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Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study.

ABSTRACT: BACKGROUND:recognising that a patient is nearing the end of life is essential, to enable professional carers to discuss prognosis and preferences for end of life care. OBJECTIVE:investigate whether an electronic frailty index (eFI) generated from routinely collected data, can be used to predict mortality at an individual level. DESIGN:historical prospective case control study. SETTING:UK primary care electronic health records. SUBJECTS:13,149 individuals age 75 and over who died between 01/01/2015 and 01/01/2016, 1:1 matched by age and sex to individuals with no record of death in the same time period. METHODS:two subsamples were randomly selected to enable development and validation of the association between eFI 3 months prior to death and mortality. Receiver operator characteristic (ROC) analyses were used to examine diagnostic accuracy of eFI at 3 months prior to death. RESULTS:an eFI > 0.19 predicted mortality in the development sample at 75% sensitivity and 69% area under received operating curve (AUC). In the validation dataset this cut point gave 76% sensitivity, 53% specificity. CONCLUSIONS:the eFI measured at a single time point has low predictive value for individual risk of death, even 3 months prior to death. Although the eFI is a strong predictor or mortality at a population level, its use for individuals is far less clear.

SUBMITTER: Stow D

PROVIDER: S-EPMC6014267 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study.

Stow Daniel D Matthews Fiona E FE Barclay Stephen S Iliffe Steve S Clegg Andrew A De Biase Sarah S Robinson Louise L Hanratty Barbara B

Age and ageing 20180701 4

<h4>Background</h4>recognising that a patient is nearing the end of life is essential, to enable professional carers to discuss prognosis and preferences for end of life care.<h4>Objective</h4>investigate whether an electronic frailty index (eFI) generated from routinely collected data, can be used to predict mortality at an individual level.<h4>Design</h4>historical prospective case control study.<h4>Setting</h4>UK primary care electronic health records.<h4>Subjects</h4>13,149 individuals age 7 ...[more]

PMID: 29546362

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Project description:ObjectiveTo create an electronic frailty index (eFRAGICAP) using electronic health records (EHR) in Catalunya (Spain) and assess its predictive validity with a two-year follow-up of the outcomes: homecare need, institutionalization and mortality in the elderly. Additionally, to assess its concurrent validity compared to other standardized measures: the Clinical Frailty Scale (CFS) and the Risk Instrument for Screening in the Community (RISC).MethodsThe eFRAGICAP was based on the electronic frailty index (eFI) developed in United Kingdom, and includes 36 deficits identified through clinical diagnoses, prescriptions, physical examinations, and questionnaires registered in the EHR of primary health care centres (PHC). All subjects > 65 assigned to a PHC in Barcelona on 1st January, 2016 were included. Subjects were classified according to their eFRAGICAP index as: fit, mild, moderate or severe frailty. Predictive validity was assessed comparing results with the following outcomes: institutionalization, homecare need, and mortality at 24 months. Concurrent validation of the eFRAGICAP was performed with a sample of subjects (n = 333) drawn from the global cohort and the CFS and RISC. Discrimination and calibration measures for the outcomes of institutionalization, homecare need, and mortality and frailty scales were calculated.Results253,684 subjects had their eFRAGICAP index calculated. Mean age was 76.3 years (59.5% women). Of these, 41.1% were classified as fit, and 32.2% as presenting mild, 18.7% moderate, and 7.9% severe frailty. The mean age of the subjects included in the validation subsample (n = 333) was 79.9 years (57.7% women). Of these, 12.6% were classified as fit, and 31.5% presented mild, 39.6% moderate, and 16.2% severe frailty. Regarding the outcome analyses, the eFRAGICAP was good in the detection of subjects who were institutionalized, required homecare assistance, or died at 24 months (c-statistic of 0.841, 0.853, and 0.803, respectively). eFRAGICAP was also good in the detection of frail subjects compared to the CFS (AUC 0.821) and the RISC (AUC 0.848).ConclusionThe eFRAGICAP has a good discriminative capacity to identify frail subjects compared to other frailty scales and predictive outcomes.

Project description:BackgroundExisting operational definitions of frailty are personnel-costly and time-consuming, resulting in estimates with a small sample size that cannot be generalized to the population level. The objectives were to develop a multimorbidity frailty index using Taiwan's claim database, and to understand its ability to predict adverse event.MethodsThis is a retrospective cohort study. Subjects aged 65 to 100 years who have full National Health Insurance coverage in 2005 were included. We constructed the multimorbidity frailty index using cumulative deficit approach and categorized study population according to the multimorbidity frailty index quartiles: fit, mild frailty, moderate frailty and severe frailty. The multimorbidity frailty index included deficits from outpatient and inpatient diagnosis. Associations with all-cause mortality, unplanned hospitalization and intensive care unit admission were assessed using Kaplan-Meier curves and Cox regression analyses.ResultsThe multimorbidity frailty index incorporated 32 deficits, with mean multimorbidity frailty index score of 0.052 (standard deviation = 0.060) among 86,133 subjects included. Compared to subjects in fit category, subjects with severe frailty were associated with a 5.0-fold (adjusted hazard ratio, aHR 4.97; 95% confidence interval, 95% CI 4.49-5.50) increased risk of death at 1 year after adjusting for age and gender. Subjects with moderate frailty or mild frailty was associated with 3.1- (adjusted HR 3.08; 95% CI 2.80-3.39) or 1.9- (adjusted HR 1.86; 95% CI 1.71-2.01) folds increased risk, respectively.4.49-5.50). The risk trend of unplanned hospitalization and intensive care unit admission is similar among the study population. Besides, the association between the frailty categories and all three outcomes was slightly stronger among women.ConclusionThe multimorbidity frailty index was highly associated with all-cause mortality, unplanned hospitalization and ICU admission. It could serve as an efficient tool for stratifying older adults into different risk groups for planning care management programs.

Dataset Information

Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study.

Publications

Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study.

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