Frailty assessment using routine clinical data: An integrative review

Frailty indices based on routinely collected data: a scoping review

A frailty index (FI) is a frailty assessment tool calculated as the proportion of the number of health-related deficits an individual has to the total number of variables in the index. Routinely collected clinical and administrative data can be used as sources of deficits to automatically calculate FIs. This scoping review aimed to evaluate the current research landscape on routine data-based FIs. We searched seven databases to find literature published in 2013-2023. Main inclusion criteria were original research articles on FIs constructed from routine data, with deficits in at least two of the following categories: "symptoms/signs", "laboratory values", "diseases", "disabilities", and "others". From 7526 publications screened, 218 were included. Studies were primarily from North America (47.7 %), conducted in the community (35.3 %), and used routine data-based FIs for risk stratification (51.4 %). FIs were calculated using various routine data sources; however, most were initially developed and validated using hospital records. We noted geographical differences in study settings and routine data sources. We identified 611 unique deficits comprising these FIs. Most were either "diseases" (34.4 %) or "symptoms/signs" (32.1 %). Routine data-based FIs are feasible and valid risk stratification tools, but research is confined to high-income countries, their routine adoption is slow, and deficits comprising these FIs emphasise a reactive and overtly medical approach in addressing frailty. Future directions include exploring the feasibility and applicability of using routine databases for frailty assessment in lower- and middle-income countries, and leveraging non-clinical routine data through data linkages to proactively identify and manage frailty.

Association between laboratory data-based frailty index and clinical health outcomes in critically ill older patients: A retrospective correlational study

BACKGROUND

Although frailty assessment is crucial for understanding critically ill patients' prognosis, traditional frailty measures require substantial efforts and time from health care professionals. To address this limitation, the laboratory frailty index (FI-LAB) based on laboratory clinical data was developed. However, knowledge regarding its correlation with health outcomes among critically ill older patients is limited.

AIM

To identify the association between the FI-LAB and acute, mid- and long-term outcomes among critically ill older adults.

STUDY DESIGN

This retrospective correlational study used electronic health records of 2106 older patients who were admitted to the intensive care unit at a tertiary hospital in Seoul, Korea. Acute and mid-term outcomes included occurrence of delirium and in-hospital mortality, and the long-term outcome included 1-year mortality. Logistic regression was used to explore the relationships across FI-LAB, delirium, and in-hospital mortality, while Cox proportional hazard regression was used to analyse the relationship between FI-LAB and 1-year mortality.

RESULTS

Frailty assessed by FI-LAB was significantly associated with increased risk of delirium (odds ratio [OR] = 6.21, 95% confidence interval [CI] = 2.31-25.39, p = .009), in-hospital mortality (OR = 2.38, 95% CI = 1.15-5.79, p = .014), and 1-year mortality (hazard ratio = 2.47, 95% CI = 1.16-5.25, p = .019) after controlling for covariates.

CONCLUSIONS

The study highlighted the importance of using FI-LAB for screening frailty in critically ill older adults. Health care providers can improve patients' acute, mid- and long-term outcomes to develop more individualised management plans based on FI-LAB scores.

RELEVANCE TO CLINICAL PRACTICE

The FI-LAB score calculated from routine laboratory data can be used by nurses as a screening tool to identify frail older adults in critical care. Early detection of frailty would allow for closer monitoring and the implementation of interventions to reduce delirium and mortality.

Hospital frailty risk score using electronic medical records and geriatric syndromes in an acute-care hospital

BACKGROUND

This study investigated the relationship between the Hospital Frailty Risk Score (HFRS) and geriatric syndromes in acute-care hospitals.

METHODS

A cross-sectional analysis was performed on 8,205 inpatients aged ≥ 65 years from November 1, 2016, to October 31, 2021. HFRS was determined using ICD-10 codes in the electronic medical records. Cognitive impairment, depression, polypharmacy, dysphagia, malnutrition, and pain were assessed by attending nurses within 48 h of admission.

RESULTS

The cohort consisted of 3,872 men and 4,333 women, averaging 74.4 and 75.4 years old, respectively. Patients in the highest HFRS tertile (Q3) showed significantly higher risks of cognitive impairment, depression, and polypharmacy after adjusting for age, sex, and body mass index than those in the lowest tertile (Q1).

CONCLUSIONS

Elevated HFRS is significantly associated with increased risk of geriatric syndromes, highlighting its usefulness in identifying at-risk elderly patients in hospital settings without face-to-face assessments by medical staff.

Frailty Modeling Using Machine Learning Methodologies: A Systematic Review With Discussions on Outstanding Questions

Studying frailty is crucial for enhancing the health and quality of life among older adults, refining healthcare delivery methods, and tackling the obstacles linked to an aging demographic. Approaches to frailty modeling often utilise simple analytic techniques rather than available advanced machine learning methods, which may be sub-optimal. There is no large-scale systematic review on applications of machine learning methods on frailty modeling. In this study we explore the use of machine learning methods to predict or classify frailty in older persons in routinely collected data. We reviewed 181 research articles, and categorised analytic methods into three categories: generalised linear models, survival models, and non-linear models. These methods have a moderate agreement with existing frailty scores and predictive validity for adverse outcomes. Limited evidence suggests that non-linear methods outperform generalised linear methods. The top-three predictor/input variables are specific diagnosis or groups of diagnoses, functional performance (e.g., ADLs), and impaired cognition. Mortality, hospital admissions and prolonged hospital stay are the mainly predicted outcomes. Most studies utilise classical machine learning methods with cross-sectional data. Longitudinal data collected by wearable sensors have been used for frailty modeling. We also discuss the opportunities to use more advanced machine learning methods with high dimensional longitudinal data for more personalised and accessible frailty tools.

Development of a Frailty Screening Tool Using Electronic Community Pharmacy Records

BACKGROUND

Frailty is associated with increased susceptibility to medication-related harm, highlighting the importance of medication review for frail older adults. Community pharmacists are increasingly involved in the initiation of medication reviews. Yet, current frailty measurement methods are impractical in this setting. Alternative approaches, leveraging routinely collected data, are needed.

OBJECTIVE

To develop a frailty screening tool utilising routine electronic pharmacy records.

METHODS

Community-dwelling older adults (≥ 70 years) using ≥ 5 chronic medications were recruited in 196 Belgian community pharmacies. Frailty was assessed using SHARE-FI75+ (based on Fried's frailty phenotype). Model development was on the basis of a two-stage approach using multivariable logistic regression with split-sample internal validation. Stage 1 considered only electronic pharmacy record variables, while stage 2 also included other variables that can easily be collected in the community pharmacy. Model performance was evaluated for discrimination, calibration and predictive accuracy.

RESULTS

We recruited 875 participants [mean ± standard deviation (SD) age 79.3 ± 5.9 years], with 14.8% identified as frail. At stage 1, the frailty screening model included age, sex, reimbursement level of medical expenses, number of chronic medications and medication-derived comorbidities (anxiety, congestive heart failure, hypertension) [area under the receiver operating characteristic curve (AUC) 0.77, 95% confidence interval (CI) 0.69-0.85; sensitivity 78.0%; specificity 60.1%]. At stage 2, additional information on difficulties with basic activities of daily living or pharmacist's intuitive frailty assessment further improved the model (AUC 0.81, 95% CI 0.74-0.88 and AUC 0.82, 95% CI 0.75-0.89, respectively).

CONCLUSIONS

We developed a screening tool for frailty using data from electronic pharmacy records. This tool offers the opportunity for frailty screening in community pharmacy and to identify individuals that may benefit the most from medication review. External validation is warranted.

Assessing the Benefits and Harms of Pharmacotherapy in Older Adults with Frailty: Insights from Pharmacoepidemiologic Studies of Routine Health Care Data

The objective of this review is to summarize and appraise the research methodology, emerging findings, and future directions in pharmacoepidemiologic studies assessing the benefits and harms of pharmacotherapies in older adults with different levels of frailty. Older adults living with frailty are at elevated risk for poor health outcomes and adverse effects from pharmacotherapy. However, current evidence is limited due to the under-enrollment of frail older adults and the lack of validated frailty assessments in clinical trials. Recent advancements in measuring frailty in administrative claims and electronic health records (database-derived frailty scores) have enabled researchers to identify patients with frailty and to evaluate the heterogeneity of treatment effects by patients' frailty levels using routine health care data. When selecting a database-derived frailty score, researchers must consider the type of data (e.g., different coding systems), the length of the predictor assessment period, the extent of validation against clinically validated frailty measures, and the possibility of surveillance bias arising from unequal access to care. We reviewed 13 pharmacoepidemiologic studies published on PubMed from 2013 to 2023 that evaluated the benefits and harms of cardiovascular medications, diabetes medications, anti-neoplastic agents, antipsychotic medications, and vaccines by frailty levels. These studies suggest that, while greater frailty is positively associated with adverse treatment outcomes, older adults with frailty can still benefit from pharmacotherapy. Therefore, we recommend routine frailty subgroup analyses in pharmacoepidemiologic studies. Despite data and design limitations, the findings from such studies may be informative to tailor pharmacotherapy for older adults across the frailty spectrum.

The Association of Frailty and Neighborhood Disadvantage with Emergency Department Visits and Hospitalizations in Older Adults

BACKGROUND

Risk stratification and population management strategies are critical for providing effective and equitable care for the growing population of older adults in the USA. Both frailty and neighborhood disadvantage are constructs that independently identify populations with higher healthcare utilization and risk of adverse outcomes.

OBJECTIVE

To examine the joint association of these factors on acute healthcare utilization using two pragmatic measures based on structured data available in the electronic health record (EHR).

DESIGN

In this retrospective observational study, we used EHR data to identify patients aged ≥ 65 years at Atrium Health Wake Forest Baptist on January 1, 2019, who were attributed to affiliated Accountable Care Organizations. Frailty was categorized through an EHR-derived electronic Frailty Index (eFI), while neighborhood disadvantage was quantified through linkage to the area deprivation index (ADI). We used a recurrent time-to-event model within a Cox proportional hazards framework to examine the joint association of eFI and ADI categories with healthcare utilization comprising emergency visits, observation stays, and inpatient hospitalizations over one year of follow-up.

KEY RESULTS

We identified a cohort of 47,566 older adults (median age = 73, 60% female, 12% Black). There was an interaction between frailty and area disadvantage (P = 0.023). Each factor was associated with utilization across categories of the other. The magnitude of frailty's association was larger than living in a disadvantaged area. The highest-risk group comprised frail adults living in areas of high disadvantage (HR 3.23, 95% CI 2.99-3.49; P < 0.001). We observed additive effects between frailty and living in areas of mid- (RERI 0.29; 95% CI 0.13-0.45; P < 0.001) and high (RERI 0.62, 95% CI 0.41-0.83; P < 0.001) neighborhood disadvantage.

CONCLUSIONS

Considering both frailty and neighborhood disadvantage may assist healthcare organizations in effectively risk-stratifying vulnerable older adults and informing population management strategies. These constructs can be readily assessed at-scale using routinely collected structured EHR data.

Association between frailty and hepatic fibrosis in NAFLD among middle-aged and older adults: results from NHANES 2017-2020

Background

Although previous studies found that frailty is prevalent in NAFLD patients with advanced liver fibrosis and cirrhosis, studies examining the relationship are spare.

Aim

Our study aspires to investigate the potential correlation between the Frailty Index (FI) and hepatic fibrosis among middle-aged and older adults with NAFLD.

Methods

Data from the 2017-2020.03 National Health and Nutrition Examination Survey (NHANES) were utilized for this study, with a final of 2,383 participants aged 50 years and older included. The quantification of frailty was executed employing a 49-item frailty index. The recognition of hepatic steatosis and fibrosis was accomplished through the utilization of the controlling attenuation parameter (CAP) and transient elastography (TE). The relationship between the FI and hepatic fibrosis were investigated employing univariable and multivariable-adjusted logistic regression analyses. A subgroup analysis was conducted, dividing the subjects based on gender, Body Mass Index (BMI), and the presence of hyperlipidemia.

Results

The findings demonstrated a positive correlation between the FI and significant hepatic fibrosis in NAFLD, even after using multivariate logistic regression models adjusting for potential confounding factors (OR = 1.022, 95% CI, 1.004-1.041) and in tertiles (Q3vs Q1: OR = 2.004, 95% CI, 1.162-3.455). In the subgroup analysis, the correlation was more statistically significant in male (OR = 1.046, 95% CI, 1.022-1.071), under/normal weight (OR = 1.077, 95% CI, 1.009-1.150), overweight (OR = 1.040, 95% CI, 1.010-1.071), and subjects without hyperlipidemia (OR = 1.054, 95% CI, 1.012-1.097). The area under the Receiver Operating Characteristic (ROC) curve for the FI in assessing the existence of substantial fibrosis in NAFLD was 0.612 (95% CI, 0.596-0.628).

Conclusion

This study demonstrated a positive correlation between significant hepatic fibrosis and frailty, particularly among males aged 50 years and older, who were non-obese and did not have hyperlipidemia with NAFLD. Additional studies are required to further validate these findings.

Retrospective Use of the Pictorial Fit-Frail Scale for Determination of Frailty Level in Hospitalized Older Adults with a Hip Fracture

The Pictorial Fit-Frail Scale (PFFS) is a frailty tool consisting of visual images to comprehensively assess frailty across 14 domains that can be completed by health professionals, patients, or caregivers. The objective of this study was to explore the feasibility of using the PFFS retrospectively to determine a patient's frailty level using data from the hospital electronic health records (EHRs) of older adults admitted with an isolated hip fracture. A random sample of 200 hip fracture patients admitted to a Level 1 Trauma Center hospital in New Brunswick was selected for review using the PFFS. The majority (94.5%) of hospital EHRs contained the clinical information needed to populate most of the 14 PFFS domains, allowing for determination of a frailty score. The mean raw PFFS frailty score was 9.7 (SD 6.6), consistent with moderate frailty. For all patients, a Frailty Index (FI) score was calculated, with the mean being 0.27 (SD 0.18), again consistent with moderate frailty. Comparing the PFFS score to the FI score, the percentage categorized as not frail or very mildly frail fell from 33.3% to 20.1%, and those considered severely frail rose from 30.7% to 34.9%. The PFFS can be successfully used retrospectively with hospital EHRs to determine the frailty level of older patients. When converted to the FI score, there was an increase in the frequency and severity of frailty. This tool may provide a useful way to stratify older adults by frailty that can be helpful in evaluating health outcomes based on frailty levels.

Higher Hospital Frailty Risk Score Is Associated With Increased Risk of Stroke: Observational and Genetic Analyses

BACKGROUND

Frailty is a prevalent state associated with several aging-related traits and conditions. The relationship between frailty and stroke remains understudied. Here we aim to investigate whether the hospital frailty risk score (HFRS) is associated with the risk of stroke and determine whether a significant association between genetically determined frailty and stroke exists.

DESIGN

Observational study using data from All of Us research program and Mendelian Randomization analyses.

METHODS

Participants from All of Us with available electronic health records were selected for analysis. All of Us began national enrollment in 2018 and is expected to continue for at least 10 years. All of Us is recruiting members of groups that have traditionally been underrepresented in research. All participants provided informed consent at the time of enrollment, and the date of consent was recorded for each participant. Incident stroke was defined as stroke event happening on or after the date of consent to the All of Us study HFRS was measured with a 3-year look-back period before the date of consent for stroke risk. The HFRS was stratified into 4 categories: no-frailty (HFRS=0), low (HFRS ≥1 and <5), intermediate (≥5 and <15), and high (HFRS ≥15). Last, we implemented Mendelian Randomization analyses to evaluate whether genetically determined frailty is associated with stroke risk.

RESULTS

Two hundred fifty-three thousand two hundred twenty-six participants were at risk of stroke. In multivariable analyses, frailty status was significantly associated with risk of any (ischemic or hemorrhagic) stroke following a dose-response way: not-frail versus low HFRS (HR, 4.9 [CI, 3.5-6.8]; P<0.001), not-frail versus intermediate HFRS (HR, 11.4 [CI, 8.3-15.7]; P<0.001) and not-frail versus high HFRS (HR, 42.8 [CI, 31.2-58.6]; P<0.001). We found similar associations when evaluating ischemic and hemorrhagic stroke separately (P value for all comparisons <0.05). Mendelian Randomization confirmed this association by indicating that genetically determined frailty was independently associated with risk of any stroke (OR, 1.45 [95% CI, 1.15-1.84]; P=0.002).

CONCLUSIONS

Frailty, based on the HFRS was associated with higher risk of any stroke. Mendelian Randomization analyses confirmed this association providing evidence to support a causal relationship.

Calf circumference predicts frailty in older adults: the Chinese longitudinal healthy longevity survey

BACKGROUND

Although frailty is a common geriatric syndrome in old adults, a simple method to assess the degree of frailty in a person has not yet been established. In this study we have tried to establish the association between calf circumference (CC) and frailty among older Chinese people.

METHODS

We used the data obtained from the 2014 edition of the Chinese Longitudinal Healthy Longevity Survey; 1216 participants aged ≥60 years were included for the study. Body mass index, CC and waist circumference measurement data, and laboratory test results were collected. Frailty status was measured using the frailty index (FI). Participants were then classified into non-frail (FI < 0.25) and frail (FI ≥ 0.25) groups.

RESULTS

There were 874 participants (71.9%) in the non-frail group and 342 (28.1%) in the frail group. The CC was significantly different between the two groups (31.54 ± 4.16 versus 28.04 ± 4.53, P < 0.001). Logistic regression analysis revealed that CC (odds ratio = 0.947, 95% confidence interval: 0.904-0.993, P = 0.023) was an independent impact factor associated with frailty. The CC value of 28.5 cm was considered the best cut-off value in women with area under the curve (AUC) was 0.732 (P < 0.001) and 29.5 cm in men with AUC was 0.592 (P = 0.004);We created a simple prediction model for frailty that included age,sex and CC:[Formula: see text]P = e^logit(P) /1 + e^logit(P), and AUC is 0.849 (P < 0.001).

CONCLUSIONS

CC is a convenient and predictable marker of frailty in older adults.

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.

Preoperative frailty based on laboratory data and postoperative health outcomes in patients undergoing coronary artery bypass graft surgery

BACKGROUND

Frailty is associated with adverse surgical outcomes. Patients with cardiovascular diseases have many risk factors of frailty; thus, preoperative frailty evaluation is necessary to predict adverse outcomes after coronary artery bypass graft (CABG) surgery. Laboratory data based-frailty assessments are objective and not time-consuming, addressing the need for an accurate but simple frailty screening for patients awaiting CABG surgery.

OBJECTIVES

This retrospective study aimed to determine the association between laboratory based-frailty and patient health outcomes after CABG surgery.

METHODS

We evaluated 896 patients who underwent on-pump or off-pump CABG surgery between August 1, 2015 and July 31, 2020 at a tertiary hospital. The frailty index-laboratory (FI-LAB), which comprises 32 laboratory parameters and vital signs, was used for frailty assessment.

RESULTS

The patients were divided into three groups according to their preoperative FI-LAB level as low (FI-LAB <0.25, 23.0%), moderate (FI-LAB ≥0.25 to ≤0.4, 54.9%), and high (FI-LAB>0.4, 22.1%) frailty groups. In the confounder-adjusted analysis, the lengths of hospital stay and intensive care unit stay were longer by 2.20 days (p=.023) and by 0.89 days (p=.009), respectively, in the high frailty group than those in the low frailty group. The odds ratio for 30-day readmission was also 2.58 times higher in the high frailty group than that in the low frailty group.

CONCLUSION

A high preoperative FI-LAB score indicates increasing risks of adverse postoperative outcomes among CABG surgery patients. FI-LAB has potential strengths to capture the need for a more thorough frailty assessment for cardiac surgery patients.