JOURNAL OF HOSPITAL GENERAL MEDICINE
Online ISSN : 2436-018X
Review
Predictive Models of Falls: A Narrative Review
Masaki Tago Risa HirataNaoko KatsukiEiji NakataniChihiro SaitoShizuka YaitaYoshimasa OdaMidori TokushimaYuka HirakawaShun YamashitaYoshinori TokushimaHidetoshi AiharaMotoshi FujiwaraShu-ichi Yamashita
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ジャーナル フリー

2024 年 6 巻 1 号 p. 12-16

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Although many predictive models of falls have been developed, there are no firmly established and widely used predictive models of falls for health care settings in Japan. In this review, we elucidate the features of existing fall predictive models and compare them with our independently developed in-hospital fall predictive model, which includes Bedriddenness ranks (Saga Fall Risk Model 2, SFRM2). We conducted a narrative review after searching PubMed to identify studies on the development and/or validation of fall predictive models. We describe the outcomes, study participants, related factors, and development methods in those studies. A comparison was made with our developed SFRM2 and its features were analyzed. The reported fall predictive models exhibit considerable diversity in the number of items, evaluation methods, and model area under the receiving operator characteristic curve, with no consistent trends. Compared with previously developed fall prediction models, the SFRM2 only comprises eight items, allowing for simplified assessment through questionnaire-based inquiry without additional in-depth assessment upon admission.

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© 2024 JAPAN SOCIETY OF HOSPITAL GENERAL MEDICINE
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