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Background <p>Early identification of ventricular arrhythmia (VA) risk in children with acute myocarditis (AMC) is challenging, as existing tools lack pediatric targeting. AMC’s non-specific early symptoms in children lead to underdiagnosis. VA, a major complication of AMC, is linked to poor prognosis, but not all cases present at admission, highlighting the need for simple, accessible predictive indicators.</p> Methods <p>This retrospective single-center study included 312 children (1 month − 17 years) with AMC (2021–2024), divided into a training set (<i>n</i> = 208, 2021–2023) and validation set (<i>n</i> = 104, 2024). Eligibility required first-time AMC diagnosis without admission VA. Univariate analysis (<i>P</i> &lt; 0.05) and binary logistic regression identified independent VA risk factors. Continuous variables were dichotomized via receiver operating characteristic curves, and a scoring model was constructed, with internal validation of discriminative performance and calibration.</p> Results <p>Forty-four children (14.1%) developed VA, mostly within the first week. Independent risk factors were cardiac troponin I (cTnI ≥ 0.1945 ng/mL, OR = 9.114), blood urea nitrogen (BUN ≥ 4.55 mmol/L, OR = 9.796), and left ventricular fraction shortening (LVFS ≤ 0.33, OR = 6.005). The model assigned 3 points to cTnI/BUN and 2 to LVFS (total 0–8 points). At cutoff ≥ 4 points: training set (sensitivity = 70.0%, specificity = 90.4%, AUC = 0.871); validation set (sensitivity = 78.6%, specificity = 94.4%, accuracy = 92.3%, AUC = 0.837). Calibration was acceptable (Hosmer-Lemeshow <i>P</i> = 0.288).</p> Conclusion <p>The cTnI-, BUN-, and LVFS-based scoring model offers a simple, effective tool for early VA risk stratification in children with AMC. It aids targeted monitoring and intervention, improving clinical decision-making. Limitations include retrospective single-center design and small sample size; prospective multicenter validation is needed.</p>

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Development and internal validation of a scoring model for early identification of ventricular arrhythmia risk in children with acute myocarditis

  • 期刊论文

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  • 发表日期：

    2026年2月26日

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  • Changjian Li,
  • Tingjie Wen,
  • Yuhang Zhou,
  • Chunxia Lei

摘要

Background

Early identification of ventricular arrhythmia (VA) risk in children with acute myocarditis (AMC) is challenging, as existing tools lack pediatric targeting. AMC’s non-specific early symptoms in children lead to underdiagnosis. VA, a major complication of AMC, is linked to poor prognosis, but not all cases present at admission, highlighting the need for simple, accessible predictive indicators.

Methods

This retrospective single-center study included 312 children (1 month − 17 years) with AMC (2021–2024), divided into a training set (n = 208, 2021–2023) and validation set (n = 104, 2024). Eligibility required first-time AMC diagnosis without admission VA. Univariate analysis (P < 0.05) and binary logistic regression identified independent VA risk factors. Continuous variables were dichotomized via receiver operating characteristic curves, and a scoring model was constructed, with internal validation of discriminative performance and calibration.

Results

Forty-four children (14.1%) developed VA, mostly within the first week. Independent risk factors were cardiac troponin I (cTnI ≥ 0.1945 ng/mL, OR = 9.114), blood urea nitrogen (BUN ≥ 4.55 mmol/L, OR = 9.796), and left ventricular fraction shortening (LVFS ≤ 0.33, OR = 6.005). The model assigned 3 points to cTnI/BUN and 2 to LVFS (total 0–8 points). At cutoff ≥ 4 points: training set (sensitivity = 70.0%, specificity = 90.4%, AUC = 0.871); validation set (sensitivity = 78.6%, specificity = 94.4%, accuracy = 92.3%, AUC = 0.837). Calibration was acceptable (Hosmer-Lemeshow P = 0.288).

Conclusion

The cTnI-, BUN-, and LVFS-based scoring model offers a simple, effective tool for early VA risk stratification in children with AMC. It aids targeted monitoring and intervention, improving clinical decision-making. Limitations include retrospective single-center design and small sample size; prospective multicenter validation is needed.