Machine Learning-Based Prediction of Myocardial Recovery in Patients With Left Ventricular Assist Device Support

Background: Prospective studies demonstrate that aggressive pharmacological therapy combined with pump speed optimization may result in myocardial recovery in larger numbers of patients supported with left ventricular assist device (LVAD). This study sought to determine whether the use of machine learning (ML) based models predict LVAD patients with myocardial recovery resulting in pump explant. Methods: A total of 20 270 adult patients with a durable continuous-flow LVAD in the INTERMACS registry (Interagency Registry for Mechanically Assisted Circulatory Support) were included in the study. Ninety-eight raw clinical variables were screened using the least absolute shrinkage and selection operator for selection of features associated with LVAD-induced myocardial recovery. ML models were developed in the training data set (70%) and were assessed in the validation data set (30%) by receiver operating curve and Kaplan-Meier analysis. Results: Least absolute shrinkage and selection operator identified 28 unique clinical features associated with LVAD-induced myocardial recovery, including age, cause of heart failure, psychosocial risk factors, laboratory values, cardiac rate and rhythm, and echocardiographic indices. ML models achieved area under the receiver operating curve of 0.813 to 0.824 in the validation data set outperforming logistic regression-based new INTERMACS recovery risk score (area under the receiver operating curve of 0.796) and previously established LVAD recovery risk scores (INTERMACS Cardiac Recovery Score and INTERMACS Recovery Score by Topkara et al.) with area under the receiver operating curve of 0.744 and 0.748 ( P <0.05). Patients who were predicted to recover by ML models demonstrated a significantly higher incidence of myocardial recovery resulting in LVAD explant in the validation cohort compared with those who were not predicted to recover (18.8% versus 2.6% at 4 years of pump support). Conclusions: ML can be a valuable tool to identify subsets of LVAD patients who may be more likely to respond to myocardial recovery protocols.

Prognostic Value of Natriuretic Peptides for All-Cause Mortality, Right Ventricular Failure, Major Adverse Events, and Myocardial Recovery in Advanced Heart Failure Patients Receiving a Left Ventricular Assist Device: A Systematic Review

Aims: Major adverse event (MAE) rates during left ventricular assist device (LVAD) therapy in advanced heart failure (HF) patients are high, and impair quality of life and survival. Prediction and risk stratification of MAEs in order to improve patient selection and thereby outcome during LVAD therapy is therefore warranted. Circulating natriuretic peptides (NPs) are strong predictors of MAEs and mortality in chronic HF patients. However, whether NPs can identify patients who are at risk of MAEs and mortality or tend toward myocardial recovery after LVAD implantation is unclear. The aim of this systematic review is to analyze the prognostic value of circulating NP levels before LVAD implantation for all-cause mortality, MAEs and myocardial recovery after LVAD implantation.Methods and Results: Electronic databases were searched for studies analyzing circulating NP in adults with advanced HF before LVAD implantation in relation to mortality, MAEs, or myocardial recovery after LVAD implantation. Twenty-four studies published between 2008 and 2021 were included. Follow-up duration ranged from 48 hours to 5 years. Study sample size ranged from 14 to 15,138 patients. Natriuretic peptide levels were not predictive of all-cause mortality. However, NPs were predictive of right ventricular failure (RVF) and MAEs such as ventricular arrhythmias, moderate or severe aortic regurgitation, and all-cause rehospitalization. No relation between NPs and myocardial recovery was found.Conclusion: This systematic review found that NP levels before LVAD implantation are not predictive of all-cause mortality after LVAD implantation. Thus, NP levels may be of limited value in patient selection for LVAD therapy. However, NPs help in risk stratification of MAEs and may be used to identify patients who are at risk for RVF, ventricular arrhythmias, moderate or severe aortic regurgitation, and all-cause rehospitalization after LVAD implantation.