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

Publication date: Feb 16, 2024

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.

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Concepts Keywords
Forest Artificial intelligence
Hematological Diagnostics
Pandemic Health sciences


Type Source Name
disease MESH myocarditis
disease MESH COVID-19
disease IDO blood
disease IDO symptom
disease MESH Long Covid

Original Article

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