Development and validation of an electronic frailty index using routine primary care electronic health record data

Abstract

Background: frailty is an especially problematic expression of population ageing. International guidelines recommend routine identification of frailty to provide evidence-based treatment, but currently available tools require additional resource.Objectives: to develop and validate an electronic frailty index (eFI) using routinely available primary care electronic health record data.Study design and setting: retrospective cohort study. Development and internal validation cohorts were established using a randomly split sample of the ResearchOne primary care database. External validation cohort established using THIN database.Participants: patients aged 65–95, registered with a ResearchOne or THIN practice on 14 October 2008.Predictors: we constructed the eFI using the cumulative deficit frailty model as our theoretical framework. The eFI score is calculated by the presence or absence of individual deficits as a proportion of the total possible. Categories of fit, mild, moderate and severe frailty were defined using population quartiles.Outcomes: outcomes were 1-, 3- and 5-year mortality, hospitalisation and nursing home admission.Statistical analysis: hazard ratios (HRs) were estimated using bivariate and multivariate Cox regression analyses. Discrimination was assessed using receiver operating characteristic (ROC) curves. Calibration was assessed using pseudo-R2 estimates.Results: we include data from a total of 931,541 patients. The eFI incorporates 36 deficits constructed using 2,171 CTV3 codes. One-year adjusted HR for mortality was 1.92 (95% CI 1.81–2.04) for mild frailty, 3.10 (95% CI 2.91–3.31) for moderate frailty and 4.52 (95% CI 4.16–4.91) for severe frailty. Corresponding estimates for hospitalisation were 1.93 (95% CI 1.86–2.01), 3.04 (95% CI 2.90–3.19) and 4.73 (95% CI 4.43–5.06) and for nursing home admission were 1.89 (95% CI 1.63–2.15), 3.19 (95% CI 2.73–3.73) and 4.76 (95% CI 3.92–5.77), with good to moderate discrimination but low calibration estimates.Conclusions: the eFI uses routine data to identify older people with mild, moderate and severe frailty, with robust predictive validity for outcomes of mortality, hospitalisation and nursing home admission. Routine implementation of the eFI could enable delivery of evidence-based interventions to improve outcomes for this vulnerable group.

Home-based health promotion for older people with mild frailty: the HomeHealth intervention development and feasibility RCT

“…Within the original protocol (version 1.2) we aimed to use the eFI 8 to identify potentially eligible patients. This tool was only available in one of our study general practices at the time of recruitment.…”

“…This tool was only available in one of our study general practices at the time of recruitment. For this practice, they identified a random sample of participants who were classed as mildly frail (0.12-0.24 on the eFI 8 ) for postal recruitment, to test the feasibility of using this approach in a larger trial at a later date when the eFI was available at all practices.…”

“…In the original protocol, we intended to collect eFI 8 scores for participants from their GP medical records if available. The eFI is an index of cumulative deficits that totals the presence or absence of 36 frailty-related deficits out of the total, based on data already collected in GP medical records.…”

“…Previous interventions have focused on preventing decline or reducing frailty in the highest-risk populations with moderate-severe frailty, with limited success. [11][12][13] Although moderate-severe frailty has a higher risk of decline than mild frailty, older people with mild frailty are also more likely to transition back to a robust state or remain stable than those BOX 1 Commonly used definitions of mild frailty [7][8][9][10] Fried frailty phenotype Pre-frailty' defined as one or two characteristics from the following: slow gait speed, weight loss, low physical activity, low energy and weakness. 10 …”

Data Resource: the Kent Integrated Dataset (KID)

“…An electronic frailty index (eFI) has been developed by Clegg et al (17), based on linked electronic health records, allowing healthcare professionals to draw on routinely available data to generate a frailty score. The process uses primary care data to count 'deficits', including symptoms, conditions and disabilities, in people aged 65 and over.…”