Frailty Indices Research Articles

SIOG2021-0083 - Comparison of frailty indices for the prediction of quality of life and physical function in older men with metastatic castration-resistant prostate cancer

SIOG2021-0083 - Comparison of frailty indices for the prediction of quality of life and physical function in older men with metastatic castration-resistant prostate cancer

Visualizing Frailty: Exploring Radiographical Measures of Frailty in Trauma Patients

Purpose: This study assessed the relationship of core muscle sarcopenia, myosteatosis, and L1 attenuation to the 5-factor modified frailty index (mFI-5), discharge disposition, and post-admission complications in orthopedic and general trauma patients. It was hypothesized that reduced sarcopenia, L1 attenuation, and increased myosteatosis is associated with higher mFI-5 scores (≥ 0.3), discharge into care, and increased post-admission complications.Methods: This prospective cohort study was performed at a Level 1 trauma center. Patients were surveyed and metrics of the mFI-5 were used. Frail was categorized as a mFI-5 score ≥ 0.3. Recent abdominal computed tomography (CT) scans were used to extract radiographical information of total psoas cross-sectional area, psoas myosteatosis, and L1 vertebrae attenuation.Results: There were 140 patients who consented to the study, of which 83 had available abdomen and pelvis CT scans. The mean age was 43.19 (± 17.36), and 65% were male (<i>n</i> = 52). When comparing the frail (16%, <i>n</i> = 13) and not frail (84%, <i>n</i> = 70) patients, there was a significant difference in mean psoas myosteatosis (<i>p</i> < 0.0001) and the attenuation of the L1 vertebrae (<i>p</i> < 0.001). On multivariate analysis when accounting for age, myosteatosis of the psoas muscles was predictive of an mFI-5 score ≥ 0.3.Conclusion: The findings suggest that myosteatosis and L1 attenuation are associated with frailty indices (mFI-5) after traumatic injury. Future studies are needed to prospectively assess the validity of both radiographical and index-based markers of frailty in predicting post-traumatic complications, mortality, and hospital utilization.

Colorectal Cancer Screening in the Elderly: Is Age Just a Number?

IntroductionColorectal cancer screening improved outcomes for patients diagnosed between the age of 45-75. Present life expectancy is beyond this limit, yet there are no guidelines for these ages. We aim to identify outcomes after screening and intervention in patients ≥75 years and correlate with frailty. Materials and methodsRecords between 2011 and 2019 were queried. Patients ≥75 screened and treated for colorectal cancer were included. Patient demographics, perioperative mortality, age at last colonoscopy and frailty score were calculated. A Modified Frailty Index from the Canadian Study of Health and Aging Frailty Index was used. A score of 1 to 11 was calculated based on patient comorbidities. The MFI was assigned from 0 to 11: 0 signified absence of frailty and 11 indicated maximum frailty. ResultsOf 179 patients were identified, 46.3% males. 171(95%) had elective and 8 (5%) had emergent surgery. The average age was 81.8 years. All colonoscopies were performed for symptoms. A modified frailty index was retrospectively calculated; 75% of patients scored between 0 and 2 and 1% scored >6. ConclusionOlder patients who underwent colonoscopy and surgery for symptomatic colon cancer had a low mortality, 2%. The average age was 6.8 years older than the recommended cutoff for colonoscopy screening. Most patients scored 0 to 2 on the modified frailty index, suggesting that not only are older patients more fit than previously thought, but also able to tolerate colorectal interventions more liberally. Utilizing frailty indices to identify screening patterns beyond 75 years of age might prove beneficial for this patient population. Further studies are recommended.

Abstract 6988: The Role of Frailty in Identifying Benefit from Transcatheter versus Surgical Aortic Valve Replacement

Background: Frailty is associated with a higher risk for adverse outcomes after aortic valve replacement (AVR) for severe aortic valve stenosis, but whether or not frail patients have differential benefit from transcatheter (TAVR) vs. surgical (SAVR) AVR is uncertain. Methods: We linked adults ≥ 65 years old in the US CoreValve High Risk (HiR) or Surgical or Transcatheter Aortic-Valve Replacement in Intermediate Risk Patients (SURTAVI) trial to 100% Medicare claims 2/2/2011-9/30/2015. Two frailty measures, a deficit-based (DFI) and phenotype-based (PFI) frailty index, based on the Rockwood and Fried definitions of frailty respectively, were generated for linked individuals. The absolute and relative treatment effect of TAVR vs. SAVR were evaluated within frailty index (FI) tertiles for the primary endpoint of death and non-death secondary outcomes, as defined by the trials, using multivariable Cox regression. Functional status decline was additionally in TAVR vs. SAVR by FI tertile using multivariable logistic regression. Results: Of 1,442 (linkage rate = 60.0%) individuals in the linked HiR and SURTAVI trials, 741 (51.4%) individuals received TAVR and 701 (48.6%) received SAVR (mean age 81.8 ± 6.1 years, 44.0% female, mean Society of Thoracic Surgeons risk score 5.9 ± 2.7). Those in higher FI tertiles had a greater burden of comorbidities. Though the rates of death in the highest FI tertiles (DFI 38.6%, PFI 33.8%) were 2-3-fold higher than the lowest tertile (DFI 14.8%, HR 2.86, 95% CI 2.15-3.79, p < 0.001; PFI 17.9%; HR 2.05, 95% CI 1.58-2.67, p < 0.001), there were no significant differences in the relative or absolute treatment effect of SAVR vs. TAVR across FI tertiles for all death, non-death, and functional outcomes (all interaction p-values > 0.05). Results were overall consistent across individual trials and frailty definitions ( Figure ). Conclusions: Among CoreValve trial participants linked to Medicare claims, two different frailty indices based on Fried and Rockwood definitions identified individuals at higher risk of death and functional decline but no differential benefit from TAVR vs. SAVR.

Role of Frailty in Identifying Benefit From Transcatheter Versus Surgical Aortic Valve Replacement

Frailty is associated with a higher risk for adverse outcomes after aortic valve replacement (AVR) for severe aortic valve stenosis, but whether or not frail patients derive differential benefit from transcatheter (TAVR) versus surgical (SAVR) AVR is uncertain. We linked adults ≥65 years old in the US CoreValve HiR trial (High-Risk) or SURTAVI trial (Surgical or Transcatheter Aortic-Valve Replacement in Intermediate-Risk Patients) to Medicare claims, February 2, 2011, to September 30, 2015. Two frailty measures, a deficit-based and phenotype-based frailty index (FI), were generated. The treatment effect of TAVR versus SAVR was evaluated within FI tertiles for the primary end point of death and nondeath secondary outcomes, using multivariable Cox regression. Of 1442 (linkage rate =60.0%) individuals included, 741 (51.4%) individuals received TAVR and 701 (48.6%) received SAVR (mean age 81.8±6.1 years, 44.0% female). Although 1-year death rates in the highest FI tertiles (deficit-based FI 36.7% and phenotype-based FI 33.8%) were 2- to 3-fold higher than the lowest tertiles (deficit-based FI 13.4%; hazard ratio, 3.02 [95% CI, 2.26-4.02], P<0.001; phenotype-based FI 17.9%; hazard ratio, 2.05 [95% CI, 1.58-2.67], P<0.001), there were no significant differences in the relative or absolute treatment effect of SAVR versus TAVR across FI tertiles for all death, nondeath, and functional outcomes (all interaction P>0.05). Results remained consistent across individual trials, frailty definitions, and when considering the nonlinked trial data. Two different frailty indices based on Fried and Rockwood definitions identified individuals at higher risk of death and functional impairment but no differential benefit from TAVR versus SAVR.

Emergency General Surgery Procedures and Cost of Care for Older Adults in the State of Maryland.

Older adults (OAs) ≥ 65years of age, representing the fastest growing segment in the United States, are anticipated to require a greater percentage of emergency general surgery procedures (EGSPs) with an associated increase in health care costs. The aims of this study were to identify the frequency of EGSP and charges incurred by OA compared to their younger counterparts in the state of Maryland. A retrospective review of the Maryland Health Services Cost Review Commission from 2009 to 2018 was undertaken. Patients undergoing urgent or emergent ESGP were divided into 2 groups (18-64 years and ≥65 years). Data collected included demographics, APR-severity of illness (SOI), APR-risk of mortality (ROM), the EGSP (partial colectomy [PC], small bowel resection [SBR], cholecystectomy, operative management of peptic ulcer disease, lysis of adhesions, appendectomy, and laparotomy), length of stay (LOS), and hospital charges. P-values (P < .05) were significant. Of the 181,283 patients included in the study, 55,401 (38.1%) were ≥65years of age. Older adults presented with greater APR-SOI (major 37.7% vs 21.3%, extreme 5.2% vs 9.3%), greater APR-ROM (major 25.3% vs 8.7%, extreme 22.3% vs 5.3%), underwent PC (24.5% vs 10.9%) and SBR (12.8% vs 7.0%) more frequently, and incurred significantly higher median hospital charges for every EGSP, consistently between 2009 and 2018 due to increased LOS and complications when compared to those ≤65years of age. These findings stress the need for validated frailty indices and quality improvement initiatives focused on the care of OAs in emergency general surgery to maximize outcomes and optimize cost.

Development of a health data derived frailty index as a predictor of adverse outcomes in older patients with pancreatic cancer

Pancreatic cancer is a prevalent disease among older adults. Well-selected patients, based on a geriatric assessment for risk stratification, could be good candidates for chemotherapy and/or curative resection. Deficits accumulation frailty indices (FI) utilize readily available clinical data and easily obtained patient-reported information to predict hospitalization and mortality of older individuals. Retrospective data from 440 older adults (median age 76 years) with pancreatic cancer, obtained from electronic health records, was used to develop a FI and its ability to predict mortality and other geriatric and cancer related outcomes was tested. Fatigue (n = 45), infection (n = 40) and neutropenia (n = 36) were the most common registered adverse events of treatment; 153 subjects had no adverse events. The mean FI score was 0.26, 112 subjects were fit (0.0 < 0.2), 255 pre-frail (0.2 < 0.35), and 73 frail (≥ 0.35). Median survival was twelve months for the whole sample; at one year 62.5% of fit patients, 46.3% of pre-frail, and 26% of frail patients were alive. The FI categories correlated with institutionalization (p < 0.001) and non-planned hospitalization (p < 0.001). The FI categories did not correlate with the presence of Common Terminology Criteria for Adverse Events (CTCAE) grade 3–4 adverse events (p = 0.377). We conclude that patients with pancreatic cancer classified as frail with our FI had worse survival than those fit and pre-frail. Non-fit patients were also more prone to be institutionalized and have non-planned hospitalizations. The items used for this FI can be usually acquired from electronic health records and could be automated in the future, which could simplify its use as a helping tool for decisions in older patients with pancreatic cancer.

Evaluation of Frailty Assessment Compliance in Acute Care Surgery: Changing Trends, Lessons Learned

Routine frailty assessment has emerged recently in the surgical literature and is an important prognostication and risk stratification tool. The aim of our study was to review our 7-y experience with two frailty assessment tools and changing trends in their use. We performed a 7-y (2011-2017) analysis of our prospectively maintained frailty database. Frail patients were identified using the emergency general surgery and trauma specific frailty indices. Outcome measures were rates of compliance with frailty assessment, overall complications, discharge to skilled nursing facility (SNF)/rehab, and mortality over the study period. Multivariate logistic regression and Cochran-Armitage trend analyses were performed. We evaluated a total of 1045 geriatric patients (Trauma: 587, EGS: 458). Mean age was 74.5 ± 7.9 y, 74% were males, and 81% were white. Overall, 34% of the patients were frail. Compared to non-frail patients, frail patients had higher adjusted rates of complications (OR 2.4 [1.9-2.9]), mortality (OR 1.8 [1.4-2.3]), and rehab/SNF disposition (OR 3.7 [3.1-4.3]). The compliance rate of measuring frailty increased from 12% in 2011 to 78% in 2017, P < 0.001 (Figure). The complication rate decreased (33% versus 21%, P < 0.001), while the rate of discharge disposition to SNF/Rehab increased (41% versus 58%, P < 0.001). There was no difference in mortality (11% versus 9.8%, P=0.48) over the study period. Adherence to frailty measurement increased over the study period. This was accompanied by a significant decline in overall in-hospital complications. Frailty indices can be utilized to identify high-risk patients and develop post-operative strategies to improve outcomes in acute care surgery.

Derivation of an electronic frailty index for short-term mortality in heart failure: a machine learning approach

Abstract Objective Frailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure. Methods This was a retrospective observational study included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson comorbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR) and prognostic nutritional index (PNI) were analyzed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the predictive models. Comparisons were made with decision tree and multivariate logistic regression. Results A total of 8893 patients (median: age 81, Q1-Q3: 71–87 years old) were included, in whom 9% had 30-day mortality and 17% had 90-day mortality. PNI, age and NLR were the most important variables predicting 30-day mortality (importance score: 37.4, 32.1, 20.5, respectively) and 90-day mortality (importance score: 35.3, 36.3, 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariate logistic regression (area under the curve: 0.90, 0.86 and 0.86 for 30-day mortality; 0.92, 0.89 and 0.86 for 90-day mortality). Conclusions The electronic frailty index based on comorbidities, inflammation and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques. Funding Acknowledgement Type of funding sources: None.

The impact of frailty on the management and outcome of ST-elevation myocardial infarction in older patients

Abstract Background Frailty is a clinical syndrome of increased vulnerability, resulting from age-associated decline in physiological reserve, compromising the ability to cope with acute stressors. Despite an increasing number of older, frail patients presenting with ST-elevation myocardial infarction (STEMI), there remains a paucity of guidance on how to approach the management of this complex group. Purpose To evaluate the impact of frailty on the management strategy and outcomes in older patients presenting with ST-elevation myocardial infarction. Methods A retrospective cohort study was performed, using linked patient records in The Nationwide Readmission Database. All patients aged ≥75 years that presented with STEMI (2015 – 2018) were included. International Classification of Disease (10th Edition; ICD-10) codes were used to ascertain exposures and outcomes. Frailty was quantified using the Hospital Frailty Risk Score (HFRS): an ICD-10-based scoring system that has been validated against established clinical frailty indices. Outcomes included: (i) management strategy (coronary angiography ± percutaneous coronary intervention, vs. conservative management), length of stay and 30-day mortality. Outcomes were modelled using multivariable binary logistic regression. Continuous variables are presented as: mean (standard deviation). Odds ratios (OR) are given with corresponding 95% confidence intervals (CI). Results From an overall dataset of 57,133,894 admissions, 368,201 patients presenting with STEMI were identified, of which 92,067 were aged ≥75 years. The mean age was 82.4 (5.1) years; 45,768 (49.7%) were female. The mean frailty score was 5.9 (SD 4.9, range 0 - 37.7). Patients were categorised by frailty status: low (HFRS &amp;lt;5; n=46,336 [50.3%]), intermediate (HFRS 5 - 15; n=40,493 [44.0%]) and high risk (HFRS &amp;gt;15; n=5,238 [5.7%]). Characteristics of the cohort are presented in Figure 1. Frail patients were less likely to undergo invasive management: 1,873 (35.5%) of the high risk group underwent coronary angiography vs. 36,888 (79.6%) of the low risk group; OR 0.14 (95% CI 0.13 - 0.15), P&amp;lt;0.001. Length of stay in hospital increased proportionately with frailty: a 2-unit increase in HRFS was associated with one additional day in hospital (p&amp;lt;0.001). 30-day mortality increased non-linearly with increasing HFRS and was markedly higher among patients at high risk for frailty, compared with those at low risk; OR 3.70 (95% CI 3.47 - 3.94; p&amp;lt;0.001). The relationship between frailty score and outcomes is presented in Figure 2. Frailty remained the greatest single predictor of outcome following adjustment for other covariates, including age. Conclusions Frail patients presenting with STEMI are less likely to undergo invasive management and more likely to experience adverse outcomes. Quantification of frailty offers an opportunity to identify and address modifiable risk factors to improve post-STEMI outcomes in this vulnerable group. Funding Acknowledgement Type of funding sources: None.

Impact of frailty status on medium-term follow up in patients undergoing percutaneous transcatheter aortic valve implantation in a high-flow referral center with high procedural volumes

Abstract Background Transcatheter strategies to treat aortic stenosis (AS) are an established therapeutic option in older patients not candidate for open heart surgery. Current guidelines recommend the adoption of surgical scores like the Society of Thoracic Surgeons (STS) as tools for risk stratification. However, these scores may have limited predictive value in older patients. Purpose To assess the impact of frailty status on a composite endpoint comprising mortality and cardiovascular (CV) events in patients with severe AS evaluated for transcatheter aortic valve implantation (TAVI) in a high-flow and high-volume tertiary care center. Methods Consecutive patients &amp;gt;80 years referred to TAVI from January to December 2019 at our tertiary care institution were prospectively screened for frailty through a comprehensive geriatric assessment (CGA) based on physical function and the Multidimensional Prognostic Index (MPI). Physical function was evaluated by the Short Physical Performance Battery (SPPB), a tool exploring balance, gait speed, strength and endurance that produces a score ranging from 0 to 12 (lowest to highest performance). The SPPB &amp;lt;6 is an established strong predictor of mortality and disability. The MPI is a three-level score used to stratify risk of mortality (low, intermediate or high risk) based on eight key domains for frailty assessment (functional and cognitive status, nutrition, mobility and risk of pressure sores, multimorbidity, polypharmacy and co-habitation). Data on mortality and CV events at 6 and 12 months were retrieved via administrative records and/or telephone follow-up. Results Overall, 134 patients were referred for TAVI (mean age: 84±4 years; &amp;gt;90 years: 12%, women 67%). The average STS risk score was 4.6±3.0 (low risk: 49%; intermediate: 39%, high risk: 12%). Mean SPPB was 6.3±3.7 (SPPB &amp;lt;6: 32%). Ninety-five (71%) patients belonged to the MPI-low risk group, 30 (22%) to the MPI intermediate risk group and nine (7%) to the MPI high risk group. SPPB and MPI scores were moderately correlated with STS (Spearman correlation coefficient: SPPB R=0.31, p=0.01, MPI R=0.29, p=0.03, Figure Panel A and B). At 12 months, 3 (2.2%) patients died, and 11 (8.2%) were hospitalized for CV events: major bleeding, N=6 (4.5%); stroke: N=4 (3.0%); re-do: N=1 (0.7%). The probability of the composite endpoint was higher for patients at intermediate/high MPI risk (HR intermediate/high risk vs low risk: HR 2.9, 95% CI 1.1–6.8, p=0.031, Figure 1 Panel C), while no association with STS (p=0.332) was found. Conclusions In a prospectively enrolled cohort of TAVI candidates, frailty indices stratified short- and medium-term prognosis. The integrated frailty assessment could be a useful tool for early detection of patients at risk of disability, and potentially, for preventing the futility of the TAVI procedure. Funding Acknowledgement Type of funding sources: None. Figure 1

Identification of Frailty Using a Claims-Based Frailty Index in the CoreValve Studies: Findings from the EXTEND-FRAILTY Study.

BackgroundIn aortic valve disease, the relationship between claims‐based frailty indices (CFIs) and validated measures of frailty constructed from in‐person assessments is unclear but may be relevant for retrospective ascertainment of frailty status when otherwise unmeasured.Methods and ResultsWe linked adults aged ≥65 years in the US CoreValve Studies (linkage rate, 67%; mean age, 82.7±6.2 years, 43.1% women), to Medicare inpatient claims, 2011 to 2015. The Johns Hopkins CFI, validated on the basis of the Fried index, was generated for each study participant, and the association between CFI tertile and trial outcomes was evaluated as part of the EXTEND‐FRAILTY substudy. Among 2357 participants (64.9% frail), higher CFI tertile was associated with greater impairments in nutrition, disability, cognition, and self‐rated health. The primary outcome of all‐cause mortality at 1 year occurred in 19.3%, 23.1%, and 31.3% of those in tertiles 1 to 3, respectively (tertile 2 versus 1: hazard ratio, 1.22; 95% CI, 0.98–1.51; P=0.07; tertile 3 versus 1: hazard ratio, 1.73; 95% CI, 1.41–2.12; P<0.001). Secondary outcomes (bleeding, major adverse cardiovascular and cerebrovascular events, and hospitalization) were more frequent with increasing CFI tertile and persisted despite adjustment for age, sex, New York Heart Association class, and Society of Thoracic Surgeons risk score.ConclusionsIn linked Medicare and CoreValve study data, a CFI based on the Fried index consistently identified individuals with worse impairments in frailty, disability, cognitive dysfunction, and nutrition and a higher risk of death, hospitalization, bleeding, and major adverse cardiovascular and cerebrovascular events, independent of age and risk category. While not a surrogate for validated metrics of frailty using in‐person assessments, use of this CFI to ascertain frailty status among patients with aortic valve disease may be valid and prognostically relevant information when otherwise not measured.

Too frail is to fail: Frailty portends poor outcomes in the elderly with type II odontoid fractures independent of management strategy

Type-II odontoid fractures are common and highly morbid injuries, particularly among elderly patients. However, few risk stratification resources exist to predict outcomes and guide management decision making. Frailty indices have been increasingly utilized for these purposes in elective surgery, but have not been assessed for trauma. A single-center prospective trauma registry identified patients aged ≥ 80 years with type-II odontoid fractures. Frailty was the independent variable, using three independent indices: modified-5-item frailty (mFI-5), modified Charlson comorbidity (mCCI), and Davies. 97 patients had complete frailty data and sufficient follow up information, with median mIF-5 of 2 (range 0–4; 34 frail, mFI-5 > 2), median mCCI score of 6 (range 4–14), and median Davies score of 2 (range 0–7). For all indices, increasing score was associated with mortality, mIF-5 (HR = 1.76, 95%CI = 1.06–2.88), mCCI (HR = 1.10, 95%CI = 1.01–1.20), and Davies scores (HR = 1.21, 95%CI = 1.08–1.37). Median post-injury survival among patients with mIF-5 of ≤ 2 was 10-fold longer than patients with mIF-5 of > 2 (70 vs. 710 days, p = 0.0026). After adjusting for initial treatment strategy, frailty status remained an independent predictor of patient mortality; mIF-5 (HR = 1.72, 95%CI = 1.02–2.80), mCCI (HR = 1.10, 95%CI = 1.01–1.20), and Davies scores (HR = 1.21, 95%CI = 1.08–1.37). Among octogenarian patients with type-II odontoid fractures, frailty was associated with increased mortality, independent of treatment strategy.

Claims-based Frailty indices may function as long-term risk estimates for elderly patients after hospitalization.

Claims-based Frailty indices may function as long-term risk estimates for elderly patients after hospitalization.

Identifying Frail Populations for Disease Risk Prediction and Intervention Planning in the Covid-19 Era: A Focus on Social Isolation and Vulnerability.

The early identification of fragile populations in the Covid-19 era would help governments to allocate resources and plan strategies to contain consequences of the pandemic. Beyond frailty, social vulnerability to environmental stressors, such as the social distancing enforced to reduce the SARS-CoV2 contagion, can modify long-term disease risk and induce health status changes in the general population. We assessed frailty and social vulnerability indices in 1,258 Italian residents during the first lockdown phase via an on-line survey. We compared indices taking into account age categories and gender. While frailty showed a linear increase with age and was greater in females than in males, social vulnerability was higher in young adults and elders compared to middle aged and older adults, and in males than females. Both frailty and social vulnerability contributed in explaining the individual perception of the impact of Covid-19 emergency on health, which was further influenced by proactive attitudes/behaviors and social isolation. Social isolation and loneliness following the Covid-19 outbreak may exert dramatic psychosocial effects in the general population. The early detection of vulnerable categories, at risk to become ill and develop long-lasting health status changes, would help to prevent consequences on general well-being by allocating resources to targeted interventions managing psychosocial distress and increasing young adults and elderly resilience toward the post-Covid-19 crisis.

Interpretable Trials: Is Interpretability a Reason Why Clinical Trials Fail?

Background: There are clinical trials using composite measures, indices, or scales as proxy for independent variables or outcomes. Interpretability of derived measures may not be satisfying. Adopting indices of poor interpretability in clinical trials may lead to trial failure. This study aims to understand the impact of using indices of different interpretability in clinical trials.Methods: The interpretability of indices was categorized as: fair-to-poor, good, and unknown. In the literature, frailty indices were considered fair to poor interpretability. Body mass index (BMI) was highly interpretable. The other indices were of unknown interpretability. The trials were searched at clinicaltrials.gov on October 2, 2018. The use of indices as conditions/diseases or other terms was searched. The trials were grouped as completed, terminated, active, and other status. We tabulated the frequencies of frailty, BMI, and other indices.Results: There were 263,928 clinical trials found and 155,606 were completed or terminated. Among 2,115 trials adopting indices or composite measures as condition or disease, 244 adopted frailty and 487 used BMI without frailty indices. Significantly higher proportions of trials of unknown status used indices as conditions/diseases or other terms, compared to completed and terminated trials. The proportions of active trials using frailty indices were significantly higher than those of completed or terminated trials.Discussion: Clinical trial databases can be used to understand why trials may fail. Based on the findings, we suspect that using indices of poor interpretability may be associated with trial failure. Interpretability has not been conceived as an essential criterion for outcomes or proxy measures in trials. We will continue verifying the findings in other databases or data sources and apply this research method to improve clinical trial design. To prevent patients from experiencing trials likely to fail, we suggest further examining the interpretability of the indices in trials.

Association of entirely claims-based frailty indices with long-term outcomes in patients with acute myocardial infarction, heart failure, or pneumonia: a nationwide cohort study in Turkey.

BackgroundSeveral countries have increasingly focused on improving care for acute myocardial infarction (AMI), heart failure (HF), and pneumonia to reduce their readmissions and mortality rates. Frailty is becoming increasingly important to accurately predict healthcare utilization for the aging population. The preferred method for the measurement of frailty remains unclear, and current risk-adjustment models do not account for frailty. We sought to compare commonly used frailty indices in terms of the ability to predict clinical adverse outcomes in AMI, HF, and pneumonia patients.MethodsA nationwide cohort study included AMI, HF, and pneumonia with 65 years and older patients in the Turkey between January 1 and December 31, 2018. The primary predictor of interest was frailty. We used two claims-based frailty indices (Johns Hopkins Claims-Based Frailty Index and Hospital Frailty Risk Score) to assess frailty. The main outcome was all-cause long-term mortality up to 3 years. Time to death was calculated as the time period between the date of first admission and the date of death. Patients were censored as of September 30, 2020, which marked the end of the follow-up period.FindingsOf the 200,948 patients, 35,096 (17.5%) had AMI, 62,403 (31.1%) had HF, and 103,449 (51.5%) had pneumonia. Johns Hopkins Claims-Based Frailty Index (c-statistics for long-term mortality: 0.68 in AMI, 0.61 in HF, 0.64 in pneumonia) was better compared to Hospital Frailty Risk Score (c-statistics for long-term mortality: AMI=0.62, HF=0.58, pneumonia=0.62) (DeLong p<0.001 in all).InterpretationReadmission and mortality rates after AMI, HF, and pneumonia gradually increases with increasing frailty score. While the Hospital Frailty Risk Score had a better discrimination for predicting readmissions, Johns Hopkins Claims-Based Frailty Index had a better discrimination for predicting mortality. These findings should be taken into account for a better evaluation of hospital performance.FundingThis study was supported by funding from The Scientific and Technological Research Council of Turkey (grant 120S422, HK).

Trajectories of Frailty in the 5 Years Prior to Death Among U.S. Veterans Born 1927-1934.

Electronic frailty indices (eFIs) are increasingly used to identify patients at risk for morbidity and mortality. Whether eFIs capture the spectrum of frailty change, including decline, stability, and improvement, is unknown. In a nationwide retrospective birth cohort of U.S. Veterans, a validated eFI, including 31 health deficits, was calculated annually using medical record and insurance claims data (2002-2012). K-means clustering was used to assign patients into frailty trajectories measured 5 years prior to death. There were 214 250 veterans born between 1927 and 1934 (mean [SD] age at death = 79.4 [2.8] years, 99.2% male, 90.3% White) with an annual eFI in the 5 years before death. Nine frailty trajectories were identified. Those starting at nonfrail or prefrail had 2 stable trajectories (nonfrail to prefrail, n = 29 786 and stable prefrail, n = 28 499) and 2 rapidly increasing trajectories (prefrail to moderately frail, n = 28 244 and prefrail to severely frail, n = 22 596). Those who were mildly frail at baseline included 1 gradually increasing trajectory (mildly to moderately frail, n = 33 806) and 1 rapidly increasing trajectory (mildly to severely frail, n = 15 253). Trajectories that started at moderately or severely frail included 2 gradually increasing trajectories (moderately to severely frail, n = 27 662 and progressing severely frail, n = 14 478) and 1 recovering trajectory (moderately frail to mildly frail, n = 13 926). Nine frailty trajectories, including 1 recovering trajectory, were identified in this cohort of older U.S. Veterans. Future work is needed to understand whether prevention and treatment strategies can improve frailty trajectories and contribute to compression of morbidity toward the end of life.

Frailty assessed by administrative tools and mortality in patients with pneumonia admitted to the hospital and ICU in Wales

The ideal method of identifying frailty is uncertain, and data on long-term outcomes is relatively limited. We examined frailty indices derived from population-scale linked data on Intensive Care Unit (ICU) and hospitalised non-ICU patients with pneumonia to elucidate the influence of frailty on mortality. Longitudinal cohort study between 2010–2018 using population-scale anonymised data linkage of healthcare records for adults admitted to hospital with pneumonia in Wales. Primary outcome was in-patient mortality. Odds Ratios (ORs [95% confidence interval]) for age, hospital frailty risk score (HFRS), electronic frailty index (eFI), Charlson comorbidity index (CCI), and social deprivation index were estimated using multivariate logistic regression models. The area under the receiver operating characteristic curve (AUC) was estimated to determine the best fitting models. Of the 107,188 patients, mean (SD) age was 72.6 (16.6) years, 50% were men. The models adjusted for the two frailty indices and the comorbidity index had an increased odds of in-patient mortality for individuals with an ICU admission (ORs for ICU admission in the eFI model 2.67 [2.55, 2.79], HFRS model 2.30 [2.20, 2.41], CCI model 2.62 [2.51, 2.75]). Models indicated advancing age, increased frailty and comorbidity were also associated with an increased odds of in-patient mortality (eFI, baseline fit, ORs: mild 1.09 [1.04, 1.13], moderate 1.13 [1.08, 1.18], severe 1.17 [1.10, 1.23]. HFRS, baseline low, ORs: intermediate 2.65 [2.55, 2.75], high 3.31 [3.17, 3.45]). CCI, baseline < 1, ORs: ‘1–10′ 1.15 [1.11, 1.20], > 10 2.50 [2.41, 2.60]). For predicting inpatient deaths, the CCI and HFRS based models were similar, however for longer term outcomes the CCI based model was superior. Frailty and comorbidity are significant risk factors for patients admitted to hospital with pneumonia. Frailty and comorbidity scores based on administrative data have only moderate ability to predict outcome.

Contemporary Analysis of Electronic Frailty Measurement in Older Adults with Multiple Myeloma Treated in the National US Veterans Affairs Healthcare System.

Simple SummaryGeriatric and frailty assessment are recommended for all older adults with cancer undergoing systemic therapy, but assessments remain difficult to scale. The aim of this study was to use an electronic frailty index based on data from administrative claims and electronic health records—the Veterans Affairs Frailty Index (VA-FI-10)—to estimate frailty and its impact on older United States (US) military veterans treated for multiple myeloma (MM) throughout the national VA Healthcare System. We found frailty to be prevalent and strongly associated with mortality and hospitalizations—independently of age, race, and MM stage. We also showed that changing the way in which the VA-FI-10 is measured affects its classification of frailty for individual veterans but not its association with mortality. These findings support the VA-FI-10’s use in research investigating outcomes in frail veterans treated with contemporary MM therapies. We provide further insights into the VA-FI-10’s potential use in clinical practice.Electronic frailty indices based on data from administrative claims and electronic health records can be used to estimate frailty in large populations of older adults with cancer where direct frailty measures are lacking. The objective of this study was to use the electronic Veterans Affairs Frailty Index (VA-FI-10)—developed and validated to measure frailty in the national United States (US) VA Healthcare System—to estimate the prevalence and impact of frailty in older US veterans newly treated for multiple myeloma (MM) with contemporary therapies. We designed a retrospective cohort study of 4924 transplant-ineligible veterans aged ≥ 65 years initiating MM therapy within VA from 2004 to 2017. Initial MM therapy was measured using inpatient and outpatient treatment codes from pharmacy data in the VA Corporate Data Warehouse. In total, 3477 veterans (70.6%) were classified as frail (VA-FI-10 > 0.2), with 1510 (30.7%) mildly frail (VA-FI-10 > 0.2–0.3), 1105 (22.4%) moderately frail (VA-FI-10 > 0.3–0.4), and 862 (17.5%) severely frail (VA-FI-10 > 0.4). Survival and time to hospitalization decreased with increasing VA-FI-10 severity (log-rank p-value < 0.001); the VA-FI-10 predicted mortality and hospitalizations independently of age, sociodemographic variables, and measures of disease risk. Varying data sources and assessment periods reclassified frailty severity for a substantial portion of veterans but did not substantially affect VA-FI-10’s association with mortality. Our study supports use of the VA-FI-10 in future research involving older veterans with MM and provides insights into its potential use in identifying frailty in clinical practice.