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Introduction <p>Predictive models play a critical role in enhancing medication safety in clinical practice. While multiple models for adverse drug reactions (ADRs) in adults have demonstrated potential clinical utility, pediatric-specific predictive models remain understudied, with limited comprehensive evaluation of their methodological quality and reporting standards.</p> Aim <p>To map the landscape of existing risk prediction models for ADRs specifically developed or validated for pediatric patients, and to describe their characteristics, methodological quality, and reporting completeness.</p> Method <p>A systematic search was conducted in Embase, PubMed, CNKI, Wanfang, VIP, and SinoMed for studies on pediatric ADR prediction models. The information from included studies was evaluated using the Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist, the risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST), and adherence to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines was reviewed. Predictive ability of the included model, including the area under the receiver operating characteristic curve (AuROC), sensitivity, and specificity, was also reported. </p> Results <p>Out of 12,667 screened studies, 7 articles (describing 10 models) met the inclusion criteria. Study designs included case–control, nested case–control, prospective cohort, and cross-sectional studies. Logistic regression was the primary modeling method, with one study using machine learning. Common methodological limitations included unreported handling of missing data and univariable predictor screening. Model discrimination (AuROC) ranged from 0.63–0.97, with sensitivity and specificity between 52.02–98.50% and 33.33–98.79%, respectively. TRIPOD adherence varied (62.16–86.49%), with notable reporting deficiencies in blinding, sample size justification, intervention details, model usage instructions, and supplementary materials. No models underwent external validation.</p> Conclusion <p>Existing pediatric ADR prediction models are limited by methodological and reporting shortcomings. Future research should focus on rigorous model development and external validation to ensure generalizability across diverse clinical settings.</p>

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Risk prediction models for adverse drug reactions in pediatrics: a scoping review

  • 期刊论文

  • 发表日期：

    2025年11月11日

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  • Ruonan Gao,
  • Jianing Liu,
  • Liang Huang,
  • Linan Zeng,
  • Yi Zhun Zhu,
  • Qin Yu,
  • Yangtingting Gao,
  • Zheng Xu,
  • Qilin Peng,
  • Yao Xu,
  • Hailong Li,
  • Lingli Zhang

摘要

Introduction

Predictive models play a critical role in enhancing medication safety in clinical practice. While multiple models for adverse drug reactions (ADRs) in adults have demonstrated potential clinical utility, pediatric-specific predictive models remain understudied, with limited comprehensive evaluation of their methodological quality and reporting standards.

Aim

To map the landscape of existing risk prediction models for ADRs specifically developed or validated for pediatric patients, and to describe their characteristics, methodological quality, and reporting completeness.

Method

A systematic search was conducted in Embase, PubMed, CNKI, Wanfang, VIP, and SinoMed for studies on pediatric ADR prediction models. The information from included studies was evaluated using the Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist, the risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST), and adherence to Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines was reviewed. Predictive ability of the included model, including the area under the receiver operating characteristic curve (AuROC), sensitivity, and specificity, was also reported.

Results

Out of 12,667 screened studies, 7 articles (describing 10 models) met the inclusion criteria. Study designs included case–control, nested case–control, prospective cohort, and cross-sectional studies. Logistic regression was the primary modeling method, with one study using machine learning. Common methodological limitations included unreported handling of missing data and univariable predictor screening. Model discrimination (AuROC) ranged from 0.63–0.97, with sensitivity and specificity between 52.02–98.50% and 33.33–98.79%, respectively. TRIPOD adherence varied (62.16–86.49%), with notable reporting deficiencies in blinding, sample size justification, intervention details, model usage instructions, and supplementary materials. No models underwent external validation.

Conclusion

Existing pediatric ADR prediction models are limited by methodological and reporting shortcomings. Future research should focus on rigorous model development and external validation to ensure generalizability across diverse clinical settings.