SynthesisBMC geriatrics2025
Risk prediction models for falls in hospitalized older patients: a systematic review and meta-analysis.
Synthesis in BMC geriatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 5 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.Annals of medicine · 2026Pooled it
- Machine Learning for Predicting Venous Thromboembolism After Joint Arthroplasty: Systematic Review of Clinical Applicability and Model Performance.JMIR medical informatics · 2026Pooled it
- A Systematic Review of Multivariate Models for Predicting Fall-Related Injuries in Older Adults.Journal of nursing management · 2026Pooled it
- Digital Healthcare Approaches for Fall Detection and Prediction in Older Adults: A Systematic Review of Evidence from Hospital and Long-Term Care Settings.Medicina (Kaunas, Lithuania) · 2025Pooled it
- Risk prediction models for malnutrition in cancer patients: a systematic review and meta-analysis.Frontiers in nutrition · 2025Pooled it
- Explainable AI reveals temporal risk pathways in fall prediction: Extracting clinical insights from multi-horizon machine learning models.GeroScience · 2026Article
- Risk Profiles in Hospitalized Adults Based on Functional Capacity, Pressure Injury Risk, and Fall Risk: A Cluster and Multiple Correspondence Analysis.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2026Observational
- Classification of fallers and non-fallers in older adults using electrical IMU signal for gait analysis and explainable deep learning.Scientific reports · 2026Article
- Fall Prevention and Geriatric Nursing.Healthcare (Basel, Switzerland) · 2026Article
- Evaluation of a Frustrated Total Internal Reflection (FTIR) based balance sensor for objective fall risk assessment in older adults: a study protocol.BMC geriatrics · 2026Article
- Development and Validation of Machine Learning Models for Predicting Falls Among Hospitalized Older Adults: Retrospective Cross-Sectional Study.JMIR aging · 2026Article
- Beyond patient education: fall prevention knowledge, health literacy, and implementation gaps in Chinese hospitals-a patient-caregiver study.Frontiers in public health · 2026Article
- From framework to frontline: Embedding enterprise risk management into emergency department fall prevention.Journal of healthcare risk management : the journal of the American Society for Healthcare Risk Management · 2025Article
- Streamlining Patient Fall Prevention and Management Through Human-Centered AI-Based Decision Support Systems.Risk management and healthcare policy · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundExisting 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.
objectivesTo systematically review published studies of fall risk prediction models for hospitalized older adults.
methodsA 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.
resultsA 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.
conclusionsIn 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 registrationThe study was registered in the International Database of Prospectively Registered Systematic Reviews (PROSPERO) CRD42024503718.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.