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External validation of the Johns Hopkins Fall Risk Assessment Tool in older Dutch hospitalized patients.
Damoiseaux-Volman, Birgit A; van Schoor, Natasja M; Medlock, Stephanie; Romijn, Johannes A; van der Velde, Nathalie; Abu-Hanna, Ameen.
  • Damoiseaux-Volman BA; Department of Medical Informatics, Amsterdam UMC-Location AMC, Amsterdam Public Health Research Institute, University of Amsterdam, Room J1B-109, Postbus 22660, 1100 DD, Amsterdam, The Netherlands. b.a.damoiseaux@amsterdamumc.nl.
  • van Schoor NM; Department of Epidemiology and Data Science, Amsterdam UMC, Amsterdam Public Health Research Institute, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
  • Medlock S; Department of Medical Informatics, Amsterdam UMC-Location AMC, Amsterdam Public Health Research Institute, University of Amsterdam, Room J1B-109, Postbus 22660, 1100 DD, Amsterdam, The Netherlands.
  • Romijn JA; Department of Medicine, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, The Netherlands.
  • van der Velde N; Section of Geriatric Medicine, Department of Internal Medicine, Amsterdam UMC, Amsterdam Public Health Research Institute, University of Amsterdam, Amsterdam, The Netherlands.
  • Abu-Hanna A; Department of Medical Informatics, Amsterdam UMC-Location AMC, Amsterdam Public Health Research Institute, University of Amsterdam, Room J1B-109, Postbus 22660, 1100 DD, Amsterdam, The Netherlands.
Eur Geriatr Med ; 2022 Nov 23.
Article in English | MEDLINE | ID: covidwho-2229244
ABSTRACT

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 JHFRAT for older inpatients and over-prediction in the calibration. Improvements in the fall-risk assessment are warranted to improve efficiency.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Year: 2022 Document Type: Article Affiliation country: S41999-022-00719-0

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Observational study / Prognostic study Language: English Year: 2022 Document Type: Article Affiliation country: S41999-022-00719-0