Convergent validity of the electronic frailty index

Abstract

BackgroundThe electronic frailty index (eFI) has been developed and validated using routine primary care electronic health record data. The focus of the original big data study was on predictive validity as a form of criterion validation. Convergent validity is a subtype of construct validity and considered a core component of the validity of a test. ObjectiveTo investigate convergent validity between the eFI and research standard frailty measures. DesignCross-sectional validation study using data from the Community Ageing Research 75+ (CARE 75+) cohort. SettingMulti-site UK community-based cohort study. Subjects353 community-dwelling older people (median age 80 years, IQR 77 to 84), excluding care home residents and people in the terminal stage of life. Median eFI score of participants was 0.22 (IQR 0.14 to 0.31). MethodsConvergent validities between the eFI and: a research standard frailty index; the phenotype model of frailty; Clinical Frailty Scale; and Edmonton Frail Scale were assessed using scatter plots and Spearman's rank tests to estimate correlation coefficients (Spearman's rho, ) and 95% confidence intervals. ResultsResults indicate strong correlation between the eFI and both the research standard frailty index ( =0.68, 95% CI 0.62 to 0.74) and Edmonton Frail Scale ( =0.63, 95% CI 0.57 to 0.69). There was evidence for moderate correlation between the eFI and

Development and validation of an electronic frailty index using routine electronic health records: an observational study of 49,226 elderly inpatients from a general hospital in China

“…In this study, we constructed an eFI to assess frailty in elderly inpatients by using routine EHR data in a general hospital. Our results showed a strong correlation between the eFI and CGA-FI, which was consistent with the ndings of Brundle C et al in the UK [12] and Abbasi M et al in Canada [21]. Other frailty assessment tools based on routine EHR data, such as the hospital frailty risk score [11] and care assessment need score [22], also showed good agreement with traditional frailty measurements.…”

“…This study also showed that the eFI was relatively lower than the CGA-FI (eFI: mean = 0.30, max = 0.58, min = 0.08 vs. CGA-FI: mean = 0.35, max = 0.69, min = 0.10) for 85 participants in a Canadian primary care programme [21]. There were several possible reasons why our eFI values were obviously lower than those of the CGA-FI and another reported eFI [10,12]. Our eFI was based on routine EHR from a single hospitalization rather than a summary of medical records across multiple visits.…”

“…Revision (ICD-10) coding system in the UK hospital database and con rmed that it can predict adverse outcomes for older people in acute care settings [11]. Meanwhile, these time-saving assessments of frailty based on EHR have shown good consistency with traditional assessment methods, such as the Fried phenotype and CGA-FI [11,12]. However, there has been no relevant frailty research based on EHR in China, so we do not know the feasibility of developing an effective eFI from routine EHR data in China.…”

Recurrent Measurement of Frailty Is Important for Mortality Prediction: Findings from the North West Adelaide Health Study

“…Routine assessment of frailty in older adults was highlighted as important in the clinical setting, 7,8 which can feasibly be measured using routinely collected data. 6,27,28 The findings from this study have implications for the clinical setting where a more recent frailty assessment is likely to provide the best information about the health status of older adults, taking into account the dynamic nature of the frailty condition and that regular reevaluation is necessary to keep this frailty profile up to date. .72 (.35-1.49) .373 6.08 (2.38-15.57) <.001* Abbreviations: aHR, adjusted hazard ratio; CI, confidence interval; FI, frailty index; FP, frailty phenotype.…”

Identification and management of frailty in English primary care: a qualitative study of national policy

“…In terms of accuracy, a recent study has indicated that the eFI might correlate more highly with other frailty scales by including more functional items. 27 Research has also shown a strong association between a patient's eFI score and length of registration at their practice, as the tool searches across the full record and all deficit codes are treated as non-resolvable, including conditions such as UTIs, peptic ulcer, and depression. 28 Since 2017, the GP contract has focused on the identification of frailty in patients aged ≥65 years; 9 however, a key epidemiological study by Barnett et al (2012) highlighted that the onset of multimorbidity occurs 10-15 years earlier in areas of socioeconomic deprivation.…”