Identification of prognostic factors for pediatric myocarditis with a random forests algorithm-assisted approach.

CONCLUSION: Prognostic factor identification may not be straightforward in rare diseases such as pediatric myocarditis due to small cohort size in each treating facility. Findings from this report provide insights into the prognostic factors for pediatric myocarditis, and may allow clinicians to be better prepared when informing patients and their families regarding disease outcomes. IMPACT: The rate of hospitalization due to pediatric myocarditis was increasing but the mortality rate was declining over the past decade. End organ damage, including the brain and the kidney, was associated with mortality and prolonged hospital stay in pediatric myocarditis. Tachyarrhythmias and cardiac function compromise requiring ECMO were also associated with mortality and prolonged hospital stay. A data science approach combining machine learning algorithms and conventional regression modeling using a large dataset may facilitate risk factor identification and outcome correlation in rare diseases, as illustrated in this study. PMID: 33208880 [PubMed - as supplied by publisher]
Source: Pediatric Research - Category: Pediatrics Authors: Tags: Pediatr Res Source Type: research