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Published in: BMC Geriatrics 1/2022

Open Access 01-12-2022 | Geriatric Assessment | Research

Validation of an electronic frailty index with electronic health records: eFRAGICAP index

Authors: Francesc Orfila, Lucía A. Carrasco-Ribelles, Rosa Abellana, Albert Roso-Llorach, Francisco Cegri, Carlen Reyes, Concepción Violán

Published in: BMC Geriatrics | Issue 1/2022

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Abstract

Objective

To 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).

Methods

The 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.

Results

253,684 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).

Conclusion

The eFRAGICAP has a good discriminative capacity to identify frail subjects compared to other frailty scales and predictive outcomes.
Appendix
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Literature
1.
go back to reference Morley JE, Vellas B, van Kan GA, et al. Frailty consensus: a call to action. J Am Med Dir Assoc. 2013;14:392–7. CrossRef Morley JE, Vellas B, van Kan GA, et al. Frailty consensus: a call to action. J Am Med Dir Assoc. 2013;14:392–7. CrossRef
3.
go back to reference Clegg A, Young J, Iliffe S, et al. Frailty in elderly people. Lancet. 2013;381:752–62.CrossRef Clegg A, Young J, Iliffe S, et al. Frailty in elderly people. Lancet. 2013;381:752–62.CrossRef
4.
go back to reference Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–56.CrossRef Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–56.CrossRef
5.
go back to reference Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49:M85–94.CrossRef Guralnik JM, Simonsick EM, Ferrucci L, et al. A short physical performance battery assessing lower extremity function: association with self-reported disability and prediction of mortality and nursing home admission. J Gerontol. 1994;49:M85–94.CrossRef
6.
go back to reference Serra-Prat M, Papiol M, Monteis R, et al. Relationship between plasma ghrelin levels and sarcopenia in elderly subjects: A cross-sectional study. J Nutr Health Aging. 2015;19:669–72.CrossRef Serra-Prat M, Papiol M, Monteis R, et al. Relationship between plasma ghrelin levels and sarcopenia in elderly subjects: A cross-sectional study. J Nutr Health Aging. 2015;19:669–72.CrossRef
7.
go back to reference Franceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci. 2014;69(Suppl 1):S4–9.CrossRef Franceschi C, Campisi J. Chronic inflammation (inflammaging) and its potential contribution to age-associated diseases. J Gerontol A Biol Sci Med Sci. 2014;69(Suppl 1):S4–9.CrossRef
8.
go back to reference Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. Sci World J. 2001;1:323–36.CrossRef Mitnitski AB, Mogilner AJ, Rockwood K. Accumulation of deficits as a proxy measure of aging. Sci World J. 2001;1:323–36.CrossRef
10.
go back to reference Clegg A, Bates C, Young J, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016;45:353–60.CrossRef Clegg A, Bates C, Young J, et al. Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing. 2016;45:353–60.CrossRef
11.
go back to reference NHS England/LTC Team. Toolkit for general practice in supporting older people living with frailty. Update to 2014 Document, 2017. NHS England/LTC Team. Toolkit for general practice in supporting older people living with frailty. Update to 2014 Document, 2017.
12.
go back to reference Boyd PJ, Nevard M, Ford JA, et al. The electronic frailty index as an indicator of community healthcare service utilisation in the older population. Age Ageing. 2019;48:273–7.CrossRef Boyd PJ, Nevard M, Ford JA, et al. The electronic frailty index as an indicator of community healthcare service utilisation in the older population. Age Ageing. 2019;48:273–7.CrossRef
13.
go back to reference Ambagtsheer RC, Beilby J, Dabravolskaj J, et al. Application of an electronic frailty index in Australian primary care: data quality and feasibility assessment. Aging Clin Exp Res. 2019;31:653–60.CrossRef Ambagtsheer RC, Beilby J, Dabravolskaj J, et al. Application of an electronic frailty index in Australian primary care: data quality and feasibility assessment. Aging Clin Exp Res. 2019;31:653–60.CrossRef
14.
go back to reference Lansbury LN, Roberts HC, Clift E, et al. Use of the electronic Frailty Index to identify vulnerable patients: a pilot study in primary care. Br J Gen Pract. 2017;67:e751–6.CrossRef Lansbury LN, Roberts HC, Clift E, et al. Use of the electronic Frailty Index to identify vulnerable patients: a pilot study in primary care. Br J Gen Pract. 2017;67:e751–6.CrossRef
15.
go back to reference Drubbel I, de Wit NJ, Bleijenberg N, et al. Prediction of adverse health outcomes in older people using a Frailty index based on routine primary care data. J Gerontol Ser A Biol Sci Med Sci. 2013;68:301–8.CrossRef Drubbel I, de Wit NJ, Bleijenberg N, et al. Prediction of adverse health outcomes in older people using a Frailty index based on routine primary care data. J Gerontol Ser A Biol Sci Med Sci. 2013;68:301–8.CrossRef
16.
go back to reference Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. Cmaj. 2005;173:489–95.CrossRef Rockwood K, Song X, MacKnight C, et al. A global clinical measure of fitness and frailty in elderly people. Cmaj. 2005;173:489–95.CrossRef
17.
go back to reference O’Caoimh R, Gao Y, Svendrovski A, et al. The Risk Instrument for Screening in the Community (RISC): a new instrument for predicting risk of adverse outcomes in community dwelling older adults. BMC Geriatr. 2015;15:92.CrossRef O’Caoimh R, Gao Y, Svendrovski A, et al. The Risk Instrument for Screening in the Community (RISC): a new instrument for predicting risk of adverse outcomes in community dwelling older adults. BMC Geriatr. 2015;15:92.CrossRef
18.
go back to reference García-Gil MDM, Hermosilla E, Prieto-Alhambra D, et al. Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP). Inform Prim Care. 2011;19:135–45. García-Gil MDM, Hermosilla E, Prieto-Alhambra D, et al. Construction and validation of a scoring system for the selection of high-quality data in a Spanish population primary care database (SIDIAP). Inform Prim Care. 2011;19:135–45.
19.
go back to reference Bolíbar B, Fina Avilés F, Morros R, et al. SIDIAP database: electronic clinical records in primary care as a source of information for epidemiologic research. Med Clin (Barc). 2012;138:617–21.CrossRef Bolíbar B, Fina Avilés F, Morros R, et al. SIDIAP database: electronic clinical records in primary care as a source of information for epidemiologic research. Med Clin (Barc). 2012;138:617–21.CrossRef
20.
go back to reference Middleton R, Poveda JL, Orfila Pernas F, et al. Mortality, Falls, and Fracture Risk Are Positively Associated With Frailty: A SIDIAP Cohort Study of 890 000 Patients. J Gerontol Ser A. 2022;77:148–54.CrossRef Middleton R, Poveda JL, Orfila Pernas F, et al. Mortality, Falls, and Fracture Risk Are Positively Associated With Frailty: A SIDIAP Cohort Study of 890 000 Patients. J Gerontol Ser A. 2022;77:148–54.CrossRef
21.
go back to reference Church S, Rogers E, Rockwood K, et al. A scoping review of the Clinical Frailty Scale. BMC Geriatr. 2020;20:1–18.CrossRef Church S, Rogers E, Rockwood K, et al. A scoping review of the Clinical Frailty Scale. BMC Geriatr. 2020;20:1–18.CrossRef
22.
go back to reference Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc. 1999;94:496–509.CrossRef Fine JP, Gray RJ. A Proportional Hazards Model for the Subdistribution of a Competing Risk. J Am Stat Assoc. 1999;94:496–509.CrossRef
23.
go back to reference Pencina MJ, D’Agostino RB. OverallC as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23:2109–23.CrossRef Pencina MJ, D’Agostino RB. OverallC as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med. 2004;23:2109–23.CrossRef
24.
go back to reference Gerds T. Package “Pec”. Prediction error curves for risk prediction models in survival analysis; 2018. Gerds T. Package “Pec”. Prediction error curves for risk prediction models in survival analysis; 2018.
25.
go back to reference Kim DH. Measuring frailty in health care databases for clinical care and research. Ann Geriatr Med Res. 2020;24:62–74.CrossRef Kim DH. Measuring frailty in health care databases for clinical care and research. Ann Geriatr Med Res. 2020;24:62–74.CrossRef
26.
go back to reference Kane AE, Howlett SE. Sex differences in frailty: comparisons between humans and preclinical models. Mech Ageing Dev. 2021;198:111546.CrossRef Kane AE, Howlett SE. Sex differences in frailty: comparisons between humans and preclinical models. Mech Ageing Dev. 2021;198:111546.CrossRef
27.
go back to reference Serra-Prat M, Sist X, Saiz A, et al. Clinical and Functional Characterization of Pre-frailty among Elderly Patients Consulting Primary Care Centres. J Nutr Health Aging. 2016;20:653–8.CrossRef Serra-Prat M, Sist X, Saiz A, et al. Clinical and Functional Characterization of Pre-frailty among Elderly Patients Consulting Primary Care Centres. J Nutr Health Aging. 2016;20:653–8.CrossRef
28.
go back to reference O’Caoimh R, Sezgin D, O’Donovan MR, et al. Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. Age Ageing. 2021;50:96–104.CrossRef O’Caoimh R, Sezgin D, O’Donovan MR, et al. Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. Age Ageing. 2021;50:96–104.CrossRef
29.
go back to reference Pajewski NM, Lenoir K, Wells BJ, et al. Frailty Screening Using the Electronic Health Record Within a Medicare Accountable Care Organization. J Gerontol A Biol Sci Med Sci. 2019;74:1771–7.CrossRef Pajewski NM, Lenoir K, Wells BJ, et al. Frailty Screening Using the Electronic Health Record Within a Medicare Accountable Care Organization. J Gerontol A Biol Sci Med Sci. 2019;74:1771–7.CrossRef
30.
go back to reference Tabue-Teguo M, Kelaiditi E, Demougeot L, et al. Frailty index and mortality in nursing home residents in France: results from the INCUR study. J Am Med Dir Assoc. 2015;16:603–6.CrossRef Tabue-Teguo M, Kelaiditi E, Demougeot L, et al. Frailty index and mortality in nursing home residents in France: results from the INCUR study. J Am Med Dir Assoc. 2015;16:603–6.CrossRef
31.
go back to reference Simo N, Cesari M, Tchiero H, et al. Frailty index, hospital admission and number of days spent in hospital in nursing home residents: results from the incur study. J Nutr Health Aging. 2021;25:155–9.CrossRef Simo N, Cesari M, Tchiero H, et al. Frailty index, hospital admission and number of days spent in hospital in nursing home residents: results from the incur study. J Nutr Health Aging. 2021;25:155–9.CrossRef
32.
go back to reference Hripcsak G, Duke JD, Shah NH, et al. Observational health data sciences and Informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform. 2015;216:574–8.PubMedPubMedCentral Hripcsak G, Duke JD, Shah NH, et al. Observational health data sciences and Informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform. 2015;216:574–8.PubMedPubMedCentral
Metadata
Title
Validation of an electronic frailty index with electronic health records: eFRAGICAP index
Authors
Francesc Orfila
Lucía A. Carrasco-Ribelles
Rosa Abellana
Albert Roso-Llorach
Francisco Cegri
Carlen Reyes
Concepción Violán
Publication date
01-12-2022
Publisher
BioMed Central
Published in
BMC Geriatrics / Issue 1/2022
Electronic ISSN: 1471-2318
DOI
https://doi.org/10.1186/s12877-022-03090-8

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