In-hospital fall-risk screening in 4,735 geriatric patients from the LUCAS project
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In-hospital falls in older patients are frequent, but the identification of patients at risk of falling is challenging. Aim of this study was to improve the identification of high-risk patients. Therefore, a simplified screening-tool was developed, validated, and compared to the STRATIFY predictive accuracy.
Retrospective analysis of 4,735 patients; evaluation of predictive accuracy of STRATIFY and its single risk factors, as well as age, gender and psychotropic medication; splitting the dataset into a learning and a validation sample for modelling fall-risk screening and independent, temporal validation.
Geriatric clinic at an academic teaching hospital in Hamburg, Germany.
4,735 hospitalised patients ≥65 years.
Sensitivity, specificity, positive and negative predictive value, Odds Ratios, Youden-Index and the rates of falls and fallers were calculated.
There were 10.7% fallers, and the fall rate was 7.9/1,000 hospital days. In the learning sample, mental alteration (OR 2.9), fall history (OR 2.1), and insecure mobility (Barthel-Index items ‘transfer’ + ‘walking’ score = 5, 10 or 15) (OR 2.3) had the most strongest association to falls. The LUCAS Fall-Risk Screening uses these risk factors, and patients with ≥2 risk factors contributed to the high-risk group (30.9%). In the validation sample, STRATIFY SENS was 56.8, SPEC 59.6, PPV 13.5 and NPV 92.6 vs. LUCAS Fall-Risk Screening was SENS 46.0, SPEC 71.1, PPV 14.9 and NPV 92.3.
Both the STRATIFY and the LUCAS Fall-Risk Screening showed comparable results in defining a high-risk group. Impaired mobility and cognitive status were closely associated to falls. The results do underscore the importance of functional status as essential fall-risk factor in older hospitalised patients.
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- In-hospital fall-risk screening in 4,735 geriatric patients from the LUCAS project
The journal of nutrition, health & aging
Volume 17, Issue 3 , pp 264-269
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- Older patients
- in-hospital falls
- risk screening
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- Author Affiliations
- 1390. Albertinen-Haus Geriatrics Centre, University of Hamburg, Sellhopsweg 18-22, D-22459, Hamburg, Germany
- 2390. Institute of Medical Information Sciences, Biometry and Epidemiology (IBE), Ludwig-Maximilians-University Munich, Marchionistrasse 15, 81377, Munich, Germany
- 3390. Scientific Department at the University of Hamburg, Sellhopsweg 18-22, D-22459, Hamburg, Germany