Abstract 15854: Pulmonary Arterial Waveform Analysis and Supervised Machine Learning Predicts Outcomes in Heart Failure: An Analysis of the CHAMPION Trial

Abstract

Introduction: Pulmonary artery (PA) pressure monitors have been shown to prevent hospitalizations in heart failure patients using mean pressure values. PA waveform analysis offers an overall assessment of right ventricular load that combines resistance and compliance. Hypothesis: We hypothesised that clinical outcomes can be predicted from PA waveform analyses from patients with an implanted PA monitor. Methods: The CHAMPION trial database was analysed. Information about the demographics and data collection methods for this cohort have been published previously. All waveforms were analysed automatically using Python. Tau or RC time, respiratory rate, respiratory range, P asymptote, mean pulmonary arterial pressure (mPAP), diastolic pulmonary arterial pressure (dPAP) and systolic pulmonary arterial pressure (sPAP) were derived from the raw waveforms. Tau and P asymptote were calculated using phase plane analysis. Outcomes of hospitalization or mortality were adjudicated as part of the original trial. Statistics were performed in R. A decision tree statistical approach using the XGBoost algorithm was used. Contribution to the model was assessed using Shapley Additive Explanations (SHAP) values. The dataset was split into 70% train and 30% test sets. Results: 296,948 PA pressure traces from 547 patients were analysed. 3 patients had insufficient quality of trace to analyse. There were 1340 outcome events throughout the follow up period, median 664 days. The trained algorithm was able to predict clinical outcomes in the test dataset with 100% accuracy. The most significant contributors according the mean SHAP value were RC time (SHAP = 1.1), respiratory range (SHAP = 1.1) and heart rate (SHAP = 1.1). Conclusions: Detailed analysis of the PA waveform from PA pressure monitors is feasible and yields clinically relevant information. A supervised machine learning algorithm trained on this data can accurately predict hospitalization or mortality events in heart failure patients.