Abstract 10397: Unbiased Cluster Modelling for Uncovering Natural Distribution of Doppler Derived Diastolic Variable Response to Exercise Among Patients Referred to Exercise Echocardiography

Introduction: Exercise-induced diastolic dysfunction is associated with poor prognosis. Hypothesis: We studied diastolic response to exercise based on the natural distribution of Doppler variables and its relationship to adverse outcomes. Methods: In a retrospective study, 813 patients referred for exercise echocardiography (ExE) for ischemia assessment were included (Age: 58±13 years, 57% females, EF:60±7%). A 2-step unsupervised cluster model was constructed using resting and post-exercise tissue Doppler early diastolic mitral annular velocity, post-exercise pulsed Doppler-derived mitral inflow velocity in early and late diastole, and post-exercise tricuspid regurgitation velocity. Clusters were compared for a composite outcome (death, cardiac rehospitalization, acute coronary syndrome, and need for future ischemia testing). Results: Exercise-induced ischemia occurred in 116(14%). The composite outcome occurred in 196(24%) patients over a median follow-up of 3.2 years. The model yielded 2 clusters labelled low and high risk for the composite outcome, Figure 1 A,B). Compared to the low risk cluster, patients in the high risk cluster were older, had more risk factors and comorbidities, worse exercise performance, worse resting and exercise diastolic variables, and more ischemic ExE. After stratification for ischemia, clusters resulted in significant separation of risk for the outcome, regardless of the presence or absence of ischemia (Figure 1 C,D). These findings persisted after multivariate adjustment. Conclusions: In patients referred to ExE for assessment of ischemia, diastolic response to exercise involves complex interactions among diastolic variables. Appreciation of such complexity seems to add prognostic value beyond ischemia, suggesting a need for novel approaches for characterization of the natural distribution of diastolic variables, such as computational unbiased clustering.