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Predictive value of the World falls guidelines algorithm within the AGELESS-MELoR cohort
Soo Jin Sherry Lee1, Maw Pin Tan2, Sumaiyah Mat3
1Department of Medicine, Faculty of Medicine, University of Malaya 50603 Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia.
Archives of Gerontology and Geriatrics
|June 15, 2024
Summary
The World Falls Guidelines (WFG) algorithm shows high specificity but low sensitivity for predicting falls in Malaysian older adults. Regular reassessments are crucial for effective fall risk management in diverse populations.
Area of Science:
- Gerontology
- Public Health
- Epidemiology
Background:
- The World Falls Guidelines (WFG) algorithm was developed for falls risk stratification.
- Its applicability in low- and middle-income countries like Malaysia is uncertain due to differing risk factors and resource limitations.
Purpose of the Study:
- To evaluate the effectiveness of the WFG risk stratification algorithm in predicting falls among community-dwelling older adults in Malaysia.
Main Methods:
- Utilized data from the Malaysian Elders Longitudinal Research (MELR) cohort study (2013-2022).
- Included participants aged ≥55 years from Klang Valley.
- Risk stratification used baseline data; falls prediction assessed using follow-up data from waves 2, 3, and 4.
Main Results:
- The WFG algorithm demonstrated high specificity (ranging from 78.4% to 81.6%) but low sensitivity (decreasing from 51.3% at wave 2 to 26.0% at wave 4) in predicting falls.
- Falls were reported by increasing percentages of individuals across risk groups (low, intermediate, high) over time.
Conclusions:
- The WFG algorithm exhibits high specificity but diminishing sensitivity for fall prediction in the Malaysian context.
- Regular reassessments are recommended to accurately identify and manage fall risk in older adults, especially in resource-limited settings.
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