Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients.
The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures.
This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell\'s C-statistic, respectively.
Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42-3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08-2.74), 6-month mortality (HR = 2.29; 2.04-2.56), and a longer length of stay (β-coefficient = 2.00; 1.65-2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell\'s C = 0.733), and 6-month mortality (Harrell\'s C = 0.719).
An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.

To assess the applicability of Electronic Frailty Index (eFI) and Hospital Frailty Risk Score (HFRS) algorithms to Japanese administrative claims data and to evaluate their association with long-term outcomes.
A cohort study using a regional government administrative healthcare and long-term care (LTC) claims database in Japan 2014-18.
Plan enrollees aged ≥50 years.
We applied the two algorithms to the cohort and assessed the scores\' distributions alongside enrollees\' 4-year mortality and initiation of government-supported LTC. Using Cox regression and Fine-Gray models, we evaluated the association between frailty scores and outcomes as well as the models\' discriminatory ability.
Among 827,744 enrollees, 42.8% were categorised by eFI as fit, 31.2% mild, 17.5% moderate and 8.5% severe. For HFRS, 73.0% were low, 24.3% intermediate and 2.7% high risk; 35 of 36 predictors for eFI, and 92 of 109 codes originally used for HFRS were available in the Japanese system. Relative to the lowest frailty group, the highest frailty group had hazard ratios [95% confidence interval (CI)] of 2.09 (1.98-2.21) for mortality and 2.45 (2.28-2.63) for LTC for eFI; those for HFRS were 3.79 (3.56-4.03) and 3.31 (2.87-3.82), respectively. The area under the receiver operating characteristics curves for the unadjusted model at 48 months was 0.68 for death and 0.68 for LTC for eFI, and 0.73 and 0.70, respectively, for HFRS.
The frailty algorithms were applicable to the Japanese system and could contribute to the identifications of enrollees at risk of long-term mortality or LTC use.

UNASSIGNED: To assess whether the electronic frailty index (eFI) is independently associated with all-cause mortality and chemotherapy adverse reactions among older Chinese patients with lung cancer.
UNASSIGNED: This is a retrospective, single-institution, chart review, and not a prospective cohort study. All patients ≥60 years with primary lung cancer in the West China Hospital from 2010 to 2017 were included in this cohort. The eFI was established using 35 frailty-related variables in the electronic medical record (EMR) system and was cut by a value of 0.2 to classify the patients into frail (eFI ≥0.2) and robust/non-frail groups (eFI<0.2). The long-term outcome was all-cause mortality identified by government databases and telephone interviews. Short-term outcomes were any infection, bone suppression, chemotherapy discontinuation, impaired liver function, any gastrointestinal reactions and length of hospitalization. An inverse probability weighting method was used to eliminate the potential confounders. An adjusted Kaplan-Meier estimator and a weighted Cox model were used to calculate the survival and hazard ratio. A weighted logistic model was used to calculate the odds of short-term outcomes.
UNASSIGNED: A total of 997 patients were included in this study with a median follow-up of 34 months. Compared with non-frail patients, frail patients had an increased risk of mortality and shortened overall survival (hazard ratio [HR] of mortality, 1.29; 95% confidence interval [CI], 1.05 to 1.60; adjusted restricted mean survival time [aRMST] difference, -5.68 months; 95% CI, -10.15 to -1.21 months). For short-term outcomes, frail patients had increased odds of infection compared to non-frail patients (odds ratio, 1.83; 95% CI, 1.09 to 3.06). No other outcome showed a significant result.
UNASSIGNED: This study of older Chinese patients with primary lung cancer suggests that eFI-based frail patients had worse prognoses with increased risk of all-cause mortality and shortened survival times.

An electronic frailty index (eFI) has been developed and validated in the UK; it uses data from primary care electronic medical records (EMR) for effective frailty case-finding in primary care. This project examined the convergent validity of the eFI from Canadian primary care EMR data with a validated frailty index based on comprehensive geriatric assessment (FI-CGA), in order to understand its potential use in the Canadian context.
A cross-sectional validation study, using data from an integrated primary care research program for seniors living with frailty in Edmonton, AB. Eighty-five patients 65 years of age and older from six primary care physicians\' practices were recruited. Patients were excluded if they were under 65 years of age, did not provide consent to participate in the program, or were living in a long term care facility at the time of enrolment. We used scatter plots to assess linearity and Pearson correlation coefficients to examine correlations.
Results indicate a strong statistically significant correlation between the eFI and FI-CGA (r = 0.72, 95% CI 0.60-0.81, p < 0.001). A simple linear regression showed good ability of the eFI scores to predict FI-CGA scores (F (1,83) = 89.06, p < .0001, R2 = 0.51). Both indices were also correlated with age, number of chronic conditions and number of medications.
The study findings support the convergent validity of the eFI, which further justifies implementation of a case-finding tool that uses routinely collected primary care data in the Canadian context.

BACKGROUND: Identifying frailty is key to providing appropriate treatment for older people at high risk of adverse health outcomes. Screening tools proposed for primary care often involve additional workload. The electronic Frailty Index (eFI) has the potential to overcome this issue.
OBJECTIVE: To assess the feasibility and acceptability of using the eFI in primary care.
METHODS: Pilot study in one suburban primary care practice in southern England in 2016.
METHODS: Use of the eFI on the primary care TPP SystmOne database was explained to staff at the practice where a comprehensive geriatric assessment (CGA) clinic was being trialled. The practice data manager ran an eFI report for all patients (n = 6670). Date of birth was used to identify patients aged ≥75 years (n = 589). The eFI was determined for patients attending the CGA clinic (n = 18).
RESULTS: Practice staff ran the eFI reports in 5 minutes, which they reported was feasible and acceptable. The eFI range was 0.03 to 0.61 (mean 0.23) for all patients aged ≥75 years (mean 83 years, range 75 to 102 years). For CGA patients (mean 82 years, range 75 to 94 years) the eFI range was 0.19 to 0.53 (mean 0.33). Importantly, the eFI scores identified almost 12% of patients aged ≥75 years in this practice to have severe frailty.
CONCLUSIONS: It was feasible and acceptable to use the eFI in this pilot study. A higher mean eFI in the CGA patients demonstrated construct validity for frailty identification. Practice staff recognised the potential for the eFI to identify the top 2% of vulnerable patients for avoiding unplanned admissions.