Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score.

Pubmed ID: 31519694

Pubmed Central ID: PMC7364669

Journal: Diabetes care

Publication Date: Dec. 1, 2019

Affiliation: Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX ambarish.pandey@utsouthwestern.edu.

Link: https://care.diabetesjournals.org/content/diacare/early/2019/09/11/dc19-0587.full.pdf?link_time=2024-03-28_15:25:35.230965

MeSH Terms: Humans, Male, Female, Aged, Risk Factors, Cohort Studies, Middle Aged, Clinical Trials as Topic, Risk Assessment, Heart Failure, Hospitalization, Follow-Up Studies, Incidence, Time Factors, Predictive Value of Tests, Diabetes Mellitus, Type 2, Reproducibility of Results, Outpatients, Machine Learning

Grants: UL1 TR001420, U54 GM115428, UL1 TR002541, UL1 TR001102

Authors: Butler J, Fonarow GC, Berry J, Grodin JL, Pandey A, Vaduganathan M, Patel KV, Segar MW, McGuire DK, Basit M, Kannan V, Everett B, Willett D

Cite As: Segar MW, Vaduganathan M, Patel KV, McGuire DK, Butler J, Fonarow GC, Basit M, Kannan V, Grodin JL, Everett B, Willett D, Berry J, Pandey A. Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score. Diabetes Care 2019 Dec;42(12):2298-2306. Epub 2019 Sep 13.

Studies:

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 &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). 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 χ<sup>2</sup> = 9.63, <i>P</i> = 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 = 0.74 and 0.70, respectively), acceptable calibration (<i>P</i> ≥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.