Predicting Right Ventricular Failure Following Left Ventricular Assist Device Support: A Derivation-Validation Multicenter Risk Score
Kyriakopoulos CP, Taleb I, Koliopoulou AG, Ijaz N, Demertzis Z, Peruri A, Dranow E, Wever-Pinzon O, Yin MY, Shah KS, Kemeyou L, Richins TJ, Tang DG, Nemeh HW, Stehlik J, Selzman CH, Alharethi R, Caine WT, Kfoury AG, Fang JC, Cowger JA, Shah P, and Drakos SG. Predicting Right Ventricular Failure Following Left Ventricular Assist Device Support: A Derivation-Validation Multicenter Risk Score. J Heart Lung Transplant 2021; 40(4):S98.
J Heart Lung Transplant
Purpose: Despite several models predicting right ventricular failure (RVF) after durable left ventricular assist device (LVAD) support, poor performance when externally validated has limited their widespread use. We sought to derive a predictive model for RVF after LVAD implantation, and ascertain its performance in an independent cohort.
Methods: End-stage heart failure (HF) patients requiring continuous-flow LVAD were prospectively enrolled at one US program (n=477, derivation cohort), with two other US medical centers forming the validation cohort (n=321). The primary outcome was RVF incidence, defined as the need for right ventricular assist device or inotropes for >14 days. Multivariable logistic regression in the derivation set yielded a RVF predictive model, which was subsequently applied to the validation cohort, and a risk score was ultimately developed.
Results: Derivation cohort included patients less likely to be African-Americans (7% vs 37%; p<0.001), Hispanics (7% vs 30%; p<0.001), have a remote history of hypertension (49% vs 60%; p=0.002) or be bridged with short-term MCS (8% vs 16%; p=0.001), compared to the validation set. RVF incidence was 16% in the derivation and 36% in the validation cohort (p<0.001). Multivariable analysis identified 7 variables (Figure) as predictive of RVF, with the model achieving a C statistic of 0.734 (95% CI=0.674-0.794) in the derivation and 0.709 (95% CI=0.651-0.767) in the heterogeneous validation cohort. Patients were stratified into 3 RVF risk groups (all comparisons; p<0.001) (Figure).
Conclusion: We propose a novel scoring system to predict post-LVAD RVF, achieving high discriminative performance in distinct, heterogeneous LVAD cohorts.