Development and Validation of an Electronic Frailty Index Using Routine Electronic Health Records: An Observational Study From a General Hospital in China

The Implementation of Frailty Assessment Tools in the Acute Care Setting: A Scoping Review

BACKGROUND

Frailty is a syndrome of increased vulnerability to health stressors that is associated with adverse health outcomes. There is no universally accepted method of measuring frailty, and choosing among the many tools is often confusing for clinicians. Moreover, the acute care setting presents unique challenges to the operationalization of frailty measurement, and implementation into daily clinical practice has been variable. The objective of this scoping review was to map out and synthesize how frailty is being measured and used in the acute care setting.

METHODS

We used Arksey and O'Malley's methodological framework for scoping reviews. We searched MEDLINE, EMBASE, CINAHL, SCOPUS, and Google Scholar for primary studies assessing frailty in the acute care setting from inception to May 2023.

RESULTS

Our search resulted in 8834 articles, of which 2554 met inclusion criteria. Most articles (75%) were published in the last 5 years. The top three most frequently used methods of frailty measurement were the Frailty Index (41.0%), the Clinical Frailty Scale (23.3%), and the Fried Frailty Phenotype (9.3%). More than one frailty assessment tool was used in 11.2% of studies. While 99.6% of studies measured frailty assessment to evaluate the association of frailty with adverse outcomes or the validity of specific frailty tools, only 0.4% measured frailty to prospectively adapt healthcare provision.

CONCLUSION

There is an abundance of evidence demonstrating that frailty in acute care is associated with adverse health outcomes, with relatively scarce evidence on the effect of frailty assessment on prospectively adapting care. Future research focusing on the prospective management of frailty in acute care is needed.

Using Existing Clinical Data to Measure Older Adult Inpatients' Frailty at Admission and Discharge: Hospital Patient Register Study

BACKGROUND

Frailty is a widespread geriatric syndrome among older adults, including hospitalized older inpatients. Some countries use electronic frailty measurement tools to identify frailty at the primary care level, but this method has rarely been investigated during hospitalization in acute care hospitals. An electronic frailty measurement instrument based on population-based hospital electronic health records could effectively detect frailty, frailty-related problems, and complications as well be a clinical alert. Identifying frailty among older adults using existing patient health data would greatly aid the management and support of frailty identification and could provide a valuable public health instrument without additional costs.

OBJECTIVE

We aim to explore a data-driven frailty measurement instrument for older adult inpatients using data routinely collected at hospital admission and discharge.

METHODS

A retrospective electronic patient register study included inpatients aged ≥65 years admitted to and discharged from a public hospital between 2015 and 2017. A dataset of 53,690 hospitalizations was used to customize this data-driven frailty measurement instrument inspired by the Edmonton Frailty Scale developed by Rolfson et al. A 2-step hierarchical cluster procedure was applied to compute e-Frail-CH (Switzerland) scores at hospital admission and discharge. Prevalence, central tendency, comparative, and validation statistics were computed.

RESULTS

Mean patient age at admission was 78.4 (SD 7.9) years, with more women admitted (28,018/53,690, 52.18%) than men (25,672/53,690, 47.81%). Our 2-step hierarchical clustering approach computed 46,743 inputs of hospital admissions and 47,361 for discharges. Clustering solutions scored from 0.5 to 0.8 on a scale from 0 to 1. Patients considered frail comprised 42.02% (n=19,643) of admissions and 48.23% (n=22,845) of discharges. Within e-Frail-CH's 0-12 range, a score ≥6 indicated frailty. We found a statistically significant mean e-Frail-CH score change between hospital admission (5.3, SD 2.6) and discharge (5.75, SD 2.7; P<.001). Sensitivity and specificity cut point values were 0.82 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.85. Comparing the e-Frail-CH instrument to the existing Functional Independence Measure (FIM) instrument, FIM scores indicating severe dependence equated to e-Frail-CH scores of ≥9, with a sensitivity and specificity of 0.97 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.92. There was a strong negative association between e-Frail-CH scores at hospital discharge and FIM scores (rs=-0.844; P<.001).

CONCLUSIONS

An electronic frailty measurement instrument was constructed and validated using patient data routinely collected during hospitalization, especially at admission and discharge. The mean e-Frail-CH score was higher at discharge than at admission. The routine calculation of e-Frail-CH scores during hospitalization could provide very useful clinical alerts on the health trajectories of older adults and help select interventions for preventing or mitigating frailty.

Development and validation of an electronic frailty index in a national health maintenance organization

BACKGROUND

Frailty constitutes a major factor that puts the elderly at risk of health and functional deterioration.

OBJECTIVES

To develop and validate an Electronic Frailty Index based on electronic data routinely collected in the HMO.

STUDY DESIGN AND SETTING

A retrospective cohort of the HMO members.

PARTICIPANTS

120,986 patients, aged 65 years and over at the beginning of 2023.

PREDICTORS

A cumulative frailty index including 36 medical, functional, and social deficits.

OUTCOMES

One-year all-cause mortality or hospitalization.

STATISTICAL ANALYSIS

One-year hazard ratios were estimated for composite outcome of mortality or hospitalization using multivariable hierarchical Cox regression.

RESULTS

The mean EFI score increased with the Social Security Nursing Benefit. Compared to fit patients, mild, moderate, and severe frailty patients had 2.07, 3.35, and 4.4-fold increased risks of mortality or hospitalization, after controlling for covariates.

CONCLUSIONS

The findings showed that the Electronic Frailty Index version we created is valid in predicting mortality or hospitalization. In addition, the Electronic Frailty Index converged with an independent measurement produced by National Social Security.

Prognostic accuracy of 70 individual frailty biomarkers in predicting mortality in the Canadian Longitudinal Study on Aging

The frailty index (FI) uses a deficit accumulation approach to derive a single, comprehensive, and replicable indicator of age-related health status. Yet, many researchers continue to seek a single "frailty biomarker" to facilitate clinical screening. We investigated the prognostic accuracy of 70 individual biomarkers in predicting mortality, comparing each with a composite FI. A total of 29,341 individuals from the comprehensive cohort of the Canadian Longitudinal Study on Aging were included (mean, 59.4 ± 9.9 years; 50.3% female). Twenty-three blood-based biomarkers and 47 test-based biomarkers (e.g., physical, cardiac, cardiology) were examined. Two composite FIs were derived: FI-Blood and FI-Examination. Mortality status was ascertained using provincial vital statistics linkages and contact with next of kin. Areas under the curve were calculated to compare prognostic accuracy across models (i.e., age, sex, biomarker, FI) in predicting mortality. Compared to an age-sex only model, the addition of individual biomarkers demonstrated improved model fit for 24/70 biomarkers (11 blood, 13 test-based). Inclusion of FI-Blood or FI-Examination improved mortality prediction when compared to any of the 70 biomarker-age-sex models. Individual addition of seven biomarkers (walking speed, chair rise, time up and go, pulse, red blood cell distribution width, C-reactive protein, white blood cells) demonstrated an improved fit when added to the age-sex-FI model. FI scores had better mortality risk prediction than any biomarker. Although seven biomarkers demonstrated improved prognostic accuracy when considered alongside an FI score, all biomarkers had worse prognostic accuracy on their own. Rather than a single biomarker test, implementation of routine FI assessment in clinical settings may provide a more accurate and reliable screening tool to identify those at increased risk of adverse outcomes.

New horizons in frailty identification via electronic frailty indices: early implementation lessons from experiences in England and the United States

Frailty represents an integrative prognostic marker of risk that associates with a myriad of age-related adverse outcomes in older adults. As a concept, frailty can help to target scarce resources and identify subgroups of vulnerable older adults that may benefit from interventions or changes in medical management, such as pursing less aggressive glycaemic targets for frail older adults with diabetes. In practice, however, there are several operational challenges to implementing frailty screening outside the confines of geriatric medicine. Electronic frailty indices (eFIs) based on the theory of deficit accumulation, derived from routine data housed in the electronic health record, have emerged as a rapid, feasible and valid approach to screen for frailty at scale. The goal of this paper is to describe the early experience of three diverse groups in developing, implementing and adopting eFIs (The English National Health Service, US Department of Veterans Affairs and Atrium Health-Wake Forest Baptist). These groups span different countries and organisational complexity, using eFIs for both research and clinical care, and represent different levels of progress with clinical implementation. Using an implementation science framework, we describe common elements of successful implementation in these settings and set an agenda for future research and expansion of eFI-informed initiatives.

Identifying Frail Patients by Using Electronic Health Records in Primary Care: Current Status and Future Directions

With the rapidly aging population, frailty, characterized by an increased risk of adverse outcomes, has become a major public health problem globally. Several frailty guidelines or consensuses recommend screening for frailty, especially in primary care settings. However, most of the frailty assessment tools are based on questionnaires or physical examinations, adding to the clinical workload, which is the major obstacle to converting frailty research into clinical practice. Medical data naturally generated by routine clinical work containing frailty indicators are stored in electronic health records (EHRs) (also called electronic health record (EHR) data), which provide resources and possibilities for frailty assessment. We reviewed several frailty assessment tools based on primary care EHRs and summarized the features and novel usage of these tools, as well as challenges and trends. Further research is needed to develop and validate frailty assessment tools based on EHRs in primary care in other parts of the world.

Frailty assessment using routine clinical data: An integrative review

BACKGROUND

Frailty is a common but complex problem in older adults. Frailty assessment using routine clinical data has been suggested as a pragmatic approach based on electronic health records from primary care center or hospital settings.

PURPOSE

We aimed to explore the tools and outcome variables used in the published studies on frailty assessment using routine clinical data.

METHODS

An integrative literature review was conducted using the method of Whittemore and Knafl. A literature search was conducted in PubMed, EMBASE, and CINAHL from January 2010 to October 2021.

RESULTS

A total of 45 studies and thirteen frailty assessment tools were analyzed. The assessment items were generally biased toward frailty's risk factors rather than the mechanisms or phenotypes of frailty. Similar to using conventional tools, routine clinical data-based frailty was associated with adverse health outcomes.

CONCLUSIONS

Frailty assessment based on routine clinical data could efficiently evaluate frailty using electronic health records from primary care centers or hospitals. However, they need refinement to consider the risk factors, mechanisms, and frailty phenotypes.