Observational studyClinical interventions in aging2024
Validation and Improvement of the Saga Fall Risk Model: A Multicenter Retrospective Observational Study.
Observational study in Clinical interventions in aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Cognitive biases and contextual factors explaining variability in nurses' fall risk judgements: a multi-centre cross-sectional study.International journal of nursing studies advances · 2025Article
- Constructing a fall risk prediction model for hospitalized patients using machine learning.BMC public health · 2025Article
- External Validation of the Saga Fall-Related Injury Risk Model and Exploration of Common Factors in Multiple Hospitals: A Retrospective Observational Study.Clinical interventions in aging · 2025Observational
- Criterion-Related Validity of the Cognitive Function Score with the Revised Hasegawa's Dementia Scale and the Bedriddenness Rank with the Barthel Index and the Katz Index: A Multi-Center Retrospective Study.Dementia and geriatric cognitive disorders extraArticle
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Authors and funding
20 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: We conducted a pilot study in an acute care hospital and developed the Saga Fall Risk Model 2 (SFRM2), a fall prediction model comprising eight items: Bedriddenness rank, age, sex, emergency admission, admission to the neurosurgery department, history of falls, independence of eating, and use of hypnotics. The external validation results from the two hospitals showed that the area under the curve (AUC) of SFRM2 may be lower in other facilities. This study aimed to validate the accuracy of SFRM2 using data from eight hospitals, including chronic care hospitals, and adjust the coefficients to improve the accuracy of SFRM2 and validate it. Patients and Methods: This study included all patients aged ≥20 years admitted to eight hospitals, including chronic care, acute care, and tertiary hospitals, from April 1, 2018, to March 31, 2021. In-hospital falls were used as the outcome, and the AUC and shrinkage coefficient of SFRM2 were calculated. Additionally, SFRM2.1, which was modified from the coefficients of SFRM2 using logistic regression with the eight items comprising SFRM2, was developed using two-thirds of the data randomly selected from the entire population, and its accuracy was validated using the remaining one-third portion of the data. Results: Of the 124,521 inpatients analyzed, 2,986 (2.4%) experienced falls during hospitalization. The median age of all inpatients was 71 years, and 53.2% were men. The AUC of SFRM2 was 0.687 (95% confidence interval [CI]:0.678-0.697), and the shrinkage coefficient was 0.996. SFRM2.1 was created using 81,790 patients, and its accuracy was validated using the remaining 42,731 patients. The AUC of SFRM2.1 was 0.745 (95% CI: 0.731-0.758). Conclusion: SFRM2 showed good accuracy in predicting falls even on validating in diverse populations with significantly different backgrounds. Furthermore, the accuracy can be improved by adjusting the coefficients while keeping the model's parameters fixed.
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