From Code to Care and Digital Detection of Frailty: A Scoping Review of electronic Frailty Index (eFI) Applications Using Electronic Health Records

Research Article | Vol. 6, Issue 3 | Journal of Clinical Medical Research | Open Access

DOI : https://doi.org/10.46889/JCMR.2025.6301

From Code to Care and Digital Detection of Frailty: A Scoping Review of electronic Frailty Index (eFI) Applications Using Electronic Health Records

Inga Wang^1*, Cameron Lee¹, Syeda Tanzima Shefa¹, Sheng-Che Yen², Hui-Wen Lin³, Xiaoyan Li⁴, Kishor Lakshminarayanan⁵, Chiung-ju (CJ) Liu⁶

¹School of Rehabilitation Sciences and Technology, University of Wisconsin-Milwaukee, Milwaukee, WI, USA
²Department of Physical Therapy, Northeastern University, Boston, MA, USA
³Institute of Medicine, Chung Shan Medical University, Taichung, Taiwan
⁴Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
⁵Department of Sensors and Biomedical Tech, Vellore Institute of Technology, Vellore, Tamil Nadu, India
⁶Department of Occupational Therapy, University of Florida, Gainesville, FL, USA

*Correspondence author: Inga Wang, PhD, OTR/L, School of Rehabilitation Sciences and Technology, University of Wisconsin-Milwaukee, Enderis Hall 955, 2400 E Hartford Ave, Milwaukee, WI 53211, USA; Email: wang52@uwm.edu

Citation: Wang I, et al. From Code to Care and Digital Detection of Frailty: A Scoping Review of electronic Frailty Index (eFI) Applications Using Electronic Health Records. Jour Clin Med Res. 2025;6(3):1-23.

Copyright^© 2025 by Wang I, et al. All rights reserved. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Received
18 August, 2025

Accepted
07 September, 2025

Published
14 September, 2025

Abstract

The electronic Frailty Index (eFI) is a cumulative deficit model that quantifies frailty by summing health-related deficits recorded in a patient’s electronic health record. This scoping review evaluated the emerging applications of eFI models, providing an overview of the current landscape. A comprehensive search of three databases identified peer-reviewed studies published between August 2020 and July 2025. Study characteristics, including purpose, sample characteristics, eFI model details, cut-off thresholds and outcome measures were extracted. A total of 80 studies were included. Most were conducted in the UK (n = 29, 36.2%), followed by the US (n = 23, 28.7%) and Australia (n = 6, 7.5%). The most common purpose was risk stratification (n = 40, 50%), followed by model development and validation (n = 26, 32.5%). The predominant model included 36 deficits (n = 35, 46.1%), followed by 54 deficits (n = 10, 13.2%) and 31 deficits (n = 8, 10.5%). By type, the Clegg-based eFI (36 deficits) was the most frequently used (42.5%), while the Pajewski-based eFI (45-59 deficits) accounted for 21.3% and the Veterans Affairs Frailty Index (VA-FI) or adaptations represented 7.5%. Among cut-off schemes, the four-level classification (Fit: 0-0.12, Mild: >0.12-0.24, Moderate: >0.24-0.36, Severe: >0.36) was most common (24 studies, 30%), followed by the simplified three-level scheme (14 studies, 18%). Mortality was the most frequently evaluated outcome (65%), followed by hospital-related utilization such as admissions, readmissions, length of stay and emergency visits (48%). These findings highlight the widespread adoption of eFI models for risk stratification and validation, with notable variability in deficit selection and model adaptation across settings.

Keywords: Geriatric Assessment; Risk Assessment; Frailty; Electronic Frailty Index; Review

Abbreviations

eFI: electronic Frailty Index; EHR: Electronic Health Record; FI: Frailty Index

Introduction

Frailty or frail status, refers to a state of increased vulnerability to stressors due to aging and the decline of multiple physiological systems [1-3]. It has gained prominence in personalized care management due to its strong association with adverse health outcomes. Patients identified as frail have been shown to be at increased risk of surgical complications, prolonged hospitalization, delayed recovery, disability and mortality [4-7]. In oncology, for instance, frail patients are more susceptible to treatment-related toxicities, tend to experience the side effects of chemotherapy and radiation more severely and may struggle to recover between treatment cycles [8-13].

Early recognition of frailty allows healthcare providers to tailor interventions through frailty-guided clinical management [14-18]. In geriatrics, screening for frailty enables geriatricians to customize treatment plans, minimize polypharmacy and focus on non-pharmacological interventions such as rehabilitation and nutrition. In oncology, assessing frailty helps oncologists determine treatment tolerability, select safer treatment options and plan supportive care to manage potential side effects, significantly improving outcomes and maintaining patients’ quality of life [19,20]. In surgical and anesthetic care, patients identified as frail can receive tailored perioperative care, such as optimized nutrition or physical therapy, to enhance resilience and improve recovery outcomes [21,22]. In rehabilitation, assessing frailty enables therapists to prioritize fall prevention, increased monitoring, energy conservation, fatigue management and the consideration of adaptive equipment and environmental modifications [23,24].

A Frailty Index (FI) is commonly used to assess and classify a patient’s level of frailty, such as categorizing them as robust, pre-frail or frail or into graded levels like robust, mild, moderate or severe frailty. There are three widely recognized approaches to assessing frailty: (1) the frailty phenotype, which emphasizes physical characteristics; (2) the deficit-based model, which estimates frailty based on the proportion of health-related deficits present; and (3) the comprehensive geriatric assessment, a multidimensional and interdisciplinary evaluation of medical, functional and psychosocial domains.

Published in 2001, the Fried Frailty Phenotype [1,25] defines frailty as meeting at least three out of five specific criteria that indicate compromised physical energetics: (1) unintentional weight loss, (2) slowed walking speed, (3) reduced grip strength, (4) low physical activity and (5) physical exhaustion. The Fried Frailty Phenotype is conceptually simple and clinically intuitive, employing standardized performance-based measures (e.g., grip strength and gait speed) that help reduce subjectivity. It is particularly well-suited for identifying individuals at risk of physical decline or functional limitations. However, despite its clinical strengths, the Fried Phenotype requires additional performance-based testing, which limits its feasibility in routine or acute care settings where time, space or equipment may be constrained [26-28].

Introduced in 2007, the Deficit Accumulation Model (Rockwood and Mitnitski Model) [2] defines frailty as a cumulative score based on the presence of health deficits (e.g., typically between 30 and 70 items spanning symptoms, functional limitations, laboratory tests and chronic conditions) [29-31]. Each deficit is scored from 0 (absent) to 1 (present) and the FI is calculated as the total score divided by the number of deficits assessed, yielding a value between 0 and 1, with higher values indicating greater frailty. While this model allows for scalable, data-driven assessment, it requires a large number of variables, which may be difficult to collect consistently across clinical settings. It also weights all deficits equally, regardless of clinical severity. In acute settings, its utility may be limited. For example, a young patient with an aggressive illness may appear non-frail, while someone with multiple minor chronic conditions may be classified as frail despite functional independence [32].

Comprehensive Geriatric Assessment (CGA)-based frailty scores offer a holistic evaluation across multiple domains, including physical health, cognition, functional status, psychological well-being and social support. Over the past decade, several tools have been developed to streamline the CGA process, such as the Clinical Frailty Scale (CFS) , Tilburg Frailty Indicator (TFI), Edmonton Frail Scale (EFS) , Geriatric 8 (G8) Screening Tool , Frailty Index based on Comprehensive Geriatric Assessment (FI-CGA) , Frailty Trait Scale (FTS) and Kihon Checklist (KCL) [33-40]. While CGA-based approaches provide a more comprehensive view of an older adult’s overall health, they come with several limitations: lack of standardization in domain selection, complexity in data collection, time requirements and potential burden on patients. The multidimensional nature of CGA also demands standardized tools and coordinated data collection, which can be difficult to implement consistently in multidisciplinary settings. Moreover, some domains, such as social support and psychological health, often rely on subjective or self-reported data, introducing variability and potential bias in assessment [41].

The electronic Frailty Index (eFI) is grounded in the Deficit Accumulation Model that calculates frailty by identifying and summing up a set of health-related deficits recorded in a patient’s Electronic Health Record (EHR). As a frailty assessment tool, the eFI holds promise for routine clinical use due to its efficiency, low cost, minimal burden, programmability and standardized approach, making it particularly useful for population-level screening and retrospective risk stratification. However, it shares several limitations inherent to deficit-based models. Its accuracy depends on the completeness and reliability of EHR coding. Incomplete data, inconsistent coding, outdated or static diagnoses can lead to misclassification. Similarly, the eFI also lacks direct physical performance measures (e.g., grip strength, gait speed), which are fundamental to the physical construct of frailty. In addition, it does not capture patient-reported outcomes such as fatigue, mood or perceived function and may overlook psychosocial context or clinical judgment, reducing its sensitivity to functional decline and quality of life. Despite these limitations, the eFI has been shown to effectively predict hospitalization and mortality. Furthermore, automating the eFI with routinely collected EHR data remains a promising strategy, leveraging real-time clinical information to support timely and accurate frailty identification.

This scoping review evaluated the emerging applications of eFI models, providing an overview of the current landscape. The specific objectives were to: (a) summarize the characteristics of published studies using the eFI, including study purpose, sample and outcome measures and (b) provide an overview of the current landscape of eFI use, including geographic distribution, purposes of eFI applications, model variations and cut-off thresholds.

Ethical Statement

The project did not meet the definition of human subject research under the purview of the IRB according to federal regulations and therefore, was exempt.

Materials and Methods

Search Strategy

A comprehensive literature search was conducted to identify peer-reviewed studies examining the application of the eFI using EHR. The search strategy was developed and executed collaboratively by two independent reviewers to ensure methodological rigor and comprehensive coverage of the relevant literature. Three electronic databases were searched: PubMed, CINAHL and PsycINFO.

The strategy included title- and abstract-specific searches using the primary term “electronic frailty index” [Title/Abstract] OR “eFI” [Title]. To capture a broader range of studies, a combination of Medical Subject Headings (MeSH) and keyword search terms was employed. The search terms included: “electronic frailty index”[Title/Abstract] OR “frailty index”[Title/Abstract] OR “cumulative deficit model”[Title/Abstract] OR Frailty[Mesh]) AND (“electronic health record”[Title/Abstract] OR EHR[Title/Abstract] OR “electronic medical record”[Title/Abstract] OR “Electronic Health Records”[Mesh]).

Study Eligibility

Titles and abstracts were screened based on the following inclusion criteria: (1) peer-reviewed original research articles; (2) published within the last five years (August 2020-July 2025); (3) written in English; (4) conducted on human subjects; (5) studies that explicitly described an eFI model and its components, elements or items in detail; and (6) utilized EHRs or medical databases for the construction or application of the eFI.

Studies were excluded if they: (1) were non-peer-reviewed publications, such as conference abstracts, letters, editorials, erratum or commentaries; (2) were review articles, meta-analyses or scoping reviews; (3) described software updates, usability or technical features of software, without reporting eFI-related data, analysis or outcomes; or (4) were qualitative mapping or semi-structured interview studies.

Two reviewers independently screened the titles and abstracts for eligibility. Full-text articles were retrieved for studies that met the inclusion criteria or when relevance was unclear. Discrepancies were resolved through discussion or consultation with a third reviewer.

Data Extraction

A standardized data extraction form was developed to systematically capture relevant information from each included study. The following details were collected: (a) first author, year of publication and country of study (or data source location); (b) purpose of the study, (c) purpose of eFI application (e.g., risk stratification, eFI model development and validation, clinical decision support, population surveillance); (d) study sample characteristics, including sample size, care setting; (e) study design (e.g., retrospective cohort, cross-sectional, etc.); (f) eFI model details, including the number and types of deficits included; (g) eFI cut-off thresholds used to classify frailty (e.g., robust, pre-frail, frail); (h) outcomes for which the eFI was applied (e.g., mortality prediction, hospitalization risk, functional decline); and (i) concurrent comorbidity or frailty measures used alongside the eFI (e.g., other comorbidity index).

In this review, each study was assigned to one of four eFI application categories based on its primary objective and use of the eFI. Studies were classified as risk stratification if they used the eFI to categorize individuals into risk groups (e.g., frail vs non-frail) to predict adverse outcomes or to assess how frailty status influences mortality, adverse outcomes or disease progression. eFI model development and validation included studies focused on creating, refining or validating an eFI model using EHR data. Clinical decision support referred to studies that integrated the eFI into clinical workflows to guide care pathway decisions. Population surveillance encompassed studies that applied the eFI to describe the epidemiology, prevalence and progression of frailty at both individual and population levels over time.

Data were independently extracted by two reviewers and cross-verified for consistency. Any discrepancies were discussed and resolved through consensus or consultation with a third reviewer, if necessary.

Data Synthesis

Data were tabulated and summarized using frequency counts and percentages to describe key study characteristics, including year of publication, country, purpose of eFI application, commonly used eFI models and applied cut-off thresholds. Study outcomes were also synthesized and the most frequently used deficits across eFI models were identified.

Results

Search Results

Our literature search identified 125 records, of which 80 (64%) met the study’s inclusion criteria. Most of the excluded studies were those with unclear or insufficient descriptions of the eFI model, while several others used the abbreviation “EFI” to refer to terms different from the intended “electronic frailty index”. Among the 80 included studies, 44 reported the list of eFI deficits/items, either in the main manuscript or in the supplementary materials. Fig. 1 presents the flow diagram of the search strategy.

Year

Table 1 summarizes the included studies in eFI applications. Among the included studies, 5 were published in 2020, 11 in 2021, 20 in 2022, 14 in 2023, 12 in 2024 and 18 in 2025.

Country

Most studies (n = 29, 36.2%) were conducted in the UK, followed by 23 studies (28.7%) from the US and 6 studies (7.5%) from Australia. Together, these three countries accounted for 72.5% of the studies in this scoping review.

Study Design

Almost all studies employed a retrospective cohort design, as the data were derived from EHRs.

Purpose of eFI Application

The most common purpose is risk stratification (n = 40, 50%), followed by eFI model development and validation (n = 26, 32.5%). Population surveillance was reported in 12 studies (15%), while clinical decision support was the least common purpose, with only 2 studies (2.5%).

Number of Deficits

Of the studies included, 76 (95%) reported the number of deficits used in their eFI model, while 4 studies (5%) did not provide this information. The number of deficits varied substantially, ranging from 15 to 80. The most common number of deficits was 36 (reported in 35 studies, 46.1%), followed by 54 deficits (10 studies, 13.2%) and 31 deficits (8 studies, 10.5%). By model type, the Clegg-based eFI (36 deficits) [42] was the most widely used, accounting for 42.5% of studies. The Pajewski-based eFI (variations between 45 and 59 deficits) [43] represented 21.3%, while approximately 7.5% of studies employed the Veterans Affairs Frailty Index (VA-FI) [44] or its adaptations.

eFI Cut-off Thresholds

Among the reported eFI cut-offs, the Clegg, et al., based scheme (Fit: 0-0.12, Mild: >0.12-0.24, Moderate: >0.24-0.36, Severe: >0.36) was the most widely applied, used in approximately 24 studies (30%). The second most common approach was the simplified 3-level scheme (Fit: ≤0.10, Pre-frail: 0.10-0.21, Frail: >0.21), reported in about 14 studies (18%). The third most frequently applied was the expanded 5-level scheme (Non-frail: <0.10, Pre-frail: 0.10-0.20, Mild: 0.20-0.30, Moderate: 0.30-0.40, Severe: >0.40), used in roughly 10 studies (13%).

Outcome Measures

The most evaluated outcome was mortality across varying time frames, reported in 65% of studies. The second most frequent outcome was hospital-related utilization (e.g., length of stay, hospital admissions and readmissions, emergency visits), included in 48% of studies. Institutionalization and care transitions (e.g., transfer to skilled nursing facilities, initiation of long-term care or home care services) were reported in 23% of studies. Finally, disease-specific or functional outcomes (e.g., morbidity, complications, functional measures) were assessed in 10-26% of studies.

Concurrent Comorbidity or Frailty Measures

Few studies collected concurrent comorbidity or frailty measures. The Charlson Comorbidity Index (CCI) [45] was reported in 13 studies (17.1%). The Clinical Frailty Scale (CFS) [46] appeared in 7 studies (9.2%), while the Hospital Frailty Risk Score (HFRS) [47] was reported in 6 studies (7.9%).

Common Deficits in eFI Models

From the 44 included studies, a total of 1,669 individual deficits were extracted. After applying synonym mapping and merging clinically equivalent terms, the 40 most frequently reported deficits were identified. Ranked by frequency of mention, the leading deficits were: lung disease (COPD, asthma, respiratory issues, dyspnea) (40 mentions), hypertension (35 mentioned), diabetes, osteoporosis, atrial fibrillation, arthritis (27 mentioned), hearing impairment (including deafness, hearing problems), heart failure, fall history / risk of falls, peripheral vascular disease, thyroid disease, weight loss (anorexia, weight loss and anorexia) (24 mentioned), dementia / cognitive impairment (20 mentioned), cerebrovascular disease (stroke/TIA), urinary incontinence, coronary artery disease (CHD, MI, ischemic heart disease), visual impairment (blindness, poor vision), depression, peptic ulcer, polypharmacy, chronic kidney disease (18 mentioned), skin ulcer, activity limitation, anemia, cancer / malignancy, anxiety, sleep disturbance / insomnia, dizziness, urinary system disease (general), chronic pain (14 mentioned), fragility fracture, housebound status, social vulnerability, heart valve disease, hypotension, requirement for care (dependency), Parkinson’s disease, foot problems and fatigue (10 mentioned).

Author (Year)	Country	*Objective (Purpose of eFI application)**	Sample	eFI model (adapted from)	eFI Cut-offs	Outcome Measures	Comorbidity / Frailty Measures (Concurrent validation measures)
Broad (2020) [51]	UK	To examine the convergent validity of the eFI with the CFS (b)	Community dwelling people 65 years or over (n = 265)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild frailty: > 0.12 to 0.24 Moderate frailty > 0.24 to 0.36 Severe frailty: >0.36	n/a	CFS
Coventry (2020) [52]	UK	To assess whether depression, alone or in combination with frailty, contributes to loss of independence in instrumental ADLs among older adults (a)	The Community Ageing Research 75+ Study (CARE75+) (n = 553)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: 0.13 to 0.24 Moderate: 0.25 to 0.36 Severely frail: >0.36	1. Nottingham Extended ADL (NEADL) scale 2. Geriatric Depression Scale (GDS)	n/a
Masoli (2020) [53]	UK	To estimate associations between baseline BP and mortality/cardiovascular outcomes in a primary-care population aged above 75, stratified by frailty (a)	Patients aged above 75 (n = 415,980)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36	1. All-cause mortality 2. Incident cardiovascular events:	n/a
Ravensbergen (2020) [54]	NL	To evaluate whether an eFI can be used as a measure for daily functioning in research with community-dwelling older persons (b)	Community-dwelling older persons aged ≥75 years (n = 11,476)	50-deficits (eFI-U; Drubbel, 2013)	n/a	1. eFI-Utrecht (eFI-U) 2. GARS	n/a
Stewart (2020) [55]	US	To create a novel screening tool that identified patients who were most likely to benefit from pharmacist in-home medication reviews (b)	Patients aged 60 years or older with physical or cognitive impairments and enrolled in home-based primary care or transitional and supportive care (n = 25)	59-deficits (Pajewski, 2019)	Non-frail: < 0.1 Prefrail: > 0.10 to ≤ 0.21 Frail: > 0.21	1. Age, gender 2. eFI score 3. LACE+ index score 4. Number of Beers Criteria medications	CCI
Callahan (2021) [56]	US	To evaluate the association of an eFI with post-operative outcomes for non-emergency surgeries (a)	Patients aged 65 or more, who underwent non-emergency surgery (n = 4,831)	54-deficits (Pajewski, 2019)	Fit: ≤ 0.10 Pre-frail: 0.10 to ≤ 0.21 Frail: > 0.21	1. Inpatient length of stay 2. Discharge destination 3. 30-day readmission 4. 6-month all-cause mortality	CCI
Cheng (2021) [57]	US	To update the VA-FI (VA-FI-9) using ICD-10 codes (VA-FI-10) and assess whether VA-FI-10 maintains stability in measuring prevalence of frailty after the transition to ICD-10 (b)	Veterans aged 65 years and older	31-deficits (VA-FI)	Non-frail: ≤ 0.1 Prefrail: >0.1 to 0.2 Mildly frail: >0.2 to 0.3 Moderately frail: >0.3 to 0.4 Severely frail: >0.4	n/a	n/a
Gagesch (2021) [58]	CH	To develop an eFI in geriatric patients (b)	Patients within Swiss Frailty Network and Repository (SFNR)	55-deficits	n/a	1. Clinical Frailty Instrument (cFI) based on the Fried phenotype	n/a
Hollinghurst (2021) [59]	UK	To compare two different tools (eFI vs HFRS) for identifying frailty (b)	Patients aged 65+ who were admitted as an emergency (n = 126,600)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mildly frail: >0.12 to 0.24 Moderately frail: >0.24 to 0.36 Severely frail: >0.36	1. Mortality 2. Hospital resource use 3. Care home admissions	HFRS
Ju (2021) [60]	HK	To develop a new eFI model using machine learning (gradient boosting) (b)	Patients with heart failure (n = 8,893)	16-deficits (new eFI model)	n/a	1. 30-day mortality 2. 90-day mortality	n/a
Liang (2021) [61]	China	To develop a new eFI model (b)	Patients ≥65 years old (n = 49,226)	45-deficits (Pajewski, 2019)	Non-frail: 0 to 0.14 Frail: > 0.15	1. Hospital stay 2. Hospital death 3. Hospitalized costs	CGA
Lo (2021) [62]	AU	To develop a new eFI model (b)	Inpatients ≥ 70 years (development cohort n = 151, validation cohort n = 999)	39-deficits (eFI-AH)	Non-frail: < 0.25 Frail: > 0.25	n/a	REFS
Pugh (2021) [63]	UK	To study trends in patient characteristics for adult critical care admissions and potential effects of resource constraints on admission patterns and processes of care in Wales (a)	Patients in Wales Critical Care (n = 85,629)	n/a (Clegg, 2016)	Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36	1. Planned and unplanned admission 2. Comorbidity 3. Advanced respiratory support 4. Advanced cardiovascular support 5. Mortality	n/a
Szakmany (2021) [64]	UK	To examine frailty indices derived from population-scale linked data on ICU and hospitalised non-ICU patients with pneumonia to elucidate the influence of frailty on mortality (a)	Patients with a diagnosis of pneumonia or flu (n = 133,604)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36	1. In-patient mortality 2. 6-month mortality 3. 1-year mortality	HFRS CCI
Shen (2021) [65]	CN	To assess whether the eFI is associated with mortality and chemotherapy adverse reactions (a)	Patients with lung cancer (n = 1,263)	35-deficits (Searle, 2008)	Robust/Non-frail: 0 to < 0.2 Frail: ≥ 0.20	1. Overall Survival 2. Infection, bone suppression, neutropenia, thrombocytopenia, decreased hemoglobin, chemotherapy discontinuation, impaired liver function, gastrointestinal reactions 3. Length of hospitalization	n/a
Wilkinson (2021) [66]	UK	To assess frailty and atrial fibrillation (AF) prevalence in adults ≥65, evaluate oral anticoagulation (OAC) use and analyze AF outcomes by frailty level (d)	Patients aged ≥65 years (n = 536,955)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36	1. All-cause mortality and first stroke 2. First gastrointestinal or intracranial bleed, fall and TIA	n/a
Cheng (2022) [67]	US	To measure frailty and assess its prognostic value by applying an eFI to older patients with NSCLC in the Veterans Affairs (VA) health-care system (a)	Patients ≥65 years old with non-small cell lung cancer (NSCLC) (n = 42,204)	31-deficits (VA-FI)	Non-frail: 0 to 0.1 Pre-frail: 0.1 to 0.2 Mildly frail: 0.2 to 0.3 Moderate-to-severely frail: >0.3	1. All-cause mortality 2. Acute hospitalizations 3. ER visits	n/a
Cook (2022) [68]	UK	To determine the impact of frailty on patient-reported outcomes following hip and knee arthroplasty (a)	Patients who had a hip arthroplasty (n = 42,512) and who had a knee arthroplasty (n = 49,208)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mildly frail: >0.12 to 0.24 Moderately frail: >0.24 to 0.36 Severely frail: >0.36	1. The Oxford hip and knee scores (12-item questionnaire)	n/a
Fogg (2022) [69]	UK	To describe the structure of the dataset, cohort characteristics and planned analyses (d)	Patients aged ≥50 years (n = 2,177,656)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mildly frail: 0.13 to 0.24 Moderately frail: 0.25 to 0.36 Severely frail: >0.36	1. Prevalence of long-term conditions 2. Prevalence of frailty deficits	n/a
Hickson (2022) [70]	UK	To evaluate the benefits of a model of care in which a dietitian provided care to patients at risk of malnutrition and frailty (c)	Patients aged ≥50 years, eFI 0.26 to 0.36 or BMI < 19 kgm-2 (n = 189)	36-deficits	Fit: 0 to 0.12 Mildly frail: >0.12 to 0.24 Moderately frail: >0.24 to 0.36 Severely frail: >0.36	1. Frailty status 2. Nutritional status 3. Anthropometric measures 4. Dietary aims 5. Cost savings 6. Patient satisfaction	n/a
Hollinghurst (2022) [71]	UK	To investigate associations between dysphagia and age, frailty, gender and deprivation (a)	Individuals aged 65+ (n = 400,000)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mildly frail: >0.12 to 0.24 Moderately frail: >0.24 to 0.36 Severely frail: >0.36	1. Dysphagia	n/a
Ibrahim (2022) [72]	UK	To evaluate the feasibility of assessing sarcopenia and frailty among older people attending fracture clinics (d)	Patients aged ≥65 years with a single wrist or upper arm fracture	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: 0.13 to 0.24 Moderate: 0.25 to 0.36 Severely frail: >0.36	1. Falls and fractures 2. CGA 3. Acceptability of the assessments	FFP FRAIL Scale SOF CFS PRISMA-7
Le (2022) [73]	US	To compare 4 electronic frailty metrics within a surgical population (a)	Patients who underwent abdominal surgical procedures (n = 37,186)	38-deficits (FI-CSHA model, Howlett, 2014)	n/a	1. 30-day postoperative morbidity 2. 30-day major postoperative complications	HFRS mFI-5 RAI
Lewis (2022) [74]	AU	To examine the feasibility of deriving an eFI and to describe the prevalence of frailty (d)	Australian Medicine Insight data platform (n = 79,251)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild frailty: > 0.12 to 0.24 Moderate frailty > 0.24 to 0.36 Severe frailty: >0.36	n/a	n/a
Mak (2022) [75]	SE	To develop an eFI (b)	Patients with unplanned admissions (n = 18,225)	48-deficits (Pajewski, 2019)	Fit: ≤ 0.15 Mild frailty: 0.16 to 0.2 Moderate frailty: 0.21 to 0.25 Severe frailty: > 0.25	1. In-hospital, 30-day and 6-month all-cause mortality 2. 30-day readmission to any of the 9 included clinics 3. Length of stay	CCI CFS HFRS
Nishimura (2022) [76]	JP	To assess the applicability of eFI and to evaluate their association with long-term outcomes (b)	Participants aged ≥50 years (n = 827,744)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mildly frail: >0.12 to 0.24 Moderately frail: >0.24 to 0.36 Severely frail: >0.36	1. All-cause mortality 2. Use of government-supported long-term care (LTC)	n/a
Oates (2022) [27]	AU	To describe how an AI optimization technique called partial genetic algorithms can be used to refine the subset of features used to calculate an FI and favor features that have the least cost of acquisition (b)	Residents aged 75 years and over (n = 592)	35-deficits (Clegg, 2016)	Frail: > 0.21	n/a	n/a
Orfila (2022) [77]	ES	To develop an eFI (eFRAGICAP) (b)	Individuals from the Information System for the Development of Research in Primary Care (SIDIAP) database (n = 253,684)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild frailty: > 0.12 to 0.24 Moderate frailty > 0.24 to 0.36 Severe frailty: >0.36	1. All-cause mortality 2. Institutionalization 3. Inclusion in a homecare program	CFS RISC
Sarwar (2022) [78]	AU	To develop a new eFI model (b)	Residents in the residential aged care homes (n = 2,588)	32-deficits (reFI model)	Fit: ≤ 0.13 Mildly frail: 0.13 to ≤ 0.26 Moderately frail: 0.26 to ≤ 0.39 Severely frail: > 0.39	1. 1, 3 and 5 years mortality	n/a
Semelka (2022) [79]	US	To examine the association between frailty and COVID-19 mRNA vaccine antibody response (a)	Adults aged 55 and older with self-reported Moderna or Pfizer COVID-19 mRNA vaccine (n = 1,677)	54-deficits (Pajewski, 2019)	Non-frail: < 0.1 Prefrail: > 0.10 to ≤ 0.21 Frail: > 0.21	1. Odds of nonseroconversion 2. Odds of seroreversion	n/a
Shrauner (2022) [80]	US	To assess the association between eFI and risk of cardiovascular mortality and risk of atherosclerotic CVD (ASCVD) events (a)	Veterans aged ≥65 years (n = 3,068,439)	31-deficits	Not frail: < 0.1 Pre-frail: > 0.1 to < 0.2 Mildly frail: > 0.2 to < 0.3 Moderately frail: > 0.3 to < 0.4 Severely frail: > 0.4	1. Cardiovascular mortality 2. Myocardial infarction/fatal myocardial infarction, stroke/fatal stroke and cardiovascular events	n/a
Stow (2022) [81]	UK	To study the association between area-level, multi-domain deprivation and frailty trajectories in the last year of life and over 1 year in a matched non-end-of-life sample (d)	People age ≥ 75 at end of life (n = 13,149) and age-, sex- and practice-matched individuals not at end of life (n = 13,149)	36-deficits (Clegg, 2016)	n/a	n/a	n/a
Summerfield (2022) [82]	UK	To study the association between old age and frailty on the time to cancer diagnosis (a)	Cancer patients (n = 7,460)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36	1. Time to diagnosis	n/a
Wilkinson (2022) [83]	UK	To study the association between oral anticoagulation (OAC) and outcomes for people with frailty and whether there is overall net benefit from treatment in people with atrial fibrillation (AF) (a)	Patients with a diagnosis of non-valvular AF (n = 89,996)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36	1. All-cause mortality 2. Stroke 3. Severe bleeding 4. TIA 5. Fall	n/a
Wilkinson (2022) [84]	UK	To study the risk of frailty development in CKD and the impact on mortality and end-stage kidney disease (ESKD) (a)	Patients aged ≥40 years (n = 819,893)	36-deficits (Clegg, 2016)	Non-frail: ≤ 4 deﬁcits Mild frailty: 5-8 deﬁcits Moder-ate frailty: 9-12 deﬁcits Severe frailty: ≥13 deﬁcits		n/a
Zhou (2022) [85]	HK	To develop a scalable predictive model in the form of an eFI (b)	Patients with pulmonary hypertension (PHTN) (n = 2,560)	15-deficits (new eFI model)	n/a	1. Complications: Development of cardiovascular and renal complications, diabetes mellitus after diagnosis of PHTN 2. Mortality after diagnosis of PHTN	n/a
Cheong (2023) [86]	UK	To (i) investigate the prevalence of anticholinergic prescribing for older patients and (ii) examine anticholinergic burden according to frailty status (d)	Patients aged ≥65 (n = 529,095)	36-deficits (Clegg, 2016)	Fit: 0 to <0.12 Mildly frail: >0.12 to < 0.24 Moderately frail: >0.24 to <0.36 Severely frail: >0.36	1. Measurement of anticholinergic burden, Anticholinergic Cognitive Burden (ACB) scale	n/a
Fujita (2023) [87]	AU	To compare characteristics and predictive ability between the electronic frailty index-acute hospital (eFI-AH) with the hospital frailty risk score (HFRS) (a)	Patients aged ≥75 years admitted to an Australian metropolitan tertiary referral hospital (n = 6,771)	37-deficits (eFI-AH)	Robust: < 0.05 Prefrail: 0.05 to < 0.25 Frail: > 0.25	1. In-hospital mortality 2. Long hospital stay (>10 days) 3. All-cause readmission 4. Fall-related readmission within 28 days from hospital discharge	HFRS
Cheng (2023) [88]	US	To describe the distribution of frailty status at pre-treatment by therapy type (b)	Older adults with acute myeloid leukemia (n = 166)	54-deficits (Pajewski, 2019)	Fit: < 0.10 Prefrail: > 0.10 to < 0.21 Frail: > 0.21	1. Overall survival	HCT-CI
Chao (2023) [89]	US	To examine the current health status (d)	Gulf War (GW) veterans (n = 703)	31-deficits (VA-FI)	Non-frail: 0 to 0.1 Pre-frail: 0.1 to 0.2 Mildly frail: 0.2 to 0.3 Moderately frail: 0.3 to 0.4 Severely frail: > 0.4	1. Kansas Gulf War and Health Questionnaire	n/a
Dulai (2023) [90]	UK	To evaluate the association between the validated NHS eFI and outcomes post-AF ablation (a)	Patients who had undergone atrial fibrillation (AF) ablation and aged ≥ 65 (n = 248)	36-deficits (Clegg, 2013)	n/a	1. Time to atrial arrhythmia 2. Recurrence of an atrial arrhythmia	n/a
DuMontier (2023) [91]	US	To assess the construct validity of the VA-FI (b)	Veterans (n = 132)	31-deficits (VA-FI)	Non-frail: ≤ 0.2 Mild-moderately frail: >0.2 to 0.4 Severely frail: > 0.4	n/a	CGA CFS CCI
Elhussein (2023) [92]	UK	To identify and characterize older people with complex health needs based on healthcare resource use or frailty using large population-based linked records (d)	People aged > 65 Hospitalization cohort (n = 90,597); Frailty cohort (n = 110,225); Polypharmacy cohort (n = 116,076)	36-deficits (Clegg, 2016)	n/a	1. Use of oral bisphosphonates, statins and anti-hypertensives (1-month, 3-month and 1-year point prevalence) 2. Prescriptions of preventive treatments 3. Treatment episodes	n/a
Khanna (2023) [93]	US	To examine the association of frailty with adverse events after elective noncardiac surgery (a)	Patients who underwent noncardiac surgical procedures (n = 50,456)	54-deficits (Pajewski, 2019)	Fit: < 0.10 Prefrail: > 0.10 to < 0.21 Frail: > 0.21	1. 30-day mortality 2. 30-day readmission 3. 30-day ED visit after surgery 4. Unexpected ICU admission after surgery 5. PSI 90 6. HAC CMS 7. Other hospital morbidity 8. Transfer to SNF after surgery	CCI
La (2023) [94]	US	To use the VA-FI to measure the prevalence of frailty in veterans and to determine whether frail patients have inferior survival compared to non-frail veterans (a)	Veterans with acute myeloid leukemia (AML) (n = 1,166)	31-deficits (VA-FI)	Non-frail: 0 to 0.1 Pre-frail: 0.1 to 0.2 Mildly frail: 0.2 to 0.3 Moderate-to-severely frail: >0.3	1. Overall Survival	n/a
Lin (2023) [95]	TW	To investigate the predictive utility of an eFI in community elders (a)	Participants aged 65-80 years (n = 427)	80-deficits	Low risk: eFI ≤ the median value High risk: eFI > the median value	1. Falls 2. ER visits 3. Hospitalizations within 24 months	n/a
Nishimura (2023) [50]	JP	To assess whether frailty at the time of oral anticoagulant (OAC) initiation is associated with subsequent bleeding or embolic events (a)	Patients aged ≥65 years with nonvalvular atrial fibrillation (AF) (n = 12,585)	35-deficits	Fit: 0 to 0.12 Mildly frail: >0.12 to 0.24 Moderately frail: >0.24 to 0.36 Severely frail: >0.36	1. Bleeding events 2. Embolic events	n/a
Mak (2023) [96]	SE	To examine the association of frailty with mortality and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures (a)	COVID-19 patients (n = 3,980)	48-deficits (Mak 2022 eFI model)	Fit: ≤0.15 Mild frailty: >0.15 to 0.2 Moderate frailty: >0.2 to 0.25 Severe frailty: >0.25	1. In-hospital mortality 2. 30-day mortality 3. 6-month mortality 4. 30-day readmission 5. Length of hospital stay	CCI CFS HFRS
Stutsrim (2023) [97]	US	To examine the association of an automated eFI with outcomes (a)	Patients undergoing open, lower extremity vascular surgery (n = 295)	54-deficits (Pajewski, 2019)	Fit: < 0.10 Prefrail: > 0.10 to < 0.21 Frail: > 0.21	1. Length of stay 2. 30-day readmission rate 3. Discharge destination 4. 1-year all-cause mortality	CCI
Walsh (2023) [98]	UK	To explore and predict trends in onset, prevalence and progression of frailty and the dynamics of frailty-related healthcare demand, outcomes and costs in the ageing population (d)	Adults aged ≥50 (n = 2,171,497)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: 0.13 to 0.24 Moderate: 0.25 to 0.36 Severe: >0.36	1. eFI score	n/a
Archer (2024) [99]	UK	To develop and externally validate the eFalls prediction model using routinely collected primary care electronic health records (EHR)	Patients aged ≥65 years (n = 660,417)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36	1. Any (one or more) ED attendance or hospital admission for a fall or fracture	n/a
Bunch (2024) [100]	US	To determine whether CT-derived measurements of muscle and adipose tissue are associated with frailty (a)	Patients aged 55 years and older (n = 886)	54-deficits (Pajewski, 2019)	Non-frail: < 0.1 Prefrail: > 0.10 to ≤ 0.21 Frail: > 0.21	1. CT image L3 cross-sectional area	n/a
Fogg (2024) [101]	UK	To describe primary and secondary care service use and associated costs by eFI (a)	Patients aged ≥50 (n = 2,171,497)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: 0.13 to 0.24 Moderate: 0.25 to 0.36 Severely frail: >0.36	1. Primary care service use 2. Secondary care service use 3. Cost	n/a
Georgiev (2024) [102]	UK	To evaluate the utility of machine learning models developed using comprehensive linked routine primary and secondary care data to predict future dementia diagnosis (a)	Patients (n = 144,113)	35-deficits (Clegg, 2016)	n/a	1. Dementia code in either primary, secondary or death 2. All-cause mortality	n/a
Kong (2024) [103]	AU	To evaluate the comparability of frailty assessment tools (eFI, retrospective eFI (reFI) and CFS) in older residents of care facilities (b)	Individuals aged 65 or older (n = 813)	36-deficits (Clegg 2016) and 32-deficits Retrospective eFI (reFI) (Sarwar, 2022)	eFI Fit: 0 to 0.12 Mildly frail: >0.12 to 0.24 Moderately frail: >0.24 to 0.36 Severely frail: >0.36 reFI Fit: 0 to 0.13 Mildly frail: >0.13 to 0.26 Moderately frail: >0.26 to 0.39 Severely frail: >0.39	1. 32-deficits Retrospective eFI (reFI) (Sarwar 2022)	CFS
Lenoir (2024) [104]	US	To examine the joint association of these factors on acute healthcare utilization using two pragmatic measures (a)	Patients aged ≥ 65 years (n =47,566)	n/a (Pajewski, 2019)	Non-frail: < 0.1 Prefrail: > 0.10 to ≤ 0.21 Frail: > 0.21	1. Acute healthcare utilization 2. ED visits, observation stays and inpatient visits over one year of follow-up 3. Mortality	n/a
Lowry (2024) [105]	UK	To assess the relationship between the eFI and outcomes in older patients hospitalized with acute myocardial infarction (a)	Patients with cardiovascular disease (n = 4,670)	36-deficits (Clegg, 2016)	Frail: ≥0.12 Mild frailty: 0.12-0.24 Moderate frailty: 0.25-0.36 Severe frailty: >0.36	1. All-cause mortality at 12 months 2. In-patient all-cause mortality 3. Hospital admission due to non-fatal myocardial condition 4. Cardiovascular death at 12 months 5. All-cause mortality at 3-years	CCI
Short (2024) [106]	UK	To examine the effect of frailty on mortality and other relevant outcomes (a)	Patients diagnosed with pleural disease (n = 54,566)	36-deficits (Clegg 2016)	Fit: 0 to 0.12 Mildly frail: >0.12 to 0.24 Moderately frail: >0.24 to 0.36 Severely frail: >0.36	1. Time to all-cause mortality 2. Time to pleural disease-related mortality 3. Time to first all-cause 4. Hospital admission after diagnosis; 5. Time to pleural disease related hospital admission 6. Re-admission within 90 day	CCI
Nakamaru (2024) [49]	JP	To assess the trajectory of frailty and the course of frailty progression to clinical outcomes of older patients with atrial fibrillation (AF) (c)	Residents aged <75 years (n = 6,247)	35-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mildly frail: >0.12 to 0.24 Moderately frail: >0.24 to 0.36 Severely frail: >0.36	1. Death 2. Embolism 3. Major bleeding	n/a
Sikron (2024) [107]	IL	To develop a new eFI model (Meuhedet Electronic Frailty Index [MEFI]) (b)	Patients of Meuhedet HMO aged 65 years and over (n = 20,986)	36-deficits (MEFI model)	n/a	1. One-year all-cause mortality 2. Hospitalization	CCI
Solsky (2024) [108]	US	To examine the Impact of frailty on adjuvant therapy (a)	Patients with breast cancer >65 years undergoing surgery (n = 133)	54-deficits (Pajewski, 2019)	Fit: ≤ .10 Pre-frail: > 0.10 to ≤.21 Frail: > 0.21	n/a	n/a
Wu (2024) [109]	US	To study the association between frailty and treatment selection (a)	Patients with advanced non-small cell lung cancer (NSCLC) (n = 1,547)	31-deficits (VA-FI)	Non-frail: ≤ 0.2 Frail: >0.2	1. Receipt of intensive over non-intensive first-line treatment 2. Overall survival	n/a
Almatrafi (2025) [110]	UK	To describe the prevalence of frailty and comorbidity in those invited for lung cancer screening (LCS) and evaluate their associations with response to telephone risk assessment invitation and subsequent uptake of LCS	Patients randomized to the intervention arm of the Yorkshire Lung Screening Trial (YLST) (n = 27,761)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36	1. Response to the invitation for telephone lung cancer risk assessment 2. Uptake of low-dose computed tomography (LDCT) screening	n/a
Auyeung (2025) [111]	HK	To develop a new eFI model (HK eFI) (b)	Hong Kong Hospital Authority (HA) database (2015, 1.14 million; 2016, 1.19 million; 2017,1.25 million; 2018, 1.31 million; 2019, 1.38 million)	38-deficits	Fit: ≤ 0.125 Mild frailty: > 0.125 to 0.25 Moderate frailty: > 0.25 to 0.375 Severe frailty: >0.375	1. One-year mortality 2. Two-year mortality 3. Three-year mortality 4. Unplanned readmission	n/a
Best (2025) [112]	UK	To valid a previously developed eFI version 2 (eFI2) model (b)	Patients ≥65 years old (n = 660,417)	36-deficits (eFI2 model)	n/a	1. Hospitalization with fall or fragility fracture 2. Care home admission 3. All-cause mortality 4. New home care package	n/a
Duy (2025) [113]	US	To investigate whether an eFI is associated with visual field loss in glaucoma (a)	Subjects ≥65 years old with glaucoma (n = 1,163)	54-deficits (Pajewski, 2019)	Fit: ≤ 0.10 Pre-frail: 0.10 to ≤ 0.21 Frail: > 0.21	Baseline Mean Deviation (MD) Change in Mean Deviation (MD)	n/a
Duy (2025) [114]	US	To compare the association of glaucoma and glaucoma suspect diagnoses with eFI (a)	Adults ≥65 years old with glaucoma (n = 4,147)	54-deficits (Pajewski, 2019)	Fit: ≤ 0.10 Pre-frail: 0.10 to ≤ 0.21 Frail: > 0.21	n/a	n/a
Fogg (2025) [115]	UK	To estimate frailty prevalence and severity among patients conveyed to hospital by ambulance, assess changes over time and identify key patient and incident characteristics to guide recommendations (d)	People aged ≥50 living in Wales (n = 1,264,094)	36-deficits (Clegg, 2016)	Fit: 0 to < 0.12 Mild: 0.12 to < 0.24 Moderate: >0.24 to < 0.36 Severely frail: >0.36	n/a	n/a
Kayo (2025) [116]	JP	To assess whether the eFI and VA-FI can be applied to administrative databases in Japan and evaluate the association between frailty and health outcomes (a)	Patients with cancer aged ≥65 years (n =5,176)	36-deficits (Clegg, 2016) and 31-deficits (VA-FI)	eFI (Clegg, 2016) Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36 VA-FI Non-frail: 0 to < 0.1 Pre-frail: > 0.1 to 0.2 Mildly frail: > 0.2 to 0.3 Moderately frail: > 0.3 to 0.4 Severely frail: > 0.4	1. All-cause mortality 2. The first observation of a decline in care-need level or all-cause mortality, 3. The first observation of a decline in care-need level, the first observation of long-term care facility (LTCF) admission or all-cause mortality	n/a
Kochar (2025) [117]	US	To develop a new eFI model (MGB-eFI) (b)	Patients who were ≥ 60 years (n = 518,449)	31-deficits (MGB-eFI model)	Robust: < 0.1 Prefrail: 0.1 to 0.2 Frail: 0.2 to 0.3 Very frail: > 0.3	1. Time-to-mortality 2. Time to subsequent acute care visits 3. Hospital re-admissions within 90 days	n/a
Li (2025) [118]	CN	To develop a new eFI model (b)	Participants aged 65 or older (n = 28,144)	30-deficits (new eFI model)	Robust: ≤ 0.10 Prefrail: > 0.10 to <0.25 Frail: ≥ 0.25	1. Hospitalization 2. Mortality	n/a
Namugosa (2025) [119]	US	To assess whether the eFI can serve as a proxy for frailty by comparing it with gait speed in older women with moderate-to-severe UI (b)	Patients aged 70 years and older, seeking treatment for urinary incontinence (UI)	36-deficits (Clegg, 2016)	Fit: 0 to 0.12 Mild: >0.12 to 0.24 Moderate: >0.24 to 0.36 Severely frail: >0.36	1. Gait speed	n/a
Ruiz-Beltrán (2025) [120]	MX	To evaluate the predictive ability of traditional risk scores and frailty assessment for 30-day mortality in elderly patients undergoing cardiac surgery (a)	Patients aged ≥75 years old who underwent cardiac surgery (n = 203)	n/a (Clegg, 2016)	n/a	1. 30-day mortality	CCI
Shiffer (2025) [121]	IT	To develop a new eFI model and to evaluate its prediction of outcomes among geriatric patients presenting to the emergency department (ED) (b)	Patients ≥65 years old (n =21,537)	45-deficits (new eFI model)	Non-frail: < 0.124 Mildly frail: 0.125 to 0.159 Moderately frail: 0.159 to 0.204 Severely frail: > 0.205	1. Hospitalization rates 2. In-hospital mortality 3. ICU admission 4. 30-day ED readmission	n/a
Sikron (2025) [122]	IL	To describe the dynamic trajectory of frailty and determine what deteriorates first (d)	Meuhedet members aged 65 years and over (n = 119,952)	36-deficits (Meuhedet Electronic Frailty Index [MEFI])	Fit: 0 to 0.12 Mildly frail: 0.13 to 0.24 Moderately frail: 0.25 to 0.36 Severely frail: >0.36	Frailty status	CCI
Tu (2025) [123]	KR	To evaluate the prognostic value of frailty measures (FMs) for all-cause mortality in older patients with NSCLC (a)	Patients with non-small cell lung cancer (NSCLC) aged ≥65 years (n = 4,799)	28-deficits (Clegg, 2016)	Non-frail: < 0.04 Frail: >0.04	1. 1-year mortality 2. 3-year mortality 3. 5-year mortality	27-item FI-Lab 40-item FI-combined
Velez (2025) [124]	US	To examine whether patients with diabetic retinopathy who are frail have higher rates of loss to follow-up compared with less-frail patients (a)	Patients aged 55 years or older (n = 249)	50+ deficits (Pajewski, 2019)	Fit: < 0.1 Prefrail: > 0.10 to ≤ 0.21 Frail: > 0.21	1. Loss to follow-up (LTFU)	n/a
Wunnava (2025) [125]	UK	To improve the understanding whether there are differences in clinical outcomes between vascular patients with chronic limb-threatening ischemia (CLTI) age over and under 80 (a)	Patients with CLTI (n = 183)	36-deficits (NHS England)	Fit: 0 to 0.12 Mild: 0.13 to 0.24 Moderate: 0.25 to 0.36 Severely frail: >0.36	1. Overall survival 2. Rates of major adverse limb (MALE) 3. Rates of cardiovascular events (MACE) 4. Complication rates 5. 30-day reintervention 6. Readmission rates	n/a
Zhang (2025) [126]	CN	To examine the association between eFI and mortality and adverse events (a)	Patients ≥65 years old (n = 1,424)	45-deficits (Pajewski, 2019)	Non-frail: < 0.27 Frail: ≥ 0.27	1. Duration of hospital stay 2. Hospital charges 3. Mortality status 4. Unfavorable prognosis	n/a
Zhou (2025) [127]	US	To develop a large language model-based binary classifier using accurately phenotyped datasets (Vulnerable Elders-13 Survey [VES-13] and eFI) to identify preoperative frailty from clinical notes (b)	Patients undergoing spine surgery (n = 479) and geriatric patients undergoing spine surgery (n = 998)	54-deficits (Theou, 2013)	n/a		n/a
*Purpose of eFI application: (a) Risk stratification, (b) eFI model development and validation, (c) Clinical decision support and (d) Population surveillance. ADL = activities of daily living; CCI = Charlson Comorbidity Index; CGA-FI = Comprehensive Geriatric Assessment Frailty Index; CFS = Clinical Frailty Scale; CMS: Centers for Medicare and Medicaid Services; ED: Emergency department; eFI = electronic Frailty Index; FFP = Fried Frailty Phenotype (FFP); GARS = The Groningen Activities Restriction Scale; HAC: Hospital-Acquired Condition; HCT-CI = Hematopoietic Cell Transplantation Comorbidity Index; HFRS = Hospital Frailty Risk Score; ICU: Intensive care unit; mFI-5 = 5-Factor Modified Frailty Index; PSI 90: Center for Medicare and Medicaid Services Patient Safety Indicator 90; RAI = Risk Analysis Index; REFS = Reported Edmonton Frail Scale; RISC = Risk Instrument for Screening in the Community; SOF = Study of Osteoporotic Fracture Criteria for Frailty (SOF); SNF: Skilled nursing facility; VA-FI = Veterans Affairs Frailty Index

Table 1: Summary of the included studies in eFI applications.

Figure 1: Flow diagram of searching strategy.

Discussion

This scoping review examined the use of eFI models in the past five years. Most studies originated from the UK and US, with risk stratification being the most common application, followed by model development and validation. The Clegg-based eFI (36 deficits) and Pajewski-based eFI (45-59 deficits) were the most frequently applied, though considerable variation in deficit selection was observed in the Pajewski-based models. Overall, findings underscore the growing adoption of eFI for predicting outcomes such as mortality and healthcare utilization.

The process of developing and validating eFI models was consistently observed across the studies reviewed. Model development typically followed the cumulative deficit framework, with researchers identifying candidate health deficits from prior literature, clinical expertise or established models. Around 30-40 deficits were usually selected, with ideal candidates meeting the following criteria [38]: (1) association with health status and adverse outcomes, (2) prevalence that increases with age, (3) should not reach a ceiling effect at an early age (i.e., it should not be present in nearly everyone at midlife or earlier) and (4) covers multiple physiological systems. Additional considerations included: (a) prevalence ≥1% in the study population, (b) adequate data completeness, (c) each deficit should capture a distinct aspect of health rather than duplicating another variable (i.e., non-redundancy) and (d) can be reliably and repeatedly measured in the dataset or clinical setting [48].

Deficits commonly cover chronic diseases (e.g., diabetes, heart failure), laboratory values (e.g., albumin, cholesterol), functional impairments (e.g., falls, incontinence), symptoms (e.g., fatigue, weight loss), cognitive or mood disorders (e.g., dementia, depression) and sensory impairments (e.g., vision or hearing loss). To operationalize these deficits, researchers must match each candidate deficit with the corresponding data elements available in their system, most often using ICD or ICPC diagnosis codes, as well as prescription records, laboratory thresholds or functional assessments. Relevant data are then extracted from these EHR-coded sources to construct the eFI. Each deficit was coded as present (1) or absent (0) and the eFI score is calculated as the proportion of deficits present. The cut-offs may be determined within the development dataset using (a) empirical distribution-based (quantiles/percentiles), (b) predefined thresholds from earlier validated models or (c) outcome-driven thresholds (Cox regression, ROC curve analysis). Frailty categories were chosen for clinical practicality using cut-off thresholds. Model validation involved testing predictive validity against adverse outcomes, most commonly mortality and hospital utilization, using statistical methods such as Cox regression, ROC curves and AUC analyses. eFI models were also compared with other frailty measures, such as the Clinical Frailty Scale (CFS), to assess convergent validity. Although many studies adopted established eFI models such as the Clegg-based and Pajewski-based indices, we found considerable variation in their implementation, largely due to differences in coding systems across healthcare platforms. For example, the 36-deficit Clegg model was modified to 35 deficits in Japanese claims data because the variable “falls” was not captured, requiring recalculation of the frailty score using only the available diagnostic records [49,50]. Such adaptations underscore the practical challenges of applying standardized eFI models across diverse datasets and highlight the need for programming eFI algorithms directly into EHR platforms. Embedding eFI models within EHR systems not only ensures consistency and comparability but also facilitates their integration into clinical workflows for standardized risk stratification and decision support, while simultaneously providing researchers with readily available eFI data for secondary analyses and population-level research.

There were several limitations in this study. First, this review included only peer-reviewed, published studies, which may have introduced publication bias by overrepresenting risk stratification findings while underrepresenting purely descriptive results. The review was limited to English-language studies published between August 2020 and July 2025, which may have excluded relevant non-English evidence as well as earlier foundational work or very recent publications. Many studies cited the Clegg-based or Pajewski-based eFI models but did not provide itemized lists of individual deficits. As a result, our quantification of deficits across models may be subject to bias, although we attempted to follow a clear and transparent protocol whenever possible. Last, the process of merging clinically equivalent terms (e.g., “COPD,” “lung disease,” “respiratory disease”) involved subjective judgment, which may have introduced misclassification or bias in identifying the most common deficits.

Conclusion

This review provided an overview of the current landscape of eFI applications. The findings indicated that certain countries placed greater emphasis on integrating eFI into healthcare and demonstrated notable variability in deficit selection and model adaptation across different settings.

Conflict of Interest

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Financial Disclosure

Part of the study was supported by the UW-Milwaukee Promoting Equity, Diversity and Academic Success Through Aging Research Program (UWM STAR, R25AG076406).

Acknowledgment

None.

Author’s Contribution

All authors read and approved the final manuscript.

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Author Info

Inga Wang^1*, Cameron Lee¹, Syeda Tanzima Shefa¹, Sheng-Che Yen², Hui-Wen Lin³, Xiaoyan Li⁴, Kishor Lakshminarayanan⁵, Chiung-ju (CJ) Liu⁶

Citation

From Code to Care and Digital Detection of Frailty: A Scoping Review of electronic Frailty Index (eFI) Applications Using Electronic Health Records

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