Why the study?
Heart failure is a major health concern with poor prognosis, creating an urgent clinical need for an easy and accurate screening method.
Does a machine learning model incorporating integrated ECG and PCG features accurately screen for heart failure in adult patients?
Population
1,017 individuals in China
Comparison
Five machine learning algorithms incorporating clinical, PCG, and ECG parameters
Design
Multicenter diagnostic validation study
Key result
The CatBoost machine learning model integrating electrocardiogram, phonocardiogram, and clinical features accurately detected heart failure with an area under the curve of 0.998.
Authors
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May aid noninvasive HF screening; hypothesis-generating and should not yet change practice.
Cohort (n=1,017)
Independent reviewers blinded to feature analyses and model predictions
Yes
Does a machine learning model incorporating integrated ECG and PCG features accurately screen for heart failure in adult patients?
Effect estimate: AUC 0.998 (95% CI 0.996-1.000)
A machine learning model integrating ECG and PCG features with clinical variables demonstrated excellent diagnostic performance for early heart failure screening.
Chee et al. (2025) conducted a cohort in Heart failure (n=1,017). CatBoost machine learning model integrating ECG, PCG, and clinical features was evaluated on Area under the curve (AUC) for heart failure detection (CatBoost model, training set) (AUC 0.998, 95% CI 0.996-1.000). The CatBoost machine learning model integrating electrocardiogram, phonocardiogram, and clinical features accurately detected heart failure with an area under the curve of 0.998.