Frailty Screening Using the Electronic Health Record Within a Medicare Accountable Care Organization

Kidney outcomes with SGLT2 inhibitor versus DPP4 inhibitor use in older adults with diabetes

BACKGROUND

While the kidney-protective effects of sodium-glucose co-transporter 2 (SGLT2) inhibitors have attracted much attention, there are limited real-world clinical data examining the effects of SGLT2 inhibitors on kidney function in older individuals. We aimed to compare the kidney outcomes between SGLT2 inhibitor and dipeptidyl peptidase 4 (DPP4) inhibitor use in older adults with diabetes.

METHODS

Using a nationwide claims database, we studied 6354 older adults (≥60 years of age) who had diabetes and were newly initiated on SGLT2 inhibitors or DPP4 inhibitors. A 1:4 propensity score matching algorithm was used to compare changes in estimated glomerular filtration rate (eGFR) between SGLT2 inhibitor and DPP4 inhibitor users. The primary outcome was a decrease in the rate of eGFR, which was obtained using a linear mixed-effects model with an unstructured covariance.

RESULTS

Following propensity score matching, 6354 individuals including 1271 SGLT2 inhibitor users and 5083 DPP4 inhibitor users {median age 68 years [interquartile range (IQR) 65-70], male 60.4%, median eGFR 69.0 ml/min/1.73 m2 [IQR 59.1-79.0], median haemoglobin A1c [HbA1c] 6.9% [IQR 6.5-7.4]} were analysed. SGLT2 inhibitor users had a slower eGFR decline than did DPP4 inhibitor users [-0.97 ml/min/1.73 m2/year (95% CI -1.24 to -0.70) versus -1.83 ml/min/1.73 m2/year (95% CI -1.97 to -1.69); P for interaction <.001]. This finding remained consistent across subgroups based on age, sex, body mass index, HbA1c level, renin-angiotensin system inhibitor use and baseline eGFR. Additionally, the risk of a ≥20%, ≥30% and ≥40% decrease in eGFR from baseline was significantly lower in SGLT2 inhibitor users than in DPP4 inhibitor users.

CONCLUSIONS

Our analysis, utilizing a nationwide epidemiological dataset, demonstrated that the decrease in eGFR was slower in individuals ≥60 years of age with diabetes who were prescribed SGLT2 inhibitors compared with those prescribed DPP4 inhibitors, suggesting a potential advantage of SGLT2 inhibitors for kidney outcomes even in older individuals with diabetes.

Application of an Electronic Frailty Index to Identify High-Risk Older Adults Using Electronic Health Record Data

BACKGROUND

Measurement of frailty is limited in clinical practice. Existing electronic frailty indices (eFIs) are derived from routine primary care encounters, with near-complete health condition capture. We aimed to develop an eFI from routinely collected clinical data and evaluate its performance in older adults without complete health condition capture.

METHODS

Using Electronic Health Record (EHR) data from an integrated regional health system, we created a cohort of patients who were ≥ 60 years on January 1, 2017 with two outpatient encounters in 3 years prior or one outpatient encounter in 2 years prior. We developed an eFI based on 31 age-related deficits identified using diagnostic and procedure codes. Frailty status was categorized as robust (eFI < 0.1), prefrail (0.1-0.2), frail (0.2-0.3), and very frail (> 0.3). We estimated cumulative incidence of mortality, acute care visits and readmissions by frailty, and fit Cox proportional hazards models. We repeated analyses in a sub-cohort of patients who receive primary care in the system.

RESULTS

Among 518,449 patients, 43% were male with a mean age of 72 years; 73% were robust, 16% were pre-frail, 7% were frail, and 4% were very frail. Very frail older adults had a significantly higher risk for mortality (HR: 4.1, 95% CI: 4.0-4.3), acute care visits (HR: 5.5, 95% CI: 5.4-5.6), and 90-day readmissions (HR: 2.1, 95% CI: 2.1-2.2) than robust older adults. In a primary care sub-cohort, while prevalence of deficits was higher, associations with outcomes were similar.

CONCLUSIONS

This eFI identified older adults at increased risk for adverse health outcomes even when data from routine primary care visits were not available. This tool can be integrated into EHRs for frailty assessment at scale.

An Electronic Frailty Index Based on Deficit Accumulation May Predict Glaucomatous Visual Field Progression

Purpose

To investigate whether an electronic frailty index (eFI) is associated with visual field loss in glaucoma.

Patients and Methods

We identified 1163 subjects ≥65 years old with glaucoma (1082 right eyes and 1042 left eyes) who had a calculable baseline eFI, and who had reliable visual fields at baseline and final follow-up. Multivariable linear regression models adjusting for demographic and clinical variables were used to assess the association between eFI and mean deviation at baseline and the change in mean deviation over time in each eye.

Results

Being pre-frail or frail was not associated with baseline MD, except in the right eye where being pre-frail was associated with a higher baseline MD. Increasing level of eFI was negatively correlated with change in MD (p<0.05 both eyes), but not baseline MD. Moreover, being frail was significantly associated with a more significant decline in MD in both eyes (Right eye: Beta -0.89, 95% CI (-1.71, -0.063), p=0.035; Left eye: Beta -1.25, 95% CI (-2.17, -0.34), p=0.007). Notably, baseline IOP was not associated with MD at baseline or the change in MD in the multivariable models.

Conclusion

Glaucoma patients who are frail may be at higher risk of experiencing visual field decline, independent of baseline IOP. Future studies should investigate whether interventions to improve frailty can decrease risk of glaucoma progression.

Impact of Pre-Existing Frailty on Cardiotoxicity Among Breast Cancer Patients Receiving Adjuvant Therapy

BACKGROUND

Prior research suggests that breast cancer patients with a high burden of frailty may face an increased risk of cardiotoxicity.

OBJECTIVES

This study sought to examine the association between frailty and cardiotoxicity rates in female breast cancer patients receiving adjuvant therapy after surgery.

METHODS

We analyzed data from the OneFlorida+ clinical research network, focusing on breast cancer patients treated with adjuvant chemotherapy and targeted therapy from 2012 to 2022. Cardiovascular rates during adjuvant treatments were calculated based on pre-existing frailty, measured using the cumulative deficit frailty index (electronic health record frailty index). We employed multivariable Gray's method to examine the association between frailty with cardiotoxicity.

RESULTS

The final cohort included 2,050 patients (mean age 50.6 years), with 415 (20.2%) experiencing nonfatal adverse cardiovascular events after adjuvant therapy. The incidence of adverse cardiovascular events was 17.8% in robust, 23.2% in prefrail, and 29.4% in frail patients. In multivariable analysis, prefrail (adjusted subdistribution HR [sHR]: 1.35; 95% CI: 1.06-1.71; P = 0.015) and frail (adjusted sHR: 1.70; 95% CI: 1.11-2.61; P = 0.015) patients had a higher likelihood of experiencing adverse cardiovascular events compared with robust patients. Among non-Hispanic White and Black patients, prefrail (adjusted sHR: 1.48; 95% CI: 1.04-2.11; P = 0.031; and adjusted sHR: 1.59; 95% CI: 1.06-2.37; P = 0.024, respectively) and frail (adjusted sHR: 1.96; 95% CI: 1.10-3.50; P = 0.022; and adjusted sHR: 2.13; 95% CI: 1.11-4.10; P = 0.023, respectively) patients were more likely to experience adverse cardiovascular events compared with robust patients. No significant differences were observed in other racial/ethnic groups.

CONCLUSIONS

These findings highlight the need for close monitoring of cardiotoxicity in frail breast cancer patients undergoing adjuvant treatments to improve cardiovascular risk management.

Impact of Lookback Duration on the Performance of a Claims-Based Frailty Proxy in Women With Stage I-III Breast Cancer

BACKGROUND

Frailty is an important prognostic indicator in older women with breast cancer. The Faurot frailty index, a validated claims-based frailty proxy measure, uses healthcare billing codes during a user-specified ascertainment window to predict frailty. We assessed how the duration of frailty ascertainment affected the ability of the Faurot frailty index to predict one-year mortality in women with stage I-II breast cancer.

METHODS

We included 128 857 women (66+ years) with stage I-III breast cancer in the SEER-Medicare database (2003-2019). The Faurot frailty index was calculated using 3-, 6-, 8-, and 12-month ascertainment windows prior to diagnosis or using all-available lookback. Associations between the Faurot frailty index using each window and one-year all-cause mortality were estimated using Kaplan-Meier curves. Discrimination of one-year mortality risk was assessed using C-statistics.

RESULTS

Five percent of women died during the year following diagnosis. Higher Faurot scores were associated with increased mortality risk for all frailty ascertainment windows. Differences in one-year mortality risk for women with high vs. low Faurot frailty scores were reduced when using all-available lookback (16% vs. 2%, difference = 15%, 95% CI 0.14-0.15) compared to shorter windows (e.g., 8 months: 25% vs. 2%, difference = 23%, 95% CI 0.22-0.24). C-statistics ranged from 0.758 (all-available lookback) to 0.770 (12 months) and were robust in subgroups defined by age, race, ethnicity, region, stage, and cancer subtype.

CONCLUSIONS

The Faurot frailty index performed well across 3- to 12-month frailty ascertainment windows in women with breast cancer. Researchers should employ this index to address confounding by frailty in studies of cancer populations.

Novel Electronic Health Record Strategies to Identify Frailty Among Hospitalized Older Adults with Multiple Chronic Conditions

A growing number of aging adults are living with multiple chronic conditions (MCC). Older adults living with MCC are predisposed to developing frailty, a state of decreased physiologic reserve that increases risk for geriatric syndromes and associated morbidity and mortality. The electronic frailty index (eFI) is computed passively using structured EHR data and can aid in prospective screening. Unfortunately, certain diagnoses, such as functional status assessments in unstructured documentation, are less likely captured by eFI, potentially underestimating the degree of frailty. Here, we discuss current gaps for using eFI to identify frail older adults living with MCC, and artificial intelligence (AI) approaches to enhance eFI accuracy. Accurate and routine frailty assessment can aid the generalist providing care to older adults living with MCC across multiple care settings to optimize physiologic reserve for these vulnerable patients.

Improving Surgical Outcomes for Older Adults with Adoption of Technological Advances in Comprehensive Geriatric Assessment

Frailty is a well-recognized predictor of poor surgical outcomes for older adults, yet effective measurements and interventions remain limited. Technological advances offer an opportunity to address this gap and improve surgical care for older adults. This paper reviews the background of frailty and comprehensive geriatric assessments in surgery, and how technological innovations can advance frailty measurement and intervention in surgical settings. We review two broad areas of technological advancement as applied to frailty in surgery: 1) Innovation in the use of electronic health records (EHR) using Artificial Intelligence (AI) and Machine Learning (ML), and 2) Novel uses for wearable sensors and mobile health (mHealth) applications. We explore the integration of AI and ML with EHR systems, which can surpass traditional comorbidity indices by providing comprehensive health assessments and enhancing prediction models. Innovations like the electronic Frailty Index (eFI) show promise in expanding the reach of frailty assessments and facilitating real-time screening. Additionally, wearable devices and mobile health (mHealth) applications offer new ways to monitor and improve physical activity, nutrition, and psychological well-being, supporting perioperative rehabilitation. While these technologies present challenges, such as the need for infrastructure, training, and data interoperability, they offer promising strategies to facilitate the assessment and management of frailty among surgical patients. Continued research and tailored implementation strategies will be essential to fully realize the potential of these advancements in improving surgical outcomes for frail older adults.

Unleashing frailty from laboratory into real world: A critical step toward frailty-guided clinical care of older adults

Understanding patients' degree of frailty is crucial for tailoring clinical care for older adults based on their physiologic reserve and health needs ("frailty-guided clinical care"). Two prerequisites for frailty-guided clinical care are: (1) access to frailty information at the point of care and (2) evidence to inform decisions based on frailty information. Recent advancements include web-based frailty assessment tools and their electronic health records integration for time-efficient, standardized assessments in clinical practice. Additionally, database frailty scores from administrative claims and electronic health records data enable scalable assessments and evaluation of the effectiveness and safety of medical interventions across different frailty levels using real-world data. Given limited evidence from clinical trials, real-world database studies can complement trial results and help treatment decisions for individuals with frailty. This article, based on the Thomas and Catherine Yoshikawa Award lecture I gave at the American Geriatrics Society Annual Meeting in Long Beach, California, on May 5, 2023, outlines our group's contributions: (1) developing and integrating a frailty index calculator (Senior Health Calculator) into the electronic health records at an academic medical center; (2) developing a claims-based frailty index for Medicare claims; (3) applying this index to evaluate the effect of medical interventions for patients with and without frailty; and (4) efforts to disseminate frailty assessment tools through the launch of the eFrailty website and the forthcoming addition of the claims-based frailty index to the Centers for Medicare and Medicaid Services Chronic Conditions Data Warehouse. This article concludes with future directions for frailty-guided clinical care.

The Association Between an Electronic Health Record (EHR)-Embedded Frailty Index and Patient-Reported Outcomes Among Patients with Metastatic Non-Small-Cell Lung Cancer on Immunotherapy: A Brief Report

Background: While frailty is a well-established predictor of overall mortality among patients with metastatic non-small cell lung cancer (mNSCLC), its association with patient-reported outcomes is not well-characterized. The goal of this study was to examine the association between an electronic frailty index (eFI) score and patient-reported outcome measures along with prognostic awareness among patients with mNSCLC receiving immunotherapy. Methods: In a cross-sectional study, patients with mNSCLC who were on immunotherapy completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC-QLQ-C30) and the National Cancer Institute Patient Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). We utilized bivariate analyses to compare quality of life, symptoms, supportive services, and prognostic awareness among 3 groups defined by e-frailty status. Results: Sixty patients (mean age 62.5 years, 75% Caucasian, 60% women) participated. Most patients were pre-frail (68%), with 13% being frail and 18% non-frail. Pre-frail and frail patients had significantly lower physical function scores (mean 83.9 fit vs 74.8 pre-frail vs 60.0 frail, P = .04) and higher rates of self-reported pain (75% frail vs 41.5% pre-frail vs 18.2% fit; P = .04) compared to non-frail patients. We found no differences in palliative referral rates. Conclusion: Pre-frail and frail mNSCLC patients identified by the eFI have higher rates of pain and physical functional impairments than non-frail patients. These findings highlight the importance of emphasizing preventive interventions targeting social needs, functional limitations, and pain management, especially among pre-frail patients to reduce further decline.

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

BACKGROUND

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

OBJECTIVES

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

STUDY DESIGN AND SETTING

A retrospective cohort of the HMO members.

PARTICIPANTS

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

PREDICTORS

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

OUTCOMES

One-year all-cause mortality or hospitalization.

STATISTICAL ANALYSIS

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

RESULTS

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

CONCLUSIONS

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

Promoting serious illness conversations in primary care through telehealth among persons living with cognitive impairment

BACKGROUND

serious illness conversations (SIC), particularly for persons living with cognitive impairment (PLCI), inconsistently happen in primary care. Pragmatic, scalable strategies are needed to promote SIC for PLCI.

DESIGN

Pragmatic, prospective single-arm pilot study that occurred between July 1, 2021 and May 30, 2022 across seven primary care practices in North Carolina.

PARTICIPANTS

Community-dwelling patients aged 65 and older with known or probable mild cognitive impairment or dementia (with decision-making capacity) and their care partners (if available).

INTERVENTION

SIC telehealth intervention (TeleVoice) via video or telephone to assist PLCI in discussing their current goals, values, and future medical preferences, while facilitating documentation within the EHR.

MAIN OUTCOMES

Main feasibility outcomes included reach/enrollment, intervention completion, and adoption rates at the clinic and provider level. Primary effectiveness outcomes included SIC documentation and quality within the EHR and usage of advance care planning billing (ACP) codes.

RESULTS

Of the 163 eligible PLCI approached, 107 (66%) enrolled (mean age 83.7 years, 68.2% female, 16.8% Black, 22% living in a geographic area of high socioeconomic disadvantage) and 81 (76%) completed the SIC telehealth intervention; 45 care partners agreed to participate (mean age 71.5 years, 80% female). Adoption at clinic level was 50%, while 75% of providers within these clinics participated. Among PLCI that completed the intervention, SIC documentation and usage of ACP billing codes was 100% and 96%, respectively, with 96% (n = 78) having high-quality SIC documentation. No significant differences were observed between telephone and video visits.

CONCLUSION

These findings provide preliminary evidence to support the feasibility of conducting SICs through telehealth to specifically meet the needs of community-dwelling PLCI. Further investigation of the sustainability of the intervention and its long-term impact on patient and caregiver outcomes is needed.

Cross-sectional and longitudinal associations among healthcare costs and deficit accumulation

BACKGROUND

Type 2 diabetes mellitus and overweight/obesity increase healthcare costs. Both are also associated with accelerated aging. However, the contributions of this accelerated aging to increased healthcare costs are unknown.

METHODS

We use data from a 8-year longitudinal cohort followed at 16 U.S. clinical research sites. Participants were adults aged 45-76 years with established type 2 diabetes and overweight or obesity who had enrolled in the Action for Health in Diabetes clinical trial. They were randomly (1:1) assigned to either an intensive lifestyle intervention focused on weight loss versus a comparator of diabetes support and education. A validated deficit accumulation frailty index (FI) was used to characterize biological aging. Discounted annual healthcare costs were estimated using national databases in 2012 dollars. Descriptive characteristics were collected by trained and certified staff.

RESULTS

Compared with participants in the lowest tertile (least frail) of baseline FI, those in the highest tertile (most frail) at Year 1 averaged $714 (42%) higher medication costs, $244 (22%) higher outpatient costs, and $800 (134%) higher hospitalization costs (p < 0.001). At Years 4 and 8, relatively greater increases in FI (third vs. first tertile) were associated with an approximate doubling of total healthcare costs (p < 0.001). Mean (95% confidence interval) relative annual savings in healthcare costs associated with randomization to the intensive lifestyle intervention were $437 ($195, $579) per year during Years 1-4 and $461 ($232, $690) per year during Years 1-8. These were attenuated and the 95% confidence interval no longer excluded $0 after adjustment for the annual FI differences from baseline.

CONCLUSIONS

Deficit accumulation frailty tracks well with healthcare costs among adults with type 2 diabetes and overweight or obesity. It may serve as a useful marker to project healthcare needs and as an intermediate outcome in clinical trials.

The Complex Relationship Between Social and Functional Needs in Frail Older Adults

Background

There has been a growing interest in integrating social and function-focused care into health care settings. Little is known about what older adults perceive as the needs that impact their lives, and the resources to address patients' social and functional needs often exist outside of traditional health care settings.

Methods

Our objective was to understand frail older adults' and community organizations' perspectives on what social and functional needs impact older adults' health, the support they receive, and how organizations and health systems could partner to address these needs. We conducted semi-structured interviews with patients and community-based organizations. Patients were aged 65 years or older, frail (electronic frailty index greater than 0.21), and at an increased geographic risk of unmet social needs (Area Deprivation Index greater than or equal to the 75th percentile). Staff were from organizations that provided social and/or functional resources to older adults. We used an inductive content analysis approach and the constant comparative method to analyze the data and identify themes.

Results

We interviewed 23 patients and 28 staff from 22 distinct organizations. We found that social, financial, and functional needs were common and highly intertwined among older adults with frailty, but the support they received at home, from their health care providers, and from community organizations was highly varied.

Limitations

Our sample was limited to participants from one county, so the results may not be generalizable to other areas. We only inter-viewed organizations and patients with frailty.

Conclusions

Health systems and community organizations have distinct areas of expertise, and purposeful collaboration between them could be important in addressing the needs of frail older adults.

What is the additive value of nutritional deficiency to VA-FI in the risk assessment for heart failure patients?

OBJECTIVES

To assess the impact of adding the Prognostic Nutritional Index (PNI) to the U.S. Veterans Health Administration frailty index (VA-FI) for the prediction of time-to-death and other clinical outcomes in Veterans hospitalized with Heart Failure.

METHODS

A retrospective cohort study of veterans hospitalized for heart failure (HF) from October 2015 to October 2018. Veterans ≥50 years with albumin and lymphocyte counts, needed to calculate the PNI, in the year prior to hospitalization were included. We defined malnutrition as PNI ≤43.6, based on the Youden index. VA-FI was calculated from the year prior to the hospitalization and identified three groups: robust (≤0.1), prefrail (0.1-0.2), and frail (>0.2). Malnutrition was added to the VA-FI (VA-FI-Nutrition) as a 32nd deficit with the total number of deficits divided by 32. Frailty levels used the same cut-offs as the VA-FI. We compared categories based on VA-FI to those based on VA-FI-Nutrition and estimated the hazard ratio (HR) for post-discharge all-cause mortality over the study period as the primary outcome and other adverse events as secondary outcomes among patients with reduced or preserved ejection fraction in each VA-FI and VA-FI-Nutrition frailty groups.

RESULTS

We identified 37,601 Veterans hospitalized for HF (mean age: 73.4 ± 10.3 years, BMI: 31.3 ± 7.4 kg/m2). In general, VA-FI-Nutrition reclassified 1959 (18.6%) Veterans to a higher frailty level. The VA-FI identified 1,880 (5%) as robust, 8,644 (23%) as prefrail, and 27,077 (72%) as frail. The VA-FI-Nutrition reclassified 382 (20.3%) from robust to prefrail and 1577 (18.2%) from prefrail to frail creating the modified-prefrail and modified-frail categories based on the VA-FI-Nutrition. We observed shorter time-to-death among Veterans reclassified to a higher frailty status vs. those who remained in their original group (Median of 2.8 years (IQR:0.5,6.8) in modified-prefrail vs. 6.3 (IQR:1.8,6.8) years in robust, and 2.2 (IQR:0.7,5.7) years in modified-frail vs. 3.9 (IQR:1.4,6.8) years in prefrail). The adjusted HR in the reclassified groups was also significantly higher in the VA-FI-Nutrition frailty categories with a 38% increase in overall all-cause mortality among modified-prefrail and a 50% increase among modified-frails. Similar trends of increasing adverse events were also observed among reclassified groups for other clinical outcomes.

CONCLUSION

Adding PNI to VA-FI provides a more accurate and comprehensive assessment among Veterans hospitalized for HF. Clinicians should consider adding a specific nutrition algorithm to automated frailty tools to improve the validity of risk prediction in patients hospitalized with HF.

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.

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

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

Vulnerability to functional decline is associated with noncardiovascular cause of 90-day readmission in hospitalized patients with heart failure

BACKGROUND

Hospital readmission is common among patients with heart failure. Vulnerability to decline in physical function may increase the risk of noncardiovascular readmission for these patients, but the association between vulnerability and the cause of unplanned readmission is poorly understood, inhibiting the development of effective interventions.

OBJECTIVES

We examined the association of vulnerability with the cause of readmission (cardiovascular vs. noncardiovascular) among hospitalized patients with acute decompensated heart failure.

DESIGNS, SETTINGS, AND PARTICIPANTS

This prospective longitudinal study is part of the Vanderbilt Inpatient Cohort Study.

MAIN OUTCOME AND MEASURES

The primary outcome was the cause of unplanned readmission (cardiovascular vs. noncardiovascular). The primary independent variable was vulnerability, measured using the Vulnerable Elders Survey (VES-13).

RESULTS

Among 804 hospitalized patients with acute decompensated heart failure, 315 (39.2%) experienced an unplanned readmission within 90 days of discharge. In a multinomial logistic model with no readmission as the reference category, higher vulnerability was associated with readmission for noncardiovascular causes (relative risk ratio [RRR] = 1.36, 95% confidence interval [CI]: 1.06-1.75) in the first 90 days after discharge. The VES-13 score was not associated with readmission for cardiovascular causes (RRR = 0.94, 95% CI: 0.75-1.17).

CONCLUSIONS

Vulnerability to functional decline predicted noncardiovascular readmission risk among hospitalized patients with heart failure. The VES-13 is a brief, validated, and freely available tool that should be considered in planning care transitions. Additional work is needed to examine the efficacy of interventions to monitor and mitigate noncardiovascular concerns among vulnerable patients with heart failure being discharged from the hospital.

Frailty Status Modifies the Efficacy of ICD Therapy for Primary Prevention Among Patients With HF

BACKGROUND

Implantable cardioverter-defibrillator (ICD) therapy is recommended to reduce mortality risk in patients with heart failure with reduced ejection fraction (HFrEF). Frailty is common among patients with HFrEF and is associated with increased mortality risk. Whether the therapeutic efficacy of ICD is consistent among frail and nonfrail patients with HFrEF remains unclear.

OBJECTIVES

The aim of this study was to evaluate the effect modification of baseline frailty burden on ICD efficacy for primary prevention among participants of the SCD-HeFT (Sudden Cardiac Death in Heart Failure Trial).

METHODS

Participants in SCD-HeFT with HFrEF randomized to ICD vs placebo were included. Baseline frailty was estimated using the Rockwood Frailty Index (FI), and participants were stratified into high (FI > median) vs low (FI ≤ median) frailty burden groups. Multivariable Cox models with multiplicative interaction terms (frailty × treatment arm) were constructed to evaluate whether baseline frailty status modified the treatment effect of ICD for all-cause mortality.

RESULTS

The study included 1,676 participants (mean age: 59 ± 12 years, 23% women) with a median FI of 0.30 (IQR: 0.23-0.37) in the low frailty group and 0.54 (IQR: 0.47-0.60) in the high frailty group. In adjusted Cox models, baseline frailty status significantly modified the treatment effect of ICD therapy (Pinteraction = 0.047). In separate stratified analysis by frailty status, ICD therapy was associated with a lower risk of all-cause mortality among participants with low frailty burden (HR: 0.56; 95% CI: 0.40-0.78) but not among those with high frailty burden (HR: 0.86; 95% CI: 0.68-1.09).

CONCLUSIONS

Baseline frailty modified the efficacy of ICD therapy with a significant mortality benefit observed among participants with HFrEF and a low frailty burden but not among those with a high frailty burden.

The Impact of Frailty on Adjuvant Therapies Not Offered to or Declined by Breast Cancer Surgery Patients

INTRODUCTION

The impact of frailty on adjuvant therapies not offered to or declined by elderly breast cancer surgery patients has been understudied.

METHODS

This is a retrospective review of a prospectively managed single-center database including all breast cancer patients >65 years undergoing surgery in 2021. Frailty was determined using an electronic frailty index (eFI) derived from electronic health data. Patients were categorized as Fit (eFI ≤ .10), Pre-frail (.10 < eFI ≤.21), or Frail (eFI > .21). Chart review was performed to collect data on adjuvant therapies not offered or declined. Descriptive statistics and logistic regression were performed.

RESULTS

Of 133 patients, 16.5% were frail, 46.6% were pre-frail, and 36.8% were fit. Demographics were similar among groups except age and comorbidities. Of those with adjuvant therapy indicated (n = 123), 15.4% were not offered at least one indicated therapy. Of those offered therapy, some therapy was declined in 22.7%. Frail patients more often were not offered or declined some therapy (frail: 63.2%, pre-frail 36.2%, fit: 28.2%, P = .03). Frailty was associated with having some therapy not offered or declined on univariate modeling (OR 4.4 95% CI 1.4-13.5, P = .01) but not on multivariate. Being frail was associated with higher odds of readmission at 6 months on multivariate analysis (OR 9.5, 95% CI: 1.7-54.2. P = .01).

CONCLUSION

Over half of frail patients are not offered or decline some adjuvant therapy. The impact of this requires further study. Given their higher odds of readmission, frail patients require close postoperative monitoring to prevent the interruption of adjuvant therapies.

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.

Combining the Hospital Frailty Risk Score With the Charlson and Elixhauser Multimorbidity Indices to Identify Older Patients at Risk of Poor Outcomes in Acute Care

OBJECTIVE

The Hospital Frailty Risk Score (HFRS) can be applied to medico-administrative datasets to determine the risks of 30-day mortality and long length of stay (LOS) in hospitalized older patients. The objective of this study was to compare the HFRS with Charlson and Elixhauser comorbidity indices, used separately or combined.

DESIGN

A retrospective analysis of the French medical information database. The HFRS, Charlson index, and Elixhauser index were calculated for each patient based on the index stay and hospitalizations over the preceding 2 years. Different constructions of the HFRS were considered based on overlapping diagnostic codes with either Charlson or Elixhauser indices. We used mixed logistic regression models to investigate the association between outcomes, different constructions of HFRS, and associations with comorbidity indices.

SETTING

743 hospitals in France.

PARTICIPANTS

All patients aged 75 years or older hospitalized as an emergency in 2017 (n=1,042,234).Main outcome measures: 30-day inpatient mortality and LOS >10 days.

RESULTS

The HFRS, Charlson, and Elixhauser indices were comparably associated with an increased risk of 30-day inpatient mortality and long LOS. The combined model with the highest c-statistic was obtained when associating the HFRS with standard adjustment and Charlson for 30-day inpatient mortality (adjusted c-statistics: HFRS=0.654; HFRS + Charlson = 0.676) and with Elixhauser for long LOS (adjusted c-statistics: HFRS= 0.672; HFRS + Elixhauser =0.698).

CONCLUSIONS

Combining comorbidity indices and HFRS may improve discrimination for predicting long LOS in hospitalized older people, but adds little to Charlson's 30-day inpatient mortality risk.

Association of CT-Derived Skeletal Muscle and Adipose Tissue Metrics with Frailty in Older Adults

RATIONALE AND OBJECTIVES

Tools are needed for frailty screening of older adults. Opportunistic analysis of body composition could play a role. We aim to determine whether computed tomography (CT)-derived measurements of muscle and adipose tissue are associated with frailty.

MATERIALS AND METHODS

Outpatients aged ≥ 55 years consecutively imaged with contrast-enhanced abdominopelvic CT over a 3-month interval were included. Frailty was determined from the electronic health record using a previously validated electronic frailty index (eFI). CT images at the level of the L3 vertebra were automatically segmented to derive muscle metrics (skeletal muscle area [SMA], skeletal muscle density [SMD], intermuscular adipose tissue [IMAT]) and adipose tissue metrics (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT]). Distributions of demographic and CT-derived variables were compared between sexes. Sex-specific associations of muscle and adipose tissue metrics with eFI were characterized by linear regressions adjusted for age, race, ethnicity, duration between imaging and eFI measurements, and imaging parameters.

RESULTS

The cohort comprised 886 patients (449 women, 437 men, mean age 67.9 years), of whom 382 (43%) met the criteria for pre-frailty (ie, 0.10 < eFI ≤ 0.21) and 138 (16%) for frailty (eFI > 0.21). In men, 1 standard deviation changes in SMD (β = -0.01, 95% confidence interval [CI], -0.02 to -0.001, P = .02) and VAT area (β = 0.008, 95% CI, 0.0005-0.02, P = .04), but not SMA, IMAT, or SAT, were associated with higher frailty. In women, none of the CT-derived muscle or adipose tissue metrics were associated with frailty.

CONCLUSION

We observed a positive association between frailty and CT-derived biomarkers of myosteatosis and visceral adiposity in a sex-dependent manner.

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

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

Utilizing multimodal approach to identify candidate pathways and biomarkers and predicting frailty syndrome in individuals from UK Biobank

Frailty, a prevalent clinical syndrome in aging adults, is characterized by poor health outcomes, represented via a standardized frailty-phenotype (FP), and Frailty Index (FI). While the relevance of the syndrome is gaining awareness, much remains unclear about its underlying biology. Further elucidation of the genetic determinants and possible underlying mechanisms may help improve patients' outcomes allowing healthy aging.Genotype, clinical and demographic data of subjects (aged 60-73 years) from UK Biobank were utilized. FP was defined on Fried's criteria. FI was calculated using electronic-health-records. Genome-wide-association-studies (GWAS) were conducted and polygenic-risk-scores (PRS) were calculated for both FP and FI. Functional analysis provided interpretations of underlying biology. Finally, machine-learning (ML) models were trained using clinical, demographic and PRS towards identifying frail from non-frail individuals.Thirty-one loci were significantly associated with FI accounting for 12% heritability. Seventeen of those were known associations for body-mass-index, coronary diseases, cholesterol-levels, and longevity, while the rest were novel. Significant genes CDKN2B and APOE, previously implicated in aging, were reported to be enriched in lipoprotein-particle-remodeling. Linkage-disequilibrium-regression identified specific regulation in limbic-system, associated with long-term memory and cognitive-function. XGboost was established as the best performing ML model with area-under-curve as 85%, sensitivity and specificity as 0.75 and 0.8, respectively.This study provides novel insights into increased vulnerability and risk stratification of frailty syndrome via a multi-modal approach. The findings suggest frailty as a highly polygenic-trait, enriched in cholesterol-remodeling and metabolism and to be genetically associated with cognitive abilities. ML models utilizing FP and FI + PRS were established that identified frailty-syndrome patients with high accuracy.

Strategies to Assess and Manage Frailty among Patients Diagnosed with Primary Malignant Brain Tumors

OPINION STATEMENT

Frailty refers to a biologic process that results in reduced physiologic and functional reserve. Patients diagnosed with primary malignant brain tumors experience high symptom burden from tumor and tumor-directed treatments that, coupled with previous comorbidities, may contribute to frailty. Within the primary malignant brain tumor population, frailty is known to associate with mortality, higher healthcare utilization, and increased risk of postoperative complications. As such, methods to assess and manage frailty are paramount. However, there is currently no clear consensus on how to best assess and manage frailty throughout the entirety of the disease trajectory. Given the association between frailty and health outcomes, more research is needed to determine best practice protocols for the assessment and management of frailty among patients diagnosed with primary malignant brain tumors.

Construct validity of the electronic Veterans Affairs Frailty Index against clinician frailty assessment

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.

Frailty in Aging HIV-Positive Individuals: An Evolving Healthcare Landscape

The life expectancy of people living with HIV (PLWH) has greatly increased due to advancements in combination antiretroviral treatment (cART). However, this longer life has also increased the prevalence of age-related comorbidities, such as frailty, which now manifest sooner in this group. Frailty, a term coined by the insurance industry, has been broadened to include physical, cognitive, and emotional elements and has been recognized as a critical predictor of negative health outcomes. With the median age of PLWH now in the mid-50s, treating frailty is critical given its link to chronic diseases, cognitive decline, and even death. Frailty assessment tools, such as the Frailty Phenotype (FP) and the Frailty Index (FI), are used to identify vulnerable people. Understanding the pathophysiology of frailty in PLWH indicates the role of immunological mechanisms. Frailty screening and management in this group have progressed, with specialized clinics and programs concentrating on multidisciplinary care. Potential pharmacotherapeutic solutions, as well as novel e-health programs and sensors, are in the future of frailty treatment, but it is critical to ensure that frailty evaluation is not exploited to perpetuate ageist healthcare practices. This narrative review investigates the changing healthcare environment for older people living with HIV (OPLWH), notably in high-income countries. It emphasizes the significance of identifying and managing frailty as a crucial feature of OPLWH's holistic care and well-being.

Automated Electronic Frailty Index is Associated with Non-home Discharge in Patients Undergoing Open Revascularization for Peripheral Vascular Disease

BACKGROUND

Frailty is associated with adverse surgical outcomes including post-operative complications, needs for post-acute care, and mortality. While multiple frailty screening tools exist, most are time and resource intensive. Here we examine the association of an automated electronic frailty index (eFI), derived from routine data in the Electronic Health Record (EHR), with outcomes in vascular surgery patients undergoing open, lower extremity revascularization.

METHODS

A retrospective analysis at a single academic medical center from 2015 to 2019 was completed. Information extracted from the EHR included demographics, eFI, comorbidity, and procedure type. Frailty status was defined as fit (eFI≤0.10), pre-frail (0.100.21). Outcomes included length of stay (LOS), 30-day readmission, and non-home discharge.

RESULTS

We included 295 patients (mean age 65.9 years; 31% female), with the majority classified as pre-frail (57%) or frail (32%). Frail patients exhibited a higher degree of comorbidity and were more likely to be classified as American Society of Anesthesiologist class IV (frail: 46%, pre-frail: 27%, and fit: 18%, P = 0.0012). There were no statistically significant differences in procedure type, LOS, or 30-day readmissions based on eFI. Frail patients were more likely to expire in the hospital or be discharged to an acute care facility (31%) compared to pre-frail (14%) and fit patients (15%, P = 0.002). Adjusting for comorbidity, risk of non-home discharge was higher comparing frail to pre-frail patients (OR 3.01, 95% CI 1.40-6.48).

DISCUSSION

Frail patients, based on eFI, undergoing elective, open, lower extremity revascularization were twice as likely to not be discharged home.

Automated Electronic Frailty Index-Identified Frailty Status and Associated Postsurgical Adverse Events

Importance

Electronic frailty index (eFI) is an automated electronic health record (EHR)-based tool that uses a combination of clinical encounters, diagnosis codes, laboratory workups, medications, and Medicare annual wellness visit data as markers of frailty status. The association of eFI with postanesthesia adverse outcomes has not been evaluated.

Objective

To examine the association of frailty, calculated as eFI at the time of the surgical procedure and categorized as fit, prefrail, or frail, with adverse events after elective noncardiac surgery.

Design, Setting, and Participants

This cohort study was conducted at a tertiary care academic medical center in Winston-Salem, North Carolina. The cohort included patients 55 years or older who underwent noncardiac surgery of at least 1 hour in duration between October 1, 2017, and June 30, 2021.

Exposure

Frailty calculated by the eFI tool. Preoperative eFI scores were calculated based on available data 1 day prior to the procedure and categorized as fit (eFI score: ≤0.10), prefrail (eFI score: >0.10 to ≤0.21), or frail (eFI score: >0.21).

Main Outcomes and Measures

The primary outcome was a composite of the following 8 adverse component events: 90-item Patient Safety Indicators (PSI 90) score, hospital-acquired conditions, in-hospital mortality, 30-day mortality, 30-day readmission, 30-day emergency department visit after surgery, transfer to a skilled nursing facility after surgery, or unexpected intensive care unit admission after surgery. Secondary outcomes were each of the component events of the composite.

Results

Of the 33 449 patients (median [IQR] age, 67 [61-74] years; 17 618 females [52.7%]) included, 11 563 (34.6%) were classified as fit, 15 928 (47.6%) as prefrail, and 5958 (17.8%) as frail. Using logistic regression models that were adjusted for age, sex, race and ethnicity, and comorbidity burden, patients with prefrail (odds ratio [OR], 1.24; 95% CI, 1.18-1.30; P < .001) and frail (OR, 1.71; 95% CI, 1.58-1.82; P < .001) statuses were more likely to experience postoperative adverse events compared with patients with a fit status. Subsequent adjustment for all other potential confounders or covariates did not alter this association. For every increase in eFI of 0.03 units, the odds of a composite of postoperative adverse events increased by 1.06 (95% CI, 1.03-1.13; P < .001).

Conclusions and Relevance

This cohort study found that frailty, as measured by an automatically calculated index integrated within the EHR, was associated with increased risk of adverse events after noncardiac surgery. Deployment of eFI tools may support screening and possible risk modification, especially in patients who undergo high-risk surgery.

Recent developments in frailty identification, management, risk factors and prevention: A narrative review of leading journals in geriatrics and gerontology

Frailty is an age-related clinical condition characterised by an increased susceptibility to stressors and an elevated risk of adverse outcomes such as mortality. In the light of global population ageing, the prevalence of frailty is expected to soar in coming decades. This narrative review provides critical insights into recent developments and emerging practices in frailty research regarding identification, management, risk factors, and prevention. We searched journals in the top two quartiles of geriatrics and gerontology (from Clarivate Journal Citation Reports) for articles published between 01 January 2018 and 20 December 2022. Several recent developments were identified, including new biomarkers and biomarker panels for frailty screening and diagnosis, using artificial intelligence to identify frailty, and investigating the altered response to medications by older adults with frailty. Other areas with novel developments included exercise (including technology-based exercise), multidimensional interventions, person-centred and integrated care, assistive technologies, analysis of frailty transitions, risk-factors, clinical guidelines, COVID-19, and potential future treatments. This review identified a strong need for the implementation and evaluation of cost-effective, community-based interventions to manage and prevent frailty. Our findings highlight the need to better identify and support older adults with frailty and involve those with frailty in shared decision-making regarding their care.

Assessment of an embedded primary care-derived electronic health record (EHR) frailty index (eFI) in older adults with acute myeloid leukemia

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.

Machine learning functional impairment classification with electronic health record data

BACKGROUND

Poor functional status is a key marker of morbidity, yet is not routinely captured in clinical encounters. We developed and evaluated the accuracy of a machine learning algorithm that leveraged electronic health record (EHR) data to provide a scalable process for identification of functional impairment.

METHODS

We identified a cohort of patients with an electronically captured screening measure of functional status (Older Americans Resources and Services ADL/IADL) between 2018 and 2020 (N = 6484). Patients were classified using unsupervised learning K means and t-distributed Stochastic Neighbor Embedding into normal function (NF), mild to moderate functional impairment (MFI), and severe functional impairment (SFI) states. Using 11 EHR clinical variable domains (832 variable input features), we trained an Extreme Gradient Boosting supervised machine learning algorithm to distinguish functional status states, and measured prediction accuracies. Data were randomly split into training (80%) and test (20%) sets. The SHapley Additive Explanations (SHAP) feature importance analysis was used to list the EHR features in rank order of their contribution to the outcome.

RESULTS

Median age was 75.3 years, 62% female, 60% White. Patients were classified as 53% NF (n = 3453), 30% MFI (n = 1947), and 17% SFI (n = 1084). Summary of model performance for identifying functional status state (NF, MFI, SFI) was AUROC (area under the receiving operating characteristic curve) 0.92, 0.89, and 0.87, respectively. Age, falls, hospitalization, home health use, labs (e.g., albumin), comorbidities (e.g., dementia, heart failure, chronic kidney disease, chronic pain), and social determinants of health (e.g., alcohol use) were highly ranked features in predicting functional status states.

CONCLUSION

A machine learning algorithm run on EHR clinical data has potential utility for differentiating functional status in the clinical setting. Through further validation and refinement, such algorithms can complement traditional screening methods and result in a population-based strategy for identifying patients with poor functional status who need additional health resources.

Change in a Claims-Based Frailty Index, Mortality, and Health Care Costs in Medicare Beneficiaries

BACKGROUND

A claims-based frailty index (CFI) allows measurement of frailty on a population scale. Our objective was to examine the association of changes in CFI over 12 months with mortality and Medicare costs.

METHODS

We used a 5% sample of fee-for-service Medicare beneficiaries. We estimated CFI (range: 0–1: nonfrail (<0.25), mildly frail (0.25–0.34), moderately-to-severely frail (≥0.35) on January 1, 2015 and January 1, 2016. Beneficiaries were categorized as having a large decrease (-<0.045), small decrease (-≤0.045-0.015), stable (±0.015), small increase (>0.015-0.045), or large increase (>0.045). We used Cox proportional hazards model to estimate hazard ratio (HR) for mortality adjusting for age, sex, and 2015 CFI value and compared total Medicare costs from January 1, 2016 to December 31, 2016.

RESULTS

The study population included 995 664 beneficiaries (mean age 77 years, 56.8% female). In nonfrail (n = 906 046), HR (95% confidence interval [CI]) ranged from 0.71 (0.67-0.75) for a large decrease to 2.75 (2.68-2.33) for a large increase. In moderate-to-severely frail beneficiaries (n = 16 527), the corresponding HR (95% CI) ranged from 0.63 (0.57-0.70) to 1.21 (1.06-1.38). The mean total Medicare cost per member per year (standard deviation) was from $12 149 ($83 508) in nonfrail beneficiaries to $61 155 ($345 904) in moderate-to-severely frail beneficiaries.

CONCLUSIONS

One-year changes in CFI are associated with elevated mortality risk and health care costs across all levels of frailty.

Development and validation of a hospital frailty risk measure using Canadian clinical administrative data

BACKGROUND

Accessible measures specific to the Canadian context are needed to support health system planning for older adults living with frailty. We sought to develop and validate the Canadian Institute for Health Information (CIHI) Hospital Frailty Risk Measure (HFRM).

METHODS

Using CIHI administrative data, we conducted a retrospective cohort study involving patients aged 65 years and older who were discharged from Canadian hospitals from Apr. 1, 2018, to Mar. 31, 2019. We used a 2-phase approach to develop and validate the CIHI HFRM. The first phase, construction of the measure, was based on the deficit accumulation approach (identification of age-related conditions using a 2-year look-back). The second phase involved refinement into 3 formats (continuous risk score, 8 risk groups and binary risk measure), with assessment of their predictive validity for several frailty-related adverse outcomes using data to 2019/20. We assessed convergent validity with the United Kingdom Hospital Frailty Risk Score.

RESULTS

The cohort consisted of 788 701 patients. The CIHI HFRM included 36 deficit categories and 595 diagnosis codes that cover morbidity, function, sensory loss, cognition and mood. The median continuous risk score was 0.111 (interquartile range 0.056-0.194, equivalent to 2-7 deficits); 35.1% (n = 277 000) of the cohort were found at risk of frailty (≥ 6 deficits). The CIHI HFRM showed satisfactory predictive validity and reasonable goodness-of-fit. For the continuous risk score format (unit = 0.1), the hazard ratio (HR) for 1-year risk of death was 1.39 (95% confidence interval [CI] 1.38-1.41), with a C-statistic of 0.717 (95% CI 0.715-0.720); the odds ratio for high users of hospital beds was 1.85 (95% CI 1.82-1.88), with a C-statistic of 0.709 (95% CI 0.704-0.714), and the HR of 90-day admission to long-term care was 1.91 (95% CI 1.88-1.93), with a C-statistic of 0.810 (95% CI 0.808-0.813). Compared with the continuous risk score, using a format of 8 risk groups had similar discriminatory ability and the binary risk measure had slightly weaker performance.

INTERPRETATION

The CIHI HFRM is a valid tool showing good discriminatory power for several adverse outcomes. The tool can be used by decision-makers and researchers by providing information on hospital-level prevalence of frailty to support system-level capacity planning for Canada's aging population.

Evaluation of "Care Plus," A Multidisciplinary Program to Improve Population Health for Patients With High Utilization

With rising health care costs, health systems have adopted alternative care models targeting high-need, high-cost patients to improve chronic disease management and population health. Intensive primary care teams may reduce health care utilization by tackling medical and psychosocial needs specific to this patient population. This study presents health care utilization trends from a high-intensity primary care program that employs a multidisciplinary team (including clinicians, psychologists, pharmacists, chaplaincy, and community health workers) and community partnerships. Using descriptive statistics and Poisson rates of differences, this study evaluates patient and utilization characteristics of those enrolled (n = 341) versus declined (n = 54) program participation from 2013 to 2020. Both enrolled and declined patients experienced significant reduction in emergency department and inpatient utilization, but differences between enrolled and declined patients were not statistically significant. Programs aimed at decreasing health care utilization for high-need, high-cost, medically complex patients may be best supported by interventions that simultaneously address social and behavioral health needs.

Frailty impacts immune responses to Moderna COVID-19 mRNA vaccine in older adults

BACKGROUND

Immune responses to COVID-19 mRNA vaccines have not been well characterized in frail older adults. We postulated that frailty is associated with impaired antibody and cellular mRNA vaccine responses.

METHODS

We followed older adults in a retirement facility with longitudinal clinical and serological samples from the first Moderna mRNA-1273 vaccine dose starting in February 2021 through their 3rd (booster) vaccine dose. Outcomes were antibody titers, antibody avidity, and AIM+ T cell function and phenotype. Statistical analysis used linear regression with clustered error for antibody titers over multiple timepoints with clinical predictors including, age, sex, prior infection status, and clinical frailty scale (CFS) score. T cell function analysis used linear regression models with clinical predictors and cellular memory phenotype variables.

RESULTS

Participants (n = 15) had median age of 90 years and mild, moderate, or severe frailty scores (n = 3, 7, or 5 respectively). Over the study time course, anti-spike antibody titers were 10-fold higher in individuals with lower frailty status (p = 0.001 and p = 0.005, unadjusted and adjusted for prior COVID-19 infection). Following the booster, titers to spike protein improved regardless of COVID-19 infection or degree of frailty (p = 0.82 and p = 0.29, respectively). Antibody avidity significantly declined over 6 months in all participants following 2 vaccine doses (p < 0.001), which was further impaired with higher frailty (p = 0.001). Notably, avidity increased to peak levels after the booster (p < 0.001). Overall antibody response was inversely correlated with a phenotype of immune-senescent T cells, CD8 + CD28- TEMRA cells (p = 0.036, adjusted for COVID-19 infection). Furthermore, there was increased detection of CD8 + CD28- TEMRA cells in individuals with greater frailty (p = 0.056, adjusted for COVID-19).

CONCLUSIONS

We evaluated the immune responses to the Moderna COVID-19 mRNA vaccine in frail older adults in a retirement community. A higher degree of frailty was associated with diminished antibody quantity and quality. However, a booster vaccine dose at 6 months overcame these effects. Frailty was associated with an increased immune-senescence phenotype that may contribute to the observed changes in the vaccine response. While the strength of our conclusions was limited by a small cohort, these results are important for guiding further investigation of vaccine responses in frail older adults.

Using machine learning to detect sarcopenia from electronic health records

Introduction

Sarcopenia (low muscle mass and strength) causes dysmobility and loss of independence. Sarcopenia is often not directly coded or described in electronic health records (EHR). The objective was to improve sarcopenia detection using structured data from EHR.

Methods

Adults undergoing musculoskeletal testing (December 2017-March 2020) were classified as meeting sarcopenia thresholds for 0 (controls), ≥1 (Sarcopenia-1), or ≥2 (Sarcopenia-2) tests. Electronic health record diagnoses, medications, and laboratory testing were extracted from the Indiana Network for Patient Care. Five machine learning models were applied to EHR data for predicting sarcopenia.

Results

Of 1304 participants, 1055 were controls, 249 met Sarcopenia-1 and 76 met Sarcopenia-2. Sarcopenic participants were older, with higher fat mass, Charlson Comorbidity Index, and more chronic diseases. All models performed better for Sarcopenia-2 than Sarcopenia-1. The top performing models for Sarcopenia-1 were Logistic Regression [area under the curve (AUC) 71.59 (95% confidence interval [CI], 71.51-71.66)] and Multi-Layer Perceptron [AUC 71.48 (95%CI, 71.00-71.97)]. The top performing models for Sarcopenia-2 were Logistic Regression [AUC 91.44 (95%CI, 91.28-91.60)] and Support Vector Machine [AUC 90.81 (95%CI, 88.41-93.20)]. For the best Logistic Regression Model, important sarcopenia predictors included diabetes mellitus, digestive system complaints, signs and symptoms involving the nervous, musculoskeletal and respiratory systems, metabolic disorders, and kidney or urinary tract disorders. Opioids, corticosteroids, and antihyperlipidemic drugs were also more common among sarcopenic participants.

Conclusions

Applying machine learning models, sarcopenia can be predicted from structured data in EHR, which may be developed through future studies to facilitate large-scale early detection and intervention in clinical populations.

Frailty in older people living with HIV: current status and clinical management

This paper will update care providers on the clinical and scientific aspects of frailty which affects an increasing proportion of older people living with HIV (PLWH). The successful use of combination antiretroviral therapy has improved long-term survival in PLWH. This has increased the proportion of PLWH older than 50 to more than 50% of the HIV population. Concurrently, there has been an increase in the premature development of age-related comorbidities as well as geriatric syndromes, especially frailty, which affects an important minority of older PLWH. As the number of frail older PLWH increases, this will have an important impact on their health care delivery. Frailty negatively affects a PLWH's clinical status, and increases their risk of adverse outcomes, impacting quality of life and health-span. The biologic constructs underlying the development of frailty integrate interrelated pathways which are affected by the process of aging and those factors which accelerate aging. The negative impact of sarcopenia in maintaining musculoskeletal integrity and thereby functional status may represent a bidirectional interaction with frailty in PLWH. Furthermore, there is a growing body of literature that frailty states may be transitional. The recognition and management of related risk factors will help to mitigate the development of frailty. The application of interdisciplinary geriatric management principles to the care of older PLWH allows reliable screening and care practices for frailty. Insight into frailty, increasingly recognized as an important marker of biologic age, will help to understand the diversity of clinical status occurring in PLWH, which therefore represents a fundamentally new and important aspect to be evaluated in their health care.

Two Years with COVID-19: The Electronic Frailty Index Identifies High-Risk Patients in the Stockholm GeroCovid Study

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 (β-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.

Development of an Electronic Frailty Index for Hospitalized Older Adults in Sweden

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.

Registered report protocol: A scoping review to identify potential predictors as features for developing automated estimation of the probability of being frail in secondary care

INTRODUCTION

The impact of frailty surges, as the prevalence increases with age and the population age is rising. Frailty is associated with adverse health outcomes and increased healthcare costs. Many validated instruments to detect frailty have been developed. Using these in clinical practice takes time. Automated estimation of the probability of being frail using routinely collected data from hospital electronic health records (EHRs) would circumvent that. We aim to identify potential predictors that could be used as features for modeling algorithms on the basis of routine hospital EHR data to incorporate in an automated tool for estimating the probability of being frail.

METHODS

PubMed (MEDLINE), CINAHL Plus, Embase, and Web of Science will be searched. The studied population consists of older people (≥65 years). The first step is searching articles published ≥2018. Second, we add two published literature reviews (and the articles included therein) [Bery 2020; Bouillon, 2013] to our search results. In these reviews, articles on potential predictor variables in frailty screening tools were included from inception until March 2018. The goal is to identify and extract all potential predictors of being frail. Domain experts will be consulted to evaluate the results.

DISCUSSION

The results of the intended study will increase the quality of the developed algorithms to be used for automated estimation of the probability of being frail in secondary care. This is a promising perspective, being less labor-intensive compared to screening each individual patient by hand. Also, such an automated tool may raise awareness of frailty, especially in those patients who would not be screened for frailty by hand because they seem robust.

CONCLUSION

The identified potential predictors of being frail can be used as evidence-based input for machine learning based automated estimation of the probability of being frail using routine EHR data in the near future.

Analysis of Functional Recovery in Older Adults Discharged to Skilled Nursing Facilities and Then Home

IMPORTANCE

Although many older adults are discharged to skilled nursing facilities (SNFs) after hospitalization, rates of patients recovery afterward are unknown.

OBJECTIVE

To examine postacute functional recovery among older adults.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study was conducted among older adults treated in SNFs, then at home with home health care (HHC). Participants were a 5% random sample of Medicare fee-for-service beneficiaries discharged to community HHC after SNF stay from 2014 to 2016 with continuous part A and B enrollment in the prior 6 months. Medicare claims data from 2014 to 2016 were used, including inpatient, SNF, hospice, HHC, outpatient, carrier, and durable medical equipment data and Minimum Data Set (MDS) and Outcome Assessment Information Set (OASIS) for SNF and HHC assessments, respectively. Data were analyzed from July 20, 2020, to June 5, 2022.

EXPOSURES

Frailty was measured with a validated claims-based frailty index (CFI) (range, 0-1; higher scores indicate worse frailty) and categorized into not frail (<0.20), mildly frail (0.20-0.29), and moderately to severely frail (≥0.30).

MAIN OUTCOMES AND MEASURES

The primary outcome was functional recovery, defined by discharge from HHC with stable or improved ability to perform activities of daily living (ADL). Recovery status was examined at 15, 30, 45, 60, 75, and 90 days after discharge to HHC using OASIS. Covariates were obtained from the MDS admission file at SNF admission, including age, race and ethnicity, cognitive status, functional status, and geographic region.

RESULTS

Among 105 232 beneficiaries (mean [SD] age, 79.1 [10.6] years; 68 637 [65.2%] women; 8951 Black [8.5%], 3109 Hispanic [3.0%], and 88 583 White [84.2%] individuals), 65 796 individuals (62.5%) were discharged from HHC services with improved function over 90 days of follow-up. Among 39 436 beneficiaries not recovered, 19 612 individuals (49.7%) had mild frailty and 15 818 individuals (40.1%) had moderate to severe frailty. While 10 492 of 17 576 beneficiaries who were not frail recovered by 45 days (59.7%), 10 755 of 32 212 individuals with moderate to severe frailty had recovered (33.4%). Overall, frailty was negatively associated with functional recovery after adjustment for demographic characteristics, geographic census regions, and health-related variables, with a hazard ratio for moderate to severe frailty of 0.62 (95% CI, 0.60-0.63) compared with nonfrailty.

CONCLUSIONS AND RELEVANCE

This study found that recovery after posthospitalization SNF stay was particularly prolonged for individuals with frailty. Functional dependence in activities of daily living remained common among individuals with frailty long after discharge home.

Frailty and Age Impact Immune Responses to Moderna COVID-19 mRNA Vaccine

BACKGROUND

Immune responses to COVID-19 mRNA vaccines have not been well characterized in frail older adults. We postulated that frailty is associated with impaired antibody and cellular mRNA vaccine responses.

METHODS

We followed older adults in a retirement facility with longitudinal clinical and serological samples from the first Moderna mRNA-1273 vaccine dose starting in February 2021 through their 3rd (booster) vaccine dose. Outcomes were antibody titers, antibody avidity, and AIM+ T cell function and phenotype. Statistical analysis used antibody titers in linear mixed-effects linear regression with clinical predictors including, age, sex, prior infection status, and clinical frailty scale (CFS) score. T cell function analysis used clinical predictors and cellular phenotype variables in linear regression models.

RESULTS

Participants (n=15) had median age of 90 years and mild, moderate, or severe frailty scores (n=3, 7, or 5 respectively). After 2 vaccine doses, anti-spike antibody titers were higher in 5-fold higher in individuals with mild frailty compared to severe frailty and 9-fold higher in individuals with prior COVID-19 infection compared to uninfected (p=0.02 and p<0.001). Following the booster, titers improved regardless of COVID-19 infection or frailty. Antibody avidity significantly declined following 2 vaccine doses regardless of frailty status, but reached maximal avidity after the booster. Spike-specific CD4+ T cell responses were modulated by frailty and terminally differentiated effector memory TEMRA cells, and spike-specific TFH cell responses were inversely correlated with age. Additionally, an immune-senescent memory T cell phenotype was correlated with frailty and functional decline.

CONCLUSIONS

We described the separate influences of frailty and age on adaptive immune responses to the Moderna COVID-19 mRNA vaccine. Though overall antibody responses were robust, higher frailty diminished initial antibody quantity, and all older adults had impaired antibody avidity. Following the booster, antibody responses improved, overcoming the effects of age and frailty. CD4+ T cell responses were independently impacted by age, frailty, and burden of immune-senescence. Frailty was correlated with increased burden of immune-senescence, suggesting an immune-mediated mechanism for physiological decline.

Management and Outcomes of ST-Segment Elevation Myocardial Infarction in Hospitalized Frail Patients in the United States

Cardiovascular diseases and frailty are common conditions of aging populations and often coexist. In this study, we examined the in-hospital management, outcomes, and resource use of frail patients hospitalized for ST-segment elevation myocardial infarction (STEMI). This was a retrospective analysis of the 2005-2014 data from the Nationwide Inpatient Sample. Patients were classified into to versus 'nonfrail' using the Johns Hopkins Adjusted Clinical Groups frailty-defining diagnosis indicator. The primary outcome was STEMI management, whereas secondary outcomes were in-hospital mortality, length of stay, and cost. Outcomes were compared between frail and nonfrail patients using propensity score-matched analysis. There were 1,360,597 STEMI hospitalizations, of which 36,316 (2.7%) were frail. Propensity score-matched analysis showed that in in-hospital management options for STEMI, the odds of overall revascularization (odds ratio [OR], 0.60; 95% confidence interval [CI], 0.55 to 0.65), percutaneous coronary intervention (OR, 0.53; 95% CI, 0.49 to 0.57), and coronary angiography (OR, 0.59; 95% CI, 0.55 to 0.64) were significantly lower for frail patients. The odds of receiving coronary artery bypass grafting (OR, 1.66; 95% CI, 1.48 to 1.86) and overall hemodynamic support (OR, 1.26; 95% CI, 1.15 to 1.39) were significantly higher for frail patients. In-hospital mortality (18.7% vs 8.2%, p <0.001), length of stay (7.7 vs 3.7 days, p <0.001) and costs ($90,060 vs $63,507, p <0.001) were significantly higher in frail patients. Our findings suggest that collaborative efforts by cardiologists and cardiovascular surgeons for identifying frailty in patients with STEMI and incorporating frailty in risk estimation measures may improve management strategies, resource use and optimize patient outcomes.

Low Peak Inspiratory Flow Rates are Common Among COPD Inpatients and are Associated with Increased Healthcare Resource Utilization: A Retrospective Cohort Study

Background

Patients with chronic obstructive pulmonary disease (COPD) can have low peak inspiratory flow (PIF), especially after hospitalization for acute exacerbation of COPD (AECOPD).

Purpose

To characterize patients hospitalized for AECOPD, and to assess the prevalence of low PIF, changes in PIF after hospitalization, and the association of low PIF with healthcare resource utilization (HRU) outcomes.

Patients and Methods

A retrospective cohort study was conducted using electronic health record data of hospitalized COPD patients in the Wake Forest Baptist Health system (01/01/2017 through 06/30/2020). Patients with a first eligible AECOPD hospitalization (index hospitalization) who were discharged before 05/31/2020 were included. PIF was measured using the In-Check DIAL™ at both medium-low resistance (R-2) and high resistance (R-5) during the index hospitalization. For R-2 and R-5, PIF was divided into low PIF (< 60 L/min; < 30 L/min) and high PIF (≥ 60 L/min; ≥ 30 L/min) groups. The primary outcome was the prevalence of low PIF. The stability of PIF after hospitalization was described. Adjusted regression models evaluated associations between low PIF and subsequent 30-day readmissions, 90-day readmissions, and HRU outcomes, including hospitalizations, emergency department visits, inpatient days, and intensive care unit (ICU) days.

Results

In total, 743 patients with PIF measured at R-2 and R-5 during a AECOPD hospitalization were included. The prevalence of low PIF was 56.9% at R-2 and 14.7% at R-5. PIF values were relatively stable after hospitalization. Adjusted analyses showed significant increases in HRU (all-cause hospitalizations [31%], COPD hospitalizations [33%], COPD inpatient days [46%], and COPD ICU days [24%]) during the follow-up period among patients with low PIF (< 60 L/min) at R-2. The 30- and 90-day readmission risks were similar between patients with low PIF and high PIF.

Conclusion

Low PIF is common among patients hospitalized for AECOPD, relatively stable after hospital discharge, and associated with increased HRU.