ArticleJournal of multidisciplinary healthcare2025
Development and Validation of a Dynamic Online Nomogram for Predicting Inpatient Fall Risk: A Cohort Study.
Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: This study aimed to develop and validate a dynamic online nomogram for predicting inpatient fall risk using data from a Dryad database cohort to strengthen fall prevention strategies and enhance patient safety in hospital settings. Methods: We analyzed data from a cohort study conducted at Fukushima Medical University Hospital, with external validation using an independent dataset from Taizhou People's Hospital (2019-2023, n=2000). Following multiple imputation, 9470 cases were included and divided into training (n=6631) and validation (n=2839) sets. LASSO regression identified fall-associated factors, leading to development of two predictive models using binomial logistic regression. Model 1 incorporated all selected variables, while Model 2 emphasized clinically relevant factors. Discriminatory power, calibration, and clinical decision curve analysis were conducted for both models. Results: LASSO regression identified 14 key variables, reduced to 11 in Model 1 and 6 clinically relevant variables in Model 2. Both models demonstrated comparable performance (Z=1.152, p=0.249), with Model 2 selected for clinical applicability. Bootstrap validation showed strong performance with AUC of 0.801 (training set) and 0.796 (validation set). Calibration was adequate (Hosmer-Lemeshow test p>0.05). Decision curve analysis indicated potential intervention benefit for predicted probabilities of 1-95.1% (training) and 1-89.2% (validation). External validation in 2000 patients demonstrated robust generalizability (AUC=0.87, 95% CI: 0.80-0.93). Conclusion: We developed a predictive model for assessing fall risk among hospitalized patients. This model supports individualized patient evaluations, assists in identifying high-risk patients, and may contribute to reducing fall incidence in hospital settings.
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