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Review
. 2020 Jun;24(2):62-74.
doi: 10.4235/agmr.20.0002. Epub 2020 Apr 3.

Measuring Frailty in Health Care Databases for Clinical Care and Research

Dae Hyun Kim  1   2   3   4
Affiliations
Review

Measuring Frailty in Health Care Databases for Clinical Care and Research

Dae Hyun Kim. Ann Geriatr Med Res. 2020 Jun.

Abstract

Considering the increasing burden and serious consequences of frailty in aging populations, there is increasing interest in measuring frailty in health care databases for clinical care and research. This review synthesizes the latest research on the development and application of 21 frailty measures for health care databases. Frailty measures varied widely in terms of target population (16 ambulatory, 1 long-term care, and 4 inpatient), data source (16 claims-based and 5 electronic health records [EHR]-based measures), assessment period (6 months to 36 months), data types (diagnosis codes required for 17 measures, health service codes for 7 measures, pharmacy data for 4 measures, and other information for 9 measures), and outcomes for validation (clinical frailty for 7 measures, disability for 7 measures, and mortality for 16 measures). These frailty measures may be useful to facilitate frailty screening in clinical care and quantify frailty for large database research in which clinical assessment is not feasible.

Keywords: Electronic health records; Frailty; Healthcare administrative claims.

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Conflict of interest statement

CONFLICT OF INTEREST The author provides paid consultative services to Alosa Health, a nonprofit educational organization with no relationship to any drug or device manufacturers.

Figures

Fig. 1.
Fig. 1.
Approaches to developing a frailty measure in health care databases. The literature applied three general approaches to develop a frailty measure for health care databases. When a dataset containing information on a reference standard measure of frailty was not available, frailty was measured using diagnosis and health service codes selected based on clinical knowledge (approach 1) or cluster analysis using diagnosis codes, hospital days, and total costs (approach 2). When a dataset with a reference standard measure of frailty was available, a variable selection method (e.g., penalized regression or machine learning technique) was used to select diagnosis and health service codes to measure frailty (approach 3). EHR, electronic health records.
Fig. 2.
Fig. 2.
Considerations in choosing a database-derived frailty measure. EHR, electronic health records.
See this image and copyright information in PMC

References

    1. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381:752–62. - PMC - PubMed
    1. Collard RM, Boter H, Schoevers RA, Oude Voshaar RC. Prevalence of frailty in community-dwelling older persons: a systematic review. J Am Geriatr Soc. 2012;60:1487–92. - PubMed
    1. Kim KJ, Shin J, Choi J, Won CW. Discrepancies in the prevalence of known frailty scales: Korean Frailty and Aging Cohort Study. Ann Geriatr Med Res. 2018;22:137–44. - PMC - PubMed
    1. Kojima G. Prevalence of frailty in nursing homes: a systematic review and meta-analysis. J Am Med Dir Assoc. 2015;16:940–5. - PubMed
    1. Hajek A, Bock JO, Saum KU, Matschinger H, Brenner H, Holleczek B, et al. Frailty and healthcare costs-longitudinal results of a prospective cohort study. Age Ageing. 2018;47:233–41. - PubMed

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