• Gojiro Nakagami, Shinichiroh Yokota, Aya Kitamura, Toshiaki Takahashi, Kojiro Morita, Hiroshi Noguchi, Kazuhiko Ohe, and Hiromi Sanada.
• Department of Gerontological Nursing/Wound Care Management, Graduate School of Medicine, Faculty of Medicine, The University of Tokyo, Building 5, 7-3-1, Hongo, Bunkyo-ku, Tokyo 1130033, Japan; Global Nursing Research Center, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 1130033, Japan.
• Int J Nurs Stud. 2021 Jul 1; 119: 103932.

BackgroundIn hospitals, nurses are responsible for pressure injury risk assessment using several kinds of risk assessment scales. However, their predictive validity is insufficient to initiate targeted preventive strategy for each patient. The use of electronic health records with machine learning technique is a promising strategy to provide automated clinical decision-making aid.ObjectiveThe purpose of this study was to construct a predictive model for pressure injury development which included feature variables that can be collected on the first day of hospitalization by nurses who routinely input the data to electronic health records.DesignRetrospective observational cohort study.SettingThis study was conducted at a university hospital in Japan.ParticipantsThis study used electronic health records, which include entry/discharge records, basic nursing records, and pressure injury management documents (N = 75,353).MethodsThe outcome measure was the pressure injuries which developed outside of an operation theatre and frequently appeared on the specific body parts at high risk of pressure injury development. We utilized four major classifiers: logistic regression, random forest, linear support vector machine, and extreme gradient boosting (XGBoost) with 5-fold cross-validation technique. The area under the receiver operating characteristic curve (AUC) was used for evaluating predictive performance.ResultsThe proportion of hospital-acquired pressure injuries was 0.52%. The receiver operating characteristic curves revealed the best predictive performance for the XGBoost model, achieving the highest sensitivity of 0.78±0.03 and AUC of 0.80±0.02 amongst four types of classifiers. Variables related to difficulty in activities of daily living, anorexia, and respiratory or cardiac disorders were extracted as important features.ConclusionsOur findings suggest that routinely collected health data by nurses on the first day of patient admission have the potential to help determine high-risk patients for pressure injury development. Tweetable abstract: Machine learning models on routinely collected electronic health records data successfully predict pressure injury development during hospitalization.FundingThis work was supported by a JSPS KAKENHI Grant-in-Aid for Exploratory Research (16K15865).Copyright © 2021 Elsevier Ltd. All rights reserved.

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