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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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design, setting, participants, &amp; measurements<\/jats:title>\n                    <jats:p>\n                      We studied 2766 participants in the Chronic Renal Insufficiency Cohort study without prior AF with complete cardiac biomarker (N-terminal pro\u2013B-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\n                      <jats:italic toggle=\"yes\">C<\/jats:italic>\n                      -index; calibration was evaluated graphically and with the Gr\u00f8nnesby and Borgan test.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:sup>2<\/jats:sup>\n                      ; 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\n                      <jats:italic toggle=\"yes\">C-<\/jats:italic>\n                      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\n                      <jats:italic toggle=\"yes\">C-<\/jats:italic>\n                      index of 0.67 (95% confidence interval, 0.64 to 0.70); adding N-terminal pro\u2013B-type natriuretic peptide, high-sensitivity troponin T, or both biomarkers improved the\n                      <jats:italic toggle=\"yes\">C-<\/jats:italic>\n                      index by 0.04, 0.01, and 0.04, respectively. In addition to N-terminal pro\u2013B-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.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Using machine learning algorithms, a model that included 12 standard clinical variables and cardiac-specific biomarkers N-terminal pro\u2013B-type natriuretic peptide and high-sensitivity troponin T had moderate discrimination for incident AF in a CKD population.<\/jats:p>\n                  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California"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elsayed Z.","family":"Soliman","sequence":"additional","affiliation":[{"name":"Department of Medicine, Wake Forest University, Winston-Salem, North Carolina"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amanda","family":"Anderson","sequence":"additional","affiliation":[{"name":"Department of Epidemiology, Tulane University, New Orleans, Louisiana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Christenson","sequence":"additional","affiliation":[{"name":"Department of Medicine, University of Maryland, Baltimore, Maryland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James","family":"Lash","sequence":"additional","affiliation":[{"name":"Division of Nephrology, University of Illinois\u2013Chicago, Chicago, 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