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. 2022 Nov 21;77(11):2311-2319.
doi: 10.1093/gerona/glac069.

Development of an Electronic Frailty Index for Hospitalized Older Adults in Sweden

Jonathan K L Mak  1 , Sara Hägg  1 , Maria Eriksdotter  2   3 , Martin Annetorp  2   3 , Ralf Kuja-Halkola  1 , Laura Kananen  1   4 , Anne-Marie Boström  3   5 , Miia Kivipelto  2   3 , Carina Metzner  2   3 , Viktoria Bäck Jerlardtz  6 , Malin Engström  7 , Peter Johnson  8 , Lars Göran Lundberg  9 , Elisabet Åkesson  10   11 , Carina Sühl Öberg  12 , Maria Olsson  13   14 , Tommy Cederholm  3   15 , Juulia Jylhävä  1   4 , Dorota Religa  2   3
Affiliations

Development of an Electronic Frailty Index for Hospitalized Older Adults in Sweden

Jonathan K L Mak et al. J Gerontol A Biol Sci Med Sci. .

Abstract

Background: Frailty assessment in the Swedish health system relies on the Clinical Frailty Scale (CFS), but it requires training, in-person evaluation, and is often missing in medical records. We aimed to develop an electronic frailty index (eFI) from routinely collected electronic health records (EHRs) and assess its association with adverse outcomes in hospitalized older adults.

Methods: EHRs were extracted for 18 225 patients with unplanned admissions between 1 March 2020 and 17 June 2021 from 9 geriatric clinics in Stockholm, Sweden. A 48-item eFI was constructed using diagnostic codes, functioning and other health indicators, and laboratory data. The CFS, Hospital Frailty Risk Score, and Charlson Comorbidity Index were used for comparative assessment of the eFI. We modeled in-hospital mortality and 30-day readmission using logistic regression; 30-day and 6-month mortality using Cox regression; and length of stay using linear regression.

Results: Thirteen thousand one hundred and eighty-eight patients were included in analyses (mean age 83.1 years). A 0.03 increment in the eFI was associated with higher risks of in-hospital (odds ratio: 1.65; 95% confidence interval: 1.54-1.78), 30-day (hazard ratio [HR]: 1.43; 1.38-1.48), and 6-month mortality (HR: 1.34; 1.31-1.37) adjusted for age and sex. Of the frailty and comorbidity measures, the eFI had the highest area under receiver operating characteristic curve for in-hospital mortality of 0.813. Higher eFI was associated with longer length of stay, but had a rather poor discrimination for 30-day readmission.

Conclusions: An EHR-based eFI has robust associations with adverse outcomes, suggesting that it can be used in risk stratification in hospitalized older adults.

Keywords: Comorbidity; Electronic frailty index; Frailty; Geriatrics.

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Figures

Figure 1.
Figure 1.
Diagnostic performance of frailty and comorbidity measures for mortality outcomes in patients with all the measures available (n = 4 945). (A) Receiver operating characteristics (ROC) curves for in-hospital mortality; (B) Harrell’s C-statistics from Cox models for 30-day and 6-month mortality. CFS, HFRS, CCI, and eFI were considered as continuous variables in all the models. CFS = Clinical Frailty Scale; HFRS = Hospital Frailty Risk Score; CCI = Charlson Comorbidity Index; eFI = electronic frailty index.
Figure 2.
Figure 2.
Kaplan–Meier curves for all-cause mortality by categories of the electronic frailty index (n = 13 188).
Figure 3.
Figure 3.
Scatter plots of the electronic frailty index and the length of stay (n = 13 188). (A) stratified by age; (B) stratified by sex. The colored fit lines represent the fitted locally estimated scatterplot smoothing curves (LOESS).
See this image and copyright information in PMC

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