부산대학교 도서관

Introduction: Although significant cardiac reverse remodeling is a prerequisite for a left ventricular (LV) assist device (LVAD) patient to be considered for device weaning, multiple factors including patient goals, physician comfort, and center experience, weigh in on this complex decision. Existing predictive models defining recovery as device removal may under detect patients that could benefit from a targeted bridge to recovery strategy. We sought to derive and validate a predictive tool to identify patients prone to reverse remodel, independent of the complex decision of device explant.Methods: Heart failure patients (N=782) requiring LVAD were enrolled at the Utah Transplant Affiliated Hospitals (n=537) and 5 US programs (n=245). Baseline characteristics were recorded. The primary outcome was ‘responder’ incidence, defined as follow-up LV ejection fraction (LVEF) ≥40% and LV end-diastolic diameter (LVEDD) ≤6 cm within one year on LVAD support. Bootstrap imputation and lasso variable selection techniques were used to derive a predictive model which was then validated. A predictive calculator was developed and patients were classified into groups with varying potential for reverse remodeling.Results: Patients were predominantly white (84%), males (82%), aged 56±1 years. Overall, 14.8% patients were identified as responders. A subset of 10 pre-operative variables associated with reverse remodeling were included in the multivariate model achieving an optimism-corrected C-statistic of 0.75 (95% CI: 0.73-0.84) (Figure).Conclusions: The UCAR calculator is a machine learning-based multicenter and validated risk tool, implementing routine pre-operative clinical data, effectively stratifying patients into groups with varying potential for reverse remodeling. This tool can be useful in selecting patients to implement diagnostic and therapeutic protocols to promote reverse remodeling and myocardial recovery.