Key result
A deep learning-based classification model predicted heart failure categories from patient clinical profiles with an overall accuracy of 90.10%, outperforming support vector machines.
Why the study?
The authors sought to investigate the effectiveness of deep learning to predict LVEF and discriminate HF categories from patient clinical profiles.
Can deep learning models accurately predict LVEF levels and heart failure categories (HFpEF, HFmEF, HFrEF) using only patient clinical profiles?
Observational (n=303)
Yes
Can deep learning models accurately predict LVEF levels and heart failure categories (HFpEF, HFmEF, HFrEF) using only patient clinical profiles?
Deep learning models can accurately predict LVEF and classify heart failure categories using only basic clinical and demographic profiles, potentially reducing the need for routine echocardiography.
Answers are grounded in this paper and cite their sources.
Supports feasibility of deep learning for HF classification from clinical profiles; hypothesis-generating and requires prospective validation before practice change.
Background: Left ventricular ejection fraction (LVEF) is the gold standard for evaluating heart failure (HF) in coronary artery disease (CAD) patients. It is an essential metric in categorizing HF patients as preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF) ejection fraction but differs, depending on whether the ASE/EACVI or ESC guidelines are used to classify HF. Objectives: We sought to investigate the effectiveness of using deep learning as an automated tool to predict LVEF from patient clinical profiles using regression and classification trained models. We further investigate the effect of utilizing other LVEF-based thresholds to examine the discrimination ability of deep learning between HF categories grouped with narrower ranges. Methods: Data from 303 CAD patients were obtained from American and Greek patient databases and categorized based on the American Society of Echocardiography and the European Association of Cardiovascular Imaging (ASE/EACVI) guidelines into HFpEF (EF > 55%), HFmEF (50% ≤ EF ≤ 55%), and HFrEF (EF < 50%). Clinical profiles included 13 demographical and clinical markers grouped as cardiovascular risk factors, medication, and history. The most significant and important markers were determined using linear regression fitting and Chi-squared test combined with a novel dimensionality reduction algorithm based on arc radial visualization (ArcViz). Two deep learning-based models were then developed and trained using convolutional neural networks (CNN) to estimate LVEF levels from the clinical information and for classification into one of three LVEF-based HF categories. Results: A total of seven clinical markers were found important for discriminating between the three HF categories. Using statistical analysis, diabetes, diuretics medication, and prior myocardial infarction were found statistically significant ( p < 0.001). Furthermore, age, body mass index (BMI), anti-arrhythmics medication, and previous ventricular tachycardia were found important after projections on the ArcViz convex hull with an average nearest centroid (NC) accuracy of 94%. The regression model estimated LVEF levels successfully with an overall accuracy of 90%, average root mean square error (RMSE) of 4.13, and correlation coefficient of 0.85. A significant improvement was then obtained with the classification model, which predicted HF categories with an accuracy ≥93%, sensitivity ≥89%, 1-specificity <5%, and average area under the receiver operating characteristics curve (AUROC) of 0.98. Conclusions: Our study suggests the potential of implementing deep learning-based models clinically to ensure faster, yet accurate, automatic prediction of HF based on the ASE/EACVI LVEF guidelines with only clinical profiles and corresponding information as input to the models. Invasive, expensive, and time-consuming clinical testing could thus be avoided, enabling reduced stress in patients and simpler triage for further intervention.
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Alkhodari et al. (2021) conducted an observational in Heart Failure with Coronary Artery Disease (n=303). Deep learning-based classification model vs. Support vector machine (SVM) was evaluated on Overall accuracy in predicting heart failure categories. A deep learning-based classification model predicted heart failure categories from patient clinical profiles with an overall accuracy of 90.10%, outperforming support vector machines.
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