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Prediction of adverse health outcomes using an electronic frailty index among nonfrail and prefrail community elders

BACKGROUND: Early recognition of older people at risk of undesirable clinical outcomes is vital in preventing future disabling conditions. Here, we report the prognostic performance of an electronic frailty index (eFI) in comparison with traditional tools among nonfrail and prefrail community-dwelli...

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Detalles Bibliográficos
Autores principales: Lin, Kun-Pei, Li, Hsin-Yi, Chen, Jen-Hau, Lu, Feng-Ping, Wen, Chiung-Jung, Chou, Yi-Chun, Wu, Meng-Chen, (Derrick) Chan, Ding-Cheng, Chen, Yung-Ming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10408173/
https://www.ncbi.nlm.nih.gov/pubmed/37550602
http://dx.doi.org/10.1186/s12877-023-04160-1
Descripción
Sumario:BACKGROUND: Early recognition of older people at risk of undesirable clinical outcomes is vital in preventing future disabling conditions. Here, we report the prognostic performance of an electronic frailty index (eFI) in comparison with traditional tools among nonfrail and prefrail community-dwelling older adults. The study is to investigate the predictive utility of a deficit-accumulation eFI in community elders without overt frailty. METHODS: Participants aged 65–80 years with a Clinical Frailty Scale of 1–3 points were recruited and followed for 2 years. The eFI score and Fried’s frailty scale were determined by using a semiautomated platform of self-reported questionnaires and objective measurements which yielded cumulative deficits and physical phenotypes from 80 items of risk variables. Kaplan–Meier method and Cox proportional hazards regression were used to analyze the severity of frailty in relation to adverse outcomes of falls, emergency room (ER) visits and hospitalizations during 2 years’ follow-up. RESULTS: A total of 427 older adults were evaluated and dichotomized by the median FI score. Two hundred and sixty (60.9%) and 167 (39.1%) elders were stratified into the low- (eFI ≤ 0.075) and the high-risk (eFI > 0.075) groups, respectively. During the follow-up, 77 (47.0%) individuals developed adverse events in the high-risk group, compared with 79 (30.5%) in the low-risk group (x(2), p = 0.0006). In multivariable models adjusted for age and sex, the increased risk of all three events combined in the high- vs. low-risk group remained significant (adjusted hazard ratio (aHR) = 3.08, 95% confidence interval (CI): 1.87–5.07). For individual adverse event, the aHRs were 2.20 (CI: 1.44–3.36) for falls; 1.67 (CI: 1.03–2.70) for ER visits; and 2.84 (CI: 1.73–4.67) for hospitalizations. Compared with the traditional tools, the eFI stratification (high- vs. low-risk) showed better predictive performance than either CFS rating (managing well vs. fit to very fit; not discriminative in hospitalizations) or Fried’s scale (prefrail to frail vs. nonfrail; not discriminative in ER visits). CONCLUSION: The eFI system is a useful frailty tool which effectively predicts the risk of adverse healthcare outcomes in nonfrail and/or prefrail older adults over a period of 2 years. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-04160-1.