Title |
Evaluating frailty scores to predict mortality in older adults using data from population based electronic health records: case control study
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Published in |
Age & Ageing, March 2018
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DOI | 10.1093/ageing/afy022 |
Pubmed ID | |
Authors |
Daniel Stow, Fiona E Matthews, Stephen Barclay, Steve Iliffe, Andrew Clegg, Sarah De Biase, Louise Robinson, Barbara Hanratty |
Abstract |
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. investigate whether an electronic frailty index (eFI) generated from routinely collected data, can be used to predict mortality at an individual level. historical prospective case control study. UK primary care electronic health records. 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. 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. 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. 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 69 | 49% |
Spain | 16 | 11% |
United States | 4 | 3% |
Australia | 2 | 1% |
Poland | 1 | <1% |
France | 1 | <1% |
Unknown | 49 | 35% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 73 | 51% |
Practitioners (doctors, other healthcare professionals) | 43 | 30% |
Scientists | 23 | 16% |
Science communicators (journalists, bloggers, editors) | 3 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 106 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 19 | 18% |
Student > Master | 16 | 15% |
Student > Ph. D. Student | 12 | 11% |
Other | 10 | 9% |
Student > Postgraduate | 5 | 5% |
Other | 13 | 12% |
Unknown | 31 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 33 | 31% |
Nursing and Health Professions | 13 | 12% |
Computer Science | 5 | 5% |
Psychology | 4 | 4% |
Agricultural and Biological Sciences | 3 | 3% |
Other | 15 | 14% |
Unknown | 33 | 31% |