|
1
|
Li H, Cheng ZJ, Fu X, Liu M, Liu P, Cao W, Liang Z, Wang F, Sun B. Decoding acute myocarditis in patients with COVID-19: Early detection through machine learning and hematological indices. iScience 2024; 27:108524. [PMID: 38303719 PMCID: PMC10831249 DOI: 10.1016/j.isci.2023.108524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/14/2023] [Accepted: 11/20/2023] [Indexed: 02/03/2024] Open
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.
Collapse
Affiliation(s)
- Haiyang Li
- 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
- MRC Biostatistics Unit, University of Cambridge, Cambridge CB2 0SR, UK
| | - Zhangkai J. Cheng
- 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
| | - Xing Fu
- Group of Theoretical Biology, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China
| | - Mingtao Liu
- 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
| | - Peng Liu
- Department of Clinical Pharmacy, Dazhou Central Hospital, Dazhou 635000, China
| | - Wenhan Cao
- 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
| | - Zhiman Liang
- 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
| | - Fei Wang
- Department of Clinical Pharmacy, Dazhou Central Hospital, Dazhou 635000, China
| | - Baoqing Sun
- 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
| |
Collapse
|