Development of machine learning models for prediction of left ventricular systolic dysfunction in patients with isolated left ventricular dilation

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Abstract Background/Introduction Isolated left ventricular dilation (ILVD), in the absence of concomitant LV systolic dysfunction, is a risk factor for new reduction in LV ejection fraction (LVEF) and adverse clinical outcomes. We assessed the performance of different machine learning (ML) models in predicting new reduction in LVEF in patients with ILVD. Methods A retrospective analysis of de-identified patient records in the Tempus Database with at least two transthoracic echocardiograms (TTE) conducted between January 2019 and December 2022 were retrospectively analyzed, with the follow up period ending in January 2023. Inclusion/exclusion criteria are shown in Figure 1A. Briefly, patients with ILVD were defined based on echocardiographic parameters as those with LVEF > 50% and LV end diastolic volume indexed (LVEDVi) > 62 mL/m2 for females and > 75 mL/m2 for males, without prior LVEF < 50% and/or grade 2/3 diastolic dysfunction. The endpoint was LVEF < 40% on future (≥90 days) TTE. Five hard voting ML models and one soft voting ensemble model were trained with data parsed from TTE reports (Figure 1B). Feature importance (SHAP) was calculated, and receiver operating characteristic (ROC) and Kaplan Meier curves evaluated the final model (Figure 1C-E). Results A total of 2,962 patients met the inclusion criteria. The median time from index to last TTE was 578 (IQR = 500) days. LVEF < 40% on follow up TTE was noted in 6% of patients (177 patients, median 478 days to endpoint, IQR = 535 days). Of the six models developed, the ensemble model had the highest ROC area under the curve (0.791, Figure 1D), precision (0.163), F1-score (0.266), and specificity (0.773). The ensemble model also had the lowest predicted prevalence (25.5%), although this was higher than the observed prevalence in the test set (5.7%). Patients predicted to develop LVEF < 40% had a 8.2x higher risk of developing LVEF < 40% within five years of index (p<0.001, Figure 1E). Feature importance showed that male gender, higher heart rate, lower LVEF at index TTE, higher LVEDVi, and lower tricuspid annular plane systolic excursion had the highest mean absolute impact on the model’s output probability (Figure 1C). Conclusion Using only data parsed from the index TTE, ensembling a model for the prediction of developing LVEF < 40% in patients with ILVD and without a history of LV diastolic or systolic dysfunction performed the best. Additional analyses including clinical variables such as comorbidities, symptoms, and medications may improve the model’s performance.