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Khan SS, Matsushita K, Sang Y, Ballew SH, Grams ME, Surapaneni A, Blaha MJ, Carson AP, Chang AR, Ciemins E, Go AS, Gutierrez OM, Hwang SJ, Jassal SK, Kovesdy CP, Lloyd-Jones DM, Shlipak MG, Palaniappan LP, Sperling L, Virani SS, Tuttle K, Neeland IJ, Chow SL, Rangaswami J, Pencina MJ, Ndumele CE, Coresh J. Development and Validation of the American Heart Association's PREVENT Equations. Circulation 2024; 149:430-449. [PMID: 37947085 PMCID: PMC10910659 DOI: 10.1161/circulationaha.123.067626] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD. METHODS The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets. RESULTS Among 6 612 004 adults included, mean±SD age was 53±12 years, and 56% were women. Over a mean±SD follow-up of 4.8±3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81-1.16) and 0.94 (0.81-1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD- and heart failure-specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004-0.005] and 0.005 [0.004-0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84-1.20] versus 1.39 [1.14-1.65]; P=0.01). CONCLUSIONS PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.
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Affiliation(s)
- Sadiya S Khan
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL (S.S.K.)
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)
| | - Yingying Sang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)
- Department of Population Health, New York University Grossman School of Medicine, New York, NY (Y.S., S.H.B., J.C.)
| | - Shoshana H Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)
- Department of Population Health, New York University Grossman School of Medicine, New York, NY (Y.S., S.H.B., J.C.)
| | - Morgan E Grams
- Department of Medicine, Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY (M.E.G., A.S.)
| | - Aditya Surapaneni
- Department of Medicine, Division of Precision Medicine, New York University Grossman School of Medicine, New York, NY (M.E.G., A.S.)
| | - Michael J Blaha
- Johns Hopkins Ciccarone Center for Prevention of Cardiovascular Disease, Baltimore, MD (M.J.B.)
| | - April P Carson
- University of Mississippi Medical Center, Jackson (A.P.C.)
| | - Alexander R Chang
- Departments of Nephrology and Population Health Sciences, Geisinger Health, Danville, PA (A.R.C.)
| | | | - Alan S Go
- Division of Research, Kaiser Permanente Northern California, Oakland; Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, CA; Departments of Epidemiology, Biostatistics and Medicine, University of California, San Francisco; Department of Medicine (Nephrology), Stanford University School of Medicine, Palo Alto, CA (A.S,G.)
| | - Orlando M Gutierrez
- Departments of Epidemiology and Medicine, University of Alabama at Birmingham (O.M.G.)
| | - Shih-Jen Hwang
- National Heart, Lung, and Blood Institute, Framingham, MA (S.-J.H.)
| | - Simerjot K Jassal
- Division of General Internal Medicine, University of California, San Diego and VA San Diego Healthcare, CA (S.K.J.)
| | - Csaba P Kovesdy
- Medicine-Nephrology, Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis (C.P.K.)
| | - Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, IL (D.M.L.-J.)
| | - Michael G Shlipak
- Department of Medicine, Epidemiology, and Biostatistics, University of California, San Francisco, and San Francisco VA Medical Center (M.G.S.)
| | - Latha P Palaniappan
- Center for Asian Health Research and Education and the Department of Medicine, Stanford University School of Medicine, CA (L.P.P.)
| | | | - Salim S Virani
- Department of Medicine, The Aga Khan University, Karachi, Pakistan; Texas Heart Institute and Baylor College of Medicine, Houston (S.S.V.)
| | - Katherine Tuttle
- Providence Medical Research Center, Providence Inland Northwest Health, Spokane, WA; Kidney Research Institute and Institute of Translational Health Sciences, University of Washington, Seattle (K.T.)
| | - Ian J Neeland
- UH Center for Cardiovascular Prevention, Translational Science Unit, Center for Integrated and Novel Approaches in Vascular-Metabolic Disease (CINEMA), Harrington Heart and Vascular Institute, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, OH (I.J.N.)
| | - Sheryl L Chow
- Department of Pharmacy Practice and Administration, College of Pharmacy, Western University of Health Sciences, Pomona, CA (S.L.C.)
| | - Janani Rangaswami
- Washington DC VA Medical Center and George Washington University School of Medicine (J.R.)
| | - Michael J Pencina
- Department of Biostatistics, Duke University Medical Center, Durham, NC (M.J.P.)
| | - Chiadi E Ndumele
- Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD (C.E.N.)
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD (K.M., Y.S., S.H.B., J.C.)
- Department of Population Health, New York University Grossman School of Medicine, New York, NY (Y.S., S.H.B., J.C.)
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2
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Kumada Y, Kawai N, Ishida N, Nakamura Y, Takahashi H, Ohshima S, Ito R, Izawa H, Murohara T, Ishii H. Combined Prognostic Value of Preprocedural Protein-Energy Wasting and Inflammation Status for Amputation and/or Mortality after Lower-Extremity Revascularization in Hemodialysis Patients with Peripheral Arterial Disease. J Clin Med 2023; 13:126. [PMID: 38202133 PMCID: PMC10779791 DOI: 10.3390/jcm13010126] [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: 11/16/2023] [Revised: 12/03/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024] Open
Abstract
Protein-energy wasting is associated with inflammation and advanced atherosclerosis in hemodialysis patients. We enrolled 800 patients who had undergone successful lower-extremity revascularization, and we investigated the association among the Geriatric Nutritional Risk Index (GNRI) as a surrogate marker of protein-energy wasting, C-reactive protein (CRP), and their joint roles in predicting amputation and mortality. They were divided into lower, middle, and upper tertiles (T1, T2, and T3) according to GNRI and CRP levels, respectively. Regarding the results, the amputation-free survival rates over 8 years were 47.0%, 56.9%, and 69.5% in T1, T2, and T3 of the GNRI and 65.8%, 58.7%, and 33.2% for T1, T2, and T3 of CRP, respectively (p < 0.0001 for both). A reduced GNRI [adjusted hazard ratio (aHR) 1.78, 95% confidence interval (CI) 1.24-2.59, p = 0.0016 for T1 vs. T3] and elevated CRP (aHR 1.86, 95% CI 1.30-2.70, p = 0.0007 for T3 vs. T1) independently predicted amputation and/or mortality. When the two variables were combined, the risk was 3.77-fold higher (95% CI 1.97-7.69, p < 0.0001) in patients who occupied both T1 of the GNRI and T3 of CRP than in those who occupied both T3 of the GNRI and T1 of CRP. In conclusion, patients with preprocedurally decreased GNRI and elevated CRP levels frequently experienced amputation and mortality, and a combination of these two variables could more accurately stratify the risk.
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Affiliation(s)
- Yoshitaka Kumada
- Department of Cardiovascular Surgery, Matsunami General Hospital, Kasamatsu 501-6062, Japan; (Y.K.); (N.K.); (N.I.); (Y.N.)
| | - Norikazu Kawai
- Department of Cardiovascular Surgery, Matsunami General Hospital, Kasamatsu 501-6062, Japan; (Y.K.); (N.K.); (N.I.); (Y.N.)
| | - Narihiro Ishida
- Department of Cardiovascular Surgery, Matsunami General Hospital, Kasamatsu 501-6062, Japan; (Y.K.); (N.K.); (N.I.); (Y.N.)
| | - Yasuhito Nakamura
- Department of Cardiovascular Surgery, Matsunami General Hospital, Kasamatsu 501-6062, Japan; (Y.K.); (N.K.); (N.I.); (Y.N.)
| | - Hiroshi Takahashi
- Department of Cardiology, Fujita Health University School of Medicine, Toyoake 470-1192, Japan; (H.T.); (H.I.)
| | - Satoru Ohshima
- Department of Cardiology, Nagoya Kyoritsu Hospital, Nagoya 454-0933, Japan; (S.O.); (R.I.)
| | - Ryuta Ito
- Department of Cardiology, Nagoya Kyoritsu Hospital, Nagoya 454-0933, Japan; (S.O.); (R.I.)
| | - Hideo Izawa
- Department of Cardiology, Fujita Health University School of Medicine, Toyoake 470-1192, Japan; (H.T.); (H.I.)
| | - Toyoaki Murohara
- Department of Cardiology, Nagoya University Graduate School of Medicine, Nagoya 466-8550, Japan;
| | - Hideki Ishii
- Department of Cardiovascular Medicine, Gunma University Graduate School of Medicine, 3-39-22 Showa-machi, Maebashi 371-8511, Japan
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3
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Matsushita K, Kaptoge S, Hageman SHJ, Sang Y, Ballew SH, Grams ME, Surapaneni A, Sun L, Arnlov J, Bozic M, Brenner H, Brunskill NJ, Chang AR, Chinnadurai R, Cirillo M, Correa A, Ebert N, Eckardt KU, Gansevoort RT, Gutierrez O, Hadaegh F, He J, Hwang SJ, Jafar TH, Jassal SK, Kayama T, Kovesdy CP, Landman GW, Levey AS, Lloyd-Jones DM, Major RW, Miura K, Muntner P, Nadkarni GN, Nowak C, Ohkubo T, Pena MJ, Polkinghorne KR, Sairenchi T, Schaeffner E, Schneider MP, Shalev V, Shlipak MG, Solbu MD, Stempniewicz N, Tollitt J, Valdivielso JM, van der Leeuw J, Wang AYM, Wen CP, Woodward M, Yamagishi K, Yatsuya H, Zhang L, Dorresteijn JAN, Di Angelantonio E, Visseren FLJ, Pennells L, Coresh J. Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP. Eur J Prev Cardiol 2023; 30:8-16. [PMID: 35972749 PMCID: PMC9839538 DOI: 10.1093/eurjpc/zwac176] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 01/17/2023]
Abstract
AIMS The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. METHODS In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. RESULTS In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. CONCLUSION Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.
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Affiliation(s)
- Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Stephen Kaptoge
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Steven HJ Hageman
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yingying Sang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Shoshana H Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Luanluan Sun
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Johan Arnlov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Milica Bozic
- Vascular & Renal Translational Research Group, IRBLleida, Spain and Spanish Research Network for Renal Diseases (RedInRen. ISCIII) , Lleida , Spain
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ) and Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Nigel J Brunskill
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom, Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Alex R Chang
- Department of Nephrology and Kidney Health Research Institute, Geisinger Medical Center, Danville, Pennsylvania
| | - Rajkumar Chinnadurai
- Department of Renal Medicine, Salford Care Organisation, Northern Care Alliance NHS Foundation Trust, Salford, UK
| | - Massimo Cirillo
- Department of Public Health, University of Naples “Federico II”, Italy
| | | | - Natalie Ebert
- Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Hypertension, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany; Department of Nephrology and Medical Intensive Care, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Ron T Gansevoort
- Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Orlando Gutierrez
- Departments of Epidemiology and Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Farzad Hadaegh
- Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, LA
| | - Shih-Jen Hwang
- National Heart, Lung, and Blood Institute, Framingham, Massachusetts
| | - Tazeen H Jafar
- Program in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore, Department of Medicine, Aga Khan University, Karachi, Pakistan, and Duke Global Health Institute, Durham, Duke University, North Carolina
| | - Simerjot K Jassal
- Division of General Internal Medicine, University of California, San Diego and VA San Diego Healthcare, San Diego, California
| | - Takamasa Kayama
- Global Center of Excellence, Yamagata University Faculty of Medicine, Yamagata, Japan; Department of Public Health Medicine, Faculty of Medicine, and Health Services Research and Development Center, University of Tsukuba, Japan
| | - Csaba P Kovesdy
- Medicine-Nephrology, Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis, Tennessee
| | | | - Andrew S Levey
- Division of Nephrology, Tufts Medical Center, Boston, Massachusetts
| | | | - Rupert W Major
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom, Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Katsuyuki Miura
- NCD Epidemiology Research Center, Shiga University of Medical Science, Shiga, Japan
| | - Paul Muntner
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Girish N Nadkarni
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Christoph Nowak
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Takayoshi Ohkubo
- Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan
| | - Michelle J Pena
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Toshimi Sairenchi
- Medical Science of Nursing, Dokkyo Medical University School of Nursing, Mibu, Japan
| | - Elke Schaeffner
- Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus P Schneider
- Department of Nephrology and Hypertension, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Varda Shalev
- Institute for Health and Research and Innovation, Maccabi Healthcare Services and Tel Aviv University, Tel Aviv, Israel
| | - Michael G Shlipak
- Kidney Health Research Collaborative, University of California, San Francisco, and San Francisco VA Healthcare System, San Francisco
| | - Marit D Solbu
- Section of Nephrology, University Hospital of North Norway, Tromsø, Norway and UiT The Arctic University of Norway, Tromsø, Norway
| | - Nikita Stempniewicz
- AMGA (American Medical Group Association), Alexandria, Virginia and OptumLabs Visiting Fellow
| | - James Tollitt
- Department of Renal Medicine, Salford Care Organisation, Northern Care Alliance NHS Foundation Trust, Salford, UK; Renal Department, University of Manchester, Oxford Road, Manchester, United Kingdom
| | - José M Valdivielso
- Vascular & Renal Translational Research Group, IRBLleida, Spain and Spanish Research Network for Renal Diseases (RedInRen. ISCIII) , Lleida , Spain
| | - Joep van der Leeuw
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Angela Yee-Moon Wang
- Department of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong
| | - Chi-Pang Wen
- China Medical University Hospital, Taichung, Taiwan
| | - Mark Woodward
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Kazumasa Yamagishi
- George Institute for Global Health, Australia, and George Institute for Global Health, Imperial College, London, United Kingdom
| | - Hiroshi Yatsuya
- Department of Public Health, Fujita Health University School of Medicine, Aichi, Japan and Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Luxia Zhang
- Peking University First Hospital and Peking University, Beijing, China
| | - Jannick AN Dorresteijn
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Emanuele Di Angelantonio
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom; Health Data Science Centre, Human Technopole, Milan, Italy
| | - Frank LJ Visseren
- Department of Vascular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lisa Pennells
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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4
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Bundy JD, Rahman M, Matsushita K, Jaeger BC, Cohen JB, Chen J, Deo R, Dobre MA, Feldman HI, Flack J, Kallem RR, Lash JP, Seliger S, Shafi T, Weiner SJ, Wolf M, Yang W, Allen NB, Bansal N, He J. Risk Prediction Models for Atherosclerotic Cardiovascular Disease in Patients with Chronic Kidney Disease: The CRIC Study. J Am Soc Nephrol 2022; 33:601-611. [PMID: 35145041 PMCID: PMC8975076 DOI: 10.1681/asn.2021060747] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2021] [Accepted: 12/20/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Individuals with CKD may be at high risk for atherosclerotic cardiovascular disease (ASCVD). However, there are no ASCVD risk prediction models developed in CKD populations to inform clinical care and prevention. METHODS We developed and validated 10-year ASCVD risk prediction models in patients with CKD that included participants without self-reported cardiovascular disease from the Chronic Renal Insufficiency Cohort (CRIC) study. ASCVD was defined as the first occurrence of adjudicated fatal and nonfatal stroke or myocardial infarction. Our models used clinically available variables and novel biomarkers. Model performance was evaluated based on discrimination, calibration, and net reclassification improvement. RESULTS Of 2604 participants (mean age 55.8 years; 52.0% male) included in the analyses, 252 had incident ASCVD within 10 years of baseline. Compared with the American College of Cardiology/American Heart Association pooled cohort equations (area under the receiver operating characteristic curve [AUC]=0.730), a model with coefficients estimated within the CRIC sample had higher discrimination (P=0.03), achieving an AUC of 0.736 (95% confidence interval [CI], 0.649 to 0.826). The CRIC model developed using clinically available variables had an AUC of 0.760 (95% CI, 0.678 to 0.851). The CRIC biomarker-enriched model had an AUC of 0.771 (95% CI, 0.674 to 0.853), which was significantly higher than the clinical model (P=0.001). Both the clinical and biomarker-enriched models were well-calibrated and improved reclassification of nonevents compared with the pooled cohort equations (6.6%; 95% CI, 3.7% to 9.6% and 10.0%; 95% CI, 6.8% to 13.3%, respectively). CONCLUSIONS The 10-year ASCVD risk prediction models developed in patients with CKD, including novel kidney and cardiac biomarkers, performed better than equations developed for the general population using only traditional risk factors.
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Affiliation(s)
- Joshua D. Bundy
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana,Tulane University Translational Science Institute, New Orleans, Louisiana
| | - Mahboob Rahman
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Division of Cardiology, Johns Hopkins School of Medicine, Welch Center for Prevention, Epidemiology, and Clinical Research, Baltimore, Maryland
| | - Byron C. Jaeger
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama
| | - Jordana B. Cohen
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Jing Chen
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana,Tulane University Translational Science Institute, New Orleans, Louisiana,Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana
| | - Rajat Deo
- Cardiovascular Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Mirela A. Dobre
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, Ohio
| | - Harold I. Feldman
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - John Flack
- Department of Internal Medicine, Southern Illinois University School of Medicine, Springfield, Illinois
| | - Radhakrishna R. Kallem
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - James P. Lash
- Department of Medicine, University of Illinois College of Medicine at Chicago, Chicago, Illinois
| | - Stephen Seliger
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Tariq Shafi
- Nephrology Division, The University of Mississippi Medical Center, Jackson, Mississippi
| | - Shoshana J. Weiner
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland
| | - Myles Wolf
- Department of Medicine, Duke Clinical Research Institute, Duke University, Durham, North Carolina
| | - Wei Yang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Norrina B. Allen
- Department of Preventive Medicine, Northwestern Feinberg School of Medicine, Chicago, Illinois
| | - Nisha Bansal
- Division of Nephrology, University of Washington, Seattle, Washington
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana .,Tulane University Translational Science Institute, New Orleans, Louisiana.,Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana
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5
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Mok Y, Ballew SH, Matsushita K. Chronic kidney disease measures for cardiovascular risk prediction. Atherosclerosis 2021; 335:110-118. [PMID: 34556333 DOI: 10.1016/j.atherosclerosis.2021.09.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 09/02/2021] [Accepted: 09/08/2021] [Indexed: 02/07/2023]
Abstract
Chronic kidney disease (CKD) affects 15-20% of adults globally and causes various complications, one of the most important being cardiovascular disease (CVD). CKD has been associated with many CVD subtypes, especially severe ones like heart failure, independent of potential confounders such as diabetes and hypertension. There is no consensus in major clinical guidelines as to how to incorporate the two key measures of CKD (glomerular filtration rate and albuminuria) for CVD risk prediction. This is a critical missed opportunity to appropriately refine predicted risk and personalize prevention therapies according to CKD status, particularly since these measures are often already evaluated in clinical care. In this review, we provide an overview of CKD definition and staging, the subtypes of CVD most associated with CKD, major pathophysiological mechanisms, and the current state of CKD as a predictor of CVD in major clinical guidelines. We will introduce the novel concept of a "CKD Add-on", which allows the incorporation of CKD measures in existing risk prediction models, and the implications of taking into account CKD in the management of CVD risk.
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Affiliation(s)
- Yejin Mok
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Welch Center for Prevention, Epidemiology, and Clinical Research, USA
| | - Shoshana H Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Welch Center for Prevention, Epidemiology, and Clinical Research, USA
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Welch Center for Prevention, Epidemiology, and Clinical Research, USA.
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Streja E, Norris KC, Budoff MJ, Hashemi L, Akbilgic O, Kalantar-Zadeh K. The quest for cardiovascular disease risk prediction models in patients with nondialysis chronic kidney disease. Curr Opin Nephrol Hypertens 2021; 30:38-46. [PMID: 33186224 DOI: 10.1097/mnh.0000000000000672] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
PURPOSE OF REVIEW Cardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). However, traditional CVD risk prediction equations do not work well in patients with CKD, and inclusion of kidney disease metrics such as albuminuria and estimated glomerular filtration rate have a modest to no benefit in improving prediction. RECENT FINDINGS As CKD progresses, the strength of traditional CVD risk factors in predicting clinical outcomes weakens. A pooled cohort equation used for CVD risk prediction is a useful tool for guiding clinicians on management of patients with CVD risk, but these equations do not calibrate well in patients with CKD, although a number of studies have developed modifications of the traditional equations to improve risk prediction. The reason for the poor calibration may be related to the fact that as CKD progresses, associations of traditional risk factors such as BMI, lipids and blood pressure with CVD outcomes are attenuated or reverse, and other risk factors may become more important. SUMMARY Large national cohorts such as the US Veteran cohort with many patients with evolving CKD may be useful resources for the developing CVD prediction models; however, additional considerations are needed for the unique composition of patients receiving care in these healthcare systems, including those with multiple comorbidities, as well as mental health issues, homelessness, posttraumatic stress disorders, frailty, malnutrition and polypharmacy. Machine learning over conventional risk prediction models may be better suited to handle the complexity needed for these CVD prediction models.
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Affiliation(s)
- Elani Streja
- Harold Simmons Center for Chronic Disease Research and Epidemiology, Division of Nephrology, Hypertension and Kidney Transplantation, University of California Irvine, Orange
- Veterans Affairs Tibor Rubin Long Beach Healthcare System, Long Beach
| | - Keith C Norris
- David Geffen School of Medicine, UCLA, Los Angeles, California
| | | | - Leila Hashemi
- Veterans Affairs Greater Los Angeles Medical Center, Los Angeles, California
| | - Oguz Akbilgic
- Department of Health Informatics and Data Science, Loyola University Chicago, Maywood, Illinois, USA
| | - Kamyar Kalantar-Zadeh
- Harold Simmons Center for Chronic Disease Research and Epidemiology, Division of Nephrology, Hypertension and Kidney Transplantation, University of California Irvine, Orange
- Veterans Affairs Tibor Rubin Long Beach Healthcare System, Long Beach
- University of Tennessee at Memphis Health Sciences Center, Memphis, Tennessee
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7
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Matsushita K, Jassal SK, Sang Y, Ballew SH, Grams ME, Surapaneni A, Arnlov J, Bansal N, Bozic M, Brenner H, Brunskill NJ, Chang AR, Chinnadurai R, Cirillo M, Correa A, Ebert N, Eckardt KU, Gansevoort RT, Gutierrez O, Hadaegh F, He J, Hwang SJ, Jafar TH, Kayama T, Kovesdy CP, Landman GW, Levey AS, Lloyd-Jones DM, Major RW, Miura K, Muntner P, Nadkarni GN, Naimark DMJ, Nowak C, Ohkubo T, Pena MJ, Polkinghorne KR, Sabanayagam C, Sairenchi T, Schneider MP, Shalev V, Shlipak M, Solbu MD, Stempniewicz N, Tollitt J, Valdivielso JM, van der Leeuw J, Wang AYM, Wen CP, Woodward M, Yamagishi K, Yatsuya H, Zhang L, Schaeffner E, Coresh J. Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets. EClinicalMedicine 2020; 27:100552. [PMID: 33150324 PMCID: PMC7599294 DOI: 10.1016/j.eclinm.2020.100552] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/01/2020] [Accepted: 09/04/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. "CKD Patch" is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures. METHODS Utilizing data from 4,143,535 adults from 35 datasets, we developed several "CKD Patches" incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch. FINDINGS We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-statistic 0.027 [95% CI 0.018-0.036] and 0.010 [0.007-0.013] and categorical net reclassification improvement 0.080 [0.032-0.127] and 0.056 [0.044-0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89-3.40) in very high-risk CKD (e.g., eGFR 30-44 ml/min/1.73m2 with albuminuria ≥30 mg/g), 1.86 (1.48-2.44) in high-risk CKD (e.g., eGFR 45-59 ml/min/1.73m2 with albuminuria 30-299 mg/g), and 1.37 (1.14-1.69) in moderate risk CKD (e.g., eGFR 60-89 ml/min/1.73m2 with albuminuria 30-299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37-1.81), 1.24 (1.10-1.54), and 1.21 (0.98-1.46). INTERPRETATION The "CKD Patch" can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available. FUNDING US National Kidney Foundation and the NIDDK.
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Affiliation(s)
- Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Simerjot K Jassal
- Division of General Internal Medicine, University of California, San Diego and VA San Diego Healthcare, San Diego, California
| | - Yingying Sang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Shoshana H Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
- Corresponding author.
| | - Morgan E Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Aditya Surapaneni
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Johan Arnlov
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Nisha Bansal
- Division of Nephrology, Department of Medicine, University of Washington, Seattle, WA, United States
| | - Milica Bozic
- Vascular & Renal Translational Research Group, IRBLleida, Spain and Spanish Research Network for Renal Diseases (RedInRen. ISCIII), Lleida, Spain
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ) and Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Nigel J Brunskill
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
- Department of Cardiovascular Sciences, University of Leicester, Leicester, United Kingdom
| | - Alex R Chang
- Department of Nephrology and Kidney Health Research Institute, Geisinger Medical Center, Danville, Pennsylvania
| | - Rajkumar Chinnadurai
- Department of Renal Medicine, Salford Royal NHS Foundation Trust, Salford, United Kingdom
| | - Massimo Cirillo
- Department of Public Health, University of Naples “Federico II”, Italy
| | - Adolfo Correa
- University of Mississippi Medical Center, Jackson, United States
| | - Natalie Ebert
- Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Kai-Uwe Eckardt
- Department of Nephrology and Hypertension, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
- Department of Nephrology and Medical Intensive Care, Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Ron T Gansevoort
- Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Orlando Gutierrez
- Departments of Epidemiology and Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Farzad Hadaegh
- Prevention of Metabolic Disorders Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Jiang He
- Department of Epidemiology, Tulane University School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, United States
| | - Shih-Jen Hwang
- National Heart, Lung, and Blood Institute, Framingham, MA, United States
| | - Tazeen H Jafar
- Program in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore
- Duke Global Health Institute, Durham, Duke University, NC, United States
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Takamasa Kayama
- Global Center of Excellence, Yamagata University Faculty of Medicine, Yamagata, Japan
| | - Csaba P Kovesdy
- Medicine-Nephrology, Memphis Veterans Affairs Medical Center and University of Tennessee Health Science Center, Memphis, TN, United States
| | | | - Andrew S Levey
- Division of Nephrology, Tufts Medical Center, Boston, MA, United States
| | - Donald M Lloyd-Jones
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States
| | - Rupert W. Major
- John Walls Renal Unit, Leicester General Hospital, University Hospitals of Leicester NHS Trust, Leicester, United Kingdom
- Department of Health Sciences, University of Leicester, Leicester, United Kingdom
| | - Katsuyuki Miura
- Department of Public Health, Center for Epidemiologic Research in Asia (CERA) Shiga University of Medical Science (SUMS) Seta-Tsukinowa-cho, Shiga, Japan
| | - Paul Muntner
- Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Girish N Nadkarni
- Department of Medicine, Division of Nephrology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Christoph Nowak
- Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Takayoshi Ohkubo
- Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan
| | - Michelle J Pena
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Kevan R Polkinghorne
- Department of Nephrology, Monash Medical Centre, Monashhealth, Melbourne, Australia and Department of Medicine, and Department of Epidemiology & Preventive Medicine, Monash University, Melbourne, Australia
| | | | - Toshimi Sairenchi
- Department of Public Health, Dokkyo Medical University School of Medicine, Mibu, Japan
| | - Markus P Schneider
- Department of Nephrology and Hypertension, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Varda Shalev
- Institute for Health and Research and Innovation, Maccabi Healthcare Services and Tel Aviv University, Tel Aviv, Israel
| | - Michael Shlipak
- Department of Epidemiology and Biostatistics, University of California, San Francisco, and San Francisco VA Medical Center, San Francisco, United States
| | - Marit D Solbu
- Section of Nephrology, University Hospital of North Norway, Tromsø, Norway and UiT The Arctic University of Norway, Tromsø, Norway
| | - Nikita Stempniewicz
- AMGA (American Medical Group Association), Alexandria, Virginia and OptumLabs Visiting Fellow, United States
| | - James Tollitt
- Department of Renal Medicine, Salford Royal NHS Foundation Trust, Salford, United Kingdom
- Faculty of Biology, Medicine and Health, University of Manchester, Oxford Road, Manchester, UK
| | - José M Valdivielso
- Vascular & Renal Translational Research Group, IRBLleida, Spain and Spanish Research Network for Renal Diseases (RedInRen. ISCIII), Lleida, Spain
| | - Joep van der Leeuw
- Department of Vascular Medicine and Department of Nephrology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Angela Yee-Moon Wang
- Department of Medicine, Queen Mary Hospital, The University of Hong Kong, Hong Kong
| | - Chi-Pang Wen
- China Medical University Hospital, Taichung, Taiwan
| | - Mark Woodward
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
- George Institute for Global Health, Australia, and George Institute for Global Health, Imperial College, London, United Kingdom
| | - Kazumasa Yamagishi
- Department of Public Health Medicine, Faculty of Medicine, and Health Services Research and Development Center, University of Tsukuba, Japan
| | - Hiroshi Yatsuya
- Department of Public Health, Fujita Health University School of Medicine, Aichi, Japan
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Luxia Zhang
- Peking University First Hospital and Peking University, Beijing, China
| | - Elke Schaeffner
- Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
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8
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“Should the definition of CKD be changed to include age-adapted GFR criteria?”. Kidney Int 2020; 97:37-40. [DOI: 10.1016/j.kint.2019.08.032] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 08/27/2019] [Indexed: 01/01/2023]
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9
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Nelson RG, Grams ME, Ballew SH, Sang Y, Azizi F, Chadban SJ, Chaker L, Dunning SC, Fox C, Hirakawa Y, Iseki K, Ix J, Jafar TH, Köttgen A, Naimark DMJ, Ohkubo T, Prescott GJ, Rebholz CM, Sabanayagam C, Sairenchi T, Schöttker B, Shibagaki Y, Tonelli M, Zhang L, Gansevoort RT, Matsushita K, Woodward M, Coresh J, Shalev V. Development of Risk Prediction Equations for Incident Chronic Kidney Disease. JAMA 2019; 322:2104-2114. [PMID: 31703124 PMCID: PMC6865298 DOI: 10.1001/jama.2019.17379] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
IMPORTANCE Early identification of individuals at elevated risk of developing chronic kidney disease (CKD) could improve clinical care through enhanced surveillance and better management of underlying health conditions. OBJECTIVE To develop assessment tools to identify individuals at increased risk of CKD, defined by reduced estimated glomerular filtration rate (eGFR). DESIGN, SETTING, AND PARTICIPANTS Individual-level data analysis of 34 multinational cohorts from the CKD Prognosis Consortium including 5 222 711 individuals from 28 countries. Data were collected from April 1970 through January 2017. A 2-stage analysis was performed, with each study first analyzed individually and summarized overall using a weighted average. Because clinical variables were often differentially available by diabetes status, models were developed separately for participants with diabetes and without diabetes. Discrimination and calibration were also tested in 9 external cohorts (n = 2 253 540). EXPOSURES Demographic and clinical factors. MAIN OUTCOMES AND MEASURES Incident eGFR of less than 60 mL/min/1.73 m2. RESULTS Among 4 441 084 participants without diabetes (mean age, 54 years, 38% women), 660 856 incident cases (14.9%) of reduced eGFR occurred during a mean follow-up of 4.2 years. Of 781 627 participants with diabetes (mean age, 62 years, 13% women), 313 646 incident cases (40%) occurred during a mean follow-up of 3.9 years. Equations for the 5-year risk of reduced eGFR included age, sex, race/ethnicity, eGFR, history of cardiovascular disease, ever smoker, hypertension, body mass index, and albuminuria concentration. For participants with diabetes, the models also included diabetes medications, hemoglobin A1c, and the interaction between the 2. The risk equations had a median C statistic for the 5-year predicted probability of 0.845 (interquartile range [IQR], 0.789-0.890) in the cohorts without diabetes and 0.801 (IQR, 0.750-0.819) in the cohorts with diabetes. Calibration analysis showed that 9 of 13 study populations (69%) had a slope of observed to predicted risk between 0.80 and 1.25. Discrimination was similar in 18 study populations in 9 external validation cohorts; calibration showed that 16 of 18 (89%) had a slope of observed to predicted risk between 0.80 and 1.25. CONCLUSIONS AND RELEVANCE Equations for predicting risk of incident chronic kidney disease developed from more than 5 million individuals from 34 multinational cohorts demonstrated high discrimination and variable calibration in diverse populations. Further study is needed to determine whether use of these equations to identify individuals at risk of developing chronic kidney disease will improve clinical care and patient outcomes.
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Affiliation(s)
- Robert G. Nelson
- Chronic Kidney Disease Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, Arizona
| | - Morgan E. Grams
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Shoshana H. Ballew
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Yingying Sang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- OptumLabs, Cambridge, Massachusetts
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Layal Chaker
- Academic Center for Thyroid Diseases, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | | | - Caroline Fox
- Population Sciences Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
- The Framingham Heart Study, Framingham, Massachusetts
| | - Yoshihisa Hirakawa
- Department of Public Health and Health Systems, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kunitoshi Iseki
- Nakamura Clinic & Okinawa Asia Clinical Investigation Synergy, Okinawa, Japan
| | - Joachim Ix
- University of California, San Diego, La Jolla
- Veterans Affairs San Diego Healthcare System, San Diego
| | - Tazeen H. Jafar
- Program in Health Services and Systems Research, Duke-National University of Singapore Medical School, Singapore
- Department of Medicine, Aga Khan University, Karachi, Pakistan
- Duke Global Health Institute, Durham, Duke University, North Carolina
| | - Anna Köttgen
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Institute of Genetic Epidemiology, Faculty of Medicine and Medical Center-University of Freiburg, Freiburg, Germany
| | | | - Takayoshi Ohkubo
- Department of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan
| | - Gordon J. Prescott
- Medical Statistics Team, School of Medicine, Medical Sciences & Nutrition, University of Aberdeen, Aberdeen, United Kingdom
| | - Casey M. Rebholz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Charumathi Sabanayagam
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
| | - Toshimi Sairenchi
- Department of Public Health, Dokkyo Medical University, Tochigi, Japan
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center, Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Yugo Shibagaki
- Division of Nephrology and Hypertension, Department of Internal Medicine, St Marianna University School of Medicine, Kawasaki, Japan
| | - Marcello Tonelli
- Department of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Luxia Zhang
- Peking University Institute of Nephrology, Division of Nephrology, Peking University First Hospital, Beijing, China
| | - Ron T. Gansevoort
- Department of Nephrology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Mark Woodward
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- The George Institute for Global Health, University of Oxford, United Kingdom
- The George Institute for Global Health, University of New South Wales, Australia
| | - Josef Coresh
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Varda Shalev
- Medical Division, Maccabi Healthcare Services, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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10
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Affiliation(s)
- Hideki Ishii
- Department of Cardiology, Nagoya University Graduate School of Medicine
| | - Toyoaki Murohara
- Department of Cardiology, Nagoya University Graduate School of Medicine
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