Frailty is a clinical state resulting from age-related changes in multiple physiologic systems and accumulation of diseases that reduces patient ability to maintain homeostasis in response to stressors.¹⁾ Frailty is common in older adults, affecting one in every 10 community-dwellers^2,3) and one in every two nursing home residents,⁴⁾ and is associated with increased risks of death (relative risk [RR], 1.6–6.0), disability (RR, 1.8–2.8), institutionalization (RR, 2.6–24.0), and falls (RR, 1.2–2.4).¹⁾ Health care costs for older adults with frailty increase by up to 2-fold compared to those in their non-frail counterparts,^5,6) mainly due to inpatient care, post-acute care, and care for potentially preventable conditions.^7,8) Given the considerable clinical and societal consequences of frailty in the ever-growing aging population, assessment of frailty in clinical and population settings offers valuable opportunities for prevention and treatment through efficient use of evidence-based interventions and resources.^9-11)

Several validated tools are available to measure frailty,^12-15) which can be selected based on the purpose (screening, diagnosis, or monitoring response to interventions), setting (emergency department, inpatient, outpatient, or public health), and available resources (trained staff to perform self-report vs. objective assessment).¹⁶⁾ Although simple clinical assessment tools^17-19) and online calculators are available,²⁰⁾ frailty assessment typically requires clinical assessment in the form of a survey^21-23) or objective assessments of physical performance^24-27) conducted by a clinician (e.g., geriatrician) or trained health care professional. However, routine adoption of the frailty concept for clinical care or public health practice is variably slow across health systems in different countries,¹¹⁾ in part due to a lack of time and resources for assessment.^28,29) To overcome these barriers, there is a growing interest in the measurement of frailty using ubiquitous health care databases such as administrative claims data and electronic health records (EHRs), which are by-products of health care encounters and transactions between health care providers and health plans. Administrative claims data contain diagnosis codes, health service codes, and prescription drug data obtained from a large population of health plan members but lack detailed clinical information such as vital signs, physical examination findings, and diagnostic test results. In contrast, EHR provide clinical information not available in administrative claims data; however, much of the information is unstructured (e.g., narrative clinical notes) and may be discontinuous due to patients receiving care at multiple health systems using different EHR systems.³⁰⁾ Nonetheless, frailty scores derived from health care databases (“database-derived frailty measures”) hold promise for population-level frailty screening as well as health services and outcomes research in frail older adults who are under-represented in clinical trials.³¹⁾

This review summarizes the latest advances in frailty measurement in health care databases, mainly administrative claims data and EHR, as well as the potential applications for clinical care and research. Frailty measures requiring in-person surveys or evaluations are beyond the scope of this review. The outline is as follows: (1) literature search; (2) general approaches to frailty measurement in health care data; (3) frailty measurement in administrative claims data; (4) frailty measurement in EHR; (5) considerations in developing a database-derived frailty measure; (6) potential applications of database-derived frailty measures; (7) areas of uncertainty; and (8) conclusions.

A literature search was conducted in PubMed using the Medical Subject Headings, “frailty” AND (“administrative claims, healthcare” OR “electronic health records” OR “Medicare”), and their variations in the title field. Additional filters were applied, including publication date, January 1, 2001, to December 31, 2019, and “aged, 65+ years”. This search yielded 50 articles. Risk scores derived from health care databases that aimed to predict mortality or hospitalization were not considered as frailty measures, although they may also be correlated with frailty.^32,33) From the search results, 10 reviews or commentaries; 9 articles using frailty measures not derived from health care databases; and 8 articles not reporting development, validation, or application of database-derived frailty measures were excluded. The initial search was supplemented by an additional 29 articles from the references of the included articles. Finally, 52 articles informed this review.

Health care databases generated primarily for health care service administration, care quality assessment, and clinical care delivery generally do not contain sufficient information to derive clinically validated measures of frailty.^12-15) Therefore, frailty measures^24-27) requiring clinical assessment (e.g., gait speed, grip strength, physical activity, or cognitive function) cannot be directly calculated. In the absence of sufficient clinical information, researchers attempted to measure frailty using demographic information, diagnosis codes, or health service codes available in health care databases. The approach to developing a frailty measure depended on the availability of a dataset containing a reference standard measure of frailty and methods to select diagnosis and health service codes in health care databases (Fig. 1).

Table 1 summarizes 16 frailty measures for administrative claims data. Of these, 12 measures were developed for the United States Medicare^8,39-48) or Veterans Affairs^49,50) claims databases, including two proprietary measures; namely, the Johns Hopkins Adjusted Clinical Groups Frailty Indicators⁴³⁾ and JEN-Frailty Index,^45,46) two were developed for the Canadian claims databases,^51,52) and two were developed for the United Kingdom hospital claims database.^37,53) Database-derived frailty measures varied widely in terms of development approaches (clinical knowledge in nine measures, cluster analysis in one measure, and reference standard measures in six measures), number of variables included (nine to 109 variables), target populations (general vs. specific disease populations), and validation outcomes (clinical frailty assessment, functional status, mortality, health care utilization, or costs). Only seven of 16 measures have been compared against a clinical frailty assessment^37,54-59) and seven measures have been tested for disability^{50,52,56,57,60)} or nursing home admission.^45,52,57,60)

The comparative performance of database-derived frailty measures has not been well studied. In an analysis of Medicare Current Beneficiary Survey data, implementation of a deficit-accumulation frailty index using commonly used diagnosis codes or health service codes showed lower correlation with a reference standard frailty index and was less predictive of mortality than a frailty measure developed using a least absolute shrinkage and selection operator (LASSO) regression.⁴¹⁾ A recent study compared four Medicare claims-based frailty measures—Davidoff index,⁴⁰⁾ Faurot index,⁴⁸⁾ Segal index,³⁹⁾ and Kim index⁴¹⁾—for the ability to measure frailty phenotype, deficit-accumulation frailty index, and activities-of-daily-living dependency (requiring another person’s help to perform daily activities). Of the four measures, the Kim index showed higher C statistic for frailty phenotype (0.78 vs. 0.73–0.74) after age and sex adjustment, as well as age and sex-adjusted partial correlation with a deficit-accumulation frailty index from clinical assessment (0.55 vs. 0.18–0.32).⁵⁶⁾

These frailty measures have been applied to define population subgroups by frailty levels,⁶¹⁾ reduce confounding by frailty in examining the association between influenza vaccination and mortality,⁶²⁾ estimate health care costs attributed to frailty,⁶³⁾ and improve mortality prediction after transcatheter aortic valve replacement.⁶⁴⁾

Table 2 summarizes five frailty measures for EHR. Four measures were developed for three United States regional EHR systems^65-68) or the Veterans Affairs EHR database,⁶⁹⁾ while the e-Frailty Index was developed for the United Kingdom primary care practices,³⁵⁾ which was later implemented in a primary care EHR system in Australia.⁵⁹⁾ Clinical knowledge-based selection was used for four measures while data-driven selection without a reference standard was used for one measure. A natural language processing method to explore unstructured clinic notes was applied for two measures.^67,69)

Of these measures, the e-Frailty Index has been most widely used in the United Kingdom primary care EHR database to describe frailty trajectories before dying,⁷⁰⁾ examine the effect measure modification of systolic blood pressure and mortality relationship by frailty,⁷¹⁾ predict fractures and mortality after fractures,⁷²⁾ and assess de-intensification for diabetes and hypertension treatment regimens among older adults with frailty.⁷³⁾

Database-derived frailty measures use different types of data (e.g., diagnosis, procedure, and health service codes) collected over a pre-specified period, ranging from 6³⁹⁾ to 36⁴⁹⁾ months. Because some claims datasets record information according to a unique coding system specific to each country (e.g., Current Procedural Terminology codes and Healthcare Common Procedure Coding System codes in the United States and Read codes in the United Kingdom), the choice of datasets can affect the transportability of the frailty measures. The length of the assessment period during which codes are measured may affect the accuracy of capturing certain chronic conditions. Chronic conditions that are less likely recognized or coded by general practitioners (e.g., dementia and incontinence) may require a longer assessment period than acute conditions (e.g., acute myocardial infarction) or well recognized chronic conditions (e.g., hypertension and diabetes). A longer assessment period to calculate a frailty measure reduces the amount of follow-up data available for the main analysis.

Frailty measures developed from health care databases tend to rely on diagnoses, whereas clinical frailty assessment relies more on functional status and physical performance, factors rarely available in health care databases. Health service codes indicating clinical encounter types (e.g., home visits) and use of durable medical equipment (e.g., hospital beds or wheelchairs) seem to be important to capture functional impairment or poor physical performance, which differentiates frailty measures from comorbidity indices.⁵⁷⁾ However, including demographic characteristics in the frailty model lessens its ability to explain variation in frailty beyond demographic variables.⁵⁶⁾

Once a frailty measure is developed, the key step is its validation against a reference standard measure of frailty. Given the lack of consensus on frailty definitions,¹²⁾ prevalent activities-of-daily-living dependency can be used as an alternative outcome for validation.^40,48,56,60) However, information on a reference standard frailty measure or activities-of-daily-living dependency is not always available. Many database-derived frailty measures were tested for mortality prediction rather than for frailty itself. Although frailty is associated with mortality, it is unclear how these frailty measures can be differentiated from mortality prediction models.

Another consideration is that coding systems or coding practices may change over time or vary across geographical regions. In the United States, the International Classification of Disease system transitioned from the 9th to 10th revisions in October 2015. New billing codes are generated for new procedures and health care services and some codes are retired each year. Coding practice may be influenced by the likelihood of reimbursement for health care services, which may differ across health care systems or countries. Therefore, the performance of claims-based frailty measures should be evaluated periodically in more contemporary datasets and before application to a different health care system or country.

Lastly, the development of a frailty measure from EHR may require restricting the population to those with high rates of data completeness within an EHR system to avoid bias due to health information outside the EHR system.³⁰⁾ A predictive algorithm is available to identify those with high rates of completeness.⁷⁴⁾

Frailty measures calculated from health care databases can be useful to measure frailty and study health outcomes of older adults with frailty in clinical care and research (Table 3).

Few studies to date used a database-derived frailty measure as an outcome (i.e., change in frailty level over time) to evaluate the treatment effect. The responsiveness of a frailty measure to improvement or deterioration of health status and the minimal clinically important change have not been well studied. Diagnosis codes, which comprise a large proportion of the database-derived frailty measures, tend to be carried over visits and accumulate over time in administrative claims data or EHR, causing increased frailty score. Since older adults are more likely to seek medical care during acute illness or functional decline (informed presence bias⁷⁸⁾), the estimated frailty level may be affected by the effect of acute illness and frailty progression may be recorded more often than improvement. Furthermore, in health care databases, the information needed to estimate frailty is obtained over time as opposed to clinical trials wherein information is obtained from a discrete assessment visit (e.g., baseline or follow-up visit). Therefore, the assessment periods may overlap between outcome frailty and baseline frailty, making the two measures highly collinear. For these reasons, the utility of a database-derived frailty measure as a treatment outcome remains uncertain.

Information on functional status or physical performance is often recorded in health care databases. In United States Medicare data, the Minimum Data Set records clinicians’ assessments of functional status among nursing home patients. The Outcome and Assessment Information Set contains information on patient outcomes for individuals receiving home care. In EHR, cognitive function and physical function are documented in clinical notes by primary care physicians, specialists (e.g., geriatricians, neurologists, and psychiatrists), physical therapists, or occupational therapists. In the absence of routine assessment, these documentations tend to be inconsistently available or for a subset of patients in specific clinical contexts (e.g., after a fall event, hospitalization, or major surgery), which may not represent an individual’s usual state of health. A recent study by Kharrazi et al.⁷⁹⁾ showed that the prevalence of geriatric syndromes was underestimated when only claims and structured EHR data were analyzed; natural language processing of unstructured EHR data substantially improved detection by 1.5-fold for dementia, 3.2-fold for falls, 18.0-fold for malnutrition, and 455.9-fold for lack of social support. While these findings are promising, the contribution of unstructured EHR data for case identification depends on the health information technology infrastructure and completeness of documentation by health care providers.⁷⁹⁾ How to best combine clinical information with administrative claims data or structured EHR data requires further investigation.

The use of a database-derived frailty measure offers new opportunities to facilitate frailty screening in clinical care and quantify frailty for large population-based database research in which clinical assessment is not feasible. Several database-derived frailty measures have been validated for use in administrative claims data and EHR, with some key differences (Fig. 2): target population (16 ambulatory, 1 long-term care, and 4 inpatient), data source (16 claims-based and 5 EHR-based measures), length of the assessment period (6 to 36 months), data types required for calculation (diagnosis codes required for 17 measures, health service codes for 7 measures, pharmacy data for 4 measures, and other additional information for 9 measures), and outcomes against which a frailty measure was validated (clinical frailty assessment for 7 measures, disability for 7 measures, and mortality for 16 measures). This summary can serve as a guide to choosing a database-derived frailty measure that suits specific objectives and databases at hand.