Decoding acute myocarditis in patients with COVID-19: Early detection through machine learning and hematological indices

Haiyang Li; Zhangkai J Cheng; Xing Fu; Mingtao Liu; Peng Liu; Wenhan Cao; Zhiman Liang; Fei Wang; Baoqing Sun

doi:10.1016/j.isci.2023.108524

Decoding acute myocarditis in patients with COVID-19: Early detection through machine learning and hematological indices

iScience. 2023 Nov 23;27(2):108524. doi: 10.1016/j.isci.2023.108524. eCollection 2024 Feb 16.

Authors

Haiyang Li^{1

2}, Zhangkai J Cheng¹, Xing Fu³, Mingtao Liu¹, Peng Liu⁴, Wenhan Cao¹, Zhiman Liang¹, Fei Wang⁴, Baoqing Sun¹

Affiliations

¹ Department of Clinical Laboratory, National Clinical Research Center of Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou 510120, China.
² MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK.
³ Group of Theoretical Biology, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China.
⁴ Department of Clinical Pharmacy, Dazhou Central Hospital, Dazhou 635000, China.

Abstract

During the persistent COVID-19 pandemic, the swift progression of acute myocarditis has emerged as a profound concern due to its augmented mortality, underscoring the urgency of prompt diagnosis. This study analyzed blood samples from 5,230 COVID-19 individuals, identifying key blood and myocardial markers that illuminate the relationship between COVID-19 severity and myocarditis. A predictive model, applying Bayesian and random forest methodologies, was constructed for myocarditis' early identification, unveiling a balanced gender distribution in myocarditis cases contrary to a male predominance in COVID-19 occurrences. Particularly, older men exhibited heightened vulnerability to severe COVID-19 strains. The analysis revealed myocarditis was notably prevalent in younger demographics, and two subvariants COVID-19 progression paths were identified, characterized by symptom intensity and specific blood indicators. The enhanced myocardial marker model displayed remarkable diagnostic accuracy, advocating its valuable application in future myocarditis detection and treatment strategies amidst the COVID-19 crisis.

Keywords: Artificial intelligence; Diagnostics; Health sciences.