Machine Learning Multicenter Risk Model to Predict Right Ventricular Failure After Mechanical Circulatory Support
Authors: Taleb Iosif , Kyriakopoulos Christos P. , Fong Robyn , Ijaz Naila , Demertzis Zachary , Sideris Konstantinos , Wever-Pinzon Omar , Koliopoulou Antigone G. , Bonios Michael J. , Shad Rohan , Peruri Adithya , Hanff Thomas C. , Dranow Elizabeth , Giannouchos Theodoros V. , Krauspe Ethan , Zakka Cyril , Tang Daniel G. , Nemeh Hassan W. , Stehlik Josef , Fang James C. , Selzman Craig H. , Alharethi Rami , Caine William T. , Cowger Jennifer A. , Hiesinger William , Shah Palak , Drakos Stavros G. 
Published: January 2024

ImportanceThe existing models predicting right ventricular failure (RVF) after durable left ventricular assist device (LVAD) support might be limited, partly due to lack of external validation, marginal predictive power, and absence of intraoperative characteristics.ObjectiveTo derive and validate a risk model to predict RVF after LVAD implantation.Design, Setting, and ParticipantsThis was a hybrid prospective-retrospective multicenter cohort study conducted from April 2008 to July 2019 of patients with advanced heart failure (HF) requiring continuous-flow LVAD. The derivation cohort included patients enrolled at 5 institutions. The external validation cohort included patients enrolled at a sixth institution within the same period. Study data were analyzed October 2022 to August 2023.ExposuresStudy participants underwent chronic continuous-flow LVAD support.Main Outcome and MeasuresThe primary outcome was RVF incidence, defined as the need for RV assist device or intravenous inotropes for greater than 14 days. Bootstrap imputation and adaptive least absolute shrinkage and selection operator variable selection techniques were used to derive a predictive model. An RVF risk calculator (STOP-RVF) was then developed and subsequently externally validated, which can provide personalized quantification of the risk for LVAD candidates. Its predictive accuracy was compared with previously published RVF scores.ResultsThe derivation cohort included 798 patients (mean [SE] age, 56.1 [13.2] years; 668 male [83.7%]). The external validation cohort included 327 patients. RVF developed in 193 of 798 patients (24.2%) in the derivation cohort and 107 of 327 patients (32.7%) in the validation cohort. Preimplant variables associated with postoperative RVF included nonischemic cardiomyopathy, intra-aortic balloon pump, microaxial percutaneous left ventricular assist device/venoarterial extracorporeal membrane oxygenation, LVAD configuration, Interagency Registry for Mechanically Assisted Circulatory Support profiles 1 to 2, right atrial/pulmonary capillary wedge pressure ratio, use of angiotensin-converting enzyme inhibitors, platelet count, and serum sodium, albumin, and creatinine levels. Inclusion of intraoperative characteristics did not improve model performance. The calculator achieved a C statistic of 0.75 (95% CI, 0.71-0.79) in the derivation cohort and 0.73 (95% CI, 0.67-0.80) in the validation cohort. Cumulative survival was higher in patients composing the low-risk group (estimated &amp;lt;20% RVF risk) compared with those in the higher-risk groups. The STOP-RVF risk calculator exhibited a significantly better performance than commonly used risk scores proposed by Kormos et al (C statistic, 0.58; 95% CI, 0.53-0.63) and Drakos et al (C statistic, 0.62; 95% CI, 0.57-0.67).Conclusions and RelevanceImplementing routine clinical data, this multicenter cohort study derived and validated the STOP-RVF calculator as a personalized risk assessment tool for the prediction of RVF and RVF-associated all-cause mortality.