A systematic review of predictive models for hospital‐acquired pressure injury using machine learning

Abstract

Abstract Aims and objectives To summarize the use of machine learning (ML) for hospital‐acquired pressure injury (HAPI) prediction and to systematically assess the performance and construction process of ML models to provide references for establishing high‐quality ML predictive models. Background As an adverse event, HAPI seriously affects patient prognosis and quality of life, and causes unnecessary medical investment. At present, the performance of various scales used to predict HAPIs is still unsatisfactory. As a new statistical tool, ML has been applied to predict HAPIs. However, its performance has varied in different studies; moreover, some deficiencies in the model construction process were observed in each study. Design Systematic review. Methods Relevant articles published between 2010–2021 were identified in the PubMed, Web of Science, Scopus, Embase and CINHAL databases. Study selection was performed in accordance with the preferred reporting items for systematic reviews and meta‐analysis guidelines. The quality of the included articles was assessed using the prediction model risk of bias assessment tool. Results Twenty‐three studies out of 1793 articles were considered in this systematic review. The sample size of each study ranged from 149–75353; the prevalence of pressure injuries ranged from 0.5%–49.8%. ML showed good performance for HAPI prediction. However, some deficiencies were observed in terms of data management, data pre‐processing and model validation. Conclusions ML, as a powerful decision‐making assistance tool, is helpful for the prediction of HAPIs. However, existing studies have been insufficient in terms of data management, data pre‐processing and model validation. Future studies should address these issues to establish ML models for HAPI prediction that can be widely used in clinical practice. Relevance to Clinical Practice This review highlights that ML is helpful in predicting HAPI; however, in the process of data management, data pre‐processing and model validation, some deficiencies still need to be addressed. The ultimate goal of integrating ML into HAPI prediction is to develop a practical clinical decision‐making tool. A complete and rigorous model construction process should be followed in future studies to develop high‐quality ML models that can be applied in clinical practice.

Keywords

• machine learning
• pressure injury
• pressure ulcer
• prediction
• systematic review