Prediction of Incident Atrial Fibrillation in Chronic Kidney Disease: The Chronic Renal Insufficiency Cohort Study. Issue 7 (July 2021)

• Record Type: Journal Article
• Authors: Zelnick, Leila R.; Shlipak, Michael G.; Soliman, Elsayed Z.; Anderson, Amanda; Christenson, Robert; Lash, James; Deo, Rajat; Rao, Panduranga; Afshinnia, Farsad; Chen, Jing; He, Jiang; Seliger, Stephen; Townsend, Raymond; Cohen, Debbie L.; Go, Alan; Bansal, Nisha
• Is Part Of: Clinical journal of the American Society of Nephrology. Volume 16:Issue 7(2021)
• Page Start: 1015
• Page End: 1024
• Publication Date: 2021-07
• Subjects: cardiovascular disease -- chronic kidney disease -- clinical epidemiology -- atrial fibrillation
• DOI: 10.2215/CJN.01060121
• Languages: English
• ISSNs: 1555-9041

Abstract

Visual Abstract: Abstract : Background and objectives: Atrial 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, & measurements: We 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. Results: Mean (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 m 2 ; 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. Conclusions: Using 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.