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BACKGROUND: Frailty is increasingly present in patients with acute myocardial infarction. The electronic Frailty Index (eFI) is a validated method of identifying vulnerable older patients in the community from routine primary care data. Our aim was to assess the relationship between the eFI and outcomes in older patients hospitalised with acute myocardial infarction. STUDY DESIGN AND SETTING: Retrospective cohort study using the DataLoch Heart Disease Registry comprising consecutive patients aged 65 years or over hospitalised with a myocardial infarction between October 2013 and March 2021. METHODS: Patients were classified as fit, mild, moderate, or severely frail based on their eFI score. Cox-regression analysis was used to determine the association between frailty category and all-cause mortality. RESULTS: In 4670 patients (median age 77 years [71-84], 43% female), 1865 (40%) were classified as fit, with 1699 (36%), 798 (17%) and 308 (7%) classified as mild, moderate and severely frail, respectively. In total, 1142 patients died within 12 months of which 248 (13%) and 147 (48%) were classified as fit and severely frail, respectively. After adjustment, any degree of frailty was associated with an increased risk of all-cause death with the risk greatest in the severely frail (reference = fit, adjusted hazard ratio 2.87 [95% confidence intervals 2.24 to 3.66]). CONCLUSION: The eFI identified patients at high risk of death following myocardial infarction. Automatic calculation within administrative data is feasible and could provide a low-cost method of identifying vulnerable older patients on hospital presentation.

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OBJECTIVES: Frailty is a dynamic health state that changes over time. Our hypothesis was that there are identifiable subgroups of the older population that have specific patterns of deterioration. The objective of this study was to evaluate the application of joint latent class model in identifying trajectories of frailty progression over time and their group-specific risk of death in older people. STUDY DESIGN AND SETTING: The primary care records of UK patients, aged over 65 as of January 1, 2010, included in the Clinical Practice Research Datalink: GOLD and AURUM databases, were analyzed and linked to mortality data. The electronic frailty index (eFI) scores were calculated at baseline and annually in subsequent years (2010-2013). Joint latent class model was used to divide the population into clusters with different trajectories and associated mortality hazard ratios. The model was built in GOLD and validated in AURUM. RESULTS: Five trajectory clusters were identified and characterized based on baseline and speed of progression: low-slow, low-moderate, low-rapid, high-slow, and high-rapid. The high-rapid cluster had the highest average starting eFI score; 7.9, while the low-rapid cluster had the steepest rate of eFI progression; 1.7. Taking the low-slow cluster as reference, low-rapid and high-rapid had the highest hazard ratios: 3.73 (95% CI 3.71, 3.76) and 3.63 (3.57-3.69), respectively. Good validation was found in the AURUM population. CONCLUSION: Our research found that there are vulnerable subgroups of the older population who are currently frail or have rapid frailty progression. Such groups may be targeted for greater healthcare monitoring.

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Frailty is an important construct to measure in acute myeloid leukemia (AML). We used the Veterans Affairs Frailty Index (VA-FI) - calculated using readily available data within the VA's electronic health records - to measure frailty in U.S. veterans with AML. Of the 1166 newly diagnosed and treated veterans with AML between 2012 and 2022, 722 (62%) veterans with AML were classified as frail (VA-FI > 0.2). At a median follow-up of 252.5 days, moderate-severely frail veterans had significantly worse survival than mildly frail, and non-frail veterans (median survival 179 vs. 306 vs. 417 days, p < .001). Increasing VA-FI severity was associated with higher mortality. A model with VA-FI in addition to the European LeukemiaNet (ELN) risk classification and other covariates statistically outperformed a model containing the ELN risk and other covariates alone (p < .001). These findings support the VA-FI as a tool to expand frailty measurement in research and clinical practice for informing prognosis in veterans with AML.

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Introduction: Gulf War Illness (GWI) is a chronic, multisymptom (e.g., fatigue, muscle/joint pain, memory and concentration difficulties) condition estimated to affect 25-32% of Gulf War (GW) veterans. Longitudinal studies suggest that few veterans with GWI have recovered over time and that deployed GW veterans may be at increased risks for age-related conditions. Methods: We performed a retrospective cohort study to examine the current health status of 703 GW veterans who participated in research studies at the San Francisco VA Health Care System (SFVAHCS) between 2002 and 2018. We used the Veterans Affairs Frailty Index (VA-FI) as a proxy measure of current health and compared the VA-FIs of GW veterans to a group of randomly selected age- and sex-matched, non-GW veterans. We also examined GW veterans' VA-FIs as a function of different GWI case definitions and in relationship to deployment-related experiences and exposures. Results: Compared to matched, non-GW veterans, GW veterans had lower VA-FIs (0.10 ± 0.10 vs. 0.12 ± 0.11, p < 0.01). However, the subset of GW veterans who met criteria for severe Chronic Multisymptom Illness (CMI) at the time of the SFVAHCS studies had the highest VA-FI (0.13 ± 0.10, p < 0.001). GW veterans who had Kansas GWI exclusionary conditions had higher VA-FI (0.12 ± 0.12, p < 0.05) than veterans who were Kansas GWI cases (0.08 ± 0.08) and controls (i.e., veterans with little or no symptoms, 0.04 ± 0.06) at the time of the SFVAHCS research studies. The VA-FI was positively correlated with several GW deployment-related exposures, including the frequency of wearing flea collars. Discussion: Although GW veterans, as a group, were less frail than non-GW veterans, the subset of GW veterans who met criteria for severe CDC CMI and/or who had Kansas GWI exclusionary conditions at the time of the SFVAHCS research studies were frailest at index date. This suggests that many ongoing studies of GWI that use the Kansas GWI criteria may not be capturing the group of GW veterans who are most at risk for adverse chronic health outcomes.

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BACKGROUND: Electronic frailty indices (eFIs) can expand measurement of frailty in research and practice and have demonstrated predictive validity in associations with clinical outcomes. However, their construct validity is less well studied. We aimed to assess the construct validity of the VA-FI, an eFI developed for use in the U.S. Veterans Affairs Healthcare System. METHODS: Veterans who underwent comprehensive geriatric assessments between January 31, 2019 and June 6, 2022 at VA Boston and had sufficient data documented for a comprehensive geriatric assessment-frailty index (CGA-FI) were included. The VA-FI, based on diagnostic and procedural codes, and the CGA-FI, based on geriatrician-measured deficits, were calculated for each patient. Geriatricians also assessed the Clinical Frailty Scale (CFS), functional status (ADLs and IADLs), and 4-meter gait speed (4MGS). RESULTS: A total of 132 veterans were included, with median age 81.4 years (IQR 75.8-88.7). Across increasing levels of VA-FI (<0.2; 0.2-0.4; >0.4), mean CGA-FI increased (0.24; 0.30; 0.40). The VA-FI was moderately correlated with the CGA-FI (r 0.45, p < 0.001). Every 0.1-unit increase in the VA-FI was associated with an increase in the CGA-FI (linear regression beta 0.05; 95% confidence interval [CI] 0.03-0.06), higher CFS category (ordinal regression OR 1.69; 95% CI 1.24-2.30), higher odds of ADL dependency (logistic regression OR 1.59; 95% CI 1.20-2.11), IADL dependency (logistic regression OR 1.68; 95% CI 1.23-2.30), and a decrease in 4MGS (linear regression beta -0.07, 95% CI -0.12 to -0.02). All models were adjusted for age and race, and associations held after further adjustment for the Charlson Comorbidity Index. CONCLUSION: Our results demonstrate the construct validity of the VA-FI through its associations with clinical measures of frailty, including summary frailty measures, functional status, and objective physical performance. Our findings complement others' in showing that eFIs can capture functional and mobility domains of frailty beyond just comorbidity and may be useful to measure frailty among populations and individuals.

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INTRODUCTION: Assessing frailty is integral to treatment decision-making for older adults with acute myeloid leukemia (AML). Prior electronic frailty indices (eFI) derive from an accumulated-deficit model and are associated with mortality in older primary care populations. We evaluated use of an embedded eFI in AML by describing baseline eFI categories by treatment type and exploring associations between eFI categories, survival, and treatment received. MATERIALS AND METHODS: This was a retrospective study of subjects ≥60 years old with new AML treated at an academic medical center from 1/2018-10/2020. The eFI requires ≥2 ambulatory visits over two years and uses demographics, vitals, ICD-10 codes, outpatient labs, and available functional information from Medicare Annual Wellness Visits. Frailty was defined as fit (eFI ≤ 0.10), pre-frail (0.10 < eFI ≤ 0.21), and frail (eFI > 0.21). Chemotherapy was intensive (anthracycline-based) or less-intensive (hypomethylating agent, low dose cytarabine +/- venetoclax). Therapy type, pre-treatment characteristics, and chemotherapy cycles were compared by eFI category using chi-square and Fisher's exact tests and ANOVA. Median survival was compared by eFI category using log-rank tests stratified by therapy type. RESULTS: Among 166 older adults treated for AML (mean age 74 years, 61% male, 85% Caucasian), only 79 (48%) had a calculable eFI score before treatment. Of these, baseline eFI category was associated with treatment received (fit (n = 31): 68% intensive, 32% less intensive; pre-frail (n = 38): 37% intensive, 63% less intensive; frail (n = 10): 0% intensive, 100% less intensive; not calculable (n = 87): 48% intensive, 52% less-intensive; p < 0.01). The prevalence of congestive heart failure and secondary AML differed by frailty status (p < 0.01). Median survival did not differ between eFI categories for intensively (p = 0.48) or less-intensively (p = 0.09) treated patients. For those with less-intensive therapy who lived ≥6 months, eFI category was not associated with the number of chemotherapy cycles received (p = 0.97). The main reason for an incalculable eFI was a lack of outpatient visits in our health system prior to AML diagnosis. DISCUSSION: A primary care-derived eFI was incalculable for half of older adults with AML at an academic medical center. Frailty was associated with chemotherapy intensity but not survival or treatment duration. Next steps include testing adaptations of the eFI to the AML setting.

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INTRODUCTION: Frailty, a measure of biological aging, has been linked to worse COVID-19 outcomes. However, as the mortality differs across the COVID-19 waves, it is less clear whether a medical record-based electronic frailty index (eFI) that we have previously developed for older adults could be used for risk stratification in hospitalized COVID-19 patients. OBJECTIVES: The aim of the study was to examine the association of frailty with mortality, readmission, and length of stay in older COVID-19 patients and to compare the predictive accuracy of the eFI to other frailty and comorbidity measures. METHODS: This was a retrospective cohort study using electronic health records (EHRs) from nine geriatric clinics in Stockholm, Sweden, comprising 3,980 COVID-19 patients (mean age 81.6 years) admitted between March 2020 and March 2022. Frailty was assessed using a 48-item eFI developed for Swedish geriatric patients, the Clinical Frailty Scale, and the Hospital Frailty Risk Score. Comorbidity was measured using the Charlson Comorbidity Index. We analyzed in-hospital mortality and 30-day readmission using logistic regression, 30-day and 6-month mortality using Cox regression, and the length of stay using linear regression. Predictive accuracy of the logistic regression and Cox models was evaluated by area under the receiver operating characteristic curve (AUC) and Harrell's C-statistic, respectively. RESULTS: Across the study period, the in-hospital mortality rate decreased from 13.9% in the first wave to 3.6% in the latest (Omicron) wave. Controlling for age and sex, a 10% increment in the eFI was significantly associated with higher risks of in-hospital mortality (odds ratio = 2.95; 95% confidence interval = 2.42-3.62), 30-day mortality (hazard ratio [HR] = 2.39; 2.08-2.74), 6-month mortality (HR = 2.29; 2.04-2.56), and a longer length of stay (ß-coefficient = 2.00; 1.65-2.34) but not with 30-day readmission. The association between the eFI and in-hospital mortality remained robust across the waves, even after the vaccination rollout. Among all measures, the eFI had the best discrimination for in-hospital (AUC = 0.780), 30-day (Harrell's C = 0.733), and 6-month mortality (Harrell's C = 0.719). CONCLUSION: An eFI based on routinely collected EHRs can be applied in identifying high-risk older COVID-19 patients during the continuing pandemic.

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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.

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Background: The long-term prognosis of the cardio-metabolic and renal complications, in addition to mortality in patients with newly diagnosed pulmonary hypertension, are unclear. This study aims to develop a scalable predictive model in the form of an electronic frailty index (eFI) to predict different adverse outcomes. Methods: This was a population-based cohort study of patients diagnosed with pulmonary hypertension between January 1st, 2000 and December 31st, 2017, in Hong Kong public hospitals. The primary outcomes were mortality, cardiovascular complications, renal diseases, and diabetes mellitus. The univariable and multivariable Cox regression analyses were applied to identify the significant risk factors, which were fed into the non-parametric random survival forest (RSF) model to develop an eFI. Results: A total of 2,560 patients with a mean age of 63.4 years old (interquartile range: 38.0-79.0) were included. Over a follow-up, 1,347 died and 1,878, 437, and 684 patients developed cardiovascular complications, diabetes mellitus, and renal disease, respectively. The RSF-model-identified age, average readmission, anti-hypertensive drugs, cumulative length of stay, and total bilirubin were among the most important risk factors for predicting mortality. Pair-wise interactions of factors including diagnosis age, average readmission interval, and cumulative hospital stay were also crucial for the mortality prediction. Patients who developed all-cause mortality had higher values of the eFI compared to those who survived (P < 0.0001). An eFI ≥ 9.5 was associated with increased risks of mortality [hazard ratio (HR): 1.90; 95% confidence interval [CI]: 1.70-2.12; P < 0.0001]. The cumulative hazards were higher among patients who were 65 years old or above with eFI ≥ 9.5. Using the same cut-off point, the eFI predicted a long-term mortality over 10 years (HR: 1.71; 95% CI: 1.53-1.90; P < 0.0001). Compared to the multivariable Cox regression, the precision, recall, area under the curve (AUC), and C-index were significantly higher for RSF in the prediction of outcomes. Conclusion: The RSF models identified the novel risk factors and interactions for the development of complications and mortality. The eFI constructed by RSF accurately predicts the complications and mortality of patients with pulmonary hypertension, especially among the elderly.

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Introduction: Frailty is a state of diminished physiological reserve and can be assessed using the frailty index. Early management of frailty is crucial for preventing adverse outcomes. Intended for assessing home-living older adults, the initial release of the eFI-CGA software was prior to the coronavirus disease 2019 (COVID-19) pandemic. Methods: In addressing the increased need of virtual assessment, the eFI-CGA was upgraded to version 3.0. In this paper, we introduce the updated electronic frailty assessment tool, reporting the newly developed features and validating its use. Results: End-user experiences with the previous versions are discussed. The updated features include a search function to resume disrupted assessments. The improved user interface enabled clinicians to record care management details. Conclusion: This study represents an example of software solutions in moving from disruption to transformation, benefiting healthcare for older adults during this challenging time.

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BACKGROUND: Frailty assessment in the Swedish health system relies on the Clinical Frailty Scale (CFS), but it requires training, in-person evaluation, and is often missing in medical records. We aimed to develop an electronic frailty index (eFI) from routinely collected electronic health records (EHRs) and assess its association with adverse outcomes in hospitalized older adults. METHODS: EHRs were extracted for 18 225 patients with unplanned admissions between 1 March 2020 and 17 June 2021 from 9 geriatric clinics in Stockholm, Sweden. A 48-item eFI was constructed using diagnostic codes, functioning and other health indicators, and laboratory data. The CFS, Hospital Frailty Risk Score, and Charlson Comorbidity Index were used for comparative assessment of the eFI. We modeled in-hospital mortality and 30-day readmission using logistic regression; 30-day and 6-month mortality using Cox regression; and length of stay using linear regression. RESULTS: Thirteen thousand one hundred and eighty-eight patients were included in analyses (mean age 83.1 years). A 0.03 increment in the eFI was associated with higher risks of in-hospital (odds ratio: 1.65; 95% confidence interval: 1.54-1.78), 30-day (hazard ratio [HR]: 1.43; 1.38-1.48), and 6-month mortality (HR: 1.34; 1.31-1.37) adjusted for age and sex. Of the frailty and comorbidity measures, the eFI had the highest area under receiver operating characteristic curve for in-hospital mortality of 0.813. Higher eFI was associated with longer length of stay, but had a rather poor discrimination for 30-day readmission. CONCLUSIONS: An EHR-based eFI has robust associations with adverse outcomes, suggesting that it can be used in risk stratification in hospitalized older adults.

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OBJECTIVES: To assess the applicability of Electronic Frailty Index (eFI) and Hospital Frailty Risk Score (HFRS) algorithms to Japanese administrative claims data and to evaluate their association with long-term outcomes. STUDY DESIGN AND SETTING: A cohort study using a regional government administrative healthcare and long-term care (LTC) claims database in Japan 2014-18. PARTICIPANTS: Plan enrollees aged ≥50 years. METHODS: We applied the two algorithms to the cohort and assessed the scores' distributions alongside enrollees' 4-year mortality and initiation of government-supported LTC. Using Cox regression and Fine-Gray models, we evaluated the association between frailty scores and outcomes as well as the models' discriminatory ability. RESULTS: Among 827,744 enrollees, 42.8% were categorised by eFI as fit, 31.2% mild, 17.5% moderate and 8.5% severe. For HFRS, 73.0% were low, 24.3% intermediate and 2.7% high risk; 35 of 36 predictors for eFI, and 92 of 109 codes originally used for HFRS were available in the Japanese system. Relative to the lowest frailty group, the highest frailty group had hazard ratios [95% confidence interval (CI)] of 2.09 (1.98-2.21) for mortality and 2.45 (2.28-2.63) for LTC for eFI; those for HFRS were 3.79 (3.56-4.03) and 3.31 (2.87-3.82), respectively. The area under the receiver operating characteristics curves for the unadjusted model at 48 months was 0.68 for death and 0.68 for LTC for eFI, and 0.73 and 0.70, respectively, for HFRS. CONCLUSIONS: The frailty algorithms were applicable to the Japanese system and could contribute to the identifications of enrollees at risk of long-term mortality or LTC use.

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INTRODUCTION: To assess whether the electronic frailty index (eFI) is independently associated with all-cause mortality and chemotherapy adverse reactions among older Chinese patients with lung cancer. METHODS: This is a retrospective, single-institution, chart review, and not a prospective cohort study. All patients ≥60 years with primary lung cancer in the West China Hospital from 2010 to 2017 were included in this cohort. The eFI was established using 35 frailty-related variables in the electronic medical record (EMR) system and was cut by a value of 0.2 to classify the patients into frail (eFI ≥0.2) and robust/non-frail groups (eFI<0.2). The long-term outcome was all-cause mortality identified by government databases and telephone interviews. Short-term outcomes were any infection, bone suppression, chemotherapy discontinuation, impaired liver function, any gastrointestinal reactions and length of hospitalization. An inverse probability weighting method was used to eliminate the potential confounders. An adjusted Kaplan-Meier estimator and a weighted Cox model were used to calculate the survival and hazard ratio. A weighted logistic model was used to calculate the odds of short-term outcomes. RESULTS: A total of 997 patients were included in this study with a median follow-up of 34 months. Compared with non-frail patients, frail patients had an increased risk of mortality and shortened overall survival (hazard ratio [HR] of mortality, 1.29; 95% confidence interval [CI], 1.05 to 1.60; adjusted restricted mean survival time [aRMST] difference, -5.68 months; 95% CI, -10.15 to -1.21 months). For short-term outcomes, frail patients had increased odds of infection compared to non-frail patients (odds ratio, 1.83; 95% CI, 1.09 to 3.06). No other outcome showed a significant result. CONCLUSION: This study of older Chinese patients with primary lung cancer suggests that eFI-based frail patients had worse prognoses with increased risk of all-cause mortality and shortened survival times.

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BACKGROUND: The Veterans Affairs Frailty Index (VA-FI) is an electronic frailty index developed to measure frailty using administrative claims and electronic health records data in Veterans. An update to ICD-10 coding is needed to enable contemporary measurement of frailty. METHOD: International Classification of Diseases, ninth revision (ICD-9) codes from the original VA-FI were mapped to ICD-10 first using the Centers for Medicaid and Medicare Services (CMS) General Equivalence Mappings. The resulting ICD-10 codes were reviewed by 2 geriatricians. Using a national cohort of Veterans aged 65 years and older, the prevalence of deficits contributing to the VA-FI and associations between the VA-FI and mortality over years 2012-2018 were examined. RESULTS: The updated VA-FI-10 includes 6422 codes representing 31 health deficits. Annual cohorts defined on October 1 of each year included 2 266 191 to 2 428 115 Veterans, for which the mean age was 76 years, 97%-98% were male, 78%-79% were White, and the mean VA-FI was 0.20-0.22. The VA-FI-10 deficits showed stability before and after the transition to ICD-10 in 2015, and maintained strong associations with mortality. Patients classified as frail (VA-FI > 0.2) consistently had a hazard of death more than 2 times higher than nonfrail patients (VA-FI ≤ 0.1). Distributions of frailty and associations with mortality varied with and without linkage to CMS data and with different assessment periods for capturing deficits. CONCLUSIONS: The updated VA-FI-10 maintains content validity, stability, and predictive validity for mortality in a contemporary cohort of Veterans aged 65 years and older, and may be applied to ICD-9 and ICD-10 claims data to measure frailty.

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Background: Different scales are being used to measure frailty. This study examined the convergent validity of the electronic Frailty Index (eFI) with the Clinical Frailty Scale (CFS). Method: The cross-sectional study recruited patients from three regional community nursing teams in the South East of England. The CFS was rated at recruitment, and the eFI was extracted from electronic health records (EHRs). A McNemar test of paired data was used to compare discordant pairs between the eFI and the CFS, and an exact McNemar Odds Ratio (OR) was calculated. Findings: Of 265 eligible patients consented, 150 (57%) were female, with a mean age of 85.6 years (SD = 7.8), and 78% were 80 years and older. Using the CFS, 68% were estimated to be moderate to severely frail, compared to 91% using the eFI. The eFI recorded a greater degree of frailty than the CFS (OR = 5.43, 95%CI 3.05 to 10.40; p < 0.001). This increased to 7.8 times more likely in men, and 9.5 times in those aged over 80 years. Conclusions: This study found that the eFI overestimates the frailty status of community dwelling older people. Overestimating frailty may impact on the demand of resources required for further management and treatment of those identified as being frail.

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BACKGROUND: frailty has major implications for health and social care services internationally. The development, validation and national implementation of the electronic Frailty Index (eFI) using routine primary care data has enabled change in the care of older people living with frailty in England. AIMS: to externally validate the eFI in Wales and assess new frailty-related outcomes. STUDY DESIGN AND SETTING: retrospective cohort study using the Secure Anonymised Information Linkage (SAIL) Databank, comprising 469,000 people aged 65-95, registered with a SAIL contributing general practice on 1 January 2010. METHODS: four categories (fit; mild; moderate and severe) of frailty were constructed using recognised cut points from the eFI. We calculated adjusted hazard ratios (HRs) from Cox regression models for validation of existing outcomes: 1-, 3- and 5-year mortality, hospitalisation, and care home admission for validation. We also analysed, as novel outcomes, 1-year mortality following hospitalisation and frailty transition times. RESULTS: HR trends for the validation outcomes in SAIL followed the original results from ResearchOne and THIN databases. Relative to the fit category, adjusted HRs in SAIL (95% CI) for 1-year mortality following hospitalisation were 1.05 (95% CI 1.03-1.08) for mild frailty, 1.24 (95% CI 1.21-1.28) for moderate frailty and 1.51 (95% CI 1.45-1.57) for severe frailty. The median time (lower and upper quartile) between frailty categories was 2,165 days (lower and upper quartiles: 1,510 and 2,831) from fit to mild, 1,155 days (lower and upper quartiles: 756 and 1,610) from mild to moderate and 898 days (lower and upper quartiles: 584 and 1,275) from moderate to severe. CONCLUSIONS: further validation of the eFI showed robust predictive validity and utility for new outcomes.

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BACKGROUND: routine frailty identification and management is national policy in England, but there remains a lack of evidence on the impact of frailty on healthcare resource use. We evaluated the impact of frailty on the use and costs of general practice and hospital care. METHODS: retrospective longitudinal analysis using linked routine primary care records for 95,863 patients aged 65-95 years registered with 125 UK general practices between 2003 and 2014. Baseline frailty was measured using the electronic Frailty Index (eFI) and classified in four categories (non, mild, moderate, severe). Negative binomial regressions and ordinary least squares regressions with multilevel mixed effects were applied on the use and costs of general practice and hospital care. RESULTS: compared with non-frail status, annual general practitioner consultation incidence rate ratios (IRRs) were 1.24 (95% CI: 1.21-1.27) for mild, 1.41 (95% CI: 1.35-1.47) for moderate, and 1.52 (95% CI: 1.42-1.62) for severe frailty. For emergency hospital admissions, the respective IRRs were 1.64 (95% CI 1.60-1.68), 2.45 (95% CI 2.37-2.53) and 3.16 (95% CI: 3.00-3.33). Compared with non-frail people the IRR for inpatient days was 7.26 (95% CI 6.61-7.97) for severe frailty. Using 2013/14 reference costs, extra annual cost to the healthcare system per person was £561.05 for mild, £1,208.60 for moderate and £2,108.20 for severe frailty. This equates to a total additional cost of £5.8 billion per year across the UK. CONCLUSIONS: increasing frailty is associated with substantial increases in healthcare costs, driven by increased hospital admissions, longer inpatient stay, and increased general practice consultations.

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BACKGROUND: Primary care in UK is expected to use tools such as the electronic Frailty Index (eFI) to identify patients with frailty, which should be then validated and coded accordingly. AIM: To assess the influence of organisation and software on how eFI score and direct clinical validation occurs across practices in Leeds. METHOD: The 'minimum necessary' anonymised patient data required for the study (recorded eFI scores and frailty codes - mild, moderate or severe - with their dates of entry) was requested to the Health and Care Hub of the NHS Leeds Clinical Commissioning Group. Data from 44 185 patients from 104 practices using two different clinical software were collected. Descriptive statistics was carried out using SPSS software. RESULTS: 42 593 patients had a frailty code, 8881 had an eFI code. 7341 had both types of entry, and correlation between eFI and coded level of frailty was as expected high (85.3%), but there was statistically significant variation depending on practice and software used. When results did not match, there was a tendency to overstate, to code a level of frailty above the value to be assigned based on the numeric value of eFI, and it was more so on those practices using SystmOne software compared with those using EMIS Web. CONCLUSIONS: Although correlation was generally good, the variability encountered would indicate the need for training and also for software improvements to reduce current disparity and facilitate validation, so frailty level is adequately recorded.

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BACKGROUND: An electronic frailty index (eFI) has been developed and validated in the UK; it uses data from primary care electronic medical records (EMR) for effective frailty case-finding in primary care. This project examined the convergent validity of the eFI from Canadian primary care EMR data with a validated frailty index based on comprehensive geriatric assessment (FI-CGA), in order to understand its potential use in the Canadian context. METHODS: A cross-sectional validation study, using data from an integrated primary care research program for seniors living with frailty in Edmonton, AB. Eighty-five patients 65 years of age and older from six primary care physicians' practices were recruited. Patients were excluded if they were under 65 years of age, did not provide consent to participate in the program, or were living in a long term care facility at the time of enrolment. We used scatter plots to assess linearity and Pearson correlation coefficients to examine correlations. RESULTS: Results indicate a strong statistically significant correlation between the eFI and FI-CGA (r = 0.72, 95% CI 0.60-0.81, p < 0.001). A simple linear regression showed good ability of the eFI scores to predict FI-CGA scores (F (1,83) = 89.06, p < .0001, R2 = 0.51). Both indices were also correlated with age, number of chronic conditions and number of medications. CONCLUSIONS: The study findings support the convergent validity of the eFI, which further justifies implementation of a case-finding tool that uses routinely collected primary care data in the Canadian context.

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