Abstract 12374: Machine Learning-Based Evaluation of Elevated Left Ventricular End-Diastolic Pressure

Abstract

Background: Left ventricular end-diastolic pressure (LVEDP) is clinically relevant due to its association with diastolic dysfunction and other outcomes. The gold-standard method of measuring LVEDP is invasive intra-cardiac catheterization. Echocardiography is routinely used for non-invasive estimation of left ventricular (LV) filling pressures; however, correlation with invasive LVEDP is variable. Goal: We sought to use machine learning (ML) algorithms to predict elevated LVEDP (> 20 mmHg) using clinical, echocardiographic, and biochemical parameters. Methods: We used a cohort of 460 consecutive patients without atrial fibrillation or significant mitral valve disease who underwent transthoracic echocardiography within 24 hours of elective heart catheterization. We included patients’ clinical (e.g. age), echocardiographic (e.g. left ventricular ejection fraction), and biochemical (e.g. N-terminal brain natriuretic peptide) data. We fit logistic regression, random forest, gradient boosting, and support vector machine algorithms in a 20-iteration train-validate-test workflow and measured performance using average area under the receiver operating characteristic curve (AUROC). We also performed multi-class classification of the patients’ cardiac conditions and predicted elevated tau (> 45), the gold-standard parameter for LV diastolic dysfunction. For each outcome, logistic regression weights were used to identify clinically relevant variables. Results: ML algorithms predicted LVEDP > 20 mmHg with good performance (AUROC = 0.779). ML models showed excellent performance predicting elevated tau (AUROC = 0.830) and classifying a variety of cardiac conditions (AUROC = 0.837 - 0.973). We identified several clinical variables (e.g. BMI, heart rate) relevant to LVEDP prediction. Conclusion: Our study shows ML approaches can robustly predict elevated LVEDP and tau. ML may assist in the clinical interpretation of echocardiographic data.