Key result
Deep learning model predicts LVEF improvement in heart failure with ~96% accuracy.
Why the study?
A predictive model for improvement of left ventricular ejection fraction in patients with heart failure was needed to identify independent clinical predictors.
Does a deep learning model using clinical, laboratory, and echocardiographic features accurately predict LVEF improvement in patients with heart failure?
Observational (n=422)
No
Does a deep learning model using clinical, laboratory, and echocardiographic features accurately predict LVEF improvement in patients with heart failure?
A deep learning model incorporating baseline clinical, laboratory, and echocardiographic features can accurately predict LVEF improvement in patients with heart failure.
Answers are grounded in this paper and cite their sources.
Single-center DL model for HF LVEF improvement prediction is hypothesis-generating; prospective validation required before clinical use.
Background This study developed a deep learning model to predict improvement of left ventricular ejection fraction in patients with heart failure. Methods An internal database comprising clinical, laboratory and echocardiographic features obtained from the First Affiliated Hospital of Harbin Medical University was used to construct and train the deep learning model. Results A total of 422 cases were included in this study. 122 (28.9%) were patients with HFimpEF, 300 (71.0%) were patients with HFrEF. Multivariable analyses showed that smaller baseline left atrial anterior-posterior diameter (LAD) and left ventricular end-diastolic diameter (LVEDD), higher baseline interventricular septal thickness at diastole (IVSD) and levels of prealbumin were the independent clinical predictors of LVEF improvement. Deep learning model demonstrated an overall predict accuracy of 96% in the validation set and 89% in the training set. Conclusions Independent predictors of LVEF improvement were smaller baseline LVEDD, LAD, higher baseline IVSD and baseline levels of prealbumin. Our deep learning model had shown acceptable performance in predicting improvement of left ventricular ejection fraction in patients with heart failure. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT06070506 .
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Pan et al. (2023) conducted an observational in Heart failure (n=422). Deep learning-based prediction model (2D CNN) was evaluated on Prediction accuracy of HFimpEF in the validation set. A deep learning model using clinical, laboratory, and echocardiographic features predicted the improvement of left ventricular ejection fraction in heart failure patients with 96% accuracy.
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