{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,25]],"date-time":"2026-10-25T11:41:46Z","timestamp":1792928506721,"version":"4.5.0"},"reference-count":36,"publisher":"American Diabetes Association","issue":"12","license":[{"start":{"date-parts":[[2019,11,20]],"date-time":"2019-11-20T00:00:00Z","timestamp":1574208000000},"content-version":"vor","delay-in-days":68,"URL":"http:\/\/www.diabetesjournals.org\/content\/license"}],"content-domain":{"domain":["diabetesjournals.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,12,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>To develop and validate a novel, machine learning\u2013derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESEARCH DESIGN AND METHODS<\/jats:title>\n                    <jats:p>Using data from 8,756 patients free at baseline of HF, with &amp;lt;10% missing data, and enrolled in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, we used random survival forest (RSF) methods, a nonparametric decision tree machine learning approach, to identify predictors of incident HF. The RSF model was externally validated in a cohort of individuals with T2DM using the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>Over a median follow-up of 4.9 years, 319 patients (3.6%) developed incident HF. The RSF models demonstrated better discrimination than the best performing Cox-based method (C-index 0.77 [95% CI 0.75\u20130.80] vs. 0.73 [0.70\u20130.76] respectively) and had acceptable calibration (Hosmer-Lemeshow statistic \u03c72 = 9.63, P = 0.29) in the internal validation data set. From the identified predictors, an integer-based risk score for 5-year HF incidence was created: the WATCH-DM (Weight [BMI], Age, hyperTension, Creatinine, HDL-C, Diabetes control [fasting plasma glucose], QRS Duration, MI, and CABG) risk score. Each 1-unit increment in the risk score was associated with a 24% higher relative risk of HF within 5 years. The cumulative 5-year incidence of HF increased in a graded fashion from 1.1% in quintile 1 (WATCH-DM score \u22647) to 17.4% in quintile 5 (WATCH-DM score \u226514). In the external validation cohort, the RSF-based risk prediction model and the WATCH-DM risk score performed well with good discrimination (C-index = 0.74 and 0.70, respectively), acceptable calibration (P \u22650.20 for both), and broad risk stratification (5-year HF risk range from 2.5 to 18.7% across quintiles 1\u20135).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>We developed and validated a novel, machine learning\u2013derived risk score that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among outpatients with T2DM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2337\/dc19-0587","type":"journal-article","created":{"date-parts":[[2019,9,13]],"date-time":"2019-09-13T22:44:05Z","timestamp":1568414645000},"page":"2298-2306","update-policy":"https:\/\/doi.org\/10.2337\/ada-journal-policies","source":"Crossref","is-referenced-by-count":265,"title":["Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score"],"prefix":"10.2337","volume":"42","clinical-trial-number":[{"clinical-trial-number":"nct00000620","registry":"10.18810\/clinical-trials-gov"}],"author":[{"given":"Matthew W.","family":"Segar","sequence":"first","affiliation":[{"name":"Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muthiah","family":"Vaduganathan","sequence":"additional","affiliation":[{"name":"Brigham and Women\u2019s Hospital Heart and Vascular 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