Abstract 4134947: Explainable Machine Learning to Predict Heart Failure Outcomes Using Exercise and Clinical Data

Background: Heart failure (HF) hospitalizations are associated with disease burden and cardiovascular mortality. International guidelines highlight cardiopulmonary testing (CPET) for risk stratification and prognosis in HF. Identifying individuals at risk could direct interventions to prevent admissions. This study uses explainable machine learning (XML) tools to identify CPET and clinical variables predictive of HF hospitalizations. Hypothesis: XML analysis of CPET parameters and clinical factors can yield accurate, patient-specific prediction of HF admission within one year. Methods: A retrospective single center review of 178 CPETs in patients referred for HF was completed. Clinical data within one year of CPET trained a boosted machine learning model (XGBoost). SHAP (Shapley Additive Explanations) explainability analysis emphasized key features for model predictions. Results: 44 (24.7%) patients with CPET were admitted for HF within one year. The model predicted this outcome with an area under the receiver operating characteristic curve (AUC) of 0.772. SHAP analysis (Figure 1) selected factors for decision making: pulmonary artery systolic pressure (PASP) via cardiac catheterization or echocardiogram; oxygen consumption at the ventilatory threshold (VO 2 at VT); O 2 pulse at rest; ventilatory efficiency (VE/VCO 2 slope); minute ventilation per oxygen consumption at maximal exercise (VE/VO 2 max) during CPET; left ventricle end diastolic volume by echocardiogram; and sodium-glucose cotransporter-2 inhibitor usage. Peak VO2, predicted VO2, predicted O2 pulse, and VE/VCO2 slope were significant, consistent with past studies (Table 1). Conclusion: Our XML analysis emphasizes PASP (partially related to high left ventricular filling pressures) and VO 2 (at ventilatory threshold) as predictive for HF hospitalization within one year of CPET testing. Studies to prospectively evaluate these highlighted factors are needed to continue exploring predictive capabilities to tailor interventions.