Machine Learning and Deep Phenotyping Towards Predictive Analytics and Therapeutic Strategy in Cardiac Surgery

Abstract

Introduction: Myocardial infarction (MI) secondary to coronary artery disease (CAD) remains the most common cause of heart failure (HF), costing over $30 billion in healthcare costs. Although early revascularization is the most effective therapy to restore blood flow and salvage myocardium, to date, there are no available treatments to attenuate ischemia-reperfusion injury (IRI). Moreover, post-operative atrial fibrillation (POAF) continues to be a devastating complication following cardiac surgery, affecting 25-40% CABG and 30-40% valve patients. Human placental amniotic (HPA) tissue is known to have anti-inflammatory and wound healing properties and therefore may promote anti-arrhythmic and cardioprotective effects in patients undergoing cardiac surgery. The central hypothesis of this study is the use of predictive modeling in conjunction with HPA application improves cardioprotection against IRI and POAF following cardiac surgery. Methods: We developed predictive models for POAF using machine learning to characterize 340,860 isolated CABG patients from 2014 to 2017 from the national Society of Thoracic Surgeons database. The support-vector machine (SVM) models were assessed based on accuracy, sensitivity, and specificity, and the neural network (NN) model was compared to the currently utilized CHA2DS2-VASc score. Additionally, using a clinically relevant model of IRI, we performed an unbiased, non-hypothesis driven transcriptome and proteome analysis to elucidate cellular and molecular mechanisms of HPA xenograft-induced cardioprotection against IRI. Swine (n=3 in MI only and MI+HPA groups) were subjected to a 45-minute percutaneous IRI protocol followed by HPA placement in the treated group. Cardiac function was assessed, and tissue samples were collected post-operative day 14. Results were further supported by histology, RT-PCR, and Western blot analyses. Lastly, a retrospective study of 78 isolated CABG and 47 isolated valve patients were evaluated to determine if HPA use on the epicardial surface decreases incidence of POAF. Results: Predictive modeling using neural networks demonstrated to outperform the CHA2DS2-VASc score in predicting POAF in CABG patients. Second, we present the first comprehensive transcriptome and proteome profiles of the ischemic, border, and remote myocardium during the proliferative cardiac repair phase with HPA allograft use in swine. Our results establish HPA limited the extent of cardiac injury by 50% and preserved cardiac function. Spatial dynamic responses, as well as coordinated immune and extracellular matrix remodeling to mitigate injury, were among the key findings. Changes in protein secretion, mitochondrial bioenergetics, and inflammatory responses were also noted to contribute to cardioprotection. Third, peri-operative HPA allograft placement has demonstrated a strong reduction in the incidence of POAF following CABG and valve surgery. Discussion: We provide convincing evidence that HPA has beneficial effects on injured myocardium and POAF and can serve as a new therapeutic strategy in cardiac patients. Additionally, we were also able to demonstrate predictive modeling using machine learning holds promise in improving the incidence of POAF in cardiac surgery patients.