External validation of the Johns Hopkins Fall Risk Assessment Tool in older Dutch hospitalized patients

• Published in: European geriatric medicine 2023-02, Vol.14 (1), p.69-77
• Main Authors: Damoiseaux-Volman, Birgit A., van Schoor, Natasja M., Medlock, Stephanie, Romijn, Johannes A., van der Velde, Nathalie, Abu-Hanna, Ameen
• Format: Article
• Language: English
• Subjects: Aged; COVID-19 - epidemiology; Female; Geriatrics/Gerontology; Humans; Influenza, Human; Inpatients; Internal Medicine; Medicine; Medicine & Public Health; Reproducibility of Results; Research Paper; Risk Assessment - methods
• ISSN: 1878-7649; 1878-7657; 1878-7657
• DOI: 10.1007/s41999-022-00719-0

Key summary points Aim To assess the JHFRAT performance in a large sample of Dutch older inpatients, including its trend over time. Findings Among 17,263 older hospitalized patients, inpatient falls (2.5%) were identified by searching free text and the problem list. JHFRAT and its subcategories were significantly associated with inpatient falls, but JHFRAT showed low discrimination between fallers and non-fallers and over-prediction in the calibration. Message Falling is a serious adverse event and this paper showed that improvements in fall-risk assessment for older inpatients are warranted to improve efficiency. Purpose Fall prevention is a safety goal in many hospitals. The performance of the Johns Hopkins Fall Risk Assessment Tool (JHFRAT) in older inpatients is largely unknown. We aimed to assess the JHFRAT performance in a large sample of Dutch older inpatients, including its trend over time. Methods We used an Electronic Health Records (EHR) dataset with hospitalized patients (≥ 70), admitted for ≥ 24 h between 2016 and 2021. Inpatient falls were extracted from structured and free-text data. We assessed the association between JHFRAT and falls using logistic regression. For test accuracy, we calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Discrimination was measured by the AUC. For calibration, we plotted the predicted fall probability with the actual probability of falls. For time-related effects, we calculated the AUC per 6 months (using data of patients admitted during the 6 months’ time interval) and plotted these different AUC values over time. Furthermore, we compared the model (JHFRAT and falls) with and without adjusting for seasonal influenza, COVID-19, spring, summer, fall or winter periods. Results Data included 17,263 admissions with at least 1 JHFRAT measurement, a median age of 76 and a percentage female of 47%. The in-hospital fall prevalence was 2.5%. JHFRAT [OR = 1.11 (1.03–1.20)] and its subcategories were significantly associated with falls. For medium/high risk of falls (JHFRAT > 5), sensitivity was 73%, specificity 51%, PPV 4% and NPV 99%. The overall AUC was 0.67, varying over time between 0.62 and 0.71 (for 6 months’ time intervals). Seasonal influenza did affect the association between JHFRAT and falls. COVID-19, spring, summer, fall or winter did not affect the association. Conclusions Our results show an association between JHFRAT and falls, a low discrimination by