitus, kidney failure or transplantation, or gastrostomy tube dependence. We collected data via surveys and electronic health record abstraction. We administered the validated SCOFF DEB screening questionnaire. We compared DEB prevalence using a one-sample z-test of proportions (p0 = 0.1) and estimated DEB risk by obesity, CKD, and lifestyle counseling source using multivariable generalized linear models. RESULTS Of 74 participants, 59% identified as male, 22% as Black or African American, and 36% as Hispanic or Latino; 58% had obesity and 26% had CKD. DEB prevalence was 28% (95% CI 18-39%, p < 0.001). CKD was associated with higher DEB prevalence (adjusted RR 2.17, 95% CL 1.09 to 4.32), but obesity and lifestyle counseling source were not. CONCLUSIONS DEB prevalence is higher in youth with HTN disorders and comparable to other conditions requiring lifestyle counseling. Youth with HTN disorders may benefit from DEB screening. A higher resolution version of the Graphical abstract is available as Supplementary information.
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Affiliation(s)
- Ella C Perrin
- Department of Pediatrics, Section of Nephrology, Brenner Children's, Wake Forest University School of Medicine, One Medical Center Boulevard, Winston Salem, NC, 27157, USA
| | - Hanna L Ravi
- Department of Pediatrics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Gagana S Borra
- Department of Pediatrics, Section of Nephrology, Brenner Children's, Wake Forest University School of Medicine, One Medical Center Boulevard, Winston Salem, NC, 27157, USA
| | - Andrew M South
- Department of Pediatrics, Section of Nephrology, Brenner Children's, Wake Forest University School of Medicine, One Medical Center Boulevard, Winston Salem, NC, 27157, USA.
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston Salem, NC, USA.
- Cardiovascular Sciences Center, Wake Forest University School of Medicine, Winston Salem, NC, USA.
- Center for Biomedical Informatics, Wake Forest University School of Medicine, Winston Salem, NC, USA.
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Sperotto F, Gutiérrez-Sacristán A, Makwana S, Li X, Rofeberg VN, Cai T, Bourgeois FT, Omenn GS, Hanauer DA, Sáez C, Bonzel CL, Bucholz E, Dionne A, Elias MD, García-Barrio N, González TG, Issitt RW, Kernan KF, Laird-Gion J, Maidlow SE, Mandl KD, Ahooyi TM, Moraleda C, Morris M, Moshal KL, Pedrera-Jiménez M, Shah MA, South AM, Spiridou A, Taylor DM, Verdy G, Visweswaran S, Wang X, Xia Z, Zachariasse JM, Newburger JW, Avillach P. Clinical phenotypes and outcomes in children with multisystem inflammatory syndrome across SARS-CoV-2 variant eras: a multinational study from the 4CE consortium. EClinicalMedicine 2023; 64:102212. [PMID: 37745025 PMCID: PMC10511777 DOI: 10.1016/j.eclinm.2023.102212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 08/22/2023] [Accepted: 08/29/2023] [Indexed: 09/26/2023] Open
Abstract
Background Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. It remains unclear how MIS-C phenotypes vary across SARS-CoV-2 variants. We aimed to investigate clinical characteristics and outcomes of MIS-C across SARS-CoV-2 eras. Methods We performed a multicentre observational retrospective study including seven paediatric hospitals in four countries (France, Spain, U.K., and U.S.). All consecutive confirmed patients with MIS-C hospitalised between February 1st, 2020, and May 31st, 2022, were included. Electronic Health Records (EHR) data were used to calculate pooled risk differences (RD) and effect sizes (ES) at site level, using Alpha as reference. Meta-analysis was used to pool data across sites. Findings Of 598 patients with MIS-C (61% male, 39% female; mean age 9.7 years [SD 4.5]), 383 (64%) were admitted in the Alpha era, 111 (19%) in the Delta era, and 104 (17%) in the Omicron era. Compared with patients admitted in the Alpha era, those admitted in the Delta era were younger (ES -1.18 years [95% CI -2.05, -0.32]), had fewer respiratory symptoms (RD -0.15 [95% CI -0.33, -0.04]), less frequent non-cardiogenic shock or systemic inflammatory response syndrome (SIRS) (RD -0.35 [95% CI -0.64, -0.07]), lower lymphocyte count (ES -0.16 × 109/uL [95% CI -0.30, -0.01]), lower C-reactive protein (ES -28.5 mg/L [95% CI -46.3, -10.7]), and lower troponin (ES -0.14 ng/mL [95% CI -0.26, -0.03]). Patients admitted in the Omicron versus Alpha eras were younger (ES -1.6 years [95% CI -2.5, -0.8]), had less frequent SIRS (RD -0.18 [95% CI -0.30, -0.05]), lower lymphocyte count (ES -0.39 × 109/uL [95% CI -0.52, -0.25]), lower troponin (ES -0.16 ng/mL [95% CI -0.30, -0.01]) and less frequently received anticoagulation therapy (RD -0.19 [95% CI -0.37, -0.04]). Length of hospitalization was shorter in the Delta versus Alpha eras (-1.3 days [95% CI -2.3, -0.4]). Interpretation Our study suggested that MIS-C clinical phenotypes varied across SARS-CoV-2 eras, with patients in Delta and Omicron eras being younger and less sick. EHR data can be effectively leveraged to identify rare complications of pandemic diseases and their variation over time. Funding None.
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Affiliation(s)
- Francesca Sperotto
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Alba Gutiérrez-Sacristán
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Simran Makwana
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Xiudi Li
- Department of Biostatistics, Harvard School of Public Health, 677 Huntington Ave, Boston, MA 02115, United States
| | - Valerie N. Rofeberg
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Florence T. Bourgeois
- Department of Pediatrics, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Gilbert S. Omenn
- Dept of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, & Public Health, University of Michigan, 2017 Palmer Commons, Ann Arbor, MI 48109-2218, United States
| | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, 100-107 NCRC, 2800 Plymouth Road, Ann Arbor, MI 48109, United States
| | - Carlos Sáez
- Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politécnica de Valéncia, Camino de Vera S/N, Valencia 46022, Spain
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Emily Bucholz
- Department of Cardiology, Children's Hospital Colorado, University of Colorado Anschutz, 13123 E. 16th Ave, Aurora, CO 80045, United States
| | - Audrey Dionne
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Matthew D. Elias
- Division of Cardiology, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104, United States
| | - Noelia García-Barrio
- Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Tomás González González
- Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Richard W. Issitt
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, Great Ormond Street, London WC1N 3JH, United Kingdom
| | - Kate F. Kernan
- Department of Critical Care Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15213, United States
| | - Jessica Laird-Gion
- Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Sarah E. Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, NCRC Bldg 400, 2800 Plymouth Road, Ann Arbor, MI 48109, United States
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States
| | - Taha Mohseni Ahooyi
- Department of Biomedical Health Informatics, The Children's Hospital of Philadelphia, Roberts Building, 734 Schuylkill Ave, Philadelphia, PA 19146, United States
| | - Cinta Moraleda
- Pediatric Infectious Disease Department, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, United States
| | - Karyn L. Moshal
- Department of Infectious Diseases, Great Ormond Street Hospital for Children, Great Ormond Street, London WC1N 3JH, United Kingdom
| | - Miguel Pedrera-Jiménez
- Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n, Madrid 28041, Spain
| | - Mohsin A. Shah
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, DRIVE, 40 Bernard St, London WC1N 1LE, United Kingdom
| | - Andrew M. South
- Department of Pediatrics-Section of Nephrology, Brenner Children’s, Wake Forest University School of Medicine, Medical Center Boulevard, Winston Salem, NC 27157, United States
| | - Anastasia Spiridou
- Data Research, Innovation and Virtual Environments, Great Ormond Street Hospital for Children, DRIVE, 40 Bernard St, London WC1N 1LE, United Kingdom
| | - Deanne M. Taylor
- Department of Biomedical Health Informatics, The Children's Hospital of Philadelphia, United States
- The Department of Pediatrics, University of Pennsylvania Perelman Medical School, 3601 Civic Center Blvd, 6032 Colket, Philadelphia, PA 19104, United States
| | - Guillaume Verdy
- IAM Unit, Bordeaux University Hospital, Place amélie rabat Léon, Bordeaux 33076, France
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, United States
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, 3501 5th Avenue, BST-3 Suite 7014, Pittsburgh, PA 15260, United States
| | - Joany M. Zachariasse
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
| | - Jane W. Newburger
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, United States
- Computational Health Informatics Program, Boston Children's Hospital, 300 Longwood Avenue, Boston, MA 02115, United States
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9
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Floyd WN, Beavers DP, Jensen ET, Washburn LK, South AM. Association of antenatal corticosteroids with kidney function in adolescents born preterm with very low birth weight. J Perinatol 2023; 43:1038-1044. [PMID: 37160975 PMCID: PMC10524661 DOI: 10.1038/s41372-023-01688-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/15/2023] [Accepted: 04/26/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVE Investigate if antenatal corticosteroids (ANCS) are associated with worse kidney function in adolescence and if greater adiposity magnifies this association. STUDY DESIGN Prospective cohort of 162 14-year-olds born preterm with very low birth weight (<1500 g). Outcomes were estimated glomerular filtration rate (eGFR) and first-morning urine albumin-to-creatinine ratio (UACR). We used adjusted generalized linear models, stratified by waist-to-height ratio (WHR) ≥ 0.5. RESULTS Fifty-five percent had ANCS exposure and 31.3% had WHR ≥ 0.5. In adjusted analyses of the entire cohort, ANCS was not significantly associated with eGFR or UACR. However, the ANCS-eGFR association was greater in those with WHR ≥ 0.5 (β -16.8 ml/min/1.73 m2, 95% CL -31.5 to -2.1) vs. WHR < 0.5: (β 13.9 ml/min/1.73 m2, 95% CL -0.4 to 28.1), interaction term p = 0.02. CONCLUSION ANCS exposure was not associated with worse kidney function in adolescence, though ANCS may be associated with lower eGFR if children develop obesity by adolescence.
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Affiliation(s)
- Whitney N Floyd
- Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
| | - Daniel P Beavers
- Department of Statistical Sciences, Wake Forest University, Winston-Salem, NC, 27101, USA
| | - Elizabeth T Jensen
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
| | - Lisa K Washburn
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA
| | - Andrew M South
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, 27101, USA.
- Department of Pediatrics, Section of Nephrology, Brenner Children's, Wake Forest University School of Medicine, Winston Salem, NC, USA.
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10
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Alexander BT, South AM, August P, Bertagnolli M, Ferranti EP, Grobe JL, Jones EJ, Loria AS, Safdar B, Sequeira-Lopez MLS. Appraising the Preclinical Evidence of the Role of the Renin-Angiotensin-Aldosterone System in Antenatal Programming of Maternal and Offspring Cardiovascular Health Across the Life Course: Moving the Field Forward: A Scientific Statement From the American Heart Association. Hypertension 2023; 80:e75-e89. [PMID: 36951054 PMCID: PMC10242542 DOI: 10.1161/hyp.0000000000000227] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
There is increasing interest in the long-term cardiovascular health of women with complicated pregnancies and their affected offspring. Emerging antenatal risk factors such as preeclampsia appear to increase the risk of hypertension and cardiovascular disease across the life course in both the offspring and women after pregnancy. However, the antenatal programming mechanisms responsible are complex and incompletely understood, with roots in alterations in the development, structure, and function of the kidney, heart, vasculature, and brain. The renin-angiotensin-aldosterone system is a major regulator of maternal-fetal health through the placental interface, as well as kidney and cardiovascular tissue development and function. Renin-angiotensin-aldosterone system dysregulation plays a critical role in the development of pregnancy complications such as preeclampsia and programming of long-term adverse cardiovascular health in both the mother and the offspring. An improved understanding of antenatal renin-angiotensin-aldosterone system programming is crucial to identify at-risk individuals and to facilitate development of novel therapies to prevent and treat disease across the life course. Given the inherent complexities of the renin-angiotensin-aldosterone system, it is imperative that preclinical and translational research studies adhere to best practices to accurately and rigorously measure components of the renin-angiotensin-aldosterone system. This comprehensive synthesis of preclinical and translational scientific evidence of the mechanistic role of the renin-angiotensin-aldosterone system in antenatal programming of hypertension and cardiovascular disease will help (1) to ensure that future research uses best research practices, (2) to identify pressing needs, and (3) to guide future investigations to maximize potential outcomes. This will facilitate more rapid and efficient translation to clinical care and improve health outcomes.
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11
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Giammattei VC, Weaver DJ, South AM. Management of acute severe hypertension in youth: from the philosophical to the practical. Curr Opin Pediatr 2023; 35:251-258. [PMID: 36437756 PMCID: PMC9992153 DOI: 10.1097/mop.0000000000001209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
PURPOSE OF REVIEW Acute severe hypertension remains an uncommon but important source of morbidity and mortality in youth. However, there has been very little progress made in our understanding of how to best manage youth with acute severe hypertension to improve patient outcomes. RECENT FINDINGS Our understanding of what is acute severe hypertension is undergoing a philosophical change. Management of patients with acute severe hypertension is evolving towards more of a risk and outcomes-based approach. SUMMARY We should be intentional when we consider whether a patient has acute severe hypertension and if they are truly at an increased risk for life-threatening target organ injury. We should consider their specific risk factors to best interpret the risks and benefits of how best to treat a patient with acute severe hypertension, rather than relying on traditional approaches and conventional wisdom. We should always ask 'why' when we are pursuing a given management course. Future studies should clearly define the research questions they are investigating to best advance the field to ultimately improve patient outcomes.
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Affiliation(s)
| | - Donald J. Weaver
- Division of Nephrology and Hypertension, Department of Pediatrics, Atrium Health Levine Children's, Charlotte, NC, USA
| | - Andrew M. South
- Section of Nephrology, Department of Pediatrics, Brenner Children’s, Wake Forest University School of Medicine, Winston Salem, NC, USA
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston Salem, NC, USA
- Cardiovascular Sciences Center, Wake Forest University School of Medicine, Winston Salem, NC, USA
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12
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Falkner B, Gidding SS, Baker-Smith CM, Brady TM, Flynn JT, Malle LM, South AM, Tran AH, Urbina EM. Pediatric Primary Hypertension: An Underrecognized Condition: A Scientific Statement From the American Heart Association. Hypertension 2023; 80:e101-e111. [PMID: 36994715 DOI: 10.1161/hyp.0000000000000228] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/31/2023]
Abstract
The overall prevalence of hypertension in childhood is 2% to 5%, and the leading type of childhood hypertension is primary hypertension, especially in adolescence. As in adults, the leading risk factors for children with primary hypertension are excess adiposity and suboptimal lifestyles; however, environmental stress, low birth weight, and genetic factors may also be important. Hypertensive children are highly likely to become hypertensive adults and to have measurable target organ injury, particularly left ventricular hypertrophy and vascular stiffening. Ambulatory and home blood pressure monitoring may facilitate diagnosis. Primordial prevention of hypertension through public health implementation of healthier diet and increased physical activity will reduce the prevalence of primary hypertension, and evidence-based treatment guidelines should be implemented when hypertension is diagnosed. Further research to optimize recognition and diagnosis and clinical trials to better define outcomes of treatment are needed.
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13
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Tan BW, Tan BW, Tan AL, Schriver ER, Gutiérrez-Sacristán A, Das P, Yuan W, Hutch MR, García Barrio N, Pedrera Jimenez M, Abu-el-rub N, Morris M, Moal B, Verdy G, Cho K, Ho YL, Patel LP, Dagliati A, Neuraz A, Klann JG, South AM, Visweswaran S, Hanauer DA, Maidlow SE, Liu M, Mowery DL, Batugo A, Makoudjou A, Tippmann P, Zöller D, Brat GA, Luo Y, Avillach P, Bellazzi R, Chiovato L, Malovini A, Tibollo V, Samayamuthu MJ, Serrano Balazote P, Xia Z, Loh NHW, Chiudinelli L, Bonzel CL, Hong C, Zhang HG, Weber GM, Kohane IS, Cai T, Omenn GS, Holmes JH, Ngiam KY. Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study. EClinicalMedicine 2023; 55:101724. [PMID: 36381999 PMCID: PMC9640184 DOI: 10.1016/j.eclinm.2022.101724] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/12/2022] [Accepted: 10/12/2022] [Indexed: 11/09/2022] Open
Abstract
Background While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1-365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings Advanced age (HR 2.77, 95%CI 2.53-3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03-4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55-5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14-1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37-0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17-1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20-1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45-1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80-13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10-1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32-1.67) and 365 days (RR 1.54, 95%CI 1.21-1.96) compared to COVID-19 patients with no AKI. Interpretation COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding Authors are supported by various funders, with full details stated in the acknowledgement section.
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Affiliation(s)
- Byorn W.L. Tan
- Department of Medicine, National University Hospital, 1E Kent Ridge Road, NUHS Tower Block Level 10, Singapore 119228
| | - Bryce W.Q. Tan
- Department of Medicine, National University Hospital, 1E Kent Ridge Road, NUHS Tower Block Level 10, Singapore 119228
| | - Amelia L.M. Tan
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Emily R. Schriver
- Data Analytics Center, University of Pennsylvania Health System, 3600 Civic Center Boulevard, Philadelphia, PA 19104, USA
| | - Alba Gutiérrez-Sacristán
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Priyam Das
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Meghan R. Hutch
- Department of Preventive Medicine, Northwestern University, 750 North Lake Shore Drive, Chicago, IL 60611, USA
| | - Noelia García Barrio
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n 28041 Madrid, Spain
| | - Miguel Pedrera Jimenez
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n 28041 Madrid, Spain
| | - Noor Abu-el-rub
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, USA
| | - Bertrand Moal
- IAM Unit, Bordeaux University Hospital, Place Amélie Rabat Léon, 33076 Bordeaux, France
| | - Guillaume Verdy
- IAM Unit, Bordeaux University Hospital, Place Amélie Rabat Léon, 33076 Bordeaux, France
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, 2 Avenue De Lafayette, Boston, MA 02130, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, 2 Avenue De Lafayette, Boston, MA 02130, USA
| | - Lav P. Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy, Via Ferrata 5, 27100 Pavia, Italy
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, 149 Rue de Sèvres, 75015 Paris, France
| | - Jeffrey G. Klann
- Department of Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Andrew M. South
- Department of Pediatrics-Section of Nephrology, Brenner Children's Hospital, Wake Forest School of Medicine, Medical Center Boulevard, Winston Salem, NC 27157, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, Pittsburgh, PA 15206, USA
| | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA, 100-107 NCRC, 2800 Plymouth Road, Ann Arbor, MI 48109, USA
| | - Sarah E. Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, NCRC Bldg 400, 2800 Plymouth Road, Ann Arbor, MI, United States
| | - Mei Liu
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, 3901 Rainbow Blvd, Kansas City, KS 66160, USA
| | - Danielle L. Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk, Richards Hall, A202, Philadelphia, PA 19104, USA
| | - Ashley Batugo
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, 401 Blockley Hall 423 Guardian Drive Philadelphia, PA 19104, USA
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Zinkmattenstraße 6a, DE79108 Freiburg, Germany
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Zinkmattenstraße 6a, DE79108 Freiburg, Germany
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Zinkmattenstraße 6a, DE79108 Freiburg, Germany
| | - Gabriel A. Brat
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, 750 North Lake Shore Drive, Chicago, IL 60611, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy, Via Ferrata 5, 27100 Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Via Maugeri 4, 27100 Pavia, Italy
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy., Via Maugeri 4, 27100 Pavia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy., Via Maugeri 4, 27100 Pavia, Italy
| | | | - Pablo Serrano Balazote
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Av. de Córdoba, s/n 28041 Madrid, Spain
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, 3501 5th Avenue, BST-3 Suite 7014, Pittsburgh, PA 15260, USA
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, 5 Lower Kent Ridge Road, Singapore 119074
| | - Lorenzo Chiudinelli
- UOC Ricerca, Innovazione e Brand reputation, ASST Papa Giovanni XXIII, Bergamo, P.zza OMS 1 - 24127 Bergamo, Italy
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
- Department of Biostatistics and Bioinformatics, Duke University, 2424 Erwin Road, Durham, NC, United States
| | - Harrison G. Zhang
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Griffin M. Weber
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck Street, Boston, MA 02115, USA
| | - Gilbert S. Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, 2017B Palmer Commons, 100 Washtenaw, Ann Arbor, MI 48109-2218
| | - John H. Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, 3700 Hamilton Walk, Richards Hall, A202, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, 401 Blockley Hall 423 Guardian Drive Philadelphia, PA 19104, USA
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics, WiSDM, National University Health Systems Singapore, 1E Kent Ridge Road, NUHS Tower Block Level 8, Singapore 119228
- Corresponding author. Department of Biomedical Informatics, WiSDM, National University Health Systems Singapore, 1E Kent Ridge Road, NUHS Tower Block Level 8, Singapore 119228.
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14
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O'Shea TM, Register HM, Yi JX, Jensen ET, Joseph RM, Kuban KCK, Frazier JA, Washburn L, Belfort M, South AM, Santos HP, Shenberger J, Perrin EM, Thompson AL, Singh R, Rollins J, Gogcu S, Sanderson K, Wood C, Fry RC. Growth During Infancy After Extremely Preterm Birth: Associations with Later Neurodevelopmental and Health Outcomes. J Pediatr 2023; 252:40-47.e5. [PMID: 35987367 PMCID: PMC10242541 DOI: 10.1016/j.jpeds.2022.08.015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 07/12/2022] [Accepted: 08/11/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To evaluate associations between changes in weight, length, and weight/length ratio during infancy and outcomes later in life among individuals born extremely preterm. STUDY DESIGN Among participants in the Extremely Low Gestational Age Newborn (ELGAN) study, we measured weight and length at discharge from the neonatal intensive care unit (NICU) and at age 2 years and evaluated neurocognitive, psychiatric, and health outcomes at age 10 years and 15 years. Using multivariable logistic regression, we estimated associations between gains in weight, length, and weight/length ratio z-scores between discharge and 2 years and outcomes at 10 and 15 years. High gain was defined as the top quintile of change; low gain, as the bottom quintile of change. RESULTS High gains in weight and weight/length were associated with greater odds of obesity at 10 years, but not at 15 years. These associations were found only for females. High gain in length z-score was associated with lower odds of obesity at 15 years. The only association found between high gains in growth measures and more favorable neurocognitive or psychiatric outcomes was between high gain in weight/length and lower odds of cognitive impairment at age 10 years. CONCLUSIONS During the 2 years after NICU discharge, females born extremely preterm with high gains in weight/length or weight have greater odds of obesity at 10 years, but not at 15 years. Infants with high growth gains in the 2 years after NICU discharge have neurocognitive and psychiatric outcomes in middle childhood and adolescence similar to those of infants with lower gains in weight and weight/length.
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Affiliation(s)
- T Michael O'Shea
- Department of Pediatrics, The University of North Carolina, Chapel Hill, NC
| | - Hannah M Register
- Department of Pediatrics, The University of North Carolina, Chapel Hill, NC
| | - Joe X Yi
- Frank Porter Graham Child Development Institute, The University of North Carolina, Chapel Hill, NC
| | - Elizabeth T Jensen
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Robert M Joseph
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA
| | - Karl C K Kuban
- Department of Pediatrics and Neurology, Boston Medical Center, Boston, MA
| | - Jean A Frazier
- Eunice Kennedy Shriver Center and Department of Psychiatry, University of Massachusetts Chan Medical Center, Worcester, MA
| | - Lisa Washburn
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Mandy Belfort
- Department of Pediatric Newborn Medicine, Harvard Medical School, Boston, MA
| | - Andrew M South
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC; Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Hudson P Santos
- School of Nursing & Health Studies, University of Miami, Coral Gables, FL
| | - Jeffrey Shenberger
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Eliana M Perrin
- Department of Pediatrics, Johns Hopkins University School of Medicine and Nursing, Baltimore, MD
| | - Amanda L Thompson
- Department of Anthropology, The University of North Carolina, Chapel Hill, NC
| | - Rachana Singh
- Department of Pediatrics, Tufts Children's Hospital, Tufts University School of Medicine, Boston, MA
| | - Julie Rollins
- Department of Pediatrics, The University of North Carolina, Chapel Hill, NC
| | - Semsa Gogcu
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, NC
| | - Keia Sanderson
- Department of Internal Medicine, University of North Carolina School of Medicine, Chapel Hill, NC
| | - Charles Wood
- Department of Pediatrics, Duke University School of Medicine, Durham, NC
| | - Rebecca C Fry
- Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina, Chapel Hill, NC
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15
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South AM, Allen NB. Antenatal Programming of Hypertension: Paradigms, Paradoxes, and How We Move Forward. Curr Hypertens Rep 2022; 24:655-667. [PMID: 36227517 PMCID: PMC9712278 DOI: 10.1007/s11906-022-01227-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/28/2022] [Indexed: 01/31/2023]
Abstract
PURPOSE OF REVIEW Synthesize the clinical, epidemiological, and preclinical evidence for antenatal programming of hypertension and critically appraise paradigms and paradoxes to improve translation. RECENT FINDINGS Clinical and epidemiological studies persistently demonstrate that antenatal factors contribute to programmed hypertension under the developmental origins of health and disease framework, including lower birth weight, preterm birth, and fetal growth restriction. Preclinical mechanisms include preeclampsia, maternal diabetes, maternal undernutrition, and antenatal corticosteroid exposure. However, clinical and epidemiological studies to date have largely failed to adequately identify, discuss, and mitigate many sources and types of bias in part due to heterogeneous study designs and incomplete adherence to scientific rigor. These limitations have led to incomplete and biased paradigms as well as persistent paradoxes that have significantly limited translation into clinical and population health interventions. Improved understanding of these paradigms and paradoxes will allow us to substantially move the field forward.
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Affiliation(s)
- Andrew M South
- Department of Pediatrics, Section of Nephrology, Brenner Children's, Wake Forest University School of Medicine, One Medical Center Boulevard, Winston-Salem, NC, 27157, USA.
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
- Department of Surgery-Hypertension and Vascular Research, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
- Cardiovascular Sciences Center, Wake Forest University School of Medicine, Winston-Salem, NC, USA.
| | - Norrina B Allen
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
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16
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Gutiérrez-Sacristán A, Serret-Larmande A, Hutch MR, Sáez C, Aronow BJ, Bhatnagar S, Bonzel CL, Cai T, Devkota B, Hanauer DA, Loh NHW, Luo Y, Moal B, Ahooyi TM, Njoroge WFM, Omenn GS, Sanchez-Pinto LN, South AM, Sperotto F, Tan ALM, Taylor DM, Verdy G, Visweswaran S, Xia Z, Zahner J, Avillach P, Bourgeois FT. Hospitalizations Associated With Mental Health Conditions Among Adolescents in the US and France During the COVID-19 Pandemic. JAMA Netw Open 2022; 5:e2246548. [PMID: 36512353 PMCID: PMC9856226 DOI: 10.1001/jamanetworkopen.2022.46548] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/21/2022] [Indexed: 12/15/2022] Open
Abstract
Importance The COVID-19 pandemic has been associated with an increase in mental health diagnoses among adolescents, though the extent of the increase, particularly for severe cases requiring hospitalization, has not been well characterized. Large-scale federated informatics approaches provide the ability to efficiently and securely query health care data sets to assess and monitor hospitalization patterns for mental health conditions among adolescents. Objective To estimate changes in the proportion of hospitalizations associated with mental health conditions among adolescents following onset of the COVID-19 pandemic. Design, Setting, and Participants This retrospective, multisite cohort study of adolescents 11 to 17 years of age who were hospitalized with at least 1 mental health condition diagnosis between February 1, 2019, and April 30, 2021, used patient-level data from electronic health records of 8 children's hospitals in the US and France. Main Outcomes and Measures Change in the monthly proportion of mental health condition-associated hospitalizations between the prepandemic (February 1, 2019, to March 31, 2020) and pandemic (April 1, 2020, to April 30, 2021) periods using interrupted time series analysis. Results There were 9696 adolescents hospitalized with a mental health condition during the prepandemic period (5966 [61.5%] female) and 11 101 during the pandemic period (7603 [68.5%] female). The mean (SD) age in the prepandemic cohort was 14.6 (1.9) years and in the pandemic cohort, 14.7 (1.8) years. The most prevalent diagnoses during the pandemic were anxiety (6066 [57.4%]), depression (5065 [48.0%]), and suicidality or self-injury (4673 [44.2%]). There was an increase in the proportions of monthly hospitalizations during the pandemic for anxiety (0.55%; 95% CI, 0.26%-0.84%), depression (0.50%; 95% CI, 0.19%-0.79%), and suicidality or self-injury (0.38%; 95% CI, 0.08%-0.68%). There was an estimated 0.60% increase (95% CI, 0.31%-0.89%) overall in the monthly proportion of mental health-associated hospitalizations following onset of the pandemic compared with the prepandemic period. Conclusions and Relevance In this cohort study, onset of the COVID-19 pandemic was associated with increased hospitalizations with mental health diagnoses among adolescents. These findings support the need for greater resources within children's hospitals to care for adolescents with mental health conditions during the pandemic and beyond.
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Affiliation(s)
| | - Arnaud Serret-Larmande
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
- Department of Biostatistics and Biomedical Informatics, Hôpital Saint-Louis, Assistance Publique-Hôpitaux de Paris, Université Paris-Cité, Paris, France
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Carlos Sáez
- Biomedical Data Science Lab, Instituto Universitario de Tecnologías de la Información y Comunicaciones, Universitat Politècnica de València, València, Spain
| | - Bruce J Aronow
- Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio
| | - Surbhi Bhatnagar
- Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, Ohio
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Batsal Devkota
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois
| | - Bertrand Moal
- Unité Informatique et Archivistique Médicale, Bordeaux University Hospital, Bordeaux, France
| | - Taha Mohseni Ahooyi
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Wanjiku F M Njoroge
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Gilbert S Omenn
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor
| | - L Nelson Sanchez-Pinto
- Department of Pediatrics (Critical Care), Northwestern University Feinberg School of Medicine, Chicago, Illinois
| | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest University School of Medicine, Winston Salem, North Carolina
| | - Francesca Sperotto
- Department of Cardiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Deanne M Taylor
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | - Guillaume Verdy
- Unité Informatique et Archivistique Médicale, Bordeaux University Hospital, Bordeaux, France
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Janet Zahner
- Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- Department of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- Department of Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
| | - Florence T Bourgeois
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
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17
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Jensen ET, Yi J, Jackson W, Singh R, Joseph RM, Kuban KCK, Msall ME, Washburn L, Fry R, South AM, O’Shea TM. Analysis of Neurodevelopment in Children Born Extremely Preterm Treated With Acid Suppressants Before Age 2 Years. JAMA Netw Open 2022; 5:e2241943. [PMID: 36378311 PMCID: PMC9667324 DOI: 10.1001/jamanetworkopen.2022.41943] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
IMPORTANCE Children born preterm are at increased risk of adverse neurodevelopmental outcomes and may be particularly vulnerable to the effects of gastric acid suppression during infancy. OBJECTIVE To assess whether early acid suppressant use in infants born extremely preterm is associated with poorer neurodevelopmental outcomes. DESIGN, SETTING, AND PARTICIPANTS The Extremely Low Gestational Age Newborn study was a multicenter, longitudinal cohort study of infants born before 28 weeks' gestational age between March 22, 2002, and August 31, 2004. The current analyses were performed from September 12, 2020, through September 22, 2022. Of the 1506 infants enrolled, 284 died before discharge and 22 died before 24 months of age. An additional 2 died before age 10 years, leaving 1198 (79.5%) eligible for a visit. Of these, 889 (74%) participated in the visit at age 10. At age 10 years, the association of early-life acid suppressant use with neurocognitive, neurodevelopmental, and psychiatric symptomatology was assessed. EXPOSURES Acid suppressant use before 24 months of age was determined from medical records and from questionnaires administered to mothers. MAIN OUTCOMES AND MEASURES Neurodevelopmental assessments at age 10 years included the School-Age Differential Ability Scales-II, the Developmental Neuropsychological Assessment-II, the Autism Diagnostic Observation Schedule-2, the Social Responsiveness Scale-2, and the Child Symptom Inventory-4 for attention-deficit/hyperactivity disorder (ADHD), depression, and anxiety. RESULTS Of the 889 participants assessed at age 10 years (mean [SD] age, 9.97 [0.67] years; mean [SD] gestational age at birth, 26.1 [1.3] weeks; 455 [51.2%] male), 368 (41.4%) had received acid suppressants by 24 months of age. Associations were observed between acid suppressant use and decreased full-scale IQ z score (adjusted β, -0.29; 95% CI, -0.45 to -0.12), verbal IQ z score (adjusted β, -0.34; 95% CI, -0.52 to -0.15), nonverbal IQ z score (adjusted β, -0.22; 95% CI to -0.39 to -0.05), working memory z score (adjusted β, -0.26; 95% CI to -0.45, -0.08), autism spectrum disorder (adjusted relative risk, 1.84; 95% CI, 1.15-2.95), and epilepsy (adjusted relative risk, 2.07; 95% CI, 1.31 to 3.35). Results were robust to multiple sensitivity analyses. Use of acid suppressants was not associated with inhibitory control, ADHD, anxiety, or depression. CONCLUSIONS AND RELEVANCE The results of this cohort study suggest that early-life use of acid suppressants in extremely preterm infants may be associated with poorer neurodevelopmental outcomes and add to evidence indicating caution in use of these agents.
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Affiliation(s)
- Elizabeth T. Jensen
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Joe Yi
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill
| | - Wesley Jackson
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill
| | - Rachana Singh
- Department of Pediatrics, Tufts University School of Medicine, Boston, Massachusetts
| | - Robert M. Joseph
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, Massachusetts
| | | | - Michael E. Msall
- Kennedy Research Center on Intellectual and Neurodevelopmental Disabilities, University of Chicago Pritzker School of Medicine, Chicago, Illinois
| | - Lisa Washburn
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Rebecca Fry
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill
| | - Andrew M. South
- Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, North Carolina
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - T. Michael O’Shea
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill
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18
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Wang X, Zhang HG, Xiong X, Hong C, Weber GM, Brat GA, Bonzel CL, Luo Y, Duan R, Palmer NP, Hutch MR, Gutiérrez-Sacristán A, Bellazzi R, Chiovato L, Cho K, Dagliati A, Estiri H, García-Barrio N, Griffier R, Hanauer DA, Ho YL, Holmes JH, Keller MS, Klann MEng JG, L'Yi S, Lozano-Zahonero S, Maidlow SE, Makoudjou A, Malovini A, Moal B, Moore JH, Morris M, Mowery DL, Murphy SN, Neuraz A, Yuan Ngiam K, Omenn GS, Patel LP, Pedrera-Jiménez M, Prunotto A, Jebathilagam Samayamuthu M, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano-Balazote P, South AM, Tan ALM, Tan BWL, Tibollo V, Tippmann P, Visweswaran S, Xia Z, Yuan W, Zöller D, Kohane IS, Avillach P, Guo Z, Cai T. SurvMaximin: Robust federated approach to transporting survival risk prediction models. J Biomed Inform 2022; 134:104176. [PMID: 36007785 PMCID: PMC9707637 DOI: 10.1016/j.jbi.2022.104176] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 07/18/2022] [Accepted: 08/15/2022] [Indexed: 10/15/2022]
Abstract
OBJECTIVE For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. MATERIALS AND METHODS For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or a single center, corresponding to transfer learning. RESULTS Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. CONCLUSIONS The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.
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Affiliation(s)
- Xuan Wang
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Xin Xiong
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yuan Luo
- Department of Preventive Medicine Northwestern University, Chicago, IL, USA
| | - Rui Duan
- Department of Biostatistics, Harvard University, Boston, MA, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Meghan R Hutch
- Department of Preventive Medicine Northwestern University, Chicago, IL, USA
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA; Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Arianna Dagliati
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Romain Griffier
- IAM unit, Bordeaux University Hospital, Bordeaux, France; INSERM Bordeaux Population Health ERIAS TEAM, ERIAS - Inserm U1219 BPH, Bordeaux, France
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, MI, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mark S Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, Ann Arbor, MI, USA
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems, Singapore
| | - Gilbert S Omenn
- Depts of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, Public Health University of Michigan, Ann Arbor, MI, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University Of Kansas Medical Center
| | | | - Andrea Prunotto
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | | | | | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | | | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore
| | - Valentina Tibollo
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Zijian Guo
- Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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19
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Butler JE, Vincent C, South AM, Chanchlani R. Updates to Pediatric Ambulatory Blood Pressure Monitoring in Clinical Practice: a Review and Strategies for Expanding Access. Curr Pediatr Rep 2022. [DOI: 10.1007/s40124-022-00273-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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20
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Borra G, Perrin E, Ravi H, South AM. Abstract P046: The Association Of Sex And Gender With Disordered Eating Behavior In Youth With Hypertension. Hypertension 2022. [DOI: 10.1161/hyp.79.suppl_1.p046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
DEB (disordered eating behavior) prevalence in adolescents has been increasing steadily. Females generally have a higher prevalence compared to males in the general population, but data do not exist in adolescents with HTN, a condition that requires lifestyle counseling and is more common in males. Finally, the difference in the risk of DEB conferred between sex, a biological construct, and gender, a self-identified construct, is characterized poorly, especially in youth with HTN.
Objective:
Determine the association of sex and gender with DEB prevalence in adolescents with HTN disorders.
Hypothesis:
Females will have a higher prevalence of screening positive for DEB compared to males regardless of if defined by sex or gender.
Design/Methods:
This was a secondary analysis of a prospective cross-sectional study of adolescents aged 11-18 years with HTN disorders. We excluded patients with diabetes mellitus, kidney failure or transplantation, or gastrostomy tube dependence. We collected data via electronic health record abstraction and surveys, including the validated SCOFF DEB screening questionnaire, with a score ≥2/5 as positive, as well as self-reported sex, defined as assigned at birth, and gender, defined as that with which the participant identifies. We compared DEB prevalence by sex and gender using bivariate generalized linear models; our directed acyclic graph identified no variables in the adjustment set.
Results:
Of 74 participants, 59% (44/74) identified as male and 41% (30/74) identified as female. One participant whose sex was male declined to answer the gender question. DEB prevalence overall was 28% (21/74); it was 20% (9/45) in males and 41% (12/29) in females by both sex and gender. Females had double the risk of DEB compared to males, when defined by sex (adjusted RR 2.07, 95% CL 0.9996 to 4.28), or gender (adjusted RR 2.02, 95% CL 0.98 to 4.18). However, our findings did not meet statistical significance at
p
<0.05.
Conclusions:
Among adolescents with HTN disorders, females had double the risk of screening positive for DEB compared to males by both sex and gender. Adolescents with HTN may benefit from routine DEB screening, especially those that identify as female.
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21
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Vincent C, Chen A, Kavanagh K, South AM. Abstract P096: Association Of Maternal Blood Pressure With Offspring Blood Pressure In A Hypertensive Non-Human Primate Model. Hypertension 2022. [DOI: 10.1161/hyp.79.suppl_1.p096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Mechanisms behind primary HTN-related organ damage are not well understood and animal models are lacking. The spontaneous HTN vervet monkey (
Chlorocebus spp.
) is a potential model for studying HTN development across the lifespan.
Methods:
This is a prospective cross-sectional study of
Chlorocebus spp.
maternal-offspring dyads. We identified 10 non-pregnant HTN mothers based on prior sedated BP measurements and randomly selected a single offspring to include. We matched HTN dyads by age and offspring sex 2:1 with maternal normotensive dyads. We defined HTN by the 2017 ACC/AHA Guideline criteria. We obtained sedated BP, weight, and labs on all dyads. Our exposures were presence of maternal HTN and BP in mothers. Our outcome was offspring BP. We estimated associations between the exposures and outcome with adjusted generalized linear models.
Results:
Maternal age ranged 9.6—19.7 years and offspring age ranged 1.6—8.7 years. Maternal systolic BP was associated inversely with offspring systolic BP (
β
-0.44 mmHg, 95% CL -0.87 to -0.01) and maternal HTN was associated with lower diastolic BP (
β
-16.1 mmHg, 95% CL -30.7 to -1.6).
Conclusion:
In this vervet monkey HTN model, we observed an intriguing association of maternal HTN with lower offspring BP. The variability of the vervet monkeys’ BP over time could be related to environmental factors, and BP alone may not be the most reliable measure of cardiovascular risk in this group. Future steps include investigating association of biochemical markers and renin-angiotensin-aldosterone system components with BP and target organ damage in this model.
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Affiliation(s)
| | - Ashton Chen
- Wake Forest Sch of Medicine, Winston Salem, NC
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22
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Weaver DJ, Giammattei V, Lucas C, Sethna C, South AM. Abstract 082: Association Of Age And Blood Pressure Severity With ICD-10 Diagnosis Codes For Hypertension Disorders Among Youth Referred For Hypertension: Interim Analysis Of Data From Three Sites Of The Study Of The Epidemiology Of Pediatric Hypertension (SUPERHERO) Registry. Hypertension 2022. [DOI: 10.1161/hyp.79.suppl_1.082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Secondary HTN is thought to be the main cause of HTN in young children and those with more severe HTN. However, the rising incidence of primary HTN has called this into question. Our objective was to estimate risk of secondary HTN based on ICD-10 codes due to age and BP.
Methods:
The SUPERHERO Registry is a multicenter retrospective cohort of youth referred to subspecialty care for HTN. Inclusion criteria were initial visit for HTN disorder (per ICD-10) from 1/1/2016-12/31/2021 and age <19 years. Exclusion criteria were pregnancy, dialysis, or transplant per ICD-10. Exposures were age and BP, including z-scores, and outcomes were primary and secondary HTN by ICD-10, all at baseline. We used unadjusted generalized linear models to estimate risk of 1) secondary vs. primary HTN and 2) kidney vs. non-kidney secondary HTN.
Results:
Median age was 14.2 years [IQR 10.5, 16.4], 52% (1703/3295) had obesity, 58% (1927/3295) had primary HTN, 9% (283/3295) had non-kidney secondary HTN, and 5% (171/3295) had kidney secondary HTN. Compared to youth <13 years old, adolescents had 38% lower risk of secondary vs. primary HTN (95% CL 0.53-0.74) and 23% lower risk of kidney vs. non-kidney secondary HTN (95% CL 0.61-0.98) (Table). A 1-unit higher systolic BP z-score was associated with 23% lower risk of secondary vs. primary HTN (95% CL 0.71-0.85) and 19% lower risk of kidney vs. non-kidney secondary HTN (95% CL 0.72-0.91).
Conclusions:
Older age was associated with greater risk of primary HTN and lower risk of secondary HTN due to kidney disease. However, worse systolic BP was associated with lower risk of secondary HTN, including due to kidney disease. Ongoing analyses are validating these findings.
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Costa C, Patel P, Constantacos C, Hunter J, South AM. Abstract P094: Prevalence Of Polycystic Ovary Syndrome In Youth With Hypertension Disorders. Hypertension 2022. [DOI: 10.1161/hyp.79.suppl_1.p094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Introduction:
While Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders among adolescent females, it continues to be underdiagnosed and understudied. In particular, it is unknown whether youth with HTN may have a greater undiagnosed burden of PCOS compared to the general adolescent population. Patients with PCOS are at an increased risk for reproductive morbidities, hormone-dependent cancer, and metabolic and cardiovascular diseases. Furthermore, the relationships between metabolic syndrome (MBS) or obesity and PCOS in patients with HTN have not been fully investigated.
Objectives:
To estimate the prevalence of PCOS in adolescent females with HTN and investigate whether MBS and obesity increase this risk.
Methods:
This is an ongoing prospective cross-sectional study that has enrolled 12 of 40 participants to date. Inclusion criteria are females 10-18 years with HTN disorders of any cause and menarche for ≥2 years. Exclusion criteria are CAH, ovarian or pituitary cancer, Cushing syndrome, pregnancy, and a diagnosis of PCOS prior to HTN diagnosis. Data are collected via EHR abstraction and administered questionnaires, and include demographics, past medical, menstrual, and fertility histories, blood pressure, and physical characteristics including height, weight, and waist circumference. Our power and sample size calculations, made a priori, are based on estimating the difference in our population’s hypothesized PCOS prevalence (p1=0.25) with that of the general adolescent female population (p0=0.1) using the one-sample z-test of proportions. We will also use multivariable generalized linear models to estimate the risk of PCOS due to MBS or obesity.
Results:
The median systolic and diastolic blood pressures are 122.7 mmHg [113.8, 128.0] and 72.5 mmHg [68.8, 76.6]. The median waist circumference is 80.7 cm [76.5, 95.6]. The prevalence of PCOS is 17% (2/12), while 8% (1/12) have MBS and 58% (7/12) have obesity.
Discussion:
We are continuing to enroll new participants to reach our target enrollment of 40. Our results have the potential to educate clinicians to consider screening for PCOS in adolescent females who present with HTN to allow for prevention of the comorbidities associated with PCOS and to guide HTN treatment.
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Affiliation(s)
| | | | | | - Janel Hunter
- Wake Forest Univ Sch of Medicine, Winston-Salem, NC
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24
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Vincent C, Chen A, South AM. Abstract 093: Association Of Preterm Birth And Low Birth Weight With The Circulatory And Urinary Renin-angiotensin System In Youth With Newly Diagnosed Primary Hypertension. Hypertension 2022. [DOI: 10.1161/hyp.79.suppl_1.093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
While the RAS is altered in youth born preterm and may play a role in development of HTN, this has not been investigated in youth with primary HTN. We hypothesized that lower gestational age (GA) and lower birth weight (BW) would be associated with higher Ang II, lower Ang-(1-7), and higher Ang II/Ang-(1-7) in plasma and urine.
Methods:
This is a secondary analysis of baseline data from a pilot prospective cohort study of 30 youth aged 5-17 years with newly diagnosed primary HTN per national guidelines. Exclusion criteria were heart or chronic kidney disease and diabetes mellitus. Exposures were GA, BW, and BW z-score on the continuous scale and as binary variables, preterm birth (GA <37 weeks) and low BW (<2500 g). RAS outcomes were Ang II and Ang-(1-7) measured in plasma and urine per well-validated RIAs; urinary values standardized to urine creatinine (Cr). Plasma renin activity and aldosterone were measured per well-validated methods. We estimated associations with unadjusted generalized linear models.
Results:
Median GA was 39 weeks [IQR 36, 40] with 28% (8/30) born preterm. Mean BW was 3.12 kg (
SD
0.65) with low BW in 17% (5/30). Preterm birth was associated with lower urine Ang-(1-7)/Cr and higher aldosterone/renin ratio and urine Ang II/Ang-(1-7) (Figure). Low BW was associated with higher plasma Ang-(1-7) and urine Ang II/Cr.
Conclusion:
Early-life risk factors were associated with intrarenal RAS alterations favoring Ang II activation and Ang-(1-7) suppression in youth with primary HTN. Our findings provide novel insight into the mechanistic role that the RAS may play in HTN development in youth born preterm or with low BW that differs from youth born at term who have HTN.
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Affiliation(s)
| | - Ashton Chen
- Wake Forest Sch of Medicine, Winston Salem, NC
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25
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Carrasquillo RA, Giammattei V, Lucas CB, Sethna C, Vincent C, Viviano I, Weaver D, South AM. Abstract P093: Preterm Birth And Hypertension Severity In Youth Referred For Hypertension Disorders: A Superhero Interim Analysis. Hypertension 2022. [DOI: 10.1161/hyp.79.suppl_1.p093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Introduction:
Pediatric hypertension (HTN) is a growing concern with short and long-term adverse health effects. While children who were born preterm (<37 weeks’ gestation) likely have an increased HTN risk, it is unknown whether preterm birth is associated with more severe HTN once diagnosed.
Objective:
Determine whether youth referred for HTN disorders who were born preterm are more likely to have worse blood pressure (BP).
Methods:
This is a cross-sectional analysis of preliminary baseline data from The Study of the Epidemiology of Pediatric Hypertension (SUPERHERO) Registry, an ongoing multicenter retrospective cohort of youth referred to subspecialty clinics for HTN disorders. Inclusion criteria were <19 years of age, initial visit 1/01/2016-12/31/2021 (index date), and ICD-10 diagnostic codes for HTN disorders. Exclusion criteria were pregnancy, kidney failure on dialysis, or kidney transplantation by ICD-10 codes. We classified BP based on age, sex, and height per pediatric guidelines. We further defined high BP as elevated BP or any stage of HTN. Preterm birth was based on ICD-10 codes at the index date. We used unadjusted generalized linear models to estimate RR with 95% CL.
Results:
In the cohort, 939/3295 (29%) identified as Black/African American, 576/3295 (17%) Hispanic/Latino, 1216/3295 (37%) were female, and the median age was 14.2 years (IQR 10.5, 16.4);1703/3295 (52%) had obesity. Only 24/3295 (1%) had an ICD-10 code for preterm birth, and 1951/3228 (60%) had stage 1 or stage 2 HTN. Preterm birth ICD-10 codes were not associated with a higher risk of high BP (RR 0.78, 95% CL 0.57 to 1.06) or a higher risk of HTN (RR 0.84, 95% CL 0.56 to 1.27).
Conclusion:
Youth referred for HTN disorders who had ICD-10 codes for preterm birth did not have worse BP compared to those without these codes. It is possible that preterm birth is not accurately documented. Ongoing analyses include obtaining actual gestational age at birth and investigating the association with target organ damage.
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26
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Perrin EC, Ravi H, South AM. Abstract P090: Prevalence And Risk Factors Of Disordered Eating Behavior In Youth With Hypertension. Hypertension 2022. [DOI: 10.1161/hyp.79.suppl_1.p090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
Eating disorders in adolescents are increasing in prevalence and have adverse health consequences, but remain underdiagnosed. Patients with medical conditions that require lifestyle management, such as obesity and diabetes mellitus, have higher disordered eating behavior (DEB) risk (20–39%) than the general adolescent population (10%). Patients with CKD might also be at increased DEB risk. In youth with other conditions requiring lifestyle counseling—such as HTN—DEB prevalence and associated risk factors are unknown.
Objective:
Estimate DEB prevalence in youth with HTN compared to the general adolescent population and investigate whether obesity, CKD, or less specialized lifestyle counseling source are associated with higher DEB risk.
Design/Methods:
Prospective cross-sectional study of youth aged 11–18 years with HTN. We excluded patients with diabetes mellitus, kidney failure or transplantation, or gastrostomy tube dependence. We collected data via surveys and electronic health record abstraction. We administered the validated SCOFF DEB screening questionnaire wherein a score ≥2/5 is positive. We compared DEB prevalence using a one-sample z-test of proportions (p
0
=0.1) and estimated DEB risk by obesity, CKD, or lifestyle counseling using multivariable generalized linear models.
Results:
Of 74 participants, 59% (44/74) identified as male gender, 22% (16/74) as Black or African American, and 36% (27/74) as Hispanic; 58% (43/74) had obesity and 26% (19/74) had CKD. DEB prevalence was 28% (95% CI 18–39%,
p
<0.001). CKD was associated with higher DEB prevalence (adjusted RR 2.17, 95% CL 1.09 to 4.32), but obesity and lifestyle counseling source were not.
Conclusions:
DEB prevalence is higher in youth with HTN and comparable to that reported in other conditions requiring lifestyle counseling. Concurrent CKD is associated with more than double the risk of DEB in this population. Youth with HTN may benefit from routine DEB screening.
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Affiliation(s)
| | - Hanna Ravi
- Wake Forest Sch of Medicine, Winston Salem, NC
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27
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Miyashita Y, Hanevold C, Faino A, Scher J, Lande M, Yamaguchi I, Hernandez J, Acosta A, Weaver DJ, Thomas J, Kallash M, Ferguson M, Patel KN, South AM, Kelton M, Flynn JT. White Coat Hypertension Persistence in Children and Adolescents: The Pediatric Nephrology Research Consortium Study. J Pediatr 2022; 246:154-160.e1. [PMID: 35351534 PMCID: PMC9275430 DOI: 10.1016/j.jpeds.2022.03.036] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 03/13/2022] [Accepted: 03/24/2022] [Indexed: 02/06/2023]
Abstract
OBJECTIVES To determine whether youth with white coat hypertension on initial ambulatory blood pressure monitoring (ABPM) continue to demonstrate the same pattern on repeat ABPM. STUDY DESIGN Retrospective longitudinal cohort study of patients referred for high blood pressure (BP) and diagnosed with white coat hypertension by ABPM who had follow-up ABPM 0.5-4.6 years later at 11 centers in the Pediatric Nephrology Research Consortium. We classified ABPM phenotype using the American Heart Association guidelines. At baseline, we classified those with hypertensive BP in the clinic as "stable white coat hypertension," and those with normal BP as "intermittent white coat hypertension." We used multivariable generalized linear mixed effect models to estimate the association of baseline characteristics with abnormal ABPM phenotype progression. RESULTS Eighty-nine patients met the inclusion criteria (median age, 13.9 years; 78% male). Median interval time between ABPM measurements was 14 months. On follow-up ABPM, 61% progressed to an abnormal ABPM phenotype (23% ambulatory hypertension, 38% ambulatory prehypertension). Individuals age 12-17 years and those with stable white coat hypertension had greater proportions progressing to either prehypertension or ambulatory hypertension. In the multivariable models, baseline wake systolic BP index ≥0.9 was significantly associated with higher odds of progressing to ambulatory hypertension (OR 3.07, 95% CI 1.02-9.23). CONCLUSIONS The majority of the patients with white coat hypertension progressed to an abnormal ABPM phenotype. This study supports the 2017 American Academy of Pediatrics Clinical Practice Guideline's recommendation for follow-up of ABPM in patients with white coat hypertension.
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Affiliation(s)
- Yosuke Miyashita
- University of Pittsburgh Medical Center Children's Hospital of Pittsburgh, University of Pittsburgh School of Medicine, Pittsburgh, PA.
| | - Coral Hanevold
- Seattle Children’s Hospital, University of Washington School of Medicine, Seattle, WA
| | - Anna Faino
- Core for Biostatistics, Epidemiology and Analytics in Research, Seattle Children’s Research Institute, Seattle, WA
| | - Julia Scher
- University of Rochester Medical Center, Rochester, NY
| | - Marc Lande
- University of Rochester Medical Center, Rochester, NY
| | - Ikuyo Yamaguchi
- The University of Oklahoma Health Sciences Center, Oklahoma City, OK
| | | | - Alisa Acosta
- Texas Children’s Hospital, Baylor College of Medicine, Houston, TX
| | | | - Jason Thomas
- Helen DeVos Children’s Hospital, Grand Rapids, MI
| | - Mahmoud Kallash
- Nationwide Children’s Hospital, The Ohio State University College of Medicine, Columbus, OH
| | | | | | - Andrew M. South
- Brenner Children’s, Wake Forest School of Medicine, Winston-Salem, NC
| | - Megan Kelton
- Seattle Children’s Hospital, University of Washington School of Medicine, Seattle, WA
| | - Joseph T. Flynn
- Seattle Children’s Hospital, University of Washington School of Medicine, Seattle, WA
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28
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Chmielewski J, Chaudhry PM, Harer MW, Menon S, South AM, Chappell A, Griffin R, Askenazi D, Jetton J, Starr MC, Selewski DT, Sarkar S, Kent A, Fletcher J, Abitbol CL, DeFreitas M, Duara S, Charlton JR, Swanson JR, Guillet R, D’Angio C, Mian A, Rademacher E, Mhanna MJ, Raina R, Kumar D, Jetton JG, Brophy PD, Colaizy TT, Klein JM, Arikan AA, Rhee CJ, Goldstein SL, Nathan AT, Kupferman JC, Bhutada A, Rastogi S, Bonachea E, Ingraham S, Mahan J, Nada A, Cole FS, Davis TK, Dower J, Milner L, Smith A, Fuloria M, Reidy K, Kaskel FJ, Soranno DE, Gien J, Gist KM, Chishti AS, Hanna MH, Hingorani S, Juul S, Wong CS, Joseph C, DuPont T, Ohls R, Staples A, Rohatgi S, Sethi SK, Wazir S, Khokhar S, Perazzo S, Ray PE, Revenis M, Mammen C, Synnes A, Wintermark P, Zappitelli M, Woroniecki R, Sridhar S. Documentation of acute kidney injury at discharge from the neonatal intensive care unit and role of nephrology consultation. J Perinatol 2022; 42:930-936. [PMID: 35676535 PMCID: PMC9280854 DOI: 10.1038/s41372-022-01424-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 04/29/2022] [Accepted: 05/27/2022] [Indexed: 11/09/2022]
Abstract
OBJECTIVE To investigate whether NICU discharge summaries documented neonatal AKI and estimate if nephrology consultation mediated this association. STUDY DESIGN Secondary analysis of AWAKEN multicenter retrospective cohort. EXPOSURES AKI severity and diagnostic criteria. OUTCOME AKI documentation on NICU discharge summaries using multivariable logistic regression to estimate associations and test for causal mediation. RESULTS Among 605 neonates with AKI, 13% had documented AKI. Those with documented AKI were more likely to have severe AKI (70.5% vs. 51%, p < 0.001) and SCr-only AKI (76.9% vs. 50.1%, p = 0.04). Nephrology consultation mediated 78.0% (95% CL 46.5-109.4%) of the total effect of AKI severity and 82.8% (95% CL 70.3-95.3%) of the total effect of AKI diagnostic criteria on documentation. CONCLUSION We report a low prevalence of AKI documentation at NICU discharge. AKI severity and SCr-only AKI increased odds of AKI documentation. Nephrology consultation mediated the associations of AKI severity and diagnostic criteria with documentation.
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Affiliation(s)
- Jennifer Chmielewski
- Department of Pediatrics, Division of Nephrology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Paulomi M. Chaudhry
- Department of Pediatrics, Division of Neonatology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Matthew W. Harer
- Department of Pediatrics, Division of Neonatology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Shina Menon
- Division of Nephrology, University of Washington and Seattle Children’s Hospital, Seattle, WA, USA
| | - Andrew M. South
- Department of Pediatrics, Section of Nephrology, Brenner Children’s, Wake Forest School of Medicine, Winston Salem, NC, USA.,Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Ashley Chappell
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Russell Griffin
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - David Askenazi
- Department of Pediatrics, Division of Nephrology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jennifer Jetton
- Division of Nephrology, Dialysis and Transplantation, Stead Family Department of Pediatrics, University of Iowa, Iowa City, IA, USA
| | - Michelle C. Starr
- Department of Pediatrics, Division of Nephrology, Indiana University School of Medicine, Indianapolis, IN, USA.,Pediatric and Adolescent Comparative Effectiveness Research, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.,Correspondence and requests for materials should be addressed to Michelle C. Starr.
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29
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Zhang HG, Dagliati A, Shakeri Hossein Abad Z, Xiong X, Bonzel CL, Xia Z, Tan BWQ, Avillach P, Brat GA, Hong C, Morris M, Visweswaran S, Patel LP, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Samayamuthu MJ, Bourgeois FT, L'Yi S, Maidlow SE, Moal B, Murphy SN, Strasser ZH, Neuraz A, Ngiam KY, Loh NHW, Omenn GS, Prunotto A, Dalvin LA, Klann JG, Schubert P, Vidorreta FJS, Benoit V, Verdy G, Kavuluru R, Estiri H, Luo Y, Malovini A, Tibollo V, Bellazzi R, Cho K, Ho YL, Tan ALM, Tan BWL, Gehlenborg N, Lozano-Zahonero S, Jouhet V, Chiovato L, Aronow BJ, Toh EMS, Wong WGS, Pizzimenti S, Wagholikar KB, Bucalo M, Cai T, South AM, Kohane IS, Weber GM. International electronic health record-derived post-acute sequelae profiles of COVID-19 patients. NPJ Digit Med 2022; 5:81. [PMID: 35768548 PMCID: PMC9242995 DOI: 10.1038/s41746-022-00623-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 05/19/2022] [Indexed: 11/10/2022] Open
Abstract
The risk profiles of post-acute sequelae of COVID-19 (PASC) have not been well characterized in multi-national settings with appropriate controls. We leveraged electronic health record (EHR) data from 277 international hospitals representing 414,602 patients with COVID-19, 2.3 million control patients without COVID-19 in the inpatient and outpatient settings, and over 221 million diagnosis codes to systematically identify new-onset conditions enriched among patients with COVID-19 during the post-acute period. Compared to inpatient controls, inpatient COVID-19 cases were at significant risk for angina pectoris (RR 1.30, 95% CI 1.09–1.55), heart failure (RR 1.22, 95% CI 1.10–1.35), cognitive dysfunctions (RR 1.18, 95% CI 1.07–1.31), and fatigue (RR 1.18, 95% CI 1.07–1.30). Relative to outpatient controls, outpatient COVID-19 cases were at risk for pulmonary embolism (RR 2.10, 95% CI 1.58–2.76), venous embolism (RR 1.34, 95% CI 1.17–1.54), atrial fibrillation (RR 1.30, 95% CI 1.13–1.50), type 2 diabetes (RR 1.26, 95% CI 1.16–1.36) and vitamin D deficiency (RR 1.19, 95% CI 1.09–1.30). Outpatient COVID-19 cases were also at risk for loss of smell and taste (RR 2.42, 95% CI 1.90–3.06), inflammatory neuropathy (RR 1.66, 95% CI 1.21–2.27), and cognitive dysfunction (RR 1.18, 95% CI 1.04–1.33). The incidence of post-acute cardiovascular and pulmonary conditions decreased across time among inpatient cases while the incidence of cardiovascular, digestive, and metabolic conditions increased among outpatient cases. Our study, based on a federated international network, systematically identified robust conditions associated with PASC compared to control groups, underscoring the multifaceted cardiovascular and neurological phenotype profiles of PASC.
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Affiliation(s)
- Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | | | - Xin Xiong
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Bryce W Q Tan
- Department of Medicine, National University Hospital, Singapore, Singapore
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University Of Kansas Medical Center, Kansas City, MO, USA
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | | | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research (MICHR) Informatics, University of Michigan, Ann Arbor, MI, USA
| | - Bertrand Moal
- IAM unit, Bordeaux University Hospital, Bordeaux, France
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore, Singapore
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health Systems Singapore, Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Andrea Prunotto
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Lauren A Dalvin
- Department of Ophthalmology, Mayo Clinic, Rochester, NY, USA
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | | | - Vincent Benoit
- IT Department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | | | - Ramakanth Kavuluru
- Division of Biomedical Informatics (Department of Internal Medicine), University of Kentucky, Lexington, KY, USA
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA.,Population Health and Data Science, VA Boston Healthcare System, Boston, MA, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore, Singapore
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Vianney Jouhet
- IAM unit, INSERM Bordeaux Population Health ERIAS TEAM, Bordeaux University Hospital / ERIAS - Inserm, U1219 BPH, Bordeaux, France
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Bruce J Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA
| | - Emma M S Toh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Wei Gen Scott Wong
- Department of Medicine, National University Health Systems Singapore, Singapore, Singapore
| | - Sara Pizzimenti
- Scientific Direction, IRCCS Ca' Granda Ospedale Maggiore Policlinico di Milano, Milan, Italy
| | | | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | | | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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Weber GM, Hong C, Xia Z, Palmer NP, Avillach P, L'Yi S, Keller MS, Murphy SN, Gutiérrez-Sacristán A, Bonzel CL, Serret-Larmande A, Neuraz A, Omenn GS, Visweswaran S, Klann JG, South AM, Loh NHW, Cannataro M, Beaulieu-Jones BK, Bellazzi R, Agapito G, Alessiani M, Aronow BJ, Bell DS, Benoit V, Bourgeois FT, Chiovato L, Cho K, Dagliati A, DuVall SL, Barrio NG, Hanauer DA, Ho YL, Holmes JH, Issitt RW, Liu M, Luo Y, Lynch KE, Maidlow SE, Malovini A, Mandl KD, Mao C, Matheny ME, Moore JH, Morris JS, Morris M, Mowery DL, Ngiam KY, Patel LP, Pedrera-Jimenez M, Ramoni RB, Schriver ER, Schubert P, Balazote PS, Spiridou A, Tan ALM, Tan BWL, Tibollo V, Torti C, Trecarichi EM, Wang X, Kohane IS, Cai T, Brat GA. International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality. NPJ Digit Med 2022; 5:74. [PMID: 35697747 PMCID: PMC9192605 DOI: 10.1038/s41746-022-00601-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 03/11/2022] [Indexed: 01/08/2023] Open
Abstract
Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach.
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Affiliation(s)
- Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Mark S Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, USA
| | | | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Arnaud Serret-Larmande
- Department of biomedical informatics, Hôpital Européen Georges Pompidou, Assistance Publique - Hôpitaux de Paris, Paris, France
| | - Antoine Neuraz
- Department of biomedical informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris (APHP), University of Paris, Paris, France
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, USA
| | - Andrew M South
- Department of Pediatrics-Section of Nephrology, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, USA
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore, Singapore, Singapore
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, Data Analytics Research Center, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy, Pavia, Italy
| | - Giuseppe Agapito
- Department of Legal, Economic and Social Sciences, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | - Mario Alessiani
- Department of Surgery, ASST Pavia, Lombardia Region Health System, Pavia, Italy
| | - Bruce J Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, USA
| | - Douglas S Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, USA
| | - Vincent Benoit
- IT department, Innovation & Data, APHP Greater Paris University Hospital, Paris, France
| | | | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Italy, Pavia, Italy
| | - Scott L DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, USA
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Richard W Issitt
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, UK, London, UK
| | - Molei Liu
- Department of Biostatistics, Harvard School of Public Health, Boston, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, USA
| | - Kristine E Lynch
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, USA
| | - Sarah E Maidlow
- Michigan Institute for Clinical and Health Research, University of Michigan, Ann Arbor, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, USA
| | - Chengsheng Mao
- Department of Preventive Medicine, Northwestern University, Chicago, USA
| | - Michael E Matheny
- VA Informatics and Computing Infrastructure, Tennessee Valley Healthcare System Veterans Affairs Medical Center, Nashville, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Jeffrey S Morris
- Department of Biostatistics, Epidemiology, and Biostatistics, University of Pennysylvania Perelman School of Medicine, Philadelphia, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, USA
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore, Singapore
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, USA
| | | | - Rachel B Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, USA
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, USA
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, USA
| | | | - Anastasia Spiridou
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, UK, London, UK
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Byorn W L Tan
- Department of Medicine, National University Hospital, Singapore, Singapore, Singapore
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Carlo Torti
- Department of Medical and Surgical Sciences, Infectious and Tropical Disease Unit, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | - Enrico M Trecarichi
- Department of Medical and Surgical Sciences, Infectious and Tropical Disease Unit, University Magna Graecia of Catanzaro, Italy, Catanzaro, Italy
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
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Klann JG, Strasser ZH, Hutch MR, Kennedy CJ, Marwaha JS, Morris M, Samayamuthu MJ, Pfaff AC, Estiri H, South AM, Weber GM, Yuan W, Avillach P, Wagholikar KB, Luo Y, Omenn GS, Visweswaran S, Holmes JH, Xia Z, Brat GA, Murphy SN. Distinguishing Admissions Specifically for COVID-19 From Incidental SARS-CoV-2 Admissions: National Retrospective Electronic Health Record Study. J Med Internet Res 2022; 24:e37931. [PMID: 35476727 PMCID: PMC9119395 DOI: 10.2196/37931] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 04/22/2022] [Accepted: 04/22/2022] [Indexed: 01/16/2023] Open
Abstract
BACKGROUND Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. Electronic health record (EHR)-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. Although the need to improve classification of COVID-19 versus incidental SARS-CoV-2 is well understood, the magnitude of the problems has only been characterized in small, single-center studies. Furthermore, there have been no peer-reviewed studies evaluating methods for improving classification. OBJECTIVE The aims of this study are to, first, quantify the frequency of incidental hospitalizations over the first 15 months of the pandemic in multiple hospital systems in the United States and, second, to apply electronic phenotyping techniques to automatically improve COVID-19 hospitalization classification. METHODS From a retrospective EHR-based cohort in 4 US health care systems in Massachusetts, Pennsylvania, and Illinois, a random sample of 1123 SARS-CoV-2 PCR-positive patients hospitalized from March 2020 to August 2021 was manually chart-reviewed and classified as "admitted with COVID-19" (incidental) versus specifically admitted for COVID-19 ("for COVID-19"). EHR-based phenotyping was used to find feature sets to filter out incidental admissions. RESULTS EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in an average of 26% of hospitalizations (although this varied widely over time, from 0% to 75%). The top site-specific feature sets had 79%-99% specificity with 62%-75% sensitivity, while the best-performing across-site feature sets had 71%-94% specificity with 69%-81% sensitivity. CONCLUSIONS A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.
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Affiliation(s)
- Jeffrey G Klann
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Zachary H Strasser
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Chris J Kennedy
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, United States
| | - Jayson S Marwaha
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Ashley C Pfaff
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Hossein Estiri
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's, Wake Forest School of Medicine, Winston Salem, NC, United States
| | | | | | | | - Kavishwar B Wagholikar
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Gilbert S Omenn
- Center for Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
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Mannemuddhu SS, Macumber I, Samuels JA, Flynn JT, South AM. When Hypertension Grows Up: Implications for Transitioning Care of Adolescents and Young Adults With Hypertension From Pediatric to Adult Health Care Providers. Adv Chronic Kidney Dis 2022; 29:263-274. [PMID: 36084973 DOI: 10.1053/j.ackd.2021.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/01/2021] [Accepted: 11/15/2021] [Indexed: 11/11/2022]
Abstract
Hypertension (HTN) is an important cause of morbidity and mortality in children as well as adults. HTN and related adverse cardiovascular health develop and progress on a continuum across an individual's life course. Pediatric HTN, or even isolated elevated blood pressure as a child, increases the risk of sustained HTN and cardiovascular disease in later adulthood. Transitioning the care of adolescents and young adults who have HTN is an important but unmet health care need that could potentially have a dramatic effect on mitigating the risk of cardiovascular disease in adulthood. However, very little has been published about the transition process in this population, and considerable gaps in the field remain. We discuss the epidemiology, etiology, and management approach in youth with HTN and how they differ from adults. We contextualize HTN and cardiovascular health on a continuum across the life course. We discuss key considerations for the transition process for adolescents and young adults with HTN including the major barriers that exist. Finally, we review key immediate health care needs that are particularly important around the time of the transfer of care.
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Affiliation(s)
- Sai Sudha Mannemuddhu
- East Tennessee Children's Hospital, Knoxville, TN; Department of Medicine, University of Tennessee Health Science Center-College of Medicine, Knoxville, TN
| | - Ian Macumber
- Department of Pediatrics, Keck School of Medicine, Division of Nephrology, Children's Hospital Los Angeles, Los Angeles, CA
| | - Joshua A Samuels
- Department of Pediatrics, Pediatric Nephrology & Hypertension, McGovern Medical School at the University of Texas Health Science Center, Houston, TX
| | - Joseph T Flynn
- Department of Pediatrics, University of Washington, Division of Nephrology, Seattle Children's Hospital, Seattle, WA.
| | - Andrew M South
- Department of Pediatrics, Section of Nephrology, Wake Forest School of Medicine and Brenner Children's Hospital, Winston Salem, NC; Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston Salem, NC; Cardiovascular Sciences Center, Wake Forest School of Medicine, Winston Salem, NC
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Perrin EC, South AM. Correlation between kidney sodium and potassium handling and the renin-angiotensin-aldosterone system in children with hypertensive disorders. Pediatr Nephrol 2022; 37:633-641. [PMID: 34499251 PMCID: PMC8904647 DOI: 10.1007/s00467-021-05204-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 06/11/2021] [Accepted: 06/22/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Urine sodium and potassium are used as surrogate markers for dietary consumption in adults with hypertension, but their role in youth with hypertension and their association with components of the renin-angiotensin-aldosterone system (RAAS) are incompletely characterized. Some individuals with hypertension may have an abnormal RAAS response to dietary sodium and potassium intake, though this is incompletely described. Our objective was to investigate if plasma renin activity and serum aldosterone are associated with urine sodium and potassium in youth referred for hypertensive disorders. METHODS This pilot study was a cross-sectional analysis of baseline data from 44 youth evaluated for hypertensive disorders in a Hypertension Clinic. We recorded urine sodium and potassium concentrations normalized to urine creatinine, plasma renin activity, and serum aldosterone and calculated the sodium/potassium (UNaK) and aldosterone/renin ratios. We used multivariable generalized linear models to estimate the associations of renin and aldosterone with urine sodium and potassium. RESULTS Our cohort was diverse (37% non-Hispanic Black, 14% Hispanic), 66% were male, and median age was 15.3 years; 77% had obesity and 9% had a secondary etiology. Aldosterone was associated inversely with urine sodium/creatinine (β: -0.34, 95% CI -0.62 to -0.06) and UNaK (β: -0.09, 95% CI -0.16 to -0.03), and adjusted for estimated glomerular filtration rate and serum potassium. CONCLUSIONS Higher serum aldosterone levels, but not plasma renin activity, were associated with lower urine sodium/creatinine and UNaK at baseline in youth referred for hypertensive disorders. Further characterization of the RAAS could help define hypertension phenotypes and guide management. A higher resolution version of the Graphical abstract is available as supplementary information.
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Affiliation(s)
- Ella C Perrin
- Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Andrew M South
- Department of Pediatrics, Section of Nephrology, Brenner Children's Hospital, Wake Forest School of Medicine, One Medical Center Boulevard, Winston Salem, NC, 27157, USA. .,Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston Salem, NC, USA. .,Department of Surgery-Hypertension and Vascular Research, Wake Forest School of Medicine, Winston Salem, NC, USA. .,Center for Biomedical Informatics, Wake Forest School of Medicine, Winston Salem, NC, USA.
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Puskarich MA, Ingraham NE, Merck LH, Driver BE, Wacker DA, Black LP, Jones AE, Fletcher CV, South AM, Murray TA, Lewandowski C, Farhat J, Benoit JL, Biros MH, Cherabuddi K, Chipman JG, Schacker TW, Guirgis FW, Voelker HT, Koopmeiners JS, Tignanelli CJ. Efficacy of Losartan in Hospitalized Patients With COVID-19-Induced Lung Injury: A Randomized Clinical Trial. JAMA Netw Open 2022; 5:e222735. [PMID: 35294537 PMCID: PMC8928006 DOI: 10.1001/jamanetworkopen.2022.2735] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 01/23/2022] [Indexed: 12/14/2022] Open
Abstract
Importance SARS-CoV-2 viral entry may disrupt angiotensin II (AII) homeostasis, contributing to COVID-19 induced lung injury. AII type 1 receptor blockade mitigates lung injury in preclinical models, although data in humans with COVID-19 remain mixed. Objective To test the efficacy of losartan to reduce lung injury in hospitalized patients with COVID-19. Design, Setting, and Participants This blinded, placebo-controlled randomized clinical trial was conducted in 13 hospitals in the United States from April 2020 to February 2021. Hospitalized patients with COVID-19 and a respiratory sequential organ failure assessment score of at least 1 and not already using a renin-angiotensin-aldosterone system (RAAS) inhibitor were eligible for participation. Data were analyzed from April 19 to August 24, 2021. Interventions Losartan 50 mg orally twice daily vs equivalent placebo for 10 days or until hospital discharge. Main Outcomes and Measures The primary outcome was the imputed arterial partial pressure of oxygen to fraction of inspired oxygen (Pao2:Fio2) ratio at 7 days. Secondary outcomes included ordinal COVID-19 severity; days without supplemental o2, ventilation, or vasopressors; and mortality. Losartan pharmacokinetics and RAAS components (AII, angiotensin-[1-7] and angiotensin-converting enzymes 1 and 2)] were measured in a subgroup of participants. Results A total of 205 participants (mean [SD] age, 55.2 [15.7] years; 123 [60.0%] men) were randomized, with 101 participants assigned to losartan and 104 participants assigned to placebo. Compared with placebo, losartan did not significantly affect Pao2:Fio2 ratio at 7 days (difference, -24.8 [95%, -55.6 to 6.1]; P = .12). Compared with placebo, losartan did not improve any secondary clinical outcomes and led to fewer vasopressor-free days than placebo (median [IQR], 9.4 [9.1-9.8] vasopressor-free days vs 8.7 [8.2-9.3] vasopressor-free days). Conclusions and Relevance This randomized clinical trial found that initiation of orally administered losartan to hospitalized patients with COVID-19 and acute lung injury did not improve Pao2:Fio2 ratio at 7 days. These data may have implications for ongoing clinical trials. Trial Registration ClinicalTrials.gov Identifier: NCT04312009.
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Affiliation(s)
- Michael A. Puskarich
- Department of Emergency Medicine, University of Minnesota, Minneapolis
- Department of Emergency Medicine, Hennepin County Medical Center, Minneapolis, Minnesota
| | - Nicholas E. Ingraham
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota, Minneapolis
| | - Lisa H. Merck
- Department of Emergency Medicine, University of Florida College of Medicine, Gainesville
| | - Brian E. Driver
- Department of Emergency Medicine, Hennepin County Medical Center, Minneapolis, Minnesota
| | - David A. Wacker
- Division of Pulmonary, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Minnesota, Minneapolis
| | - Lauren Page Black
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville
| | - Alan E. Jones
- Department of Emergency Medicine, University of Mississippi Medical Center, Jackson
| | | | - Andrew M. South
- Section of Nephrology, Department of Pediatrics, Wake Forest School of Medicine and Brenner Children's Hospital, Winston Salem, North Carolina
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston Salem, North Carolina
- Department of Surgery-Hypertension and Vascular Research, Wake Forest School of Medicine, Winston Salem, North Carolina
| | - Thomas A. Murray
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis
| | - Christopher Lewandowski
- Department of Emergency Medicine, Henry Ford Hospital, Wayne State University, Detroit, Michigan
| | - Joseph Farhat
- Department of Surgery, North Memorial Medical Center, Minneapolis, Minnesota
| | - Justin L. Benoit
- Department of Emergency Medicine, University of Cincinnati, Cincinnati, Ohio
| | - Michelle H. Biros
- Department of Emergency Medicine, University of Minnesota, Minneapolis
| | - Kartik Cherabuddi
- Department of Emergency Medicine, University of Florida College of Medicine, Gainesville
| | | | - Timothy W. Schacker
- Division of Infectious Disease, Department of Medicine, University of Minnesota, Minneapolis
| | - Faheem W. Guirgis
- Department of Emergency Medicine, University of Florida College of Medicine, Jacksonville
| | - Helen T. Voelker
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis
| | - Joseph S. Koopmeiners
- Department of Biostatistics, School of Public Health, University of Minnesota, Minneapolis
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35
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Klann JG, Strasser ZH, Hutch MR, Kennedy CJ, Marwaha JS, Morris M, Samayamuthu MJ, Pfaff AC, Estiri H, South AM, Weber GM, Yuan W, Avillach P, Wagholikar KB, Luo Y, Omenn GS, Visweswaran S, Holmes JH, Xia Z, Brat GA, Murphy SN. Distinguishing Admissions Specifically for COVID-19 from Incidental SARS-CoV-2 Admissions: A National EHR Research Consortium Study. medRxiv 2022:2022.02.10.22270728. [PMID: 35350202 PMCID: PMC8963684 DOI: 10.1101/2022.02.10.22270728] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Admissions are generally classified as COVID-19 hospitalizations if the patient has a positive SARS-CoV-2 polymerase chain reaction (PCR) test. However, because 35% of SARS-CoV-2 infections are asymptomatic, patients admitted for unrelated indications with an incidentally positive test could be misclassified as a COVID-19 hospitalization. EHR-based studies have been unable to distinguish between a hospitalization specifically for COVID-19 versus an incidental SARS-CoV-2 hospitalization. From a retrospective EHR-based cohort in four US healthcare systems, a random sample of 1,123 SARS-CoV-2 PCR-positive patients hospitalized between 3/2020â€"8/2021 was manually chart-reviewed and classified as admitted-with-COVID-19 (incidental) vs. specifically admitted for COVID-19 (for-COVID-19). EHR-based phenotyped feature sets filtered out incidental admissions, which occurred in 26%. The top site-specific feature sets had 79-99% specificity with 62-75% sensitivity, while the best performing across-site feature set had 71-94% specificity with 69-81% sensitivity. A large proportion of SARS-CoV-2 PCR-positive admissions were incidental. Straightforward EHR-based phenotypes differentiated admissions, which is important to assure accurate public health reporting and research.
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36
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Weber GM, Zhang HG, L'Yi S, Bonzel CL, Hong C, Avillach P, Gutiérrez-Sacristán A, Palmer NP, Tan ALM, Wang X, Yuan W, Gehlenborg N, Alloni A, Amendola DF, Bellasi A, Bellazzi R, Beraghi M, Bucalo M, Chiovato L, Cho K, Dagliati A, Estiri H, Follett RW, García Barrio N, Hanauer DA, Henderson DW, Ho YL, Holmes JH, Hutch MR, Kavuluru R, Kirchoff K, Klann JG, Krishnamurthy AK, Le TT, Liu M, Loh NHW, Lozano-Zahonero S, Luo Y, Maidlow S, Makoudjou A, Malovini A, Martins MR, Moal B, Morris M, Mowery DL, Murphy SN, Neuraz A, Ngiam KY, Okoshi MP, Omenn GS, Patel LP, Pedrera Jiménez M, Prudente RA, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano Balazote P, Tan BW, Tanni SE, Tibollo V, Visweswaran S, Wagholikar KB, Xia Z, Zöller D, Kohane IS, Cai T, South AM, Brat GA. Authorship Correction: International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study. J Med Internet Res 2021; 23:e34625. [PMID: 34889759 PMCID: PMC8672293 DOI: 10.2196/34625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 11/10/2021] [Indexed: 11/15/2022] Open
Affiliation(s)
- Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Anna Alloni
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Danilo F Amendola
- Clinical Research Unit, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Antonio Bellasi
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Michele Beraghi
- Information Technology Department, Azienda Socio-Sanitaria Territoriale di Pavia, Pavia, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Darren W Henderson
- Department of Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Ramakanth Kavuluru
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Katie Kirchoff
- Medical University of South Carolina, Charleston, SC, United States
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Ashok K Krishnamurthy
- Department of Computer Science, Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Trang T Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Molei Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore, Singapore
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Sarah Maidlow
- Michigan Institute for Clinical & Health Research Informatics, University of Michigan, Ann Arbor, MI, United States
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | | | - Bertrand Moal
- Informatique et archivistique médicales unit, Bordeaux University Hospital, Bordeaux, France
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics, Institute for Digital Medicine, National University Health System, Singapore, Singapore
| | - Marina P Okoshi
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Lav P Patel
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Robson A Prudente
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | | | - Fernando J Sanz Vidorreta
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | | | - Byorn Wl Tan
- Department of Medicine, National University Health System, Singapore, Singapore
| | - Suzana E Tanni
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | -
- see Authors' Contributions,
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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Le TT, Gutiérrez-Sacristán A, Son J, Hong C, South AM, Beaulieu-Jones BK, Loh NHW, Luo Y, Morris M, Ngiam KY, Patel LP, Samayamuthu MJ, Schriver E, Tan ALM, Moore J, Cai T, Omenn GS, Avillach P, Kohane IS, Visweswaran S, Mowery DL, Xia Z. Multinational characterization of neurological phenotypes in patients hospitalized with COVID-19. Sci Rep 2021; 11:20238. [PMID: 34642371 PMCID: PMC8510999 DOI: 10.1038/s41598-021-99481-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/23/2021] [Indexed: 01/08/2023] Open
Abstract
Neurological complications worsen outcomes in COVID-19. To define the prevalence of neurological conditions among hospitalized patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test in geographically diverse multinational populations during early pandemic, we used electronic health records (EHR) from 338 participating hospitals across 6 countries and 3 continents (January-September 2020) for a cross-sectional analysis. We assessed the frequency of International Classification of Disease code of neurological conditions by countries, healthcare systems, time before and after admission for COVID-19 and COVID-19 severity. Among 35,177 hospitalized patients with SARS-CoV-2 infection, there was an increase in the proportion with disorders of consciousness (5.8%, 95% confidence interval [CI] 3.7-7.8%, pFDR < 0.001) and unspecified disorders of the brain (8.1%, 5.7-10.5%, pFDR < 0.001) when compared to the pre-admission proportion. During hospitalization, the relative risk of disorders of consciousness (22%, 19-25%), cerebrovascular diseases (24%, 13-35%), nontraumatic intracranial hemorrhage (34%, 20-50%), encephalitis and/or myelitis (37%, 17-60%) and myopathy (72%, 67-77%) were higher for patients with severe COVID-19 when compared to those who never experienced severe COVID-19. Leveraging a multinational network to capture standardized EHR data, we highlighted the increased prevalence of central and peripheral neurological phenotypes in patients hospitalized with COVID-19, particularly among those with severe disease.
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Affiliation(s)
- Trang T Le
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Jiyeon Son
- Department of Neurology, University of Pittsburgh, Biomedical Science Tower 3, Suite 7014, 3501 5th Avenue, Pittsburgh, PA, 15260, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Andrew M South
- Department of Pediatrics, Wake Forest School of Medicine, Winston Salem, NC, USA
| | | | - Ne Hooi Will Loh
- Department of Critical Care, National University Health Systems, Singapore, Singapore
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kee Yuan Ngiam
- Department of Surgery, National University Health Systems, Singapore, Singapore
| | - Lav P Patel
- Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Emily Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jason Moore
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Biomedical Science Tower 3, Suite 7014, 3501 5th Avenue, Pittsburgh, PA, 15260, USA.
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38
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Weber GM, Zhang HG, L'Yi S, Bonzel CL, Hong C, Avillach P, Gutiérrez-Sacristán A, Palmer NP, Tan ALM, Wang X, Yuan W, Gehlenborg N, Alloni A, Amendola DF, Bellasi A, Bellazzi R, Beraghi M, Bucalo M, Chiovato L, Cho K, Dagliati A, Estiri H, Follett RW, García Barrio N, Hanauer DA, Henderson DW, Ho YL, Holmes JH, Hutch MR, Kavuluru R, Kirchoff K, Klann JG, Krishnamurthy AK, Le TT, Liu M, Loh NHW, Lozano-Zahonero S, Luo Y, Maidlow S, Makoudjou A, Malovini A, Martins MR, Moal B, Morris M, Mowery DL, Murphy SN, Neuraz A, Ngiam KY, Okoshi MP, Omenn GS, Patel LP, Pedrera Jiménez M, Prudente RA, Samayamuthu MJ, Sanz Vidorreta FJ, Schriver ER, Schubert P, Serrano Balazote P, Tan BW, Tanni SE, Tibollo V, Visweswaran S, Wagholikar KB, Xia Z, Zöller D, Kohane IS, Cai T, South AM, Brat GA. International Changes in COVID-19 Clinical Trajectories Across 315 Hospitals and 6 Countries: Retrospective Cohort Study. J Med Internet Res 2021; 23:e31400. [PMID: 34533459 PMCID: PMC8510151 DOI: 10.2196/31400] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 09/02/2021] [Accepted: 09/02/2021] [Indexed: 02/06/2023] Open
Abstract
Background Many countries have experienced 2 predominant waves of COVID-19–related hospitalizations. Comparing the clinical trajectories of patients hospitalized in separate waves of the pandemic enables further understanding of the evolving epidemiology, pathophysiology, and health care dynamics of the COVID-19 pandemic. Objective In this retrospective cohort study, we analyzed electronic health record (EHR) data from patients with SARS-CoV-2 infections hospitalized in participating health care systems representing 315 hospitals across 6 countries. We compared hospitalization rates, severe COVID-19 risk, and mean laboratory values between patients hospitalized during the first and second waves of the pandemic. Methods Using a federated approach, each participating health care system extracted patient-level clinical data on their first and second wave cohorts and submitted aggregated data to the central site. Data quality control steps were adopted at the central site to correct for implausible values and harmonize units. Statistical analyses were performed by computing individual health care system effect sizes and synthesizing these using random effect meta-analyses to account for heterogeneity. We focused the laboratory analysis on C-reactive protein (CRP), ferritin, fibrinogen, procalcitonin, D-dimer, and creatinine based on their reported associations with severe COVID-19. Results Data were available for 79,613 patients, of which 32,467 were hospitalized in the first wave and 47,146 in the second wave. The prevalence of male patients and patients aged 50 to 69 years decreased significantly between the first and second waves. Patients hospitalized in the second wave had a 9.9% reduction in the risk of severe COVID-19 compared to patients hospitalized in the first wave (95% CI 8.5%-11.3%). Demographic subgroup analyses indicated that patients aged 26 to 49 years and 50 to 69 years; male and female patients; and black patients had significantly lower risk for severe disease in the second wave than in the first wave. At admission, the mean values of CRP were significantly lower in the second wave than in the first wave. On the seventh hospital day, the mean values of CRP, ferritin, fibrinogen, and procalcitonin were significantly lower in the second wave than in the first wave. In general, countries exhibited variable changes in laboratory testing rates from the first to the second wave. At admission, there was a significantly higher testing rate for D-dimer in France, Germany, and Spain. Conclusions Patients hospitalized in the second wave were at significantly lower risk for severe COVID-19. This corresponded to mean laboratory values in the second wave that were more likely to be in typical physiological ranges on the seventh hospital day compared to the first wave. Our federated approach demonstrated the feasibility and power of harmonizing heterogeneous EHR data from multiple international health care systems to rapidly conduct large-scale studies to characterize how COVID-19 clinical trajectories evolve.
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Affiliation(s)
- Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Harrison G Zhang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Sehi L'Yi
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Nathan P Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - William Yuan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Anna Alloni
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Danilo F Amendola
- Clinical Research Unit, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Antonio Bellasi
- Division of Nephrology, Department of Medicine, Ente Ospedaliero Cantonale, Lugano, Switzerland
| | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Michele Beraghi
- Information Technology Department, Azienda Socio-Sanitaria Territoriale di Pavia, Pavia, Italy
| | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions), Pavia, Italy
| | - Luca Chiovato
- Unit of Internal Medicine and Endocrinology, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Kelly Cho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - Arianna Dagliati
- Department of Electrical Computer and Biomedical Engineering, University of Pavia, Pavia, Italy
| | - Hossein Estiri
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Darren W Henderson
- Department of Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States.,Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Meghan R Hutch
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Ramakanth Kavuluru
- Institute for Biomedical Informatics, University of Kentucky, Lexington, KY, United States
| | - Katie Kirchoff
- Medical University of South Carolina, Charleston, SC, United States
| | - Jeffrey G Klann
- Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Ashok K Krishnamurthy
- Department of Computer Science, Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Trang T Le
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Molei Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Ne Hooi Will Loh
- Department of Anaesthesia, National University Health System, Singapore, Singapore
| | - Sara Lozano-Zahonero
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Sarah Maidlow
- Michigan Institute for Clinical & Health Research Informatics, University of Michigan, Ann Arbor, MI, United States
| | - Adeline Makoudjou
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | | | - Bertrand Moal
- Informatique et archivistique médicales unit, Bordeaux University Hospital, Bordeaux, France
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics, Institute for Digital Medicine, National University Health System, Singapore, Singapore
| | - Marina P Okoshi
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and Public Health, University of Michigan, Ann Arbor, MI, United States
| | - Lav P Patel
- Division of Medical Informatics, Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Robson A Prudente
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | | | - Fernando J Sanz Vidorreta
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Emily R Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, United States
| | - Petra Schubert
- Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, United States
| | | | - Byorn Wl Tan
- Department of Medicine, National University Health System, Singapore, Singapore
| | - Suzana E Tanni
- Internal Medicine Department, Botucatu Medical School, São Paulo State University, Botucatu, Brazil
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri SpA SB IRCCS, Pavia, Italy
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | | | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Daniela Zöller
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
| | -
- see Authors' Contributions,
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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Karere GM, Cox LA, Bishop AC, South AM, Shaltout HA, Mercado-Deane MG, Cuda S. Sex Differences in MicroRNA Expression and Cardiometabolic Risk Factors in Hispanic Adolescents with Obesity. J Pediatr 2021; 235:138-143.e5. [PMID: 33831442 PMCID: PMC8926296 DOI: 10.1016/j.jpeds.2021.03.070] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Revised: 03/25/2021] [Accepted: 03/30/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE To evaluate sex differences in microRNA (miRNA) expression, anthropometric measures, and cardiometabolic risk factors in Hispanic adolescents with obesity. STUDY DESIGN Cross-sectional study of 68 (60% male) Hispanic adolescents with obesity, aged 13-17 years, recruited from a pediatric weight management clinic. We used small RNA sequencing to identify differentially expressed circulating miRNAs. We used ingenuity pathway analysis and David bioinformatic resource tools to identify target genes for these miRNAs and enriched pathways. We used standard procedures to measure anthropometric and cardiometabolic factors. RESULTS We identified 5 miRNAs (miR-24-3p, miR-361-3p, miR-3605-5p, miR-486-5p, and miR-199b-3p) that differed between females and males. miRNA targets-enriched pathways included phosphatidylinositol 3-kinase-protein, 5' AMP-activated protein kinase, insulin resistance, sphingolipid, transforming growth factor-β, adipocyte lipolysis regulation, and oxytocin signaling pathways. In addition, there were sex differences in blood pressure, skeletal muscle mass, lean body mass, and percent body fat. CONCLUSIONS We have identified sex differences in miRNA expression in Hispanic adolescents relevant to cardiometabolic health. Future studies should focus on sex-specific mechanistic roles of miRNAs on gene pathways associated with obesity pathophysiology to support development of precision cardiometabolic interventions.
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Affiliation(s)
- Genesio M. Karere
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina,Corresponding author Department of Internal Medicine, Center for Precision Medicine, Wake Forest Baptist, Medical Center, Winston-Salem, NC 27157., Telephone: (336) 713-7561, Fax: (336) 713-7566,
| | - Laura A. Cox
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Andrew C. Bishop
- Department of Internal Medicine, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Andrew M. South
- Department of Pediatrics, Brenner Children’s Hospital, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina,Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Hossam A. Shaltout
- Department of Obstetrics and Gynecology, Wake Forest Baptist Medical Center, Winston-Salem, North Carolina
| | - Maria-Gisela Mercado-Deane
- Department of Radiology, Baylor College of Medicine, Children’s Hospital of San Antonio, San Antonio, Texas
| | - Suzanne Cuda
- Department of Pediatrics, Baylor College of Medicine, Children’s Hospital of San Antonio, San Antonio, Texas
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40
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Klann JG, Estiri H, Weber GM, Moal B, Avillach P, Hong C, Tan ALM, Beaulieu-Jones BK, Castro V, Maulhardt T, Geva A, Malovini A, South AM, Visweswaran S, Morris M, Samayamuthu MJ, Omenn GS, Ngiam KY, Mandl KD, Boeker M, Olson KL, Mowery DL, Follett RW, Hanauer DA, Bellazzi R, Moore JH, Loh NHW, Bell DS, Wagholikar KB, Chiovato L, Tibollo V, Rieg S, Li ALLJ, Jouhet V, Schriver E, Xia Z, Hutch M, Luo Y, Kohane IS, Brat GA, Murphy SN. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data. J Am Med Inform Assoc 2021; 28:1411-1420. [PMID: 33566082 PMCID: PMC7928835 DOI: 10.1093/jamia/ocab018] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 01/14/2021] [Accepted: 01/29/2021] [Indexed: 12/21/2022] Open
Abstract
OBJECTIVE The Consortium for Clinical Characterization of COVID-19 by EHR (4CE) is an international collaboration addressing coronavirus disease 2019 (COVID-19) with federated analyses of electronic health record (EHR) data. We sought to develop and validate a computable phenotype for COVID-19 severity. MATERIALS AND METHODS Twelve 4CE sites participated. First, we developed an EHR-based severity phenotype consisting of 6 code classes, and we validated it on patient hospitalization data from the 12 4CE clinical sites against the outcomes of intensive care unit (ICU) admission and/or death. We also piloted an alternative machine learning approach and compared selected predictors of severity with the 4CE phenotype at 1 site. RESULTS The full 4CE severity phenotype had pooled sensitivity of 0.73 and specificity 0.83 for the combined outcome of ICU admission and/or death. The sensitivity of individual code categories for acuity had high variability-up to 0.65 across sites. At one pilot site, the expert-derived phenotype had mean area under the curve of 0.903 (95% confidence interval, 0.886-0.921), compared with an area under the curve of 0.956 (95% confidence interval, 0.952-0.959) for the machine learning approach. Billing codes were poor proxies of ICU admission, with as low as 49% precision and recall compared with chart review. DISCUSSION We developed a severity phenotype using 6 code classes that proved resilient to coding variability across international institutions. In contrast, machine learning approaches may overfit hospital-specific orders. Manual chart review revealed discrepancies even in the gold-standard outcomes, possibly owing to heterogeneous pandemic conditions. CONCLUSIONS We developed an EHR-based severity phenotype for COVID-19 in hospitalized patients and validated it at 12 international sites.
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Affiliation(s)
- Jeffrey G Klann
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Hossein Estiri
- Laboratory of Computer Science, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Griffin M Weber
- Department of Biomedical Informatics, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Bertrand Moal
- IAM Unit, Public Health Department , Bordeaux University Hospital, Bordeaux, France
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Amelia L M Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Brett K Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Victor Castro
- Research Information Science and Computing, Mass General Brigham, Boston, Massachusetts, USA
| | - Thomas Maulhardt
- Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alon Geva
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Alberto Malovini
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, North Carolina, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Malarkodi J Samayamuthu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA
| | - Kee Yuan Ngiam
- Department of Biomedical Informatics-WisDM, National University Health System, Singapore
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Karen L Olson
- Computational Health Informatics Program, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Robert W Follett
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Riccardo Bellazzi
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
| | - Jason H Moore
- Department of Biostatistics, Epidemiology, and Informatics, Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Ne-Hooi Will Loh
- Division of Critical Care, National University Health System, Singapore
| | - Douglas S Bell
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, USA
| | | | - Luca Chiovato
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy.,Department of Internal Medicine and Medical Therapy, University of Pavia, Pavia, Italy
| | - Valentina Tibollo
- Laboratory of Informatics and Systems Engineering for Clinical Research, Istituti Clinici Scientifici Maugeri IRCCS, Pavia, Italy
| | - Siegbert Rieg
- Division of Infectious Diseases, Department of Medicine II, Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Anthony L L J Li
- National Center for Infectious Diseases, Tan Tock Seng Hospital, Singapore
| | - Vianney Jouhet
- ERIAS-INSERM U1219 BPH, Bordeaux University Hospital, Bordeaux, France
| | - Emily Schriver
- Data Analytics Center, Penn Medicine, Philadelphia, Pennsylvania, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Meghan Hutch
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Shawn N Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA.,Research Information Science and Computing , Mass General Brigham, Boston, Massachusetts, USA
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Bourgeois FT, Gutiérrez-Sacristán A, Keller MS, Liu M, Hong C, Bonzel CL, Tan ALM, Aronow BJ, Boeker M, Booth J, Cruz Rojo J, Devkota B, García Barrio N, Gehlenborg N, Geva A, Hanauer DA, Hutch MR, Issitt RW, Klann JG, Luo Y, Mandl KD, Mao C, Moal B, Moshal KL, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Patel LP, Jiménez MP, Sebire NJ, Balazote PS, Serret-Larmande A, South AM, Spiridou A, Taylor DM, Tippmann P, Visweswaran S, Weber GM, Kohane IS, Cai T, Avillach P. International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries. JAMA Netw Open 2021; 4:e2112596. [PMID: 34115127 PMCID: PMC8196345 DOI: 10.1001/jamanetworkopen.2021.12596] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
IMPORTANCE Additional sources of pediatric epidemiological and clinical data are needed to efficiently study COVID-19 in children and youth and inform infection prevention and clinical treatment of pediatric patients. OBJECTIVE To describe international hospitalization trends and key epidemiological and clinical features of children and youth with COVID-19. DESIGN, SETTING, AND PARTICIPANTS This retrospective cohort study included pediatric patients hospitalized between February 2 and October 10, 2020. Patient-level electronic health record (EHR) data were collected across 27 hospitals in France, Germany, Spain, Singapore, the UK, and the US. Patients younger than 21 years who tested positive for COVID-19 and were hospitalized at an institution participating in the Consortium for Clinical Characterization of COVID-19 by EHR were included in the study. MAIN OUTCOMES AND MEASURES Patient characteristics, clinical features, and medication use. RESULTS There were 347 males (52%; 95% CI, 48.5-55.3) and 324 females (48%; 95% CI, 44.4-51.3) in this study's cohort. There was a bimodal age distribution, with the greatest proportion of patients in the 0- to 2-year (199 patients [30%]) and 12- to 17-year (170 patients [25%]) age range. Trends in hospitalizations for 671 children and youth found discrete surges with variable timing across 6 countries. Data from this cohort mirrored national-level pediatric hospitalization trends for most countries with available data, with peaks in hospitalizations during the initial spring surge occurring within 23 days in the national-level and 4CE data. A total of 27 364 laboratory values for 16 laboratory tests were analyzed, with mean values indicating elevations in markers of inflammation (C-reactive protein, 83 mg/L; 95% CI, 53-112 mg/L; ferritin, 417 ng/mL; 95% CI, 228-607 ng/mL; and procalcitonin, 1.45 ng/mL; 95% CI, 0.13-2.77 ng/mL). Abnormalities in coagulation were also evident (D-dimer, 0.78 ug/mL; 95% CI, 0.35-1.21 ug/mL; and fibrinogen, 477 mg/dL; 95% CI, 385-569 mg/dL). Cardiac troponin, when checked (n = 59), was elevated (0.032 ng/mL; 95% CI, 0.000-0.080 ng/mL). Common complications included cardiac arrhythmias (15.0%; 95% CI, 8.1%-21.7%), viral pneumonia (13.3%; 95% CI, 6.5%-20.1%), and respiratory failure (10.5%; 95% CI, 5.8%-15.3%). Few children were treated with COVID-19-directed medications. CONCLUSIONS AND RELEVANCE This study of EHRs of children and youth hospitalized for COVID-19 in 6 countries demonstrated variability in hospitalization trends across countries and identified common complications and laboratory abnormalities in children and youth with COVID-19 infection. Large-scale informatics-based approaches to integrate and analyze data across health care systems complement methods of disease surveillance and advance understanding of epidemiological and clinical features associated with COVID-19 in children and youth.
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Affiliation(s)
- Florence T. Bourgeois
- Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | | | - Mark S. Keller
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Molei Liu
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Clara-Lea Bonzel
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Amelia L. M. Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Bruce J. Aronow
- Departments of Biomedical Informatics, Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Ohio
| | - Martin Boeker
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - John Booth
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Jaime Cruz Rojo
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Batsal Devkota
- Department of Biomedical Health Informatics and the Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Noelia García Barrio
- Department of Health Informatics, Hospital Universitario 12 de Octubre, Madrid, Spain
| | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Alon Geva
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Anesthesiology, Critical Care, and Pain Medicine, Boston Children’s Hospital, Boston, Massachusetts
| | - David A. Hanauer
- Department of Learning Health Sciences, University of Michigan, Ann Arbor
| | - Meghan R. Hutch
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois
| | - Richard W. Issitt
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | | | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois
| | - Kenneth D. Mandl
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
| | - Chengsheng Mao
- Department of Preventive Medicine, Northwestern University, Evanston, Illinois
| | - Bertrand Moal
- IAM Unit, Bordeaux University Hospital, Bordeaux, France
| | - Karyn L. Moshal
- Department of Infectious Diseases, Great Ormond Street Hospital for Children, London, United Kingdom
| | - Shawn N. Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Antoine Neuraz
- Department of Biomedical Informatics, Hôpital Necker-Enfants Malade, Assistance Publique Hôpitaux de Paris, University of Paris, Paris, France
| | - Kee Yuan Ngiam
- Department of Biomedical informatics, WiSDM, National University Health Systems Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, & School of Public Health, University of Michigan, Ann Arbor
| | - Lav P. Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City
| | | | - Neil J. Sebire
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | | | | | - Andrew M. South
- Department of Pediatrics-Section of Nephrology, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, North Carolina
| | - Anastasia Spiridou
- Digital Research, Informatics and Virtual Environments (DRIVE), Great Ormond Street Hospital for Children, London, United Kingdom
| | - Deanne M. Taylor
- Department of Biomedical Health Informatics and the Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Department of Pediatrics, Perelman Medical School at the University of Pennsylvania, Philadelphia
| | - Patric Tippmann
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Germany
| | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Griffin M. Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Isaac S. Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | - Paul Avillach
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
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Derington CG, Cohen JB, Mohanty AF, Greene TH, Cook J, Ying J, Wei G, Herrick JS, Stevens VW, Jones BE, Wang L, Zheutlin AR, South AM, Hanff TC, Smith SM, Cooper-DeHoff RM, King JB, Alexander GC, Berlowitz DR, Ahmad FS, Penrod MJ, Hess R, Conroy MB, Fang JC, Rubin MA, Beddhu S, Cheung AK, Xian W, Weintraub WS, Bress AP. Angiotensin II receptor blocker or angiotensin-converting enzyme inhibitor use and COVID-19-related outcomes among US Veterans. PLoS One 2021; 16:e0248080. [PMID: 33891615 PMCID: PMC8064574 DOI: 10.1371/journal.pone.0248080] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 02/19/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Angiotensin II receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs) may positively or negatively impact outcomes in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We investigated the association of ARB or ACEI use with coronavirus disease 2019 (COVID-19)-related outcomes in US Veterans with treated hypertension using an active comparator design, appropriate covariate adjustment, and negative control analyses. METHODS AND FINDINGS In this retrospective cohort study of Veterans with treated hypertension in the Veterans Health Administration (01/19/2020-08/28/2020), we compared users of (A) ARB/ACEI vs. non-ARB/ACEI (excluding Veterans with compelling indications to reduce confounding by indication) and (B) ARB vs. ACEI among (1) SARS-CoV-2+ outpatients and (2) COVID-19 hospitalized inpatients. The primary outcome was all-cause hospitalization or mortality (outpatients) and all-cause mortality (inpatients). We estimated hazard ratios (HR) using propensity score-weighted Cox regression. Baseline characteristics were well-balanced between exposure groups after weighting. Among outpatients, there were 5.0 and 6.0 primary outcomes per 100 person-months for ARB/ACEI (n = 2,482) vs. non-ARB/ACEI (n = 2,487) users (HR 0.85, 95% confidence interval [CI] 0.73-0.99, median follow-up 87 days). Among outpatients who were ARB (n = 4,877) vs. ACEI (n = 8,704) users, there were 13.2 and 14.8 primary outcomes per 100 person-months (HR 0.91, 95%CI 0.86-0.97, median follow-up 85 days). Among inpatients who were ARB/ACEI (n = 210) vs. non-ARB/ACEI (n = 275) users, there were 3.4 and 2.0 all-cause deaths per 100 person months (HR 1.25, 95%CI 0.30-5.13, median follow-up 30 days). Among inpatients, ARB (n = 1,164) and ACEI (n = 2,014) users had 21.0 vs. 17.7 all-cause deaths, per 100 person-months (HR 1.13, 95%CI 0.93-1.38, median follow-up 30 days). CONCLUSIONS This observational analysis supports continued ARB or ACEI use for patients already using these medications before SARS-CoV-2 infection. The novel beneficial association observed among outpatients between users of ARBs vs. ACEIs on hospitalization or mortality should be confirmed with randomized trials.
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Affiliation(s)
- Catherine G. Derington
- Department of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Jordana B. Cohen
- Department of Medicine, Renal-Electrolyte and Hypertension Division, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States of America
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - April F. Mohanty
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States of America
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Tom H. Greene
- Department of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - James Cook
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States of America
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Jian Ying
- Department of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States of America
| | - Guo Wei
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States of America
| | - Jennifer S. Herrick
- Department of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States of America
| | - Vanessa W. Stevens
- Department of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States of America
| | - Barbara E. Jones
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States of America
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Libo Wang
- Department of Medicine, Division of Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Alexander R. Zheutlin
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Andrew M. South
- Department of Pediatrics, Section of Nephrology, Brenner Children’s Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States of America
- Division of Public Health Sciences, Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston Salem, NC, United States of America
| | - Thomas C. Hanff
- Department of Medicine, Division of Cardiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Steven M. Smith
- Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL, United States of America
| | - Rhonda M. Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL, United States of America
- Department of Medicine, University of Florida, College of Medicine, Gainesville, FL, United States of America
| | - Jordan B. King
- Department of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- Institute for Health Research, Kaiser Permanente Colorado, Aurora, CO, United States of America
| | - G. Caleb Alexander
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Dan R. Berlowitz
- Department of Public Health; University of Massachusetts Lowell, Lowell, MA, United States of America
- Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA, United States of America
| | - Faraz S. Ahmad
- Department of Medicine, Division of Cardiology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States of America
| | - M. Jason Penrod
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Rachel Hess
- Department of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Molly B. Conroy
- Department of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - James C. Fang
- Department of Medicine, Division of Cardiology, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Michael A. Rubin
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States of America
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Srinivasan Beddhu
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Alfred K. Cheung
- Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, United States of America
| | - Weiming Xian
- Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA, United States of America
- Department of Pharmacology and Experimental Therapeutics, Boston University School of Medicine, Boston, MA, United States of America
| | | | - Adam P. Bress
- Department of Population Health Sciences, Division of Health System Innovation and Research, University of Utah School of Medicine, Salt Lake City, UT, United States of America
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, United States of America
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43
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Cohen JB, D'Agostino McGowan L, Jensen ET, Rigdon J, South AM. Evaluating sources of bias in observational studies of angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker use during COVID-19: beyond confounding. J Hypertens 2021; 39:795-805. [PMID: 33186321 PMCID: PMC8164085 DOI: 10.1097/hjh.0000000000002706] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Concerns over ACE inhibitor or ARB use to treat hypertension during COVID-19 remain unresolved. Although studies using more robust methodologies provided some clarity, sources of bias persist and it remains critical to quickly address this question. In this review, we discuss pernicious sources of bias using a causal model framework, including time-varying confounder, collider, information, and time-dependent bias, in the context of recently published studies. We discuss causal inference methodologies that can address these issues, including causal diagrams, time-to-event analyses, sensitivity analyses, and marginal structural modeling. We discuss effect modification and we propose a role for causal mediation analysis to estimate indirect effects via mediating factors, especially components of the renin--angiotensin system. Thorough knowledge of these sources of bias and the appropriate methodologies to address them is crucial when evaluating observational studies to inform patient management decisions regarding whether ACE inhibitors or ARBs are associated with greater risk from COVID-19.
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Affiliation(s)
- Jordana B Cohen
- Renal-Electrolyte and Hypertension Division, Department of Medicine
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Elizabeth T Jensen
- Department of Epidemiology and Prevention, Division of Public Health Sciences
| | - Joseph Rigdon
- Department of Biostatistics and Data Science, Division of Public Health Sciences
| | - Andrew M South
- Department of Epidemiology and Prevention, Division of Public Health Sciences
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital
- Department of Surgery-Hypertension & Vascular Research
- Cardiovascular Sciences Center, Wake Forest School of Medicine, Winston Salem, North Carolina, USA
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44
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Kohane IS, Aronow BJ, Avillach P, Beaulieu-Jones BK, Bellazzi R, Bradford RL, Brat GA, Cannataro M, Cimino JJ, García-Barrio N, Gehlenborg N, Ghassemi M, Gutiérrez-Sacristán A, Hanauer DA, Holmes JH, Hong C, Klann JG, Loh NHW, Luo Y, Mandl KD, Daniar M, Moore JH, Murphy SN, Neuraz A, Ngiam KY, Omenn GS, Palmer N, Patel LP, Pedrera-Jiménez M, Sliz P, South AM, Tan ALM, Taylor DM, Taylor BW, Torti C, Vallejos AK, Wagholikar KB, Weber GM, Cai T. What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask. J Med Internet Res 2021; 23:e22219. [PMID: 33600347 PMCID: PMC7927948 DOI: 10.2196/22219] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/14/2020] [Accepted: 01/10/2021] [Indexed: 12/13/2022] Open
Abstract
Coincident with the tsunami of COVID-19–related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field.
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Affiliation(s)
- Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Bruce J Aronow
- Biomedical Informatics, Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, United States
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | | | - Riccardo Bellazzi
- Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Pavia, Italy.,ICS Maugeri, Pavia, Italy
| | - Robert L Bradford
- North Carolina Translational and Clinical Sciences Institute, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Gabriel A Brat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Mario Cannataro
- Data Analytics Research Center, University Magna Graecia of Catanzaro, Catanzaro, Italy.,Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - James J Cimino
- Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, United States
| | | | - Nils Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Marzyeh Ghassemi
- Department of Computer Science and Medicine, University of Toronto, Toronto, ON, Canada
| | | | - David A Hanauer
- Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, United States
| | - John H Holmes
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Chuan Hong
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Jeffrey G Klann
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, United States
| | | | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Mohamad Daniar
- Clinical Research Informatics, Boston Children's Hospital, Boston, MA, United States
| | - Jason H Moore
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Shawn N Murphy
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.,Department of Neurology, Massachusetts General Hospital, Boston, MA, United States
| | - Antoine Neuraz
- Department of Biomedical Informatics, Necker-Enfant Malades Hospital, Assistance Publique - Hôpitaux de Paris, Paris, France.,Centre de Recherche des Cordeliers, INSERM UMRS 1138 Team 22, Université de Paris, Paris, France
| | - Kee Yuan Ngiam
- National University Health Systems, Singapore, Singapore
| | - Gilbert S Omenn
- Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, United States
| | - Nathan Palmer
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Lav P Patel
- Department of Internal Medicine, Division of Medical Informatics, University of Kansas Medical Center, Kansas City, KS, United States
| | | | - Piotr Sliz
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Amelia Li Min Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.,Department of Biomedical Informatics, National University of Singapore, Singapore, Singapore
| | - Deanne M Taylor
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Pediatrics, Perelman School of Medicine, The University of Pennsylvania, Philadelphia, PA, United States
| | - Bradley W Taylor
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Carlo Torti
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, Catanzaro, Italy
| | - Andrew K Vallejos
- Clinical and Translational Science Institute, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Kavishwar B Wagholikar
- Department of Medicine, Harvard Medical School, Boston, MA, United States.,Laboratory of Computer Science, Massachusetts General Hospital, Boston, MA, United States
| | | | - Griffin M Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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South AM, Alexander BT, Morrison JL, Sehgal A. Reply. J Pediatr 2021; 230:275-276. [PMID: 33253734 DOI: 10.1016/j.jpeds.2020.11.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 11/20/2020] [Indexed: 10/22/2022]
Affiliation(s)
- Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital and Wake Forest School of Medicine, Department of Epidemiology and Prevention, Division of Public Health Sciences, Department of Surgery-Hypertension and Vascular Research, Cardiovascular Sciences Center, Wake Forest School of Medicine, Winston Salem, North Carolina
| | - Barbara T Alexander
- Department of Physiology and Biophysics, Mississippi Center for Excellence in Perinatal Research, University of Mississippi Medical Center, Jackson, Mississippi
| | - Janna L Morrison
- Early Origins of Adult Health Research Group, Health and Biomedical Innovation, UniSA, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - Arvind Sehgal
- Monash Children's Hospital, Department of Pediatrics, Monash University, Melbourne, Australia
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Weber GM, Hong C, Palmer NP, Avillach P, Murphy SN, Gutiérrez-Sacristán A, Xia Z, Serret-Larmande A, Neuraz A, Omenn GS, Visweswaran S, Klann JG, South AM, Loh NHW, Cannataro M, Beaulieu-Jones BK, Bellazzi R, Agapito G, Alessiani M, Aronow BJ, Bell DS, Bellasi A, Benoit V, Beraghi M, Boeker M, Booth J, Bosari S, Bourgeois FT, Brown NW, Bucalo M, Chiovato L, Chiudinelli L, Dagliati A, Devkota B, DuVall SL, Follett RW, Ganslandt T, García Barrio N, Gradinger T, Griffier R, Hanauer DA, Holmes JH, Horki P, Huling KM, Issitt RW, Jouhet V, Keller MS, Kraska D, Liu M, Luo Y, Lynch KE, Malovini A, Mandl KD, Mao C, Maram A, Matheny ME, Maulhardt T, Mazzitelli M, Milano M, Moore JH, Morris JS, Morris M, Mowery DL, Naughton TP, Ngiam KY, Norman JB, Patel LP, Pedrera Jimenez M, Ramoni RB, Schriver ER, Scudeller L, Sebire NJ, Serrano Balazote P, Spiridou A, Tan AL, Tan BW, Tibollo V, Torti C, Trecarichi EM, Vitacca M, Zambelli A, Zucco C, Kohane IS, Cai T, Brat GA. International Comparisons of Harmonized Laboratory Value Trajectories to Predict Severe COVID-19: Leveraging the 4CE Collaborative Across 342 Hospitals and 6 Countries: A Retrospective Cohort Study. medRxiv 2021:2020.12.16.20247684. [PMID: 33564777 PMCID: PMC7872369 DOI: 10.1101/2020.12.16.20247684] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Objectives To perform an international comparison of the trajectory of laboratory values among hospitalized patients with COVID-19 who develop severe disease and identify optimal timing of laboratory value collection to predict severity across hospitals and regions. Design Retrospective cohort study. Setting The Consortium for Clinical Characterization of COVID-19 by EHR (4CE), an international multi-site data-sharing collaborative of 342 hospitals in the US and in Europe. Participants Patients hospitalized with COVID-19, admitted before or after PCR-confirmed result for SARS-CoV-2. Primary and secondary outcome measures Patients were categorized as "ever-severe" or "never-severe" using the validated 4CE severity criteria. Eighteen laboratory tests associated with poor COVID-19-related outcomes were evaluated for predictive accuracy by area under the curve (AUC), compared between the severity categories. Subgroup analysis was performed to validate a subset of laboratory values as predictive of severity against a published algorithm. A subset of laboratory values (CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin) was compared between North American and European sites for severity prediction. Results Of 36,447 patients with COVID-19, 19,953 (43.7%) were categorized as ever-severe. Most patients (78.7%) were 50 years of age or older and male (60.5%). Longitudinal trajectories of CRP, albumin, LDH, neutrophil count, D-dimer, and procalcitonin showed association with disease severity. Significant differences of laboratory values at admission were found between the two groups. With the exception of D-dimer, predictive discrimination of laboratory values did not improve after admission. Sub-group analysis using age, D-dimer, CRP, and lymphocyte count as predictive of severity at admission showed similar discrimination to a published algorithm (AUC=0.88 and 0.91, respectively). Both models deteriorated in predictive accuracy as the disease progressed. On average, no difference in severity prediction was found between North American and European sites. Conclusions Laboratory test values at admission can be used to predict severity in patients with COVID-19. Prediction models show consistency across international sites highlighting the potential generalizability of these models.
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Affiliation(s)
- Griffin M Weber
- Harvard Medical School, Department of Biomedical Informatics
| | - Chuan Hong
- Harvard Medical School, Department of Biomedical Informatics
| | - Nathan P Palmer
- Harvard Medical School, Department of Biomedical Informatics
| | - Paul Avillach
- Harvard Medical School, Department of Biomedical Informatics
| | | | | | | | - Arnaud Serret-Larmande
- Ho pital Européen Georges Pompidou, Assistance Publique - Ho pitaux de Paris, Department of biomedical informatics
| | | | - Gilbert S Omenn
- University of Michigan, Dept of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - John Booth
- Great Ormond Street Hospital for Children
| | - Silvano Bosari
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico
| | | | | | - Mauro Bucalo
- BIOMERIS (BIOMedical Research Informatics Solutions)
| | | | | | | | | | | | | | - Thomas Ganslandt
- Ruprecht Karls University Heidelberg Faculty of Medicine Mannheim
| | | | - Tobias Gradinger
- Ruprecht Karls University Heidelberg Faculty of Medicine Mannheim
| | | | - David A Hanauer
- University of Michigan Institute for Healthcare Policy & Innovation
| | - John H Holmes
- University of Pennsylvania Perelman School of Medicine
| | | | | | | | | | - Mark S Keller
- Harvard Medical School, Department of Biomedical Informatics
| | | | - Molei Liu
- Harvard University T H Chan School of Public Health
| | | | | | | | - Kenneth D Mandl
- Boston Children's Hospital, Computational Health Informatics Program
| | | | | | | | | | | | | | - Jason H Moore
- University of Pennsylvania Perelman School of Medicine
| | | | | | | | | | | | - James B Norman
- Harvard Medical School, Department of Biomedical Informatics
| | | | | | | | | | | | | | | | | | - Amelia Lm Tan
- Harvard Medical School, Department of Biomedical Informatics
| | | | | | | | | | | | | | | | - Isaac S Kohane
- Harvard Medical School, Department of Biomedical Informatics
| | - Tianxi Cai
- Harvard Medical School, Department of Biomedical Informatics
| | - Gabriel A Brat
- Beth Israel Deaconess Medical Center, Surgery
- Harvard Medical School, Department of Biomedical Informatics
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Cohen JB, South AM, Shaltout HA, Sinclair MR, Sparks MA. Renin-angiotensin system blockade in the COVID-19 pandemic. Clin Kidney J 2021; 14:i48-i59. [PMID: 33796285 PMCID: PMC7929063 DOI: 10.1093/ckj/sfab026] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 01/19/2021] [Indexed: 01/08/2023] Open
Abstract
In the early months of the coronavirus disease 2019 (COVID-19) pandemic, a hypothesis emerged suggesting that pharmacologic inhibitors of the renin–angiotensin system (RAS) may increase COVID-19 severity. This hypothesis was based on the role of angiotensin-converting enzyme 2 (ACE2), a counterregulatory component of the RAS, as the binding site for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), allowing viral entry into host cells. Extrapolations from prior evidence led to speculation that upregulation of ACE2 by RAS blockade may increase the risk of adverse outcomes from COVID-19. However, counterarguments pointed to evidence of potential protective effects of ACE2 and RAS blockade with regard to acute lung injury, as well as substantial risks from discontinuing these commonly used and important medications. Here we provide an overview of classic RAS physiology and the crucial role of ACE2 in systemic pathways affected by COVID-19. Additionally, we critically review the physiologic and epidemiologic evidence surrounding the interactions between RAS blockade and COVID-19. We review recently published trial evidence and propose important future directions to improve upon our understanding of these relationships.
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Affiliation(s)
- Jordana B Cohen
- Renal-Electrolyte and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew M South
- Section of Nephrology, Department of Pediatrics, Brenner Children's Hospital, Wake Forest School of Medicine, Winston Salem, NC, USA.,Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston Salem, NC, USA.,Department of Surgery, Hypertension and Vascular Research, Wake Forest School of Medicine, Winston Salem, NC, USA.,Cardiovascular Sciences Center, Wake Forest School of Medicine, Winston Salem, NC, USA
| | - Hossam A Shaltout
- Department of Surgery, Hypertension and Vascular Research, Wake Forest School of Medicine, Winston Salem, NC, USA.,Cardiovascular Sciences Center, Wake Forest School of Medicine, Winston Salem, NC, USA.,Department of Obstetrics and Gynecology, Wake Forest School of Medicine, Winston Salem, NC, USA.,Department of Pharmacology and Toxicology, School of Pharmacy, University of Alexandria, Alexandria, Egypt
| | - Matthew R Sinclair
- Division of Nephrology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.,Duke Clinical Research Institute, Durham, NC, USA
| | - Matthew A Sparks
- Division of Nephrology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA.,Renal Section, Durham VA Health Care System, Durham, NC, USA
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Le TT, Gutiérrez-Sacristán A, Son J, Hong C, South AM, Beaulieu-Jones BK, Loh NHW, Luo Y, Morris M, Ngiam KY, Patel LP, Samayamuthu MJ, Schriver E, Tan AL, Moore J, Cai T, Omenn GS, Avillach P, Kohane IS, Visweswaran S, Mowery DL, Xia Z. Multinational Prevalence of Neurological Phenotypes in Patients Hospitalized with COVID-19. medRxiv 2021. [PMID: 33655281 PMCID: PMC7924306 DOI: 10.1101/2021.01.27.21249817] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
OBJECTIVE: Neurological complications can worsen outcomes in COVID-19. We defined the prevalence of a wide range of neurological conditions among patients hospitalized with COVID-19 in geographically diverse multinational populations. METHODS: Using electronic health record (EHR) data from 348 participating hospitals across 6 countries and 3 continents between January and September 2020, we performed a cross-sectional study of hospitalized adult and pediatric patients with a positive SARS-CoV-2 reverse transcription polymerase chain reaction test, both with and without severe COVID-19. We assessed the frequency of each disease category and 3-character International Classification of Disease (ICD) code of neurological diseases by countries, sites, time before and after admission for COVID-19, and COVID-19 severity. RESULTS: Among the 35,177 hospitalized patients with SARS-CoV-2 infection, there was increased prevalence of disorders of consciousness (5.8%, 95% confidence interval [CI]: 3.7%−7.8%, pFDR<.001) and unspecified disorders of the brain (8.1%, 95%CI: 5.7%−10.5%, pFDR<.001), compared to pre-admission prevalence. During hospitalization, patients who experienced severe COVID-19 status had 22% (95%CI: 19%−25%) increase in the relative risk (RR) of disorders of consciousness, 24% (95%CI: 13%−35%) increase in other cerebrovascular diseases, 34% (95%CI: 20%−50%) increase in nontraumatic intracranial hemorrhage, 37% (95%CI: 17%−60%) increase in encephalitis and/or myelitis, and 72% (95%CI: 67%−77%) increase in myopathy compared to those who never experienced severe disease. INTERPRETATION: Using an international network and common EHR data elements, we highlight an increase in the prevalence of central and peripheral neurological phenotypes in patients hospitalized with SARS-CoV-2 infection, particularly among those with severe disease.
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Affiliation(s)
- Trang T Le
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Jiyeon Son
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chuan Hong
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Andrew M South
- Department of Pediatrics, Wake Forest School of Medicine, Winston Salem, NC, USA
| | | | - Ne Hooi Will Loh
- Department of Critical Care, National University Health Systems, Singapore
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Michele Morris
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kee Yuan Ngiam
- Department of Surgery, National University Health Systems, Singapore
| | - Lav P Patel
- Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA
| | | | - Emily Schriver
- Data Analytics Center, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Amelia Lm Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jason Moore
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | | | - Shyam Visweswaran
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Danielle L Mowery
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Zongqi Xia
- Department of Neurology, University of Pittsburgh, PA, USA
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Chappell MC, Pirro NT, South AM, Gwathmey TM. Concerns on the Specificity of Commercial ELISAs for the Measurement of Angiotensin (1-7) and Angiotensin II in Human Plasma. Hypertension 2021; 77:e29-e31. [PMID: 33399002 PMCID: PMC7878344 DOI: 10.1161/hypertensionaha.120.16724] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Affiliation(s)
- Mark C Chappell
- From the Hypertension and Vascular Research Center (M.C.C., N.T.P., A.M.S., T.M.G.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Nancy T Pirro
- From the Hypertension and Vascular Research Center (M.C.C., N.T.P., A.M.S., T.M.G.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - Andrew M South
- From the Hypertension and Vascular Research Center (M.C.C., N.T.P., A.M.S., T.M.G.), Wake Forest University School of Medicine, Winston-Salem, NC.,Department of Pediatrics (A.M.S.), Wake Forest University School of Medicine, Winston-Salem, NC
| | - TanYa M Gwathmey
- From the Hypertension and Vascular Research Center (M.C.C., N.T.P., A.M.S., T.M.G.), Wake Forest University School of Medicine, Winston-Salem, NC
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Moore JB, Benítez-Porres J, Skelton JA, Vargas-Candela A, South AM, Gómez-Huelgas R, Bernal-López MR. Examining the Effect of a 1-yr Lifestyle Intervention on Cardiometabolic and Inflammatory Biomarkers in Youth with Overweight or Obesity: A Pilot Study. Transl J ACSM 2021. [DOI: 10.1249/tjx.0000000000000153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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