Higher Frailty Index Scores Research Articles

Utility of an additive frailty tests index score for mortality risk assessment following transcatheter aortic valve replacement

BackgroundThe impact of frailty assessment on outcomes in patients undergoing transcatheter aortic valve replacement (TAVR) remains unclear. Our aim was to evaluate the individual effect of each frailty test and the utility of an additive frailty index score on short- and long-term survival following TAVR. MethodsRetrospective analysis of consecutive TAVR patients for whom a complete set of frailty tests was obtained: algorithm defined grip strength and 5-m walking tests, body mass index <20 kg/m2, Katz activities of daily living ≤4/6, serum albumin <3.5 g/dL. Frailty status was defined as having 3 or more positive frailty tests. Included were 498 patients with a mean age of 82±8 years. ResultsFrailty status, observed in 266 (53%) patients, was associated with both 30-day and 1-year mortality (6% vs. 2%, P=.016; 20% vs. 9%, P<.001; within the respective frailty groups). As compared to 0–2 frailty criteria, a higher frailty index score was associated with increased risk of death at 1 year (OR 2.23; 95% CI 1.14–4.34; P=.019 and OR 3.30; 95% CI 1.36–8.00; P=.008 for 3 and 4–5 frailty criteria met, respectively). In Cox regression analysis, frailty status was correlated with 1-year mortality (HR=2.2; 95%CI 1.25–3.96; P=.007), and a higher frailty index was associated with increased mortality risk (HR=2.0; 95% CI 1.08–3.7; P=.027; and HR=3.07; 95% CI 1.4–6.7; P=.005; for any 3, and 4–5 frailty criteria, respectively). ConclusionsFrailty status and a higher frailty index score were associated with increased 1-year mortality risk following TAVR.

External Validity of Randomized Controlled Trials on Alzheimer's Disease: The Biases of Frailty and Biological Aging.

To date, the external validity of randomized controlled trials (RCTs) on Alzheimer’s disease (AD) has been assessed only considering monodimensional variables. Nevertheless, looking at isolated and single characteristics cannot guarantee a sufficient level of appreciation of the AD patients’ complexity. The only way to understand whether the two worlds (i.e., research and clinics) deal with the same type of patients is to adopt multidimensional approaches more holistically reflecting the biological age of the individual. In the present study, we compared measures of frailty/biological aging [assessed by a Frailty Index (FI)] of a sample of patients with AD resulted eligible and subsequently included in phase III RCTs compared to patients referring to the same clinical service, but not considered for inclusion. The “RCT sample” and the “real world sample” were found to be statistically similar for all the considered sociodemographic and clinical variables. Nevertheless, the “real world sample” was found to be significantly frailer compared to the “RCT sample,” as indicated by higher FI scores [0.28 (SD 0.1) vs. 0.17 (SD 0.1); p < 0.001, respectively]. Moreover, when assessing the relationship between FI and age, we found that the correlation was almost null in the “RCT sample” (Spearman’s r = 0.01; p = 0.98), while it was statistically significant in the “real world sample” (r = 0.49; p = 0.02). The application of too rigid designs may result in the poor representativeness of RCT samples. It may even imply the study of a condition biologically different from that observed in the “real world.” The adoption of multidimensional measures capable to capture the individual’s biological age may facilitate evaluating the external validity of clinical studies, implicitly improving the interpretation of the results and their translation in the clinical arena.

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OBJECTIVES/SPECIFIC AIMS: This study is part of a parent grant evaluating antidiabetic medications and risk for heart failure in an observational cohort of Veterans with type 2 diabetes (T2DM). Confounding by indication remains a concern in many observational studies of medications because difficult to measure confounders such as frailty may influence prescribing of different medications based on patient characteristics. Frailty is a multidimensional syndrome of loss of reserves (energy, physical ability, cognition, health) that gives rise to vulnerability to adverse outcomes. The objective of this study is to determine if frailty is a potential confounder in Veterans with T2DM, that is, independently associated with exposure to a specific antidiabetic medications and hospitalization for decompensated heart failure. METHODS/STUDY POPULATION: We conducted a cross-sectional study of patients with diabetes who were hospitalized within the Veterans Health Administration (VHA) Tennessee Valley Healthcare System from 2002 to 2012. Inclusion criteria were: age 18 years or older, receive regular VHA care (prescription fill or visit at least once every 180 d), a diagnosis of T2DM. A probability sample of HF and non-HF hospitalizations was collected. HF hospitalizations were selected on the basis of meeting either a primary diagnosis code (ICD-9) and/or disease related group (DRG) code for HF. For each hospitalization using a standardized chart abstraction tool, data was abstracted on: antidiabetic medication(s), patient frailty status, and reason for hospitalization (HF or non-HF). Antidiabetic medication regimens were categorized as follows: no medication treatment, metformin alone, sulfonylurea alone, insulin alone, insulin and one oral agent, and all other regimens. Patient frailty status was measured using a modified version of the Canadian Health and Aging frailty index (FI), which generates a score (range 0–1) by dividing the number of deficits present by the number of deficits measured. Established categories for FI scores are: non frail ≤0.10, vulnerable 0.10–0.21, frail 0.22–0.45, and very frail &gt;0.45. Patient frailty status at the time of hospitalization was used as a surrogate for patient frailty at the time of prescription of antidiabetic medication; this is a limitation of this approach. Hospitalizations were classified as HF hospitalizations if 2 major or 1 major and 2 minor Framingham criteria were present. FI was compared across antidiabetic medication regimen categories and hospitalization type using analysis of variance (ANOVA) and Student t-test, respectively. RESULTS/ANTICIPATED RESULTS: Of the 500 hospitalizations reviewed, 430 patients had confirmed diabetes diagnosis, adequate data to calculate FI scores, and were included in this analysis. Patients were on average 66.9 (10.9) years old; 99% male and 75% were White. Overall, 268 patients (62.3%) were categorized as frail or very frail. The mean FI score was 0.23 (SD 0.07). FI scores were highest in patients receiving insulin alone (mean 0.26) compared with patients receiving metformin alone (mean 0.22), sulfonylurea alone (mean 0.23), or no medication (mean 0.22). The lowest mean frailty score was seen in patients taking all other drug combinations, 0.19. The differences across these patient groups were statistically significant with p&lt;0.01. Further, 75% of patients on insulin alone were frail or very frail compared with 68% on sulfonylurea alone, 58% on metformin alone, and 58% on no medication. Framingham criteria for acute HF were present for 318 of 430 patients (74.0%). FI scores were higher in patients hospitalized for HF compared with non-HF hospitalizations (mean 0.24 vs. 0.21, p&lt;0.01). A higher proportion of patients hospitalized for HF were classified as frail or very frail compared with those hospitalized for non-HF diagnosis (66.4% vs. 50.9%, p&lt;0.01). DISCUSSION/SIGNIFICANCE OF IMPACT: This study demonstrates that certain antidiabetic medications are associated with patient frailty. In addition, those patients admitted for HF have higher FI scores than those admitted for non-HF diagnoses. Further investigation is planned to assess the degree to which frailty is captured by traditional covariates used in observational studies.

Identifying Frailty and its Outcomes in Older People in Rural Tanzania

Background/Study Context: Interest in frailty is growing in low- and middle-income countries, due to demographic aging and resource limitations. However, there is a paucity of data on the nature of frailty in Africa.Methods: The study collected frailty data from people aged 70 years and over living in six villages in the rural Hai District of northern Tanzania. At baseline, a limited data set was collected for 1198 people and a more comprehensive data set for a stratified sample of 296 people. A 40-item frailty index was constructed. Data regarding mortality and dependency were collected at 3-year follow-up.Results: A higher frailty index score was significantly correlated with greater age, never having attended school, falls, mortality, and dependency in activities of daily living. Logistic regression modeling revealed functional disability and cognitive function to be significant independent predictors of the outcome “mortality or dependency.”Conclusions: In resource-poor settings, brief frailty screening assessments may be a useful way of identifying those most in need of support.

Development of a Rat Clinical Frailty Index.

Rats are a commonly used model for aging studies, and a frailty assessment tool for rats would be of considerable value. There has been a recent focus on the development of preclinical models of frailty in mice. A mouse clinical frailty index (FI) was developed based on clinical frailty assessment tools. This FI measures the accumulation of clinically evident health-related deficits in mice. This paper aimed to develop a rat clinical FI. Male Fischer 344 rats were aged from 6 to 9 months (n = 12), and from 13 to 21 months (n = 41). A FI comprised of 27 health-related deficits was developed from a review of the literature and consultation with a veterinarian. Deficits were scored 0 if absent, 0.5 if mild, or 1 if severe. A FI score was determined for each rat every 3–4 months, and for the older group mortality was assessed up to 21 months. Mean FI scores significantly increased at each time point for the older rats. A high FI score measured at both 17 months of age and terminally was also associated with decreased probability of survival as assessed with Kaplan–Meier curves. The rat clinical FI has significant value for use in aging and interventional studies.

Understanding Risk in the Oldest Old: Frailty and the Metabolic Syndrome in a Chinese Community Sample Aged 90+ Years.

To investigate the relationship between frailty and the metabolic syndrome and to evaluate how these contribute to mortality in very old people. Secondary analysis of data from the Project of Longevity and Aging in Dujiangyan. Community sample from Sichuan Province, China. People aged 90+ years (n=767; baseline age=93.7±3.4 years; 68.0% women. After a baseline health assessment, participants were followed for four years (54.0% died). A frailty index (FI) was calculated as the sum of deficits present, divided by the 35 health-related deficits considered. Relationships between the FI and the metabolic syndrome were tested; their effect on death was examined. The mean FI was 0.26 ±0.11. Higher FI scores were associated with a greater risk of death, adjusted for age, sex, education, and metabolic syndrome items. The hazard ratio was 1.03 (95% confidence interval 1.02, 1.04) for each 1% percent increase of the FI. The mortality risk did not change with the metabolic syndrome (odds ratio=0.99; 0.71-1.36). In the oldest old, frailty was a significant risk for near-term death, regardless of the metabolic syndrome. Even using age-adjusted models, the epidemiology of late life illness may need to account for frailty routinely.

FRAILTY PREDICTS MORTALITY AND BASELINE CARDIAC OUTPUT IN THE ISOLATED RAT HEART

Elderly patients represent the fastest growing population presenting for cardiac surgery. While advanced age is associated with adverse postoperative outcomes, the wide variation in the health status of elderly patients suggests that chronological age (CA) does not necessarily reflect physiological age (PA). Frailty, a geriatric syndrome associated with impaired resiliency to acute stress, is an indicator of PA that predicts negative health outcomes following cardiac surgery. To better understand the impact of CA and frailty on hearts undergoing surgical procedures, there is a need to develop an animal model of frailty in which ischemia-reperfusion injury following cardioplegic arrest can be studied. Our objective was to develop a frailty index (FI) for rats, and assess the impact of age and frailty on cardiac function and recovery from cardioplegic arrest. We followed male F344 rats (n=36) from middle age (13 months) into old age (21 months). Data on survival and 45 variables in health systems known to change with age were serially collected to develop a 30-item FI (table 1). Mean FI scores increased with age from 0.07±0.05 at 13 months to 0.15±0.09 at 17 months and 0.25±0.10 at 21 months (p<0.05). Overall survival was 50%. A high FI score (>0.250) was associated with an increase in mortality (p<0.05) (figure 1). In the isolated working heart, we compared functional recovery following delivery of Del Nido cardioplegia. Young adult rats (9 months, n=7) served as a control. Baseline CO was similar between young adult and aged rats (11.2±0.5 vs. 9.6±0.5 ml*min-1*cm tibia length-1). Percent recovery following cardioplegic arrest did not differ by age (83 vs. 84%). In contrast, frail rats (FI score >0.250, n=5) had a reduced baseline CO relative to less frail rats (FI score <0.250, n=18) (8.0±0.6 vs. 10.7±0.4 ml*min-1*cm tibia length-1, p<0.05). Recovery of CO was similar between groups (91 vs. 82%). Eighty percent of frail hearts exhibited tachyarrhythmia in baseline working heart, while only 11% of rats in the less frail group presented with arrhythmia (p<0.05). We created a 30-item FI that predicts mortality in F344 rats. Frail hearts had a reduced CO and were more likely to be arrhythmic at baseline. Further study in a larger group of frail animals is required to elucidate the impact of frailty on functional recovery from cardioplegic arrest.View Large Image Figure ViewerDownload (PPT)

Validation of a frailty index in patients undergoing curative surgery for urologic malignancy and comparison with other risk stratification tools

ObjectiveTo retrospectively validate and compare a modified frailty index predicting adverse outcomes and other risk stratification tools among patients undergoing urologic oncological surgeries. Materials and MethodsThe American College of Surgeons National Surgical Quality Improvement Program was queried from 2005 to 2013 to identify patients undergoing cystectomy, prostatectomy, nephrectomy, and nephroureterectomy. Using the Canadian Study of Health and Aging Frailty Index, 11 variables were matched to the database; 4 were also added because of their relevance in oncology patients. The incidence of mortality, Clavien-Dindo IV complications, and adverse events were assessed with patients grouped according to their modified frailty index score. ResultsWe identified 41,681 patients who were undergoing surgery for presumed urologic malignancy. Patients with a high frailty index score of >0.20 had a 3.70 odds of a Clavien-Dindo IV event (CI: 2.865–4.788, P<0.0005) and a 5.95 odds of 30-day mortality (CI: 3.72–9.51, P<0.0005) in comparison with nonfrail patients after adjusting for race, sex, age, smoking history, and procedure. Using C-statistics to compare the sensitivity and specificity of the predictive ability of different models per risk stratification tool and the Akaike information criteria to assess for the fit of the models with the data, the modified frailty index was comparable or superior to the Charlson comorbidity index but inferior to the American Society of Anesthesiologists Risk Class in predicting 30-day mortality or Clavien-Dindo IV events. When the modified frailty index was augmented with the American Society of Anesthesiologists Risk Class, the new index was superior in all aspects in comparison to other risk stratification tools. ConclusionExisting risk stratification tools may be improved by incorporating variables in our 15-point modified frailty index as well as other factors such as walking speed, exhaustion, and sarcopenia to fully assess frailty. This is relevant in diseases such as kidney and prostate cancer, where surveillance and other nonsurgical interventions exist as alternatives to a potentially complicated surgery. In these scenarios, our modified frailty index augmented by the American Society of Anesthesiologists Risk Class may help inform which patients have increased surgical complications that may outweigh the benefit of surgery although this index needs prospective validation.

The impact of frailty on care intensity in older people with intellectual disabilities.

Frailty appears to develop earlier and is more severe in people with intellectual disabilities compared to the general population. The high prevalence of frailty may lead to an increase in care intensity and associated health care costs. Therefore a longitudinal observational study was conducted to determine the effect of frailty on care intensity. The association between frailty and care intensity at baseline and follow-up (3 years later) was assessed. Furthermore, the ability of the frailty index to predict an increase in care intensity after 3 years was evaluated. This study was part of the Dutch 'Healthy aging and intellectual disabilities' (HA-ID) study. Frailty was measured at baseline with a frailty index that included 51 health-and age-related deficits. For all participants information on care intensity in seven steps was available, based on long term care indications under the Act on Exceptional Medical Expenses (AWBZ)--a law that finances specialized long-term care. 676 participants (50 years and over) with ID were included in the final analysis. In 26% of the participants, care intensity had increased during the follow-up period. Increased care during the follow-up was related to a high frailty index score at baseline, independent of gender, age, level of ID and the presence of Down syndrome (p = 0.003). After exclusion of ADL and IADL items, the frailty index remained significantly related with increasing care intensity during follow-up (p = 0.007). Our results underline that screening instruments for early detection of frailty and effective interventions are required to limit the burden of frailty for individuals and caregivers, but also to limit health care utilization.

Frailty Index to Measure Health Status in People with Systemic Sclerosis

To develop and validate, as a measure of overall health status, a Frailty Index (FI) for patients (n=1372) in the Canadian Scleroderma Research Group (CSRG) Registry. Forty-four items were selected from the CSRG database as health deficits and recoded using FI criteria. To test construct validity, we compared measurement properties of the CSRG-FI to other FI, and related it to measures of damage, age, and time since diagnosis. To test criterion validity, we compared the baseline FI to that at last recorded visit and to mortality. The mean CSRG-FI was 0.33 with a sub-maximal limit of 0.67. In patients with diffuse disease, the mean was 0.38(SD 0.14); in patients with limited disease, the mean was 0.31(SD 0.13). The CSRG-FI was weakly (but significantly) correlated with the Rodnan Skin Score (r=0.28 in people with diffuse disease; 0.18 with limited) and moderately with the Physician Assessment of Damage (r=0.51 for both limited and diffuse). The risk of death increased with higher FI scores and with higher physician ratings of damage. The area under the receiver operating characteristic curve for the baseline FI in relation to death was 0.75, higher than for other measures (range: 0.57-0.67). The FI quantifies overall health status in people with scleroderma and predicts mortality. Whether the FI might help with decisions about who might best be served by more aggressive treatment, such as bone marrow transplantation, needs to be evaluated.

Identifying frailty: do the Frailty Index and Groningen Frailty Indicator cover different clinical perspectives? a cross-sectional study

BackgroundEarly identification of frailty is important for proactive primary care. Currently, however, there is no consensus on which measure to use. Therefore, we examined whether a Frailty Index (FI), based on ICPC-coded primary care data, and the Groningen Frailty Indicator (GFI) questionnaire identify the same older people as frail.MethodsWe conducted a cross-sectional, observational study of 1,580 patients aged ≥ 60 years in a Dutch primary care center. Patients received a GFI questionnaire and were surveyed on their baseline characteristics. Frailty-screening software calculated their FI score. The GFI and FI scores were compared as continuous and dichotomised measures.ResultsFI data were available for 1549 patients (98%). 663 patients (42%) returned their GFI questionnaire. Complete GFI and FI scores were available for 638 patients (40.4%), mean age 73.4 years, 52.8% female. There was a positive correlation between the GFI and the FI (Pearson’s correlation coefficient 0.544). Using dichotomised scores, 84.3% of patients with a low FI score also had a low GFI score. In patients with a high FI score, 55.1% also had a high GFI score. A continuous FI score accurately predicted a dichotomised GFI score (AUC 0.78, 95% CI 0.74 to 0.82). Being widowed or divorced was an independent predictor of both a high GFI score in patients with a low FI score, and a high FI score in patients with a low GFI score.ConclusionsThe FI and the GFI moderately overlap in identifying frailty in community-dwelling older patients. To provide optimal proactive primary care, we suggest an initial FI screening in routine healthcare data, followed by a GFI questionnaire for patients with a high FI score or otherwise at high risk as the preferred two-step frailty screening process in primary care.

Abstract 2275: Frailty Independently Predicts Poor Outcomes But Not Quality Of Care: An Analysis Of VA Ischemic Stroke Patients

Background: Frailty is a geriatric syndrome characterized by progressive multi-system physiologic decline that is difficult to operationalize clinically. Frailty indices which represent an accumulation of age-related deficits are one method of quantifying frailty, but have not been applied to stroke patients. Our objectives were to create a frailty index (FI) and determine its independent contribution to mortality, discharge disposition and quality of care in ischemic stroke patients. Methods: Chart abstraction data were obtained on a sample of 3965 patients admitted with ischemic stroke to 131 VA hospitals in 2007. Data on 38 deficits were used to create a FI that included: co-morbidities (e.g. previous stroke, MI, diabetes), pre-stroke function (e.g. ambulatory status, functional independence measure), social history (e.g. alcohol, smoking, living alone) and first admission lab values (e.g. creatinine, glucose). The FI (expressed as a %) was calculated for each subject as the number of deficits divided by the maximum number (n=38). Multiple logistic regression models were used to determine the independent effect of FI on in-hospital mortality and discharge home, while adjusting for age, sex, and NIHSS. Quality of care was defined by a composite compliance score expressed as a percent of 14 pre-determined quality indicators; a multivariable linear regression model was used to determine the independent effect of FI on the composite score, after adjusting for age, sex, and NIHSS. Results: The mean FI was 15%. In-hospital mortality was 5% and 70% of patients were discharged home. Multivariable logistic regression results ( Table 1 ) showed that for every 1% increase in the FI, the odds of dying in hospital increased by 4% (p=0.0003) independent of age, sex and NIHSS. Similarly for every 1% increase in the FI, the odds of being discharged home was 3% lower (p &lt;0.0001). Results of the multivariable model of quality of care showed that there was no significant relationship between frailty and the composite compliance score. (p=0.34) Conclusions: Higher FI scores were associated with increased in-hospital mortality and decreased likelihood of going home, independent of age, sex and NIHSS. However quality of care was not affected by frailty. Further work is required to define the relationships between frailty and stroke outcomes and quality of care. A FI based measure of frailty could provide added information to stroke prognostic models and assessments of quality of care processes.