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Evaluating a Two-Level vs. Three-Level Fall Risk Screening Algorithm for Predicting Falls Among Older Adults
Thelma J Mielenz1, Sneha Kannoth1, Haomiao Jia2
1Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States.
Frontiers in Public Health
|September 9, 2020
Summary
The Quick-STEADI algorithm effectively identifies fall risk in older adults. Both three-level and two-level versions showed similar predictive ability, with the three-level approach indicating lower fall likelihood for low/moderate risk groups.
Area of Science:
- Gerontology
- Public Health
- Preventive Medicine
Background:
- Falls are a leading cause of preventable injury in older adults.
- The Centers for Disease Control and Prevention (CDC) developed the Stopping Elderly Accidents, Deaths, and Injuries (STEADI) algorithm for fall risk screening.
- This study adapted STEADI into a "Quick-STEADI" algorithm for clinical use.
Purpose of the Study:
- To compare the predictive abilities of three-level and two-level Quick-STEADI fall risk screening algorithms.
- To assess the qualitative implementation and feasibility of the Quick-STEADI algorithm in clinical settings.
- To evaluate the algorithm's effectiveness in predicting subsequent daily falls in older adults.
Main Methods:
- A prospective cohort study of 200 adults aged 65+ was conducted over 6 months.
- Generalized linear mixed models and receiver operating characteristic (ROC) curves with area under the curve (AUC) were used to analyze fall prediction.
- Qualitative data were collected from 8 participants and 3 screeners to evaluate the screening experience and implementation.
Main Results:
- The three-level Quick-STEADI algorithm showed that low and moderate fall risk predicted a reduced likelihood of daily falls compared to high risk (p=0.04).
- The two-level Quick-STEADI algorithm did not significantly associate 'not at-risk' individuals with reduced daily falls (p=0.13).
- Both algorithms demonstrated similar predictive ability for daily falls (AUC: 0.653 for three-level, 0.657 for two-level), and were found to be efficient and viable by users.
Conclusions:
- The Quick-STEADI algorithm is a suitable and feasible alternative for fall risk screening in clinical settings.
- Qualitative feedback supports its integration into existing healthcare workflows.
- Future research should focus on validating and implementing Quick-STEADI in community health settings to streamline fall prevention efforts.
