Previously submitted to: JMIR Nursing (no longer under consideration since Mar 12, 2026)
Date Submitted: Mar 2, 2026
Open Peer Review Period: Mar 12, 2026 - Mar 12, 2026
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Machine learning prediction model for pressure ulcers in intensive care unit patients: A systematic review and meta-analysis
ABSTRACT
Background:
The number of predictive models for pressure ulcers (PU) in intensive care unit (ICU) patients based on machine learning is constantly increasing, yet the quality and applicability of these models in clinical practice and future research remain unclear at present.
Objective:
A systematic review was conducted on the published studies regarding predictive models for the risk of PU in patients in ICU.
Methods:
China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), SinoMed, PubMed, Web of Science, The Cochrane Library and Embase were searched from inception to December 20, 2025. Data from selected studies were extracted, including study design, data source, outcome definition, sample size, predictors, model development and performance. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was used to assess the risk of bias and applicability.
Results:
This study evaluated the predictive models constructed by various machine learning algorithms and found that the machine learning models had overall good performance in predicting PU for ICU patients. The non-logistic regression（LR） models had a greater advantage in identifying high-risk patients. Clinically, such models can be prioritized for assisting in risk screening. At the same time, special attention should be paid to elderly patients, those with long-term hospitalization, malnutrition, and those receiving mechanical ventilation, and precise interventions should be implemented. However, this study has several limitations. The quality of model construction is crucial. Efforts should be made to reduce bias risks, design appropriate sample sizes, conduct external validations, and explain the models. Future studies should focus on prospective, multi-center designs, rigorous model development methods, and external validation to improve model performance and clinical applicability.
Conclusions:
This systematic review includes 13 studies and 21 models. The combined AUC of the 21 validated models is 0.8864 (95% CI: 0.8524 - 0.9204), indicating good model performance. However, among the models included in this study, only two have low bias risk, while the rest have high bias risk. Future research should be designed following the evaluation criteria of PROBAST. By following the PROBAST standards, researchers can improve the quality of the studies and also conduct large-sample, multi-center external validations. Clinical Trial: The protocol for this study is registered with PROSPERO (registration number: CRD420251030077).
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