Machine learning models using ventricular volume data classified heart failure subtypes with a test error of 4.06%, identifying end-systolic volume as a better discriminator than ejection fraction.
Do machine learning models using ventricular volume data improve the classification of heart failure subtypes compared to traditional ejection fraction cut-offs?
Machine learning analysis suggests that end-systolic volume may be a more logical discriminator than ejection fraction for classifying heart failure subtypes, challenging the traditional linear EF cut-off paradigm.
BACKGROUND: Heart failure (HF) manifests as at least two subtypes. The current paradigm distinguishes the two by using both the metric ejection fraction (EF) and a constraint for end-diastolic volume. About half of all HF patients exhibit preserved EF. In contrast, the classical type of HF shows a reduced EF. Common practice sets the cut-off point often at or near EF = 50%, thus defining a linear divider. However, a rationale for this safe choice is lacking, while the assumption regarding applicability of strict linearity has not been justified. Additionally, some studies opt for eliminating patients from consideration for HF if 40 < EF < 50% (gray zone). Thus, there is a need for documented classification guidelines, solving gray zone ambiguity and formulating crisp delineation of transitions between phenotypes. METHODS: Machine learning (ML) models are applied to classify HF subtypes within the ventricular volume domain, rather than by the single use of EF. Various ML models, both unsupervised and supervised, are employed to establish a foundation for classification. Data regarding 48 HF patients are employed as training set for subsequent classification of Monte Carlo-generated surrogate HF patients (n = 403). Next, we map consequences when EF cut-off differs from 50% (as proposed for women) and analyze HF candidates not covered by current rules. RESULTS: The training set yields best results for the Support Vector Machine method (test error 4.06%), covers the gray zone, and other clinically relevant HF candidates. End-systolic volume (ESV) emerges as a logical discriminator rather than EF as in the prevailing paradigm. CONCLUSIONS: Selected ML models offer promise for classifying HF patients (including the gray zone), when driven by ventricular volume data. ML analysis indicates that ESV has a role in the development of guidelines to parse HF subtypes. The documented curvilinear relationship between EF and ESV suggests that the assumption concerning a linear EF divider may not be of general utility over the complete clinically relevant range.
Alonso‐Betanzos et al. (2015) studied Heart failure (n=48). Machine learning models (Support Vector Machine) vs. Ejection fraction cut-off was evaluated on Classification test error. Machine learning models using ventricular volume data classified heart failure subtypes with a test error of 4.06%, identifying end-systolic volume as a better discriminator than ejection fraction.
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