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Oh W, Jayaraman P, Tandon P, Chaddha US, Kovatch P, Charney AW, Glicksberg BS, Nadkarni GN. A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19. Artif Intell Med 2024; 148:102750. [PMID: 38325922 PMCID: PMC10864255 DOI: 10.1016/j.artmed.2023.102750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 12/12/2023] [Accepted: 12/14/2023] [Indexed: 02/09/2024]
Abstract
Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.
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Affiliation(s)
- Wonsuk Oh
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Pushkala Jayaraman
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pranai Tandon
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Udit S Chaddha
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Patricia Kovatch
- Department of Scientific Computing, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander W Charney
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Character Biosciences, New York, NY, USA
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Data-Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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Robert T, Raymond L, Dancer M, Torrents J, Jourde-Chiche N, Burtey S, Béroud C, Mesnard L. Beyond the kidney biopsy: genomic approach to undetermined kidney diseases. Clin Kidney J 2024; 17:sfad099. [PMID: 38186885 PMCID: PMC10765093 DOI: 10.1093/ckj/sfad099] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Indexed: 01/09/2024] Open
Abstract
Background According to data from large national registries, almost 20%-25% of patients with end-stage kidney disease have an undetermined kidney disease (UKD). Recent data have shown that monogenic disease-causing variants are under-diagnosed. We performed exome sequencing (ES) on UKD patients in our center to improve the diagnosis rate. Methods ES was proposed in routine practice for patients with UKD including kidney biopsy from January 2019 to December 2021. Mutations were detected using a targeted bioinformatic customized kidney gene panel (675 genes). The pathogenicity was assessed using American College of Medical Genetics guidelines. Results We included 230 adult patients, median age 47.5 years. Consanguinity was reported by 25 patients. A family history of kidney disease was documented in 115 patients (50%). Kidney biopsies were either inconclusive in 69 patients (30.1%) or impossible in 71 (30.9%). We detected 28 monogenic renal disorders in 75 (32.6%) patients. Collagenopathies was the most common genetic kidney diagnosis (46.7%), with COL4A3 and COL4A4 accounting for 80% of these diagnoses. Tubulopathies (16%) and ciliopathies (14.7%) yielded, respectively, the second and third genetic kidney diagnosis category and UMOD-associated nephropathy as the main genetic findings for tubulopathies (7/11). Ten of the 22 patients having ES "first" eventually received a positive diagnosis, thereby avoiding 11 biopsies. Among the 44 patients with glomerular, tubulo-interstitial or vascular nephropathy, 13 (29.5%) were phenocopies. The diagnostic yield of ES was higher in female patients (P = .02) and in patients with a family history of kidney disease (P < .0001), reaching 56.8% when the patient had both first- and second-degree family history of renal disease. Conclusion Genetic diagnosis has provided new clinical insights by clarifying or reclassifying kidney disease etiology in over a third of UKD patients. Exome "first" may have a significant positive diagnostic yield, thus avoiding invasive kidney biopsy; moreover, the diagnostic yield remains elevated even when biopsy is impossible or inconclusive. ES provides a clinical benefit for routine nephrological healthcare in patients with UKD.
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Affiliation(s)
- Thomas Robert
- Centre of Nephrology and Renal Transplantation, Hôpital de la Conception, CHU de Marseille, Marseille, France
- Marseille Medical Genetics, Bioinformatics & Genetics, INSERM U1251, Aix-Marseille Université, Marseille, France
| | - Laure Raymond
- Genetics Department, Laboratoire Eurofins Biomnis, Lyon, France
| | - Marine Dancer
- Genetics Department, Laboratoire Eurofins Biomnis, Lyon, France
| | - Julia Torrents
- Department of Renal Pathology, CHU Timone, AP-HM, Marseille, France
| | - Noémie Jourde-Chiche
- Centre of Nephrology and Renal Transplantation, Hôpital de la Conception, CHU de Marseille, Marseille, France
- Aix-Marseille Univ, INSERM, INRAE, C2VN, Marseille, France
| | - Stéphane Burtey
- Centre of Nephrology and Renal Transplantation, Hôpital de la Conception, CHU de Marseille, Marseille, France
- Aix-Marseille Univ, INSERM, INRAE, C2VN, Marseille, France
| | - Christophe Béroud
- Marseille Medical Genetics, Bioinformatics & Genetics, INSERM U1251, Aix-Marseille Université, Marseille, France
| | - Laurent Mesnard
- Urgences Néphrologiques et Transplantation Rénale, Sorbonne Université, APHP, Hôpital Tenon, Paris, France
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Khan A, Shang N, Nestor JG, Weng C, Hripcsak G, Harris PC, Gharavi AG, Kiryluk K. Polygenic risk alters the penetrance of monogenic kidney disease. Nat Commun 2023; 14:8318. [PMID: 38097619 PMCID: PMC10721887 DOI: 10.1038/s41467-023-43878-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/22/2023] [Indexed: 12/17/2023] Open
Abstract
Chronic kidney disease (CKD) is determined by an interplay of monogenic, polygenic, and environmental risks. Autosomal dominant polycystic kidney disease (ADPKD) and COL4A-associated nephropathy (COL4A-AN) represent the most common forms of monogenic kidney diseases. These disorders have incomplete penetrance and variable expressivity, and we hypothesize that polygenic factors explain some of this variability. By combining SNP array, exome/genome sequence, and electronic health record data from the UK Biobank and All-of-Us cohorts, we demonstrate that the genome-wide polygenic score (GPS) significantly predicts CKD among ADPKD monogenic variant carriers. Compared to the middle tertile of the GPS for noncarriers, ADPKD variant carriers in the top tertile have a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile have only a 3-fold increased risk of CKD. Similarly, the GPS significantly predicts CKD in COL4A-AN carriers. The carriers in the top tertile of the GPS have a 2.5-fold higher risk of CKD, while the risk for carriers in the bottom tertile is not different from the average population risk. These results suggest that accounting for polygenic risk improves risk stratification in monogenic kidney disease.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Ning Shang
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Jordan G Nestor
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Peter C Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, USA
| | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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Chamarthi G, Orozco T, Shell P, Fu D, Hale-Gallardo J, Jia H, Shukla AM. Electronic Phenotype for Advanced Chronic Kidney Disease in a Veteran Health Care System Clinical Database: Systems-Based Strategy for Model Development and Evaluation. Interact J Med Res 2023; 12:e43384. [PMID: 37486757 PMCID: PMC10411421 DOI: 10.2196/43384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 04/14/2023] [Accepted: 04/17/2023] [Indexed: 07/25/2023] Open
Abstract
BACKGROUND Identifying advanced (stages 4 and 5) chronic kidney disease (CKD) cohorts in clinical databases is complicated and often unreliable. Accurately identifying these patients can allow targeting this population for their specialized clinical and research needs. OBJECTIVE This study was conducted as a system-based strategy to identify all prevalent Veterans with advanced CKD for subsequent enrollment in a clinical trial. We aimed to examine the prevalence and accuracy of conventionally used diagnosis codes and estimated glomerular filtration rate (eGFR)-based phenotypes for advanced CKD in an electronic health record (EHR) database. We sought to develop a pragmatic EHR phenotype capable of improving the real-time identification of advanced CKD cohorts in a regional Veterans health care system. METHODS Using the Veterans Affairs Informatics and Computing Infrastructure services, we extracted the source cohort of Veterans with advanced CKD based on a combination of the latest eGFR value ≤30 ml·min-1·1.73 m-2 or existing International Classification of Diseases (ICD)-10 diagnosis codes for advanced CKD (N18.4 and N18.5) in the last 12 months. We estimated the prevalence of advanced CKD using various prior published EHR phenotypes (ie, advanced CKD diagnosis codes, using the latest single eGFR <30 ml·min-1·1.73 m-2, utilizing two eGFR values) and our operational EHR phenotypes of a high-, intermediate-, and low-risk advanced CKD cohort. We evaluated the accuracy of these phenotypes by examining the likelihood of a sustained reduction of eGFR <30 ml·min-1·1.73 m-2 over a 6-month follow-up period. RESULTS Of the 133,756 active Veteran enrollees at North Florida/South Georgia Veterans Health System (NF/SG VHS), we identified a source cohort of 1759 Veterans with advanced nondialysis CKD. Among these, 1102 (62.9%) Veterans had diagnosis codes for advanced CKD; 1391(79.1%) had the index eGFR <30 ml·min-1·1.73 m-2; and 928 (52.7%), 480 (27.2%), and 315 (17.9%) Veterans had high-, intermediate-, and low-risk advanced CKD, respectively. The prevalence of advanced CKD among Veterans at NF/SG VHS varied between 1% and 1.5% depending on the EHR phenotype. At the 6-month follow-up, the probability of Veterans remaining in the advanced CKD stage was 65.3% in the group defined by the ICD-10 codes and 90% in the groups defined by eGFR values. Based on our phenotype, 94.2% of high-risk, 71% of intermediate-risk, and 16.1% of low-risk groups remained in the advanced CKD category. CONCLUSIONS While the prevalence of advanced CKD has limited variation between different EHR phenotypes, the accuracy can be improved by utilizing two eGFR values in a stratified manner. We report the development of a pragmatic EHR-based model to identify advanced CKD within a regional Veterans health care system in real time with a tiered approach that allows targeting the needs of the groups at risk of progression to end-stage kidney disease.
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Affiliation(s)
- Gajapathiraju Chamarthi
- Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville, FL, United States
| | - Tatiana Orozco
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Popy Shell
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Devin Fu
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Jennifer Hale-Gallardo
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Huanguang Jia
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
| | - Ashutosh M Shukla
- Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville, FL, United States
- Advanced Chronic Kidney Disease and Home Dialysis Program, North Florida/South Georgia Veteran Healthcare System, Gainesville, FL, United States
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Guo X, Wang W, Ma Y, Liang Y, Zhou Y, Cai G. 24-h Urinary Calcium Excretion and Renal Outcomes in Hospitalized Patients with and without Chronic Kidney Disease. J Clin Med 2023; 12:4600. [PMID: 37510715 PMCID: PMC10380443 DOI: 10.3390/jcm12144600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/07/2023] [Accepted: 06/26/2023] [Indexed: 07/30/2023] Open
Abstract
This study investigated the impact of 24-h urinary calcium excretion (UCaE) on renal function decline in hospitalized patients with and without chronic kidney disease (CKD). This study enrolled 3815 CKD patients in stages 1-4 and 1133 non-CKD patients admitted to the First Center of the Chinese PLA General Hospital between January 2014 and July 2022. The primary outcome for CKD patients was a composite of CKD progression, defined as a 40% decline in estimated glomerular filtration rate (eGFR) or end-stage kidney disease. Annual eGFR change was the secondary outcome. For non-CKD patients, the primary outcome was an eGFR decline of ≥20% or CKD incidence, while annual eGFR change was the secondary outcome. The association between UCaE and kidney function decline was assessed using Cox proportional hazards and generalized linear models. Primary outcomes were observed in 813 CKD patients and 109 non-CKD patients over a median follow-up of 3.0 and 4.1 years, respectively. For CKD patients, every 1-mmol/d increase in UCaE was associated with a 15% decreased risk of CKD progression. The hazard ratio (HR) was 0.85, with a 95% confidence interval (CI) of 0.77-0.93. For non-CKD patients, the risk of renal function decline decreased by 11%. The multivariate models indicated that there was an annual decrease in eGFR in both CKD and non-CKD patients, with a reduction of 0.122 mL/min/1.73 m2/year (p < 0.001) and 0.046 mL/min/1.73 m2/year (p = 0.004), respectively, for every 1-mmol/d increase in UCaE. CKD experiences a decrease in 24-h UCaE as early as stage 1, with a significant decline in stage 4. CKD and non-CKD patients with lower UCaE levels are at an increased risk of renal decline, regardless of other variables.
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Affiliation(s)
- Xinru Guo
- School of Medicine, Nankai University, Tianjin 300071, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing 100853, China
| | - Wanling Wang
- National Engineering Laboratory for Medical Big Data Application Technology, Chinese PLA General Hospital, Beijing 100853, China
- Medical Big Data Research Center, Medical Innovation Research Division of Chinese PLA General Hospital, Beijing 100853, China
| | - Yangyang Ma
- Department of Information, First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
| | - Yanjun Liang
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing 100853, China
| | - Yena Zhou
- School of Medicine, Nankai University, Tianjin 300071, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing 100853, China
| | - Guangyan Cai
- School of Medicine, Nankai University, Tianjin 300071, China
- Department of Nephrology, First Medical Center of Chinese PLA General Hospital, Chinese PLA Institute of Nephrology, State Key Laboratory of Kidney Diseases, National Clinical Research Center of Kidney Diseases, Beijing 100853, China
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Raza Abidi SS, Naqvi A, Worthen G, Vinson A, Abidi S, Kiberd B, Skinner T, West K, Tennankore KK. Multiview Clustering to Identify Novel Kidney Donor Phenotypes for Assessing Graft Survival in Older Transplant Recipients. KIDNEY360 2023; 4:951-961. [PMID: 37291713 PMCID: PMC10371275 DOI: 10.34067/kid.0000000000000190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 05/08/2023] [Indexed: 06/10/2023]
Abstract
Key Points An unsupervised machine learning clustering algorithm identified distinct deceased kidney donor phenotypes among older recipients. Recipients of certain donor phenotypes were at a relatively higher risk of all-cause graft loss even after accounting for recipient factors. The use of unsupervised clustering to support kidney allocation systems may be an important area for future study. Background Older transplant recipients are at a relatively increased risk of graft failure after transplantation, and some of this risk may relate to donor characteristics. Unsupervised clustering using machine learning may be a novel approach to identify donor phenotypes that may then be used to evaluate outcomes for older recipients. Using a cohort of older recipients, the purpose of this study was to (1 ) use unsupervised clustering to identify donor phenotypes and (2 ) determine the risk of death/graft failure for recipients of each donor phenotype. Methods We analyzed a nationally representative cohort of kidney transplant recipients aged 65 years or older captured using the Scientific Registry of Transplant Recipients between 2000 and 2017. Unsupervised clustering was used to generate phenotypes using donor characteristics inclusive of variables in the kidney donor risk index (KDRI). Cluster assignment was internally validated. Outcomes included all-cause graft failure (including mortality) and delayed graft function. Differences in the distribution of KDRI scores were also compared across the clusters. All-cause graft failure was compared for recipients of donor kidneys from each cluster using a multivariable Cox survival analysis. Results Overall, 23,558 donors were separated into five clusters. The area under the curve for internal validation of cluster assignment was 0.89. Recipients of donor kidneys from two clusters were found to be at high risk of all-cause graft failure relative to the lowest risk cluster (adjusted hazards ratio, 1.86; 95% confidence interval, 1.69 to 2.05 and 1.73; 95% confidence interval, 1.61 to 1.87). Only one of these high-risk clusters had high proportions of donors with established risk factors (i.e. , hypertension, diabetes). KDRI scores were similar for the highest and lowest risk clusters (1.40 [1.18–1.67] and 1.37 [1.15–1.65], respectively). Conclusions Unsupervised clustering can identify novel donor phenotypes comprising established donor characteristics that, in turn, may be associated with different risks of graft loss for older transplant recipients.
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Affiliation(s)
- Syed Sibte Raza Abidi
- Division of Nephrology, Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada
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Khan A, Shang N, Nestor JG, Weng C, Hripcsak G, Harris PC, Gharavi AG, Kiryluk K. Polygenic risk affects the penetrance of monogenic kidney disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.07.23289614. [PMID: 37214819 PMCID: PMC10197721 DOI: 10.1101/2023.05.07.23289614] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Background Chronic kidney disease (CKD) is a genetically complex disease determined by an interplay of monogenic, polygenic, and environmental risks. Most forms of monogenic kidney diseases have incomplete penetrance and variable expressivity. It is presently unknown if some of the variability in penetrance can be attributed to polygenic factors. Methods Using the UK Biobank (N=469,835 participants) and the All of Us (N=98,622 participants) datasets, we examined two most common forms of monogenic kidney disorders, autosomal dominant polycystic kidney disease (ADPKD) caused by deleterious variants in the PKD1 or PKD2 genes, and COL4A-associated nephropathy (COL4A-AN caused by deleterious variants in COL4A3, COL4A4, or COL4A5 genes). We used the eMERGE-III electronic CKD phenotype to define cases (estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m2 or kidney failure) and controls (eGFR >90 mL/min/1.73m2 in the absence of kidney disease diagnoses). The effects of the genome-wide polygenic score (GPS) for CKD were tested in monogenic variant carriers and non-carriers using logistic regression controlling for age, sex, diabetes, and genetic ancestry. Results As expected, the carriers of known pathogenic and rare predicted loss-of-function variants in PKD1 or PKD2 had a high risk of CKD (ORmeta=17.1, 95% CI: 11.1-26.4, P=1.8E-37). The GPS was comparably predictive of CKD in both ADPKD variant carriers (ORmeta=2.28 per SD, 95%CI: 1.55-3.37, P=2.6E-05) and non-carriers (ORmeta=1.72 per SD, 95% CI=1.69-1.76, P< E-300) independent of age, sex, diabetes, and genetic ancestry. Compared to the middle tertile of the GPS distribution for non-carriers, ADPKD variant carriers in the top tertile had a 54-fold increased risk of CKD, while ADPKD variant carriers in the bottom tertile had only a 3-fold increased risk of CKD. Similarly, the GPS was predictive of CKD in both COL4-AN variant carriers (ORmeta=1.78, 95% CI=1.22-2.58, P=2.38E-03) and non-carriers (ORmeta=1.70, 95%CI: 1.68-1.73 P Conclusions Variable penetrance of kidney disease in ADPKD and COL4-AN is partially explained by differences in polygenic risk profiles. Accounting for polygenic factors has the potential to improve risk stratification in monogenic kidney disease and may have implications for genetic counseling.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Ning Shang
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Jordan G. Nestor
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Peter C. Harris
- Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Ali G. Gharavi
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
| | - Krzysztof Kiryluk
- Division of Nephrology, Dept of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY
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Wu Z, Lohmöller J, Kuhl C, Wehrle K, Jankowski J. Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk. Circ Res 2023; 132:1084-1100. [PMID: 37053282 DOI: 10.1161/circresaha.123.321765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/15/2023]
Abstract
The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.
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Affiliation(s)
- Zhuojun Wu
- Institute of Molecular Cardiovascular Research (Z.W., J.J.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
- Department of Radiology (C.K.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
| | - Johannes Lohmöller
- Medical Faculty, and Department of Computer Science, Communication and Distributed Systems (COMSYS) (J.L., K.W.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
| | - Christiane Kuhl
- Department of Radiology (C.K.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
| | - Klaus Wehrle
- Institute of Molecular Cardiovascular Research (Z.W., J.J.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
- Medical Faculty, and Department of Computer Science, Communication and Distributed Systems (COMSYS) (J.L., K.W.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
| | - Joachim Jankowski
- Institute of Molecular Cardiovascular Research (Z.W., J.J.), Rheinisch-Westfälische Technische Hochschule Aachen University, Germany
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht, The Netherlands (J.J.)
- Aachen-Maastricht Institute for Cardiorenal Disease (AMICARE), University Hospital Rheinisch-Westfälische Technische Hochschule Aachen, Germany (J.J.)
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Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review. J Nephrol 2023; 36:1101-1117. [PMID: 36786976 PMCID: PMC10227138 DOI: 10.1007/s40620-023-01573-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 01/01/2023] [Indexed: 02/15/2023]
Abstract
OBJECTIVES In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the available evidence on these innovative techniques to improve CKD diagnosis and patient management. METHODS We included English language studies retrieved from PubMed. The review is therefore to be classified as a "rapid review", since it includes one database only, and has language restrictions; the novelty and importance of the issue make missing relevant papers unlikely. We extracted 16 variables, including: main aim, studied population, data source, sample size, problem type (regression, classification), predictors used, and performance metrics. We followed the Preferred Reporting Items for Systematic Reviews (PRISMA) approach; all main steps were done in duplicate. RESULTS From a total of 648 studies initially retrieved, 68 articles met the inclusion criteria. Models, as reported by authors, performed well, but the reported metrics were not homogeneous across articles and therefore direct comparison was not feasible. The most common aim was prediction of prognosis, followed by diagnosis of CKD. Algorithm generalizability, and testing on diverse populations was rarely taken into account. Furthermore, the clinical evaluation and validation of the models/algorithms was perused; only a fraction of the included studies, 6 out of 68, were performed in a clinical context. CONCLUSIONS Machine learning is a promising tool for the prediction of risk, diagnosis, and therapy management for CKD patients. Nonetheless, future work is needed to address the interpretability, generalizability, and fairness of the models to ensure the safe application of such technologies in routine clinical practice.
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10
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Abstract
Hundreds of different genetic causes of chronic kidney disease are now recognized, and while individually rare, taken together they are significant contributors to both adult and pediatric diseases. Traditional genetics approaches relied heavily on the identification of large families with multiple affected members and have been fundamental to the identification of genetic kidney diseases. With the increased utilization of massively parallel sequencing and improvements to genotype imputation, we can analyze rare variants in large cohorts of unrelated individuals, leading to personalized care for patients and significant research advancements. This review evaluates the contribution of rare disorders to patient care and the study of genetic kidney diseases and highlights key advancements that utilize new techniques to improve our ability to identify new gene-disease associations.
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Affiliation(s)
- Mark D Elliott
- Division of Nephrology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA;
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Institute for Genomic Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Hila Milo Rasouly
- Division of Nephrology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA;
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA;
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
- Institute for Genomic Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
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11
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Zhou XJ, Zhong XH, Duan LX. Integration of artificial intelligence and multi-omics in kidney diseases. FUNDAMENTAL RESEARCH 2023; 3:126-148. [PMID: 38933564 PMCID: PMC11197676 DOI: 10.1016/j.fmre.2022.01.037] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 12/14/2021] [Accepted: 01/24/2022] [Indexed: 10/18/2022] Open
Abstract
Kidney disease is a leading cause of death worldwide. Currently, the diagnosis of kidney diseases and the grading of their severity are mainly based on clinical features, which do not reveal the underlying molecular pathways. More recent surge of ∼omics studies has greatly catalyzed disease research. The advent of artificial intelligence (AI) has opened the avenue for the efficient integration and interpretation of big datasets for discovering clinically actionable knowledge. This review discusses how AI and multi-omics can be applied and integrated, to offer opportunities to develop novel diagnostic and therapeutic means in kidney diseases. The combination of new technology and novel analysis pipelines can lead to breakthroughs in expanding our understanding of disease pathogenesis, shedding new light on biomarkers and disease classification, as well as providing possibilities of precise treatment.
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Affiliation(s)
- Xu-Jie Zhou
- Renal Division, Peking University First Hospital, Beijing 100034, China
- Kidney Genetics Center, Peking University Institute of Nephrology, Beijing 100034, China
- Key Laboratory of Renal Disease, Ministry of Health of China, Beijing 100034, China
- Key Laboratory of Chronic Kidney Disease Prevention and Treatment (Peking University), Ministry of Education, Beijing 100034, China
| | - Xu-Hui Zhong
- Department of Pediatrics, Peking University First Hospital, Beijing, China
| | - Li-Xin Duan
- The Big Data Research Center, University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu 611731, China
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12
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Liu L, Khan A, Sanchez-Rodriguez E, Zanoni F, Li Y, Steers N, Balderes O, Zhang J, Krithivasan P, LeDesma RA, Fischman C, Hebbring SJ, Harley JB, Moncrieffe H, Kottyan LC, Namjou-Khales B, Walunas TL, Knevel R, Raychaudhuri S, Karlson EW, Denny JC, Stanaway IB, Crosslin D, Rauen T, Floege J, Eitner F, Moldoveanu Z, Reily C, Knoppova B, Hall S, Sheff JT, Julian BA, Wyatt RJ, Suzuki H, Xie J, Chen N, Zhou X, Zhang H, Hammarström L, Viktorin A, Magnusson PKE, Shang N, Hripcsak G, Weng C, Rundek T, Elkind MSV, Oelsner EC, Barr RG, Ionita-Laza I, Novak J, Gharavi AG, Kiryluk K. Genetic regulation of serum IgA levels and susceptibility to common immune, infectious, kidney, and cardio-metabolic traits. Nat Commun 2022; 13:6859. [PMID: 36369178 PMCID: PMC9651905 DOI: 10.1038/s41467-022-34456-6] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Accepted: 10/25/2022] [Indexed: 11/13/2022] Open
Abstract
Immunoglobulin A (IgA) mediates mucosal responses to food antigens and the intestinal microbiome and is involved in susceptibility to mucosal pathogens, celiac disease, inflammatory bowel disease, and IgA nephropathy. We performed a genome-wide association study of serum IgA levels in 41,263 individuals of diverse ancestries and identified 20 genome-wide significant loci, including 9 known and 11 novel loci. Co-localization analyses with expression QTLs prioritized candidate genes for 14 of 20 significant loci. Most loci encoded genes that produced immune defects and IgA abnormalities when genetically manipulated in mice. We also observed positive genetic correlations of serum IgA levels with IgA nephropathy, type 2 diabetes, and body mass index, and negative correlations with celiac disease, inflammatory bowel disease, and several infections. Mendelian randomization supported elevated serum IgA as a causal factor in IgA nephropathy. African ancestry was consistently associated with higher serum IgA levels and greater frequency of IgA-increasing alleles compared to other ancestries. Our findings provide novel insights into the genetic regulation of IgA levels and its potential role in human disease.
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Affiliation(s)
- Lili Liu
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Atlas Khan
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Elena Sanchez-Rodriguez
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Francesca Zanoni
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Yifu Li
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Nicholas Steers
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Olivia Balderes
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Junying Zhang
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Priya Krithivasan
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Robert A. LeDesma
- grid.16750.350000 0001 2097 5006Lewis Thomas Laboratory, Department of Molecular Biology, Princeton University, Princeton, NJ USA
| | - Clara Fischman
- grid.25879.310000 0004 1936 8972Department of Medicine, University of Pennsylvania, Philadelphia, PA USA
| | - Scott J. Hebbring
- grid.280718.40000 0000 9274 7048Center for Human Genetics, Marshfield Clinic Research Institute, Marshfield, WI USA
| | - John B. Harley
- grid.239573.90000 0000 9025 8099Center of Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital, Cincinnati, OH USA ,grid.24827.3b0000 0001 2179 9593Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH USA ,grid.413848.20000 0004 0420 2128US Department of Veterans Affairs Medical Center, Cincinnati, OH USA
| | - Halima Moncrieffe
- grid.239573.90000 0000 9025 8099Center of Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital, Cincinnati, OH USA ,grid.24827.3b0000 0001 2179 9593Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Leah C. Kottyan
- grid.239573.90000 0000 9025 8099Center of Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital, Cincinnati, OH USA ,grid.24827.3b0000 0001 2179 9593Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Bahram Namjou-Khales
- grid.239573.90000 0000 9025 8099Center of Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital, Cincinnati, OH USA ,grid.24827.3b0000 0001 2179 9593Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH USA
| | - Theresa L. Walunas
- grid.16753.360000 0001 2299 3507Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Rachel Knevel
- grid.62560.370000 0004 0378 8294Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
| | - Soumya Raychaudhuri
- grid.62560.370000 0004 0378 8294Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
| | - Elizabeth W. Karlson
- grid.62560.370000 0004 0378 8294Division of Rheumatology, Immunology and Allergy, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA USA
| | - Joshua C. Denny
- grid.152326.10000 0001 2264 7217Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN USA
| | - Ian B. Stanaway
- grid.34477.330000000122986657Kidney Research Institute, Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA USA
| | - David Crosslin
- grid.34477.330000000122986657Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington, Seattle, WA USA
| | - Thomas Rauen
- grid.1957.a0000 0001 0728 696XDepartment of Nephrology, RWTH University of Aachen, Aachen, Germany
| | - Jürgen Floege
- grid.1957.a0000 0001 0728 696XDepartment of Nephrology, RWTH University of Aachen, Aachen, Germany
| | - Frank Eitner
- grid.1957.a0000 0001 0728 696XDepartment of Nephrology, RWTH University of Aachen, Aachen, Germany ,grid.420044.60000 0004 0374 4101Kidney Diseases Research, Bayer Pharma AG, Wuppertal, Germany
| | - Zina Moldoveanu
- grid.265892.20000000106344187Department of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL USA
| | - Colin Reily
- grid.265892.20000000106344187Department of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL USA
| | - Barbora Knoppova
- grid.265892.20000000106344187Department of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL USA
| | - Stacy Hall
- grid.265892.20000000106344187Department of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL USA
| | - Justin T. Sheff
- grid.265892.20000000106344187Department of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL USA
| | - Bruce A. Julian
- grid.265892.20000000106344187Department of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL USA
| | - Robert J. Wyatt
- grid.267301.10000 0004 0386 9246Division of Pediatric Nephrology, University of Tennessee Health Sciences Center, Memphis, TN USA
| | - Hitoshi Suzuki
- grid.258269.20000 0004 1762 2738Department of Nephrology, Juntendo University Faculty of Medicine, Tokyo, Japan
| | - Jingyuan Xie
- grid.16821.3c0000 0004 0368 8293Department of Nephrology, Institute of Nephrology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Nan Chen
- grid.16821.3c0000 0004 0368 8293Department of Nephrology, Institute of Nephrology, Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xujie Zhou
- grid.11135.370000 0001 2256 9319Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
| | - Hong Zhang
- grid.11135.370000 0001 2256 9319Renal Division, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, China
| | - Lennart Hammarström
- grid.4714.60000 0004 1937 0626Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
| | - Alexander Viktorin
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Patrik K. E. Magnusson
- grid.4714.60000 0004 1937 0626Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ning Shang
- grid.21729.3f0000000419368729Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - George Hripcsak
- grid.21729.3f0000000419368729Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Chunhua Weng
- grid.21729.3f0000000419368729Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Tatjana Rundek
- grid.26790.3a0000 0004 1936 8606Department of Neurology, University of Miami, Miami, FL USA ,grid.26790.3a0000 0004 1936 8606Evelyn F. McKnight Brain Institute, University of Miami, Miami, FL USA
| | - Mitchell S. V. Elkind
- grid.21729.3f0000000419368729Department of Neurology, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Elizabeth C. Oelsner
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - R. Graham Barr
- grid.21729.3f0000000419368729Division of General Medicine, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA ,grid.21729.3f0000000419368729Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY USA
| | - Iuliana Ionita-Laza
- grid.21729.3f0000000419368729Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY USA
| | - Jan Novak
- grid.265892.20000000106344187Department of Microbiology and Medicine, University of Alabama at Birmingham, Birmingham, AL USA
| | - Ali G. Gharavi
- grid.21729.3f0000000419368729Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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Which patients with CKD will benefit from genomic sequencing? Synthesizing progress to illuminate the future. Curr Opin Nephrol Hypertens 2022; 31:541-547. [PMID: 36093902 PMCID: PMC9594128 DOI: 10.1097/mnh.0000000000000836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE OF REVIEW This review will summarize and synthesize recent findings in regard to monogenic kidney disorders, including how that evidence is being translated into practice. It will add to existing key knowledge to provide context for clinicians in consolidating existing practice and approaches. RECENT FINDINGS Whilst there are long established factors, which indicate increased likelihood of identifying a monogenic cause for kidney disease, these can now be framed in terms of the identification of new genes, new indications for genomic testing and new evidence for clinical utility of genomic testing in nephrology. Further, inherent in the use of genomics in nephrology are key concepts including robust informed consent, variant interpretation and return of results. Recent findings of variants in genes related to complex or broader kidney phenotypes are emerging in addition to understanding of de novo variants. Phenocopy phenomena are indicating a more pragmatic use of broader gene panels whilst evidence is emerging of a role in unexplained kidney disease. Clinical utility is evolving but is being successfully demonstrated across multiple domains of outcome and practice. SUMMARY We provide an updated framework of evidence to guide application of genomic testing in chronic kidney disease (CKD), building upon existing principles and knowledge to indicate how the practice and implementation of this can be applied today. There are clearly established roles for genomic testing for some patients with CKD, largely those with suspected heritable forms, with these continuing to expand as new evidence emerges.
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14
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Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia. BMC Nephrol 2022; 23:320. [PMID: 36151531 PMCID: PMC9502610 DOI: 10.1186/s12882-022-02947-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 09/13/2022] [Indexed: 11/15/2022] Open
Abstract
Background Electronic health records can be used for population-wide identification and monitoring of disease. The Territory Kidney Care project developed algorithms to identify individuals with chronic kidney disease (CKD) and several commonly comorbid chronic diseases. This study aims to describe the development and validation of our algorithms for CKD, diabetes, hypertension, and cardiovascular disease. A secondary aim of the study was to describe data completeness of the Territory Kidney Care database. Methods The Territory Kidney Care database consolidates electronic health records from multiple health services including public hospitals (n = 6) and primary care health services (> 60) across the Northern Territory, Australia. Using the database (n = 48,569) we selected a stratified random sample of patients (n = 288), which included individuals with mild to end-stage CKD. Diagnostic accuracy of the algorithms was tested against blinded manual chart reviews. Data completeness of the database was also described. Results For CKD defined as CKD stage 1 or higher (eGFR of any level with albuminuria or persistent eGFR < 60 ml/min/1.732, including renal replacement therapy) overall algorithm sensitivity was 93% (95%CI 89 to 96%) and specificity was 73% (95%CI 64 to 82%). For CKD defined as CKD stage 3a or higher (eGFR < 60 ml/min/1.732) algorithm sensitivity and specificity were 93% and 97% respectively. Among the CKD 1 to 5 staging algorithms, the CKD stage 5 algorithm was most accurate with > 99% sensitivity and specificity. For related comorbidities – algorithm sensitivity and specificity results were 75% and 97% for diabetes; 85% and 88% for hypertension; and 79% and 96% for cardiovascular disease. Conclusions We developed and validated algorithms to identify CKD and related chronic diseases within electronic health records. Validation results showed that CKD algorithms have a high degree of diagnostic accuracy compared to traditional administrative codes. Our highly accurate algorithms present new opportunities in early kidney disease detection, monitoring, and epidemiological research. Supplementary Information The online version contains supplementary material available at 10.1186/s12882-022-02947-9.
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15
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Khan A, Kiryluk K. Kidney disease progression and collider bias in GWAS. Kidney Int 2022; 102:476-478. [PMID: 35988936 DOI: 10.1016/j.kint.2022.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 06/03/2022] [Accepted: 06/10/2022] [Indexed: 02/01/2023]
Abstract
New genome-wide meta-analysis for longitudinal kidney function decline identified several genetic loci related to kidney disease progression. The study illustrated the complexity of modeling longitudinal traits in genome-wide association studies and highlighted the issue of a collider bias that can be introduced when a kidney disease progression phenotype is adjusted for baseline kidney function. Herein, we briefly outline the key findings of this study, their limitations, and implications for future studies.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA.
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16
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Cabreros I, Agniel D, Martino SC, Damberg CL, Elliott MN. Predicting Race And Ethnicity To Ensure Equitable Algorithms For Health Care Decision Making. HEALTH AFFAIRS (PROJECT HOPE) 2022; 41:1153-1159. [PMID: 35914194 DOI: 10.1377/hlthaff.2022.00095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Algorithms are currently used to assist in a wide array of health care decisions. Despite the general utility of these health care algorithms, there is growing recognition that they may lead to unintended racially discriminatory practices, raising concerns about the potential for algorithmic bias. An intuitive precaution against such bias is to remove race and ethnicity information as an input to health care algorithms, mimicking the idea of "race-blind" decisions. However, we argue that this approach is misguided. Knowledge, not ignorance, of race and ethnicity is necessary to combat algorithmic bias. When race and ethnicity are observed, many methodological approaches can be used to enforce equitable algorithmic performance. When race and ethnicity information is unavailable, which is often the case, imputing them can expand opportunities to not only identify and assess algorithmic bias but also combat it in both clinical and nonclinical settings. A valid imputation method, such as Bayesian Improved Surname Geocoding, can be applied to standard data collected by public and private payers and provider entities. We describe two applications in which imputation of race and ethnicity can help mitigate potential algorithmic biases: equitable disease screening algorithms using machine learning and equitable pay-for-performance incentives.
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Affiliation(s)
| | - Denis Agniel
- Denis Agniel, RAND Corporation, Santa Monica, California
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17
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Akwo EA, Chen HC, Liu G, Triozzi JL, Tao R, Yu Z, Chung CP, Giri A, Ikizler TA, Stein CM, Siew ED, Feng Q, Robinson-Cohen C, Hung AM. Phenome-Wide Association Study of UMOD Gene Variants and Differential Associations With Clinical Outcomes Across Populations in the Million Veteran Program a Multiethnic Biobank. Kidney Int Rep 2022; 7:1802-1818. [PMID: 35967117 PMCID: PMC9366371 DOI: 10.1016/j.ekir.2022.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 04/22/2022] [Accepted: 05/09/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction Common variants in the UMOD gene are considered an evolutionary adaptation against urinary tract infections (UTIs) and have been implicated in kidney stone formation, chronic kidney disease (CKD), and hypertension. However, differences in UMOD variant-phenotype associations across population groups are unclear. Methods We tested associations between UMOD/PDILT variants and up to 1528 clinical diagnosis codes mapped to phenotype groups in the Million Veteran Program (MVP), using published phenome-wide association study (PheWAS) methodology. Associations were tested using logistic regression adjusted for age, sex, and 10 principal components of ancestry. Bonferroni correction for multiple comparisons was applied. Results Among 648,593 veterans, mean (SD) age was 62 (14) years; 9% were female, 19% Black, and 8% Hispanic. In White patients, the rs4293393 UMOD risk variant associated with increased uromodulin was associated with increased odds of CKD (odds ratio [OR]: 1.22, 95% CI: 1.20-1.24, P = 5.90 × 10-111), end-stage kidney disease (OR: 1.17, 95% CI: 1.11-1.24, P = 2.40 × 10-09), and hypertension (OR: 1.03, 95% CI: 1.05-1.05, P = 2.11 × 10-06) and significantly lower odds of UTIs (OR: 0.94, 95% CI: 0.92-0.96, P = 1.21 × 10-10) and kidney calculus (OR: 0.85, 95% CI: 0.83-0.86, P = 4.27 × 10-69). Similar findings were observed across UMOD/PDILT variants. The rs77924615 PDILT variant had stronger associations with acute cystitis in White female (OR: 0.73, 95% CI: 0.59-0.91, P = 4.98 × 10-03) versus male (OR: 0.99, 95% CI: 0.89-1.11, P = 8.80 × 10-01) (P interaction = 0.01) patients. In Black patients, the rs77924615 PDILT variant was significantly associated with pyelonephritis (OR: 0.65, 95% CI: 0.54-0.79, P = 1.05 × 10-05), whereas associations with UMOD promoter variants were attenuated. Conclusion Robust associations were observed between UMOD/PDILT variants linked with increased uromodulin expression and lower odds of UTIs and calculus and increased odds of CKD and hypertension. However, these associations varied significantly across ancestry groups and sex.
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Affiliation(s)
- Elvis A. Akwo
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Center for Kidney Disease, Nashville, Tennessee, USA
- VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - Hua-Chang Chen
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ge Liu
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jefferson L. Triozzi
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ran Tao
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Genetics Institute, Nashville, Tennessee, USA
| | - Zhihong Yu
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Cecilia P. Chung
- VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA
- Vanderbilt Genetics Institute, Nashville, Tennessee, USA
- Division of Rheumatology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ayush Giri
- Vanderbilt Genetics Institute, Nashville, Tennessee, USA
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - T. Alp Ikizler
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Center for Kidney Disease, Nashville, Tennessee, USA
- VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - C. Michael Stein
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Edward D. Siew
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Center for Kidney Disease, Nashville, Tennessee, USA
- VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Cassianne Robinson-Cohen
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Center for Kidney Disease, Nashville, Tennessee, USA
- VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - Adriana M. Hung
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Center for Kidney Disease, Nashville, Tennessee, USA
- VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA
| | - the VA Million Veteran Program12
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Center for Kidney Disease, Nashville, Tennessee, USA
- VA Tennessee Valley Healthcare System, Nashville, Tennessee, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt Genetics Institute, Nashville, Tennessee, USA
- Division of Rheumatology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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18
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Cross-ancestry genome-wide polygenic score predicts chronic kidney disease. Nat Med 2022; 28:1355-1356. [PMID: 35739270 DOI: 10.1038/s41591-022-01871-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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19
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Khan A, Turchin MC, Patki A, Srinivasasainagendra V, Shang N, Nadukuru R, Jones AC, Malolepsza E, Dikilitas O, Kullo IJ, Schaid DJ, Karlson E, Ge T, Meigs JB, Smoller JW, Lange C, Crosslin DR, Jarvik GP, Bhatraju PK, Hellwege JN, Chandler P, Torvik LR, Fedotov A, Liu C, Kachulis C, Lennon N, Abul-Husn NS, Cho JH, Ionita-Laza I, Gharavi AG, Chung WK, Hripcsak G, Weng C, Nadkarni G, Irvin MR, Tiwari HK, Kenny EE, Limdi NA, Kiryluk K. Genome-wide polygenic score to predict chronic kidney disease across ancestries. Nat Med 2022; 28:1412-1420. [PMID: 35710995 PMCID: PMC9329233 DOI: 10.1038/s41591-022-01869-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 05/11/2022] [Indexed: 01/03/2023]
Abstract
Chronic kidney disease (CKD) is a common complex condition associated with high morbidity and mortality. Polygenic prediction could enhance CKD screening and prevention; however, this approach has not been optimized for ancestrally diverse populations. By combining APOL1 risk genotypes with genome-wide association studies (GWAS) of kidney function, we designed, optimized and validated a genome-wide polygenic score (GPS) for CKD. The new GPS was tested in 15 independent cohorts, including 3 cohorts of European ancestry (n = 97,050), 6 cohorts of African ancestry (n = 14,544), 4 cohorts of Asian ancestry (n = 8,625) and 2 admixed Latinx cohorts (n = 3,625). We demonstrated score transferability with reproducible performance across all tested cohorts. The top 2% of the GPS was associated with nearly threefold increased risk of CKD across ancestries. In African ancestry cohorts, the APOL1 risk genotype and polygenic component of the GPS had additive effects on the risk of CKD.
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Affiliation(s)
- Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Michael C Turchin
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Vinodh Srinivasasainagendra
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ning Shang
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Rajiv Nadukuru
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alana C Jones
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Ozan Dikilitas
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Daniel J Schaid
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Elizabeth Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Tian Ge
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - James B Meigs
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute, Cambridge, MA, USA
| | - Jordan W Smoller
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA
| | - David R Crosslin
- Division of Biomedical Informatics and Genomics, John W. Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, USA
| | - Pavan K Bhatraju
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Jacklyn N Hellwege
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Paulette Chandler
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Laura Rasmussen Torvik
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Alex Fedotov
- Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, USA
| | - Cong Liu
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | | | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Noura S Abul-Husn
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Judy H Cho
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Ali G Gharavi
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Wendy K Chung
- Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Girish Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Division of General Internal Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nita A Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA.
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20
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Hill C, Avila-Palencia I, Maxwell AP, Hunter RF, McKnight AJ. Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease. FRONTIERS IN NEPHROLOGY 2022; 2:923068. [PMID: 37674991 PMCID: PMC10479694 DOI: 10.3389/fneph.2022.923068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 05/30/2022] [Indexed: 09/08/2023]
Abstract
Chronic kidney disease (CKD) was the 12th leading cause of death globally in 2017 with the prevalence of CKD estimated at ~9%. Early detection and intervention for CKD may improve patient outcomes, but standard testing approaches even in developed countries do not facilitate identification of patients at high risk of developing CKD, nor those progressing to end-stage kidney disease (ESKD). Recent advances in CKD research are moving towards a more personalised approach for CKD. Heritability for CKD ranges from 30% to 75%, yet identified genetic risk factors account for only a small proportion of the inherited contribution to CKD. More in depth analysis of genomic sequencing data in large cohorts is revealing new genetic risk factors for common diagnoses of CKD and providing novel diagnoses for rare forms of CKD. Multi-omic approaches are now being harnessed to improve our understanding of CKD and explain some of the so-called 'missing heritability'. The most common omic analyses employed for CKD are genomics, epigenomics, transcriptomics, metabolomics, proteomics and phenomics. While each of these omics have been reviewed individually, considering integrated multi-omic analysis offers considerable scope to improve our understanding and treatment of CKD. This narrative review summarises current understanding of multi-omic research alongside recent experimental and analytical approaches, discusses current challenges and future perspectives, and offers new insights for CKD.
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Affiliation(s)
| | | | | | | | - Amy Jayne McKnight
- Centre for Public Health, Queen’s University Belfast, Belfast, United Kingdom
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21
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Majmundar M, Ibarra G, Kumar A, Doshi R, Shah P, Mehran R, Reed GW, Puri R, Kapadia SR, Bangalore S, Kalra A. Invasive Versus Medical Management in Patients With Chronic Kidney Disease and Non-ST-Segment-Elevation Myocardial Infarction. J Am Heart Assoc 2022; 11:e025205. [PMID: 35713283 PMCID: PMC9238658 DOI: 10.1161/jaha.121.025205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/25/2021] [Accepted: 04/04/2022] [Indexed: 02/02/2023]
Abstract
Background The role of invasive management compared with medical management in patients with non-ST-segment-elevation myocardial infarction (NSTEMI) and advanced chronic kidney disease (CKD) is uncertain, given the increased risk of procedural complications in patients with CKD. We aimed to compare clinical outcomes of invasive management with medical management in patients with NSTEMI-CKD. Methods and Results We identified NSTEMI and CKD stages 3, 4, 5, and end-stage renal disease admissions using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes from the Nationwide Readmission Database 2016 to 2018. Patients were stratified into invasive and medical management. Primary outcome was mortality (in-hospital and 6 months after discharge). Secondary outcomes were in-hospital postprocedural complications (acute kidney injury requiring dialysis, major bleeding) and postdischarge 6-month safety and major adverse cardiovascular events. Out of 141 052 patients with NSTEMI-CKD, 85 875 (60.9%) were treated with invasive management, whereas 55 177 (39.1%) patients were managed medically. In propensity-score matched cohorts, invasive strategy was associated with lower in-hospital (CKD 3: odds ratio [OR], 0.47 [95% CI, 0.43-0.51]; P<0.001; CKD 4: OR, 0.79 [95% CI, 0.69-0.89]; P<0.001; CKD 5: OR, 0.72 [95% CI, 0.49-1.06]; P=0.096; end-stage renal disease: OR, 0.51 [95% CI, 0.46-0.56]; P<0.001) and 6-month mortality. Invasive management was associated with higher in-hospital postprocedural complications but no difference in postdischarge safety outcomes. Invasive management was associated with a lower hazard of major adverse cardiovascular events at 6 months in all CKD groups compared with medical management. Conclusions Invasive management was associated with lower mortality and major adverse cardiovascular events but minimal increased in-hospital complications in patients with NSTEMI-CKD compared with medical management, suggesting patients with NSTEMI-CKD should be offered invasive management.
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Affiliation(s)
- Monil Majmundar
- Department of CardiologyMaimonides Medical Center, BrooklynNew YorkNY
| | - Gabriel Ibarra
- Department of Internal MedicineBrown UniversityProvidenceRI
| | - Ashish Kumar
- Department of Internal MedicineCleveland Clinic Akron GeneralAkronOH
| | - Rajkumar Doshi
- Division of CardiologySt. Joseph’s University Medical CenterPatersonNJ
| | - Palak Shah
- Department of Internal MedicineNew York Medical College/Metropolitan HospitalNew YorkNY
| | - Roxana Mehran
- The Zena and Michael A. Wiener Cardiovascular InstituteIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Grant W. Reed
- Department of Cardiovascular MedicineHeart, Vascular, and Thoracic Institute, Cleveland ClinicClevelandOH
| | - Rishi Puri
- Department of Cardiovascular MedicineHeart, Vascular, and Thoracic Institute, Cleveland ClinicClevelandOH
| | - Samir R. Kapadia
- Department of Cardiovascular MedicineHeart, Vascular, and Thoracic Institute, Cleveland ClinicClevelandOH
| | | | - Ankur Kalra
- Division of Cardiovascular MedicineKrannert Cardiovascular Research CenterIndiana University School of MedicineIndianapolisIN
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22
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Genetics in chronic kidney disease: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int 2022; 101:1126-1141. [PMID: 35460632 PMCID: PMC9922534 DOI: 10.1016/j.kint.2022.03.019] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/16/2022] [Accepted: 03/29/2022] [Indexed: 01/19/2023]
Abstract
Numerous genes for monogenic kidney diseases with classical patterns of inheritance, as well as genes for complex kidney diseases that manifest in combination with environmental factors, have been discovered. Genetic findings are increasingly used to inform clinical management of nephropathies, and have led to improved diagnostics, disease surveillance, choice of therapy, and family counseling. All of these steps rely on accurate interpretation of genetic data, which can be outpaced by current rates of data collection. In March of 2021, Kidney Diseases: Improving Global Outcomes (KDIGO) held a Controversies Conference on "Genetics in Chronic Kidney Disease (CKD)" to review the current state of understanding of monogenic and complex (polygenic) kidney diseases, processes for applying genetic findings in clinical medicine, and use of genomics for defining and stratifying CKD. Given the important contribution of genetic variants to CKD, practitioners with CKD patients are advised to "think genetic," which specifically involves obtaining a family history, collecting detailed information on age of CKD onset, performing clinical examination for extrarenal symptoms, and considering genetic testing. To improve the use of genetics in nephrology, meeting participants advised developing an advanced training or subspecialty track for nephrologists, crafting guidelines for testing and treatment, and educating patients, students, and practitioners. Key areas of future research, including clinical interpretation of genome variation, electronic phenotyping, global representation, kidney-specific molecular data, polygenic scores, translational epidemiology, and open data resources, were also identified.
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23
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Rogers JR, Pavisic J, Ta CN, Liu C, Soroush A, Cheung YK, Hripcsak G, Weng C. Leveraging electronic health record data for clinical trial planning by assessing eligibility criteria's impact on patient count and safety. J Biomed Inform 2022; 127:104032. [PMID: 35189334 PMCID: PMC8920749 DOI: 10.1016/j.jbi.2022.104032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/14/2022] [Accepted: 02/15/2022] [Indexed: 10/19/2022]
Abstract
OBJECTIVE To present an approach on using electronic health record (EHR) data that assesses how different eligibility criteria, either individually or in combination, can impact patient count and safety (exemplified by all-cause hospitalization risk) and further assist with criteria selection for prospective clinical trials. MATERIALS AND METHODS Trials in three disease domains - relapsed/refractory (r/r) lymphoma/leukemia; hepatitis C virus (HCV); stages 3 and 4 chronic kidney disease (CKD) - were analyzed as case studies for this approach. For each disease domain, criteria were identified and all criteria combinations were used to create EHR cohorts. Per combination, two values were derived: (1) number of eligible patients meeting the selected criteria; (2) hospitalization risk, measured as the hazard ratio between those that qualified and those that did not. From these values, k-means clustering was applied to derive which criteria combinations maximized patient counts but minimized hospitalization risk. RESULTS Criteria combinations that reduced hospitalization risk without substantial reductions on patient counts were as follows: for r/r lymphoma/leukemia (23 trials; 9 criteria; 623 patients), applying no infection and adequate absolute neutrophil count while forgoing no prior malignancy; for HCV (15; 7; 751), applying no human immunodeficiency virus and no hepatocellular carcinoma while forgoing no decompensated liver disease/cirrhosis; for CKD (10; 9; 23893), applying no congestive heart failure. CONCLUSIONS Within each disease domain, the more drastic effects were generally driven by a few criteria. Similar criteria across different disease domains introduce different changes. Although results are contingent on the trial sample and the EHR data used, this approach demonstrates how EHR data can inform the impact on safety and available patients when exploring different criteria combinations for designing clinical trials.
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Affiliation(s)
- James R. Rogers
- Department of Biomedical Informatics, Columbia University, New York, NY
| | - Jovana Pavisic
- Department of Pediatrics, Division of Pediatric Hematology, Oncology, and Stem Cell Transplantation, Columbia University Irving Medical Center, New York, NY
| | - Casey N. Ta
- Department of Biomedical Informatics, Columbia University, New York, NY
| | - Cong Liu
- Department of Biomedical Informatics, Columbia University, New York, NY
| | - Ali Soroush
- Department of Biomedical Informatics, Columbia University, New York, NY,Division of Gastroenterology, Department of Medicine, Columbia University Irving Medical Center, New York, NY
| | | | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, NY,Medical Informatics Services, New York-Presbyterian Hospital, New York, NY
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY, United States.
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24
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Affiliation(s)
- Daigoro Hirohama
- Department of Medicine, Renal Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA.,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Katalin Susztak
- Department of Medicine, Renal Electrolyte and Hypertension Division, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. .,Institute of Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA. .,Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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25
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Xu K, Kiryluk K. Mapping GWAS loci to kidney genes and cell types. Kidney Int 2021; 101:447-450. [PMID: 34774560 DOI: 10.1016/j.kint.2021.10.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Accepted: 10/21/2021] [Indexed: 11/29/2022]
Affiliation(s)
- Katherine Xu
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, New York, USA.
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26
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Cervantes CE, Sperati CJ. From Dropsy to Chart Biopsy: Opportunities and Pitfalls of Electronic Health Records. KIDNEY360 2021; 2:1399-1401. [PMID: 35373111 PMCID: PMC8786136 DOI: 10.34067/kid.0004392021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 08/05/2021] [Indexed: 02/04/2023]
Affiliation(s)
- C. Elena Cervantes
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - C. John Sperati
- Division of Nephrology, Johns Hopkins University School of Medicine, Baltimore, Maryland
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27
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Kenner BJ, Abrams ND, Chari ST, Field BF, Goldberg AE, Hoos WA, Klimstra DS, Rothschild LJ, Srivastava S, Young MR, Go VLW. Early Detection of Pancreatic Cancer: Applying Artificial Intelligence to Electronic Health Records. Pancreas 2021; 50:916-922. [PMID: 34629446 PMCID: PMC8542068 DOI: 10.1097/mpa.0000000000001882] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 11/08/2021] [Indexed: 12/12/2022]
Abstract
ABSTRACT The potential of artificial intelligence (AI) applied to clinical data from electronic health records (EHRs) to improve early detection for pancreatic and other cancers remains underexplored. The Kenner Family Research Fund, in collaboration with the Cancer Biomarker Research Group at the National Cancer Institute, organized the workshop entitled: "Early Detection of Pancreatic Cancer: Opportunities and Challenges in Utilizing Electronic Health Records (EHR)" in March 2021. The workshop included a select group of panelists with expertise in pancreatic cancer, EHR data mining, and AI-based modeling. This review article reflects the findings from the workshop and assesses the feasibility of AI-based data extraction and modeling applied to EHRs. It highlights the increasing role of data sharing networks and common data models in improving the secondary use of EHR data. Current efforts using EHR data for AI-based modeling to enhance early detection of pancreatic cancer show promise. Specific challenges (biology, limited data, standards, compatibility, legal, quality, AI chasm, incentives) are identified, with mitigation strategies summarized and next steps identified.
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Affiliation(s)
| | - Natalie D. Abrams
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD
| | - Suresh T. Chari
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | | | - David S. Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY
| | | | - Sudhir Srivastava
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD
| | - Matthew R. Young
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD
| | - Vay Liang W. Go
- UCLA Center for Excellence in Pancreatic Diseases, University of California, Los Angeles, Los Angeles, CA
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28
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Quantitative disease risk scores from EHR with applications to clinical risk stratification and genetic studies. NPJ Digit Med 2021; 4:116. [PMID: 34302027 PMCID: PMC8302667 DOI: 10.1038/s41746-021-00488-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 05/06/2021] [Indexed: 12/30/2022] Open
Abstract
Labeling clinical data from electronic health records (EHR) in health systems requires extensive knowledge of human expert, and painstaking review by clinicians. Furthermore, existing phenotyping algorithms are not uniformly applied across large datasets and can suffer from inconsistencies in case definitions across different algorithms. We describe here quantitative disease risk scores based on almost unsupervised methods that require minimal input from clinicians, can be applied to large datasets, and alleviate some of the main weaknesses of existing phenotyping algorithms. We show applications to phenotypic data on approximately 100,000 individuals in eMERGE, and focus on several complex diseases, including Chronic Kidney Disease, Coronary Artery Disease, Type 2 Diabetes, Heart Failure, and a few others. We demonstrate that relative to existing approaches, the proposed methods have higher prediction accuracy, can better identify phenotypic features relevant to the disease under consideration, can perform better at clinical risk stratification, and can identify undiagnosed cases based on phenotypic features available in the EHR. Using genetic data from the eMERGE-seq panel that includes sequencing data for 109 genes on 21,363 individuals from multiple ethnicities, we also show how the new quantitative disease risk scores help improve the power of genetic association studies relative to the standard use of disease phenotypes. The results demonstrate the effectiveness of quantitative disease risk scores derived from rich phenotypic EHR databases to provide a more meaningful characterization of clinical risk for diseases of interest beyond the prevalent binary (case-control) classification.
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