Machine Learning-Based Prediction of Myocardial Recovery in Patients With Left Ventricular Assist Device Support.
Pubmed ID: 34949101
Pubmed Central ID: PMC8766904
Journal: Circulation. Heart failure
Publication Date: 01/01/2022
Affiliation: Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY.
MeSH Terms: Humans, Adult, Cohort Studies, Adolescent, Middle Aged, Heart Failure, Young Adult, Prospective Studies, Incidence, Heart Ventricles, Registries, Myocardium, Machine Learning, Heart-Assist Devices
Grants: K08 HL146964
Authors: Topkara VK, Jain R, Sayer G, Uriel N, Elias P, Burkhoff D
Cite As: Topkara VK, Elias P, Jain R, Sayer G, Burkhoff D, Uriel N. Machine Learning-Based Prediction of Myocardial Recovery in Patients With Left Ventricular Assist Device Support. Circ Heart Fail 2022 Jan;15(1):e008711. Epub 2021 Dec 24.
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 (<i>P</i><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.