Citation

Dormosh N, Schut MC, Heymans MW, van der Velde N, Abu-Hanna A. J. Gerontol. A Biol. Sci. Med. Sci. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Gerontological Society of America)

DOI

10.1093/gerona/glab311

PMID

34637510

Abstract

BACKGROUND: Currently used prediction tools have limited ability to identify community-dwelling older people at high risk for falls. Prediction models utilizing Electronic Heath Records (EHR) provide opportunities but up to now showed limited clinical value as risk stratification tool; because of among others the underestimation of falls prevalence. The aim of this study was to develop a fall prediction model for community-dwelling older people using a combination of structured data and free text of primary care EHR and to internally validate its predictive performance.

METHODS: EHR data of individuals aged 65 or over. Age, sex, history of falls, medications and medical conditions were included as potential predictors. Falls were ascertained from the free text. We employed the Bootstrap-enhanced penalized logistic regression with the least absolute shrinkage and selection operator to develop the prediction model. We used 10-fold cross-validation to internally validate the prediction strategy. Model performance was assessed in terms of discrimination and calibration.

RESULTS: Data of 36,470 eligible participants were extracted from the dataset. The number of participants who fell at least once was 4,778 (13.1%). The final prediction model included age, sex, history of falls, two medications and five medical conditions. The model had a median area under the receiver operating curve of 0.705 (IQR 0.700-0.714).

CONCLUSIONS: Our prediction model to identify older people at high risk for falls achieved fair discrimination, and had reasonable calibration. It can be applied in clinical practice as it relies on routinely collected variables and does not require mobility assessment tests.

Language: en

Keywords

Accidental falls; fall prevention; fall prediction; free text; routinely collected data