Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study

Author:

Zelnick Leila R.ORCID,Shlipak Michael G.,Soliman Elsayed Z.,Anderson Amanda,Christenson Robert,Lash James,Deo Rajat,Rao Panduranga,Afshinnia Farsad,Chen Jing,He Jiang,Seliger StephenORCID,Townsend Raymond,Cohen Debbie L.,Go Alan,Bansal Nisha

Abstract

Background and objectivesAtrial fibrillation (AF) is common in CKD and associated with poor kidney and cardiovascular outcomes. Prediction models developed using novel methods may be useful to identify patients with CKD at highest risk of incident AF. We compared a previously published prediction model with models developed using machine learning methods in a CKD population.Design, setting, participants, & measurementsWe studied 2766 participants in the Chronic Renal Insufficiency Cohort study without prior AF with complete cardiac biomarker (N-terminal pro–B-type natriuretic peptide and high-sensitivity troponin T) and clinical data. We evaluated the utility of machine learning methods as well as a previously validated clinical prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology [CHARGE]-AF, which included 11 predictors, using original and re-estimated coefficients) to predict incident AF. Discriminatory ability of each model was assessed using the ten-fold cross-validated C-index; calibration was evaluated graphically and with the Grønnesby and Borgan test.ResultsMean (SD) age of participants was 57 (11) years, 55% were men, 38% were Black, and mean (SD) eGFR was 45 (15) ml/min per 1.73 m2; 259 incident AF events occurred during a median of 8 years of follow-up. The CHARGE-AF prediction equation using original and re-estimated coefficients had C-indices of 0.67 (95% confidence interval, 0.64 to 0.71) and 0.67 (95% confidence interval, 0.64 to 0.70), respectively. A likelihood-based boosting model using clinical variables only had a C-index of 0.67 (95% confidence interval, 0.64 to 0.70); adding N-terminal pro–B-type natriuretic peptide, high-sensitivity troponin T, or both biomarkers improved the C-index by 0.04, 0.01, and 0.04, respectively. In addition to N-terminal pro–B-type natriuretic peptide and high-sensitivity troponin T, the final model included age, non-Hispanic Black race/ethnicity, Hispanic race/ethnicity, cardiovascular disease, chronic obstructive pulmonary disease, myocardial infarction, peripheral vascular disease, use of angiotensin-converting enzyme inhibitor/angiotensin receptor blockers, calcium channel blockers, diuretics, height, and weight.ConclusionsUsing machine learning algorithms, a model that included 12 standard clinical variables and cardiac-specific biomarkers N-terminal pro–B-type natriuretic peptide and high-sensitivity troponin T had moderate discrimination for incident AF in a CKD population.

Funder

National Institute of Diabetes and Digestive and Kidney Diseases

National Center for Advancing Translational Sciences

Johns Hopkins University

University of Maryland General Clinical Research Center

Michigan Institute for Clinical and Health Research

University of Illinois at Chicago Clinical and Translational Science

Tulane COBRE for Clinical and Translational Research in Cardiometabolic Diseases

Kaiser Permanente NIH/National Center for Research Resources University of California, San Francisco-Clinical and Translational Science Institute

Northwest Kidney Centers

Roche Diagnostics

Publisher

American Society of Nephrology (ASN)

Subject

Transplantation,Nephrology,Critical Care and Intensive Care Medicine,Epidemiology

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