Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection

• Published in: Scientific reports 2021-04, Vol.11 (1), p.8992-10, Article 8992
• Main Authors: Krittanawong, Chayakrit, Virk, Hafeez Ul Hassan, Kumar, Anirudh, Aydar, Mehmet, Wang, Zhen, Stewart, Matthew P., Halperin, Jonathan L.
• Format: Article
• Language: English
• Subjects: 631/114/1305; 631/114/2415; 692/4019/2776; Adult; Coronary artery; Coronary Artery Disease - mortality; Coronary Vessels; Databases, Factual; Deep Learning; Electronic Health Records; Electronic medical records; Female; Hospital Mortality; Humanities and Social Sciences; Humans; Learning algorithms; Machine learning; Male; Middle Aged; Models, Cardiovascular; Mortality; multidisciplinary; Neural networks; Patients; Regression analysis; Risk Assessment; Rupture, Spontaneous - mortality; Science; Science (multidisciplinary); Sensitivity analysis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-88172-0

Machine learning (ML) and deep learning (DL) can successfully predict high prevalence events in very large databases (big data), but the value of this methodology for risk prediction in smaller cohorts with uncommon diseases and infrequent events is uncertain. The clinical course of spontaneous coronary artery dissection (SCAD) is variable, and no reliable methods are available to predict mortality. Based on the hypothesis that machine learning (ML) and deep learning (DL) techniques could enhance the identification of patients at risk, we applied a deep neural network to information available in electronic health records (EHR) to predict in-hospital mortality in patients with SCAD. We extracted patient data from the EHR of an extensive urban health system and applied several ML and DL models using candidate clinical variables potentially associated with mortality. We partitioned the data into training and evaluation sets with cross-validation. We estimated model performance based on the area under the receiver-operator characteristics curve (AUC) and balanced accuracy. As sensitivity analyses, we examined results limited to cases with complete clinical information available. We identified 375 SCAD patients of which mortality during the index hospitalization was 11.5%. The best-performing DL algorithm identified in-hospital mortality with AUC 0.98 (95% CI 0.97–0.99), compared to other ML models ( P