Myocarditis-A Helpful Algorithm to Overcome Diagnostic Challenges in the Pediatric Population.

Area of Science:

• Pediatric Cardiology
• Infectious Diseases
• Clinical Decision Support

Background:

• Myocarditis is a serious condition in children presenting with symptoms like chest pain, tachycardia, and tachypnea.
• Accurate and timely diagnosis is crucial for effective management and improved patient outcomes.
• Existing diagnostic methods may benefit from decision support tools, especially in emergency settings.

Purpose of the Study:

• To identify clinical differences between pediatric myocarditis cases and controls presenting with chest pain, tachycardia, and/or tachypnea.
• To develop and evaluate a decision tree algorithm for the rapid diagnosis of pediatric myocarditis.

Main Methods:

• Retrospective case-control study of electronic medical records from 2003-2020.
• Inclusion criteria: children (0-18 years) with chest pain, tachycardia, and/or tachypnea, diagnosed with or suspected myocarditis.
• Analysis of demographic and clinical data; decision tree developed using Recursive Partitioning and Regression Trees (rpart).

Main Results:

• 73 myocarditis patients and 292 non-myocarditis controls were analyzed.
• Myocarditis group showed higher respiratory rate, heart rate, white blood cell count, and lower blood pressure compared to controls.
• Decision tree achieved 85.2% overall accuracy, with 100% accuracy in children aged 0-2.5 years and 69% in those aged 2.5-18 years.

Conclusions:

• Clinical and laboratory findings align with existing literature on pediatric myocarditis.
• The developed decision tree shows promise as a diagnostic aid for myocarditis in children aged 2.5 years and younger.
• The decision tree's diagnostic utility is limited in older pediatric patients (2.5-18 years).