Risk Prediction Models for Falls in Hospitalized Older Patients: a Systematic Review and Meta-analysis

Overview

Journal BMC Geriatr

Publisher Biomed Central

Date 2025 Jan 14

PMID 39810076

Authors

Anli Mao

Jie Su

Mingzhu Ren

Shuying Chen

Huafang Zhang

Affiliations

Soon will be listed here.

Abstract

Background: Existing fall risk assessment tools in clinical settings often lack accuracy. Although an increasing number of fall risk prediction models have been developed for hospitalized older patients in recent years, it remains unclear how useful these models are for clinical practice and future research.

Objectives: To systematically review published studies of fall risk prediction models for hospitalized older adults.

Methods: A search was performed of the Web of Science, PubMed, Cochrane Library, CINAHL, MEDLINE, and Embase databases: to retrieve studies of predictive models related to falls in hospitalized older adults from their inception until January 11, 2024. Extraction of data from included studies, including study design, data sources, sample size, predictors, model development and performance, etc. Risk of bias and applicability were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist.

Results: A total of 8086 studies were retrieved, and after screening, 13 prediction models from 13 studies were included. Four models were externally validated. Eight models reported discrimination metrics and two models reported calibration metrics. The most common predictors of falls were mobility, fall history, medications, and psychiatric disorders. All studies indicated a high risk of bias, primarily due to inadequate study design and methodological flaws. The AUC values of 8 models ranged from 0.630 to 0.851.

Conclusions: In the present study, all included studies had a high risk of bias, primarily due to the lack of prospective study design, inappropriate data analysis, and the absence of robust external validation. Future studies should prioritize the use of rigorous methodologies for the external validation of fall risk prediction models in hospitalized older adults.

Trial Registration: The study was registered in the International Database of Prospectively Registered Systematic Reviews (PROSPERO) CRD42024503718.

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