Predicting Harm: A Systematic Review of Pediatric Pressure Injury Models and Their Clinical Shortcomings

Significance: Pediatric pressure injuries (PIs) are a distinct and preventable clinical challenge, yet risk prediction models tailored to children remain underdeveloped. This systematic review critically evaluates existing pediatric PI prediction models to assess their methodological rigor, predictive performance, and clinical applicability. Recent Advances: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, nine databases were searched to identify studies developing or validating PI risk prediction models for hospitalized patients younger than 18 years. Twelve models from nine studies were included. Risk of bias and applicability were assessed using the Prediction Model Risk Of Bias Assessment Tool (PROBAST) and PROBAST + AI tool. Critical Issues: All models demonstrated acceptable discrimination (area under the curve AUC range: 0.612–0.978), with seven exceeding an AUC of 0.75. However, calibration was inconsistently reported, and only two models evaluated clinical utility—just one showed net benefit across a realistic threshold range. All models were rated as high risk of bias, and 10 had major concerns regarding applicability. Common methodological flaws included low events per variable <10, inappropriate categorization of continuous variables, poor handling of missing data, and lack of external validation. Most models were developed in single-center studies from China, limiting generalizability. Compared with adult PI models, pediatric models lacked age stratification, standardized outcome definitions, and robust validation. The first application of PROBAST + AI for evaluating machine learning prediction models highlighted algorithmic fairness and ethical risks within these models, but it showed insufficient interpretability regarding aspects such as the optimization process and the transparency of “black box” data leakage. Future Directions: To improve predictive accuracy and clinical relevance, future models should adopt the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis, PROBAST, and PROBAST + AI standards, use multicenter data, stratify by age and clinical setting, and focus on early-stage PIs. Incorporating objective measures and evaluating clinical utility will enhance model integration into practice. PROBAST + AI, in alignment with the advancements in information technology, requires widespread attention for its practical utility and ease of use to be further validated and optimized. Hongying Pan Yihong Xu Conclusion: Current pediatric PI prediction models show promise but fall short in methodological rigor and clinical applicability. Addressing these gaps is essential to support early identification and targeted prevention in pediatric care.