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Abstract Background Frailty in individuals can be operationalized as the accumulation of health deficits, for which several trends have been observed in Western countries. Less is known about deficit accumulation in China, the country with the world's largest number of older adults. Methods This study analyzed data from the Beijing Longitudinal Study of Aging, to evaluate the relationship between age and deficit accumulation in men and women and to evaluate the impact of frailty on mortality. Community dwelling people aged 55+ years at baseline (n = 3275) were followed every two to three years between 1992 and 2000, during which time 36% died. A Frailty Index was constructed using 35 deficits, drawn from a range of health problems, including symptoms, disabilities, disease, and psychological difficulties. Results Most deficits increased the eight-year risk of death and were more lethal in men than in women, although women had a higher mean level of frailty (Frailty Index = 0.11 ± 0.10 for men, 0.14 ± 0.12 for women). The Frailty Index increased exponentially with age, with a similar rate in men and women (0.038 vs. 0.039; r > 0.949, P < 0.01). A dose-response relationship was observed as frailty increased. Conclusions A Frailty Index employed in a Chinese sample, showed properties comparable with Western data, but deficit accumulation appeared to be more lethal than in the West.

Abstract Background Frailty can be measured in relation to the accumulation of deficits using a frailty index. A frailty index can be developed from most ageing databases. Our objective is to systematically describe a standard procedure for constructing a frailty index. Methods This is a secondary analysis of the Yale Precipitating Events Project cohort study, based in New Haven CT. Non-disabled people aged 70 years or older (n = 754) were enrolled and re-contacted every 18 months. The database includes variables on function, cognition, co-morbidity, health attitudes and practices and physical performance measures. Data came from the baseline cohort and those available at the first 18-month follow-up assessment. Results Procedures for selecting health variables as candidate deficits were applied to yield 40 deficits. Recoding procedures were applied for categorical, ordinal and interval variables such that they could be mapped to the interval 0–1, where 0 = absence of a deficit, and 1= full expression of the deficit. These individual deficit scores were combined in an index, where 0= no deficit present, and 1= all 40 deficits present. The values of the index were well fit by a gamma distribution. Between the baseline and follow-up cohorts, the age-related slope of deficit accumulation increased from 0.020 (95% confidence interval, 0.014–0.026) to 0.026 (0.020–0.032). The 99% limit to deficit accumulation was 0.6 in the baseline cohort and 0.7 in the follow-up cohort. Multivariate Cox analysis showed the frailty index, age and sex to be significant predictors of mortality. Conclusion A systematic process for creating a frailty index, which relates deficit accumulation to the individual risk of death, showed reproducible properties in the Yale Precipitating Events Project cohort study. This method of quantifying frailty can aid our understanding of frailty-related health characteristics in older adults.

Abstract Background On average, health worsens with age, but many people have periods of improvement. A stochastic model provides an excellent description of how such changes occur. Given that cognition also changes with age, we wondered whether the same model might also describe the accumulation of errors in cognitive test scores in community-dwelling older adults. Methods In this prospective cohort study, 8954 older people (aged 65+ at baseline) from the Canadian Study of Health and Aging were followed for 10 years. Cognitive status was defined by the number of errors on the 100-point Modified Min-Mental State Examination. The error count was chosen to parallel the deficit count in the general model of aging, which is based on deficit accumulation. As with the deficit count, a Markov chain transition model was employed, with 4 parameters. Results On average, the chance of making errors increased linearly with the number of errors present at each time interval. Changes in cognitive states were described with high accuracy (R2 = 0.96) by a modified Poisson distribution, using four parameters: the background chance of accumulating additional errors, the chance of incurring more or fewer errors, given the existing number, and the corresponding background and incremental chances of dying. Conclusion The change in the number of errors in a cognitive test corresponded to a general model that also summarizes age-related changes in deficits. The model accounts for both improvement and deterioration and appears to represent a clinically relevant means of quantifying how various aspects of health status change with age.

Abstract Background People age at remarkably different rates, but how to estimate trajectories of senescence is controversial. Methods In a secondary analysis of a representative cohort of Canadians aged 65 and over (n = 2914) we estimated a frailty index based on the proportion of 20 deficits observed in a structured clinical examination. The construct validity of the index was examined through its relationship to chronological age (CA). The criterion validity was examined in its ability to predict mortality, and in relation to other predictions about aging. From the frailty index, relative (to CA) fitness and frailty were estimated, as was an individual's biological age. Results The average value of the frailty index increased with age in a log-linear relationship (r = 0.91; p < 0.001). In a Cox regression analysis, biological age was significantly more highly associated with death than chronological age. The average increase in the frailty index (i.e. the average accumulation of deficits) amongst those with no cognitive impairment was 3 per cent per year. Conclusions The frailty index is a sensitive predictor of survival. As the index includes items not traditionally related to adverse health outcomes, the finding is compatible with a view of frailty as the failure to integrate the complex responses required to maintain function.

We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine techniques of modern machine learning with an interpretable interaction network, where health variables are coupled by explicit pair-wise interactions within a stochastic dynamical system. Our dynamic joint interpretable network (DJIN) model is scalable to large longitudinal data sets, is predictive of individual high-dimensional health trajectories and survival from baseline health states, and infers an interpretable network of directed interactions between the health variables. The network identifies plausible physiological connections between health variables as well as clusters of strongly connected health variables. We use English Longitudinal Study of Aging (ELSA) data to train our model and show that it performs better than multiple dedicated linear models for health outcomes and survival. We compare our model with flexible lower-dimensional latent-space models to explore the dimensionality required to accurately model aging health outcomes. Our DJIN model can be used to generate synthetic individuals that age realistically, to impute missing data, and to simulate future aging outcomes given arbitrary initial health states.

Abstract Objective Early frailty may be captured by a frailty index (FI) based entirely on vital signs and laboratory tests. Our aim was to examine associations between a laboratory‐based FI (FI‐Lab) and adverse health outcomes, and investigate how this changed with age. Methods Up to 8988 individuals aged 20+ years from the 2003‐2004 and 2005‐2006 National Health and Nutrition Examination Survey cohorts were included. Characteristics of the FI‐Lab were compared to those of a self‐reported clinical FI. Associations between each FI and health care use, self‐reported health, and disability were examined in the full sample and across age groups. Results Laboratory‐based FI scores increased with age but did not demonstrate expected sex differences. Women aged 20‐39 years had higher FI scores than men; this pattern reversed after age 60 years. FI‐Lab scores were associated with poor self‐reported health (odds ratio[95% confidence interval]: 1.46[1.39‐1.54]), high health care use (1.35[1.29‐1.42]), and high disability (1.41[1.32‐1.50]), even among those aged 20‐39 years. Conclusion Higher FI‐Lab scores were associated with poor health outcomes at all ages. Associations in the youngest group support the notion that deficit accumulation occurs across the lifespan. FI‐Lab scores could be utilized as an early screening tool to identify deficit accumulation at the cellular and molecular level before they become clinically visible.

Background: Although there is no strict definition of frailty, it is generally accepted as a state of high vulnerability for adverse health outcomes at older age. Associations between frailty and mortality, dependence, and hospitalization have been shown. We measured the frailty level of older people with intellectual disabilities (ID). Furthermore variation in gender, age, and level of ID were identified. Results were compared to a frailty study in the general European population. Methods: This research elaborates on a large cross-sectional study: Healthy Ageing with Intellectual Disability (HA-ID). Nine hundred-eighty-two men and women (greater than or equal to 50yr) with ID were included. Based on the collected data, we developed a frailty index with 51 health-related deficits, and calculated a frailty index score between 0 and 1 for each individual. Deficits included physical, social and psychological problems. Results: The mean frailty index score was 0.27 (standard deviation 0.13). Frailty was positively correlated with age (r = 0.297, p less than 0.001). More severe ID was associated with higher frailty scores (beta = 0.440, p less than 0.001). The upper limit of the FI was 0.69, which was consistent for all age categories. Conclusion: As people with ID are getting older, the question whether additional years are spent in good health becomes salient. Here, people with ID over age 50 had frailty scores similar to most elderly people over 75y. Future research is needed to confirm if frail elderly people with ID have an increased risk of adverse health outcomes. (Contains 4 figures and 3 tables.)

BackgroundThe World Wide Web allows access to patient/care partner perspectives on the lived experience of dementia. We were interested in how symptoms that care partners target for tracking relate to dementia stage, and whether dementia could be staged using only these online profiles of targeted symptoms. ObjectivesTo use clinical data where the dementia stage is known to develop a model that classifies an individual’s stage of dementia based on their symptom profile and to apply this model to classify dementia stages for subjects from a Web-based dataset. MethodsAn Artificial Neural Network (ANN) was used to identify the relationships between the dementia stages and individualized profiles of people with dementia obtained from the 60-item SymptomGuide (SG). The clinic-based training dataset (n=320), with known dementia stages, was used to create an ANN model for classifying stages in Web-based users (n=1930). ResultsThe ANN model was trained in 66% of the 320 Memory Clinic patients, with the remaining 34% used to test its accuracy in classification. Training and testing staging distributions were not significantly different. In the 1930 Web-based profiles, 309 people (16%) were classified as having mild cognitive impairment, 36% as mild dementia, 29% as moderate, and 19% as severe. In both the clinical and Web-based symptom profiles, most symptoms became more common as the stage of dementia worsened (eg, mean 5.6 SD 5.9 symptoms in the MCI group versus 11.9 SD 11.3 in the severe). Overall, Web profiles recorded more symptoms (mean 7.1 SD 8.0) than did clinic ones (mean 5.5 SD 1.8). Even so, symptom profiles were relatively similar between the Web-based and clinical datasets. ConclusionSymptoms targeted for online tracking by care partners of people with dementia can be used to stage dementia. Even so, caution is needed to assure the validity of data collected online as the current staging algorithm should be seen as an initial step.

BackgroundThe Web offers unprecedented access to the experience of people with dementia and their care partners, but data gathered online need to be validated to be useful. ObjectiveTo test the construct validity of an informant Web-based data collection to assess dementia symptoms in relation to the 15-point Dependence Scale (DS). MethodsIn an online survey posted on the DementiaGuide website, care partners of people with dementia built individualized profiles from the 60-item SymptomGuide and completed a questionnaire, which included the DS and a staging tool. ResultsIn the 250 profilees (155, 62% women, mean age 77 years), increasing dependence was associated with a greater chance of institutionalization. For example, no one at the lowest levels of dependence (DS score < 5, n = 33) was in long-term care, compared with half (13/25) of the profilees at the highest levels of dependence (DS score > 12) being in institutions (χ24 = 27.9, P < .001). The Web-based DS was correlated with the number of symptoms: higher DS scores were associated with a higher stage of dementia (F > 50, P < .001). ConclusionIn an online survey, the Web-based DS showed good construct validity, potentially demonstrating how the Web can be used to learn more about dementia progression and how it relates to symptoms experienced by patients across the course of dementing illnesses. Even so, caution is needed to assure the validity of data collected online.

Background. Many definitions of frailty exist, but few have been directly compared. We compared the relationship between a definition of frailty based on a specific phenotype with one based on an index of deficit accumulation. Methods. The data come from all 2305 people 70 years old and older who composed the clinical examination cohort of the second wave of the Canadian Study of Health and Aging. We tested convergent validity by correlating the measures with each other and with other health status measures, and analyzed cumulative index distributions in relation to phenotype. To test criterion validity, we evaluated survival (institutionalization and all-cause mortality) by frailty index (FI) score, stratified by the phenotypic definitions as 'robust,' 'pre-frail,' and 'frail.' Results. The measures correlated moderately well with each other (R = 0.65) and with measures of function (phenotypic definition R = 0.66: FI R = 0.73) but less well with cognition (phenotypic definition R = -0.35: FI R = - 0.58). The median FI scores increased from 0.12 for the robust to 0.30 for the pre-frail and 0.44 for the frail. Survival was also lower with increasing frailty, and institutionalization was more common, but within each phenotypic class, there were marked differences in outcomes based on the FI values--e.g., among robust people, the median 5-year survival for those with lower FI values was 85%, compared with 55% for those with higher FI values. Conclusion. The phenotypic definition of frailty, which offers ready clinical operationalization, discriminates broad levels of risk. The FI requires additional clinical translation, but allows the risk of adverse outcomes to be defined more precisely.

This review article summarizes how frailty can be considered in relation to deficit accumulation. Recalling that frailty is an age-associated, nonspecific vulnerability, we consider symptoms, signs, diseases, and disabilities as deficits, which are combined in a frailty index. An individual's frailty index score reflects the proportion of potential deficits present in that person, and indicates the likelihood that frailty is present. Although based on a simple count, the frailty index shows several interesting properties, including a characteristic rate of accumulation, a submaximal limit, and characteristic changes with age in its distribution. The frailty index, as a state variable, is able to quantitatively summarize vulnerability. Future studies include the application of network analyses and stochastic analytical techniques to the evaluation of the frailty index and the description of other state variables in relation to frailty.

Although, on average, cognition declines with age, cognition in older adults is a dynamic process. Hypertension is associated with greater decline in cognition with age, but whether treatment of hypertension affects this is uncertain. Here, we modelled dynamics of cognition in relation to the treatment of hypertension, to see if treatment effects might better be discerned by a model that included baseline measures of cognition and consequent mortalityThis is a secondary analysis of the Hypertension in the Very Elderly Trial (HYVET), a double blind, placebo controlled trial of indapamide, with or without perindopril, in people aged 80+ years at enrollment. Cognitive states were defined in relation to errors on the Mini-Mental State Examination, with more errors signifying worse cognition. Change in cognitive state was evaluated using a dynamic model of cognitive transition. In the model, the probabilities of transitions between cognitive states is represented by a Poisson distribution, with the Poisson mean dependent on the baseline cognitive state. The dynamic model of cognitive transition was good (R(2) = 0.74) both for those on placebo and (0.86) for those on active treatment. The probability of maintaining cognitive function, based on baseline function, was slightly higher in the actively treated group (e.g., for those with the fewest baseline errors, the chance of staying in that state was 63% for those on treatment, compared with 60% for those on placebo). Outcomes at two and four years could be predicted based on the initial state and treatment.A dynamic model of cognition that allows all outcomes (cognitive worsening, stability improvement or death) to be categorized simultaneously detected small but consistent differences between treatment and control groups (in favour of treatment) amongst very elderly people treated for hypertension. The model showed good fit, and suggests that most change in cognition in very elderly people is small, and depends on their baseline state and on treatment. Additional work is needed to understand whether this modelling approach is well suited to the valuation of small effects, especially in the face of mortality differences between treatment groups.ClinicalTrials.gov NCT0012281.