Why the study?
With the increasing worldwide prevalence of cardiovascular diseases, early prediction and accurate assessment of heart failure risk are crucial to meet clinical demand.
Can machine learning models using electronic health records accurately predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year readmission in patients hospitalized with heart failure?
Population
13,602 hospitalized patients with newly diagnosed HF at a single center in China
Comparison
Machine learning models predicting outcomes using 79 variables vs each other
Design
Single-center retrospective study
Follow-up
1 year
Authors
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May support EHR-based risk stratification in HF; hypothesis-generating, requires prospective validation before practice change.
Can machine learning models using electronic health records accurately predict 1-year in-hospital mortality, use of positive inotropic agents, and 1-year readmission in patients hospitalized with heart failure?
Machine learning models utilizing routine electronic health record data can accurately predict mortality, readmission, and inotrope use in hospitalized heart failure patients, potentially aiding clinical risk stratification.
Lv et al. (2021) studied this question.