Although apolipoprotein E (ApoE) polymorphism is associated with variable risks of several illnesses, and with mortality, no persuasive relationship has been demonstrated with frailty. Here, the clinical examination cohort (n=1452 older adults, aged 70+ years at baseline) of the Canadian Study of Health and Aging was evaluated, with 5-year follow-up data. Frailty was defined using both the phenotypic definition from the Cardiovascular Health Study (Frailty-CHS) and the 'Frailty Index', from which age-specific trajectories of deficit accumulation can be estimated. In age-sex adjusted analyses, people with ApoE 4 allele had a higher risk of death (hazard ratio [HR]=1.20; 95% confidence interval: 1.01-1.45), but this relationship was not significant when adjusted for cognitive impairment (1.06; 95% confidence interval: 0.88-1.27). There was no association between frailty and ApoE polymorphism, defined in age-sex adjusted models either as Frailty-CHS (ApoE4 HR 1.17; 95% confidence interval: 0.98-1.40, frailty HR 1.37; 95% confidence interval: 1.28-1.46) or by the Frailty Index (ApoE4 HR 1.07; 95% confidence interval: 0.90-1.29, frailty HR 35.3; 95% confidence interval: 20.4-61.1). The data do not support an association between ApoE polymorphism and frailty. This result did not depend on how frailty was defined.
To evaluate the potential of an artificial neural network (ANN) in predicting survival in elderly Canadians, using self-report data.
Cohort study with up to 72 months follow-up.
Forty self-reported characteristics were obtained from the community sample of the Canadian Study of Health and Aging. An individual frailty index score was calculated as the proportion of deficits experienced. For the ANN, randomly selected participants formed the training sample to derive relationships between the variables and survival and the validation sample to control overfitting. An ANN output was generated for each subject. A separate testing sample was used to evaluate the accuracy of prediction.
A total of 8,547 Canadians aged 65 to 99, of whom 1,865 died during 72 months of follow-up.
The output of an ANN model was compared with an unweighted frailty index in predicting survival patterns using receiver operating characteristic (ROC) curves.
The area under the ROC curve was 86% for the ANN and 62% for the frailty index. At the optimal ROC value, the accuracy of the frailty index was 70.0%. The ANN accuracy rate over 10 simulations in predicting the probability of individual survival mean+/-standard deviation was 79.2+/-0.8%.
An ANN provided more accurate survival classification than an unweighted frailty index. The data suggest that the concept of biological redundancy might be operationalized from health survey data.
The prevalence of frailty increases with age in older adults, but frailty is largely unreported for younger adults, where its associated risk is less clear. Furthermore, less is known about how frailty changes over time among younger adults. We estimated the prevalence and outcomes of frailty, in relation to accumulation of deficits, across the adult lifespan.
We analyzed data for community-dwelling respondents (age 15-102 years at baseline) to the longitudinal component of the National Population Health Survey, with seven two-year cycles, beginning 1994-1995. The outcomes were death, use of health services and change in health status, measured in terms of a Frailty Index constructed from 42 self-reported health variables.
The sample consisted of 14,713 respondents (54.2% women). Vital status was known for more than 99% of the respondents. The prevalence of frailty increased with age, from 2.0% (95% confidence interval [CI] 1.7%-2.4%) among those younger than 30 years to 22.4% (95% CI 19.0%-25.8%) for those older than age 65, including 43.7% (95% CI 37.1%-50.8%) for those 85 and older. At all ages, the 160-month mortality rate was lower among relatively fit people than among those who were frail (e.g., 2% v. 16% at age 40; 42% v. 83% at age 75 or older). These relatively fit people tended to remain relatively fit over time. Relative to all other groups, a greater proportion of the most frail people used health services at baseline (28.3%, 95% CI 21.5%-35.5%) and at each follow-up cycle (26.7%, 95% CI 15.4%-28.0%).
Deficits accumulated with age across the adult spectrum. At all ages, a higher Frailty Index was associated with higher mortality and greater use of health care services. At younger ages, recovery to the relatively fittest state was common, but the chance of complete recovery declined with age.
With aging, health deficits accumulate: people with few deficits for their age are fit, and those with more are frail. Despite recent reports of improved health in old age, how deficit accumulation is changing is not clear. Our objectives were to evaluate changes over 30 years in the degree of deficit accumulation and in the relationship between frailty and mortality in older adults.
We analyzed data from two population based, prospective longitudinal cohorts, assembled in 1971-1972 and 2000-2001, respectively. Residents of Gothenburg Sweden, systematically drawn from the Swedish population registry. The 1901-1902 cohort (N = 973) had a response rate of 84.8%; the 1930 cohort (N = 500) had a response rate of 65.1%. A frailty index using 36 deficits was calculated using data from physical examinations, assessments of physical activity, daily, sensory and social function, and laboratory tests. We evaluated mortality over 12.5 years in relation to the frailty index.
Mean frailty levels were the same (x¯ = 0.20, p = .37) in the 1901-1902 cohort as in the 1930 cohort. Although the frailty index was linked to the risk of death in both cohorts, the hazards ratio decreased from 1.67 per 0.1 increment in the frailty index for the first cohort to 1.32 for the second cohort (interaction term p = .005).
Although frailty was as common at age 70 as before, its lethality appears to be less. Just why this is so should be explored further.
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Models of human mortality include a factor that summarises intrinsic differences in individual rates of ageing, commonly called frailty. Frailty also describes a clinical syndrome of apparent vulnerability. In a representative, cross-sectional, Canadian survey (n = 66,589) we calculated a frailty index as the mean accumulation of deficits and previously showed it to increase exponentially with age. Here, its density function exhibited a monotonic change in shape, being least skewed at the oldest ages. Although the shape gradually changed, the frailty index was well fitted by a gamma distribution. Of note, the variation coefficient, initially high, decreased from middle age on. Being able to quantify frailty means that health risks can be summarised at both the individual and group levels.
The frailty index (FI) is used to measure the health status of ageing individuals. An FI is constructed as the proportion of deficits present in an individual out of the total number of age-related health variables considered. The purpose of this study was to systematically assess whether dichotomizing deficits included in an FI affects the information value of the whole index.
Secondary analysis of three population-based longitudinal studies of community dwelling individuals: Nova Scotia Health Survey (NSHS, n = 3227 aged 18+), Survey of Health, Ageing and Retirement in Europe (SHARE, n = 37546 aged 50+), and Yale Precipitating Events Project (Yale-PEP, n = 754 aged 70+). For each dataset, we constructed two FIs from baseline data using the deficit accumulation approach. In each dataset, both FIs included the same variables (23 in NSHS, 70 in SHARE, 33 in Yale-PEP). One FI was constructed with only dichotomous values (marking presence or absence of a deficit); in the other FI, as many variables as possible were coded as ordinal (graded severity of a deficit). Participants in each study were followed for different durations (NSHS: 10 years, SHARE: 5 years, Yale PEP: 12 years).
Within each dataset, the difference in mean scores between the ordinal and dichotomous-only FIs ranged from 0 to 1.5 deficits. Their ability to predict mortality was identical; their absolute difference in area under the ROC curve ranged from 0.00 to 0.02, and their absolute difference between Cox Hazard Ratios ranged from 0.001 to 0.009.
Analyses from three diverse datasets suggest that variables included in an FI can be coded either as dichotomous or ordinal, with negligible impact on the performance of the index in predicting mortality.
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Several machine learning techniques (multilayer and single layer perceptron, logistic regression, least square linear separation and support vector machines) are applied to calculate the risk of death from two biomedical data sets, one from patient care records, and another from a population survey. Each dataset contained multiple sources of information: history of related symptoms and other illnesses, physical examination findings, laboratory tests, medications (patient records dataset), health attitudes, and disabilities in activities of daily living (survey dataset). Each technique showed very good mortality prediction in the acute patients data sample (AUC up to 0.89) and fair prediction accuracy for six year mortality (AUC from 0.70 to 0.76) in individuals from epidemiological database surveys. The results suggest that the nature of data is of primary importance rather than the learning technique. However, the consistently superior performance of the artificial neural network (multi-layer perceptron) indicates that nonlinear relationships (which cannot be discerned by linear separation techniques) can provide additional improvement in correctly predicting health outcomes.
Using data from seven studies from Canada, Australia and the United States we were able to evaluate the heterogeneity of health status for 34,095 people aged 60 years and older. We found that relative heterogeneity (measured by the coefficient of variation) decreased with age. The coefficient of variation also decreased as a function of deficit accumulation (i.e. was lower when people demonstrated more deficits) displaying a power-law relationship. The exponent was close to 0.5, and therefore belongs to the dynamic universality class, which is typical for complex dynamical networks. We interpret this as showing that decline in relative heterogeneity is a robust finding, and represents, at the group level, increased vulnerability amongst elderly people.
The purpose of this study was to examine the association of disability and co-morbidity with frailty in older adults. 2305 participants aged 65+ from the second wave of the Canadian Study of Health and Aging (CSHA), a prospective population-based cohort study, comprised the study sample. Following a standard procedure, two different frailty index (FI) measures were constructed from 37 deficits by dividing the recorded deficits by the total number of measures. One version excluded disability and co-morbidity items, the other included them. Time to death was measured for up to five years. Frailty was defined using either the frailty phenotype or a cut-point applied to each FI. Of people defined as frail using the frailty phenotype, 15/416 (3.6%) experienced neither disability nor co-morbidity. Using 0.25 as the cut-point score for the FI (without disability/co-morbidity) resulted in 101/1176 (8.6%) frail participants that had neither disability nor co-morbidity. Activities of daily living (ADL) limitations and co-morbidities occurred more often among people with the highest levels of frailty. The first ADLs to become impaired with increasing frailty were bathing, managing medication, and cooking with more than 25% of older adults with a FI score (without disability/co-morbidity) >0.22 experiencing dependency on them. The hazard ratio (HR) per 0.1 increase in FI score was 1.25 (95% CI: 1.20-1.30) when disability and co-morbidity were included in the index and 1.21 (1.16-1.25) when they were not included. In conclusion, disability and co-morbidity greatly overlap with other deficits that might be used to define frailty and add to their ability to predict mortality.
Objectives were to develop a frailty index (FI) based on a standard comprehensive geriatric assessment (CGA) derived from a clinical examination; to assess the validity of the FI-CGA and to compare its precision with other frailty measures.
Secondary analysis of a prospective cohort study, with five-year follow-up data.
Second phase of the Canadian Study of Health and Aging (CSHA-2); clinical examinations were performed in clinics, nursing homes, and patients' homes.
People selected (as either cognitively impaired cases or unimpaired controls) to receive the CSHA-2 clinical examination (n = 2305; women = 1431).
Clinical and performance-based measures and diagnostic data were extracted to correspond to the 10 impairment domains and the single comorbidity domain of a CGA. The proportion of deficits accumulated in each domain was calculated to yield the FI-CGA. The FI-CGA was validated and its predictive ability compared with other frailty measures.
Within the seven grades of fitness/frailty identified, subjects with greater frailty were older, less educated, and more likely to be women. The FI-CGA correlated highly with a previously validated, empirically-derived frailty index (r = 0.76). Frailty was associated with higher risk of death (for each increment in frailty, the hazard ratio, adjusted for age, sex and education, was 1.23 (95% CI 1.18-1.29) and institutionalization (HR 1.20; 1.10-1.32).
In a population survey, the FI-CGA is a valid means of quantifying frailty from routinely collected data.