Why the study?
Heart failure with mildly reduced ejection fraction is recognized as a unique phenotype, but risk stratification models for mortality and HF re-hospitalization are lacking.
Do machine learning models improve risk prediction for mortality and re-hospitalization in patients with HFmrEF compared to traditional models?
Population
Patients with HFmrEF (45-49%) enrolled in the TOPCAT trial
Comparison
Eight ML-based risk prediction models
Design
Post-hoc prognostic model development and validation study
Follow-up
Maximum of 6 years
Key result
Machine learning models outperformed traditional models in predicting mortality and heart failure re-hospitalization in patients with mildly reduced ejection fraction, with LASSO Cox regression achieving a C-index of 0.83 for 6-year mortality.
Authors
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ML models may aid HFmrEF risk stratification; leaves open prospective validation before clinical adoption.
Observational (n=519)
Yes
Do machine learning models improve risk prediction for mortality and re-hospitalization in patients with HFmrEF compared to traditional models?
Effect estimate: C-index 0.83 (95% CI 0.68-0.94)
Machine learning models, particularly LASSO Cox regression and random forest, outperform traditional models in predicting mortality and re-hospitalization in patients with HFmrEF, highlighting the prognostic importance of KCCQ scores.
Zhao et al. (2022) conducted an observational in Heart failure with mildly reduced ejection fraction (HFmrEF) (n=519). Machine learning models (LASSO Cox regression and Random Forest) vs. Traditional statistical models was evaluated on 6-year all-cause mortality prediction (LASSO Cox regression) (C-index 0.83, 95% CI 0.68-0.94). Machine learning models outperformed traditional models in predicting mortality and heart failure re-hospitalization in patients with mildly reduced ejection fraction, with LASSO Cox regression achieving a C-index of 0.83 for 6-year mortality.