Left ventricular segmental strain and the prediction of cancer therapy-related cardiac dysfunction

Abstract

Abstract Aims We aimed to determine the early changes and predictive value of left ventricular (LV) segmental strain measures in women with breast cancer receiving doxorubicin. Methods and results In a cohort of 237 women with breast cancer receiving doxorubicin with or without trastuzumab, 1151 echocardiograms were prospectively acquired over a median (Q1–Q3) of 7 (2–24) months. LV ejection fraction (LVEF) and 36 segmental strain measures were core lab quantified. A supervised machine learning (ML) model was then developed using random forest regression to identify segmental strain measures predictive of nadir LVEF post-doxorubicin completion. Cancer therapy-related cardiac dysfunction (CTRCD) was defined as a ≥10% absolute LVEF decline pre-treatment to a value <50%. Median (Q1–Q3) baseline age was 48 (41–57) years. Thirty-five women developed CTRCD, and eight of these developed symptomatic heart failure. From pre-treatment to doxorubicin completion, longitudinal strain worsened across the basal and mid-LV segments but not in the apical segments; circumferential strain worsened primarily in the septum; radial strain worsened uniformly and transverse strain remained unchanged across all LV segments. In the ML model, anterolateral and inferoseptal circumferential strain were the most predictive features; longitudinal and transverse strain in the basal inferoseptal, anterior, basal anterolateral, and apical lateral segments were also top predictive features. The addition of predictive segmental strain measures to a model including age, cancer therapy regimen, hypertension, and LVEF increased the area under the curve (AUC) from 0.70 (95% confidence interval (CI) 0.60–0.80) to 0.87 (95% CI 0.81–0.92), ΔAUC = 0.18 (95% CI 0.08–0.27) for the prediction of CTRCD. Conclusion Our findings suggest that segmental strain measures can enhance cardiotoxicity risk prediction in women with breast cancer receiving doxorubicin.