Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: The WATCH-DM risk score

Matthew W. Segar, Muthiah Vaduganathan, Kershaw V. Patel, Darren K. McGuire, Javed Butler, Gregg C. Fonarow, Mujeeb Basit, Vaishnavi Kannan, Justin L. Grodin, Brendan Everett, Duwayne Willett, Jarett Berry, Ambarish Pandey

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

OBJECTIVE To develop and validate a novel, machine learning–derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM). RESEARCH DESIGN AND METHODS Using data from 8,756 patients free at baseline of HF, with <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). RESULTS 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 0.75–0.80] vs. 0.73 [0.70–0.76] respectively) and had acceptable calibration (Hosmer-Lemeshow statistic x2 5 9.63, P 5 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 £7) to 17.4% in quintile 5 (WATCH-DM score ‡14). In the external validation cohort, the RSF-based risk prediction model and the WATCH-DM risk score performed well with good discrimination (C-index 5 0.74 and 0.70, respectively), acceptable calibration (P ‡0.20 for both), and broad risk stratification (5-year HF risk range from 2.5 to 18.7% across quintiles 1–5). CONCLUSIONS We developed and validated a novel, machine learning–derived risk score that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among outpatients with T2DM.

Original languageEnglish (US)
Pages (from-to)2298-2306
Number of pages9
JournalDiabetes care
Volume42
Issue number12
DOIs
StatePublished - Dec 1 2019

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism
  • Advanced and Specialized Nursing

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