Interpretable machine learning for high-dimensional trajectories of aging health.

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.

Frailty among middle-aged and older Canadians: population norms for the frailty index using the Canadian Longitudinal Study on Aging.

BACKGROUND: frailty is a public health priority now that the global population is ageing at a rapid rate. A scientifically sound tool to measure frailty and generate population-based reference values is a starting point. OBJECTIVE: in this report, our objectives were to operationalize frailty as deficit accumulation using a standard frailty index (FI), describe levels of frailty in Canadians ≥45 years old and provide national normative data. DESIGN: this is a secondary analysis of the Canadian Longitudinal Study on Aging (CLSA) baseline data. SETTING/PARTICIPANTS: about 51,338 individuals (weighted to represent 13,232,651 Canadians), aged 45-85 years, from the tracking and comprehensive cohorts of CLSA. METHODS: after screening all available variables in the pooled dataset, 52 items were selected to construct an FI. Descriptive statistics for the FI and normative data derived from quantile regressions were developed. RESULTS: the average age of the participants was 60.3 years (95% confidence interval [CI]: 60.2-60.5), and 51.5% were female (95% CI: 50.8-52.2). The mean FI score was 0.07 (95% CI: 0.07-0.08) with a standard deviation of 0.06. Frailty was higher among females and with increasing age, and scores >0.2 were present in 4.2% of the sample. National normative data were identified for each year of age for males and females. CONCLUSIONS: the standardized frailty tool and the population-based normative frailty values can help inform discussions about frailty, setting a new bar in the field. Such information can be used by clinicians, researchers, stakeholders and the general public to understand frailty, especially its relationship with age and sex.

Blood-brain barrier leakage in systemic lupus erythematosus is associated with gray matter loss and cognitive impairment.

OBJECTIVES: To examine the association between blood-brain barrier (BBB) integrity, brain volume and cognitive dysfunction in adult patients with systemic lupus erythematosus (SLE). METHODS: A total of 65 ambulatory patients with SLE and 9 healthy controls underwent dynamic contrast-enhanced MRI scanning, for quantitative assessment of BBB permeability. Volumetric data were extracted using the VolBrain pipeline. Global cognitive function was evaluated using a screening battery consisting of tasks falling into five broad cognitive domains, and was compared between patients with normal versus extensive BBB leakage. RESULTS: Patients with SLE had significantly higher levels of BBB leakage compared with controls (p=0.04). Extensive BBB leakage (affecting over >9% of brain volume) was identified only in patients with SLE (16/65; 24.6%), who also had smaller right and left cerebral grey matter volumes compared with controls (p=0.04). Extensive BBB leakage was associated with lower global cognitive scores (p=0.02), and with the presence of impairment on one or more cognitive tasks (p=0.01). CONCLUSION: Our findings provide evidence for a link between extensive BBB leakage and changes in both brain structure and cognitive function in patients with SLE. Future studies should investigate the mechanisms underlying BBB-mediated cognitive impairment, validate the diagnostic utility of BBB imaging, and determine the potential of targeting the BBB as a therapeutic strategy in patients with SLE.

Informative frailty indices from binarized biomarkers.

Frailty indices (FIs) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FIs from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a "quantile" methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey and the Canadian Study of Health and Aging data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, and the upper limits of the FI observed in various data sets, and also highlights general difficulties in obtaining absolute scales for comparing FIs between studies.

Is frailty a stable predictor of mortality across time? Evidence from the Cognitive Function and Ageing Studies.

Background: age-specific mortality reduction has been accompanied by a decrease in the prevalence of some diseases and an increase in others. Whether populations are becoming 'healthier' depends on which aspect of health is being considered. Frailty has been proposed as an integrative measure to quantify health status. Objective: to investigate changes in the near-term lethality of frailty before and after a 20-year interval using the frailty index (FI), a summary of age-related health deficit accumulation. Design: baseline data from the Cognitive Function and Ageing Studies (CFAS) in 1991 (n = 7,635) and 2011 (n = 7,762). Setting: three geographically distinct UK centres (Newcastle, Cambridgeshire and Nottingham). Subjects: individuals aged 65 and over (both institutionalised and community-living). Methods: a 30-item frailty score was used, which includes morbidities, risk factors and subjective measures of disability. Missing items were imputed using multiple imputations by chained equations. Binomial regression was used to investigate the relationship between frailty, age, sex and cohort. Two-year mortality was modelled using logistic regression. Results: mean frailty was slightly higher in CFAS II (0.19, 95% confidence interval (CI): 0.19-0.20) than CFAS I (0.18, 95% CI: 0.17-0.18). Two-year mortality in CFAS I was higher than in CFAS II (odds ratio (OR) = 1.16, 95% CI: 1.03-1.30). The association between frailty and 2-year mortality was non-linear with an OR of ~1.6 for each 0.10 increment in the FI. Conclusions: the relationship between frailty and mortality did not significantly differ across the studies. Severe frailty as an indicator of mortality is shown to be a stable construct.

Frailty affects the initial treatment response and time to recovery of mobility in acutely ill older adults admitted to hospital.

Objectives: to investigate how frailty and mobility impairment affect recovery of balance and mobility in acutely ill older patients. Design: secondary analysis of cohort study. Setting: general and geriatric medicine inpatient units, QEII Health Sciences Centre, Dalhousie University, Canada. Subjects: four hundred and nine older adults (mean age = 81 ± 7 standard deviation, 64% women). Methods: we constructed a frailty index based on a comprehensive geriatric assessment (FI-CGA), at baseline (2 weeks before admission; mean 0.31 ± 0.10), and on admission (mean 0.40 ± 0.10), and recorded Hierarchical Assessment of Balance and Mobility (HABAM) scores daily. Recovery was measured as the difference in HABAM scores between discharge and admission. Results: the odds of no or incomplete recovery increased by 1.06 (95% confidence interval: 1.01-1.11) for each 0.1 increment in the baseline FI-CGA. Recovery odds were similarly dependent on age, but independent of baseline HABAM scores. Recovery time was related to Day 1 HABAM scores, initial treatment response and change in the FI-CGA from baseline to admission (r = 0.35, P < 0.001). Recovery time was independent of age. Patients whose mobility improved within 48 h (n = 113; 28%) showed greater improvement and quicker recovery. Conclusions: frailer patients are at a greater risk of incomplete or lengthier recovery from impaired mobility and balance. Tracking mobility and balance might help providers, patients and families understand the course of acute illness in older adults.

Frailty status at admission to hospital predicts multiple adverse outcomes.

Aims: frailty is proposed as a summative measure of health status and marker of individual vulnerability. We aimed to investigate the discriminative capacity of a frailty index (FI) derived from interRAI Comprehensive Geriatric Assessment for Acute Care (AC) in relation to multiple adverse inpatient outcomes. Methods: in this prospective cohort study, an FI was derived for 1,418 patients ≥70 years across 11 hospitals in Australia. The interRAI-AC was administered at admission and discharge by trained nurses, who also screened patients daily for geriatric syndromes. Results: in adjusted logistic regression models an increase of 0.1 in FI was significantly associated with increased likelihood of length of stay >28 days (odds ratio [OR]: 1.29 [1.10-1.52]), new discharge to residential aged care (OR: 1.31 [1.10-1.57]), in-hospital falls (OR: 1.29 [1.10-1.50]), delirium (OR: 2.34 [2.08-2.63]), pressure ulcer incidence (OR: 1.51 [1.23-1.87]) and inpatient mortality (OR: 2.01 [1.66-2.42]). For each of these adverse outcomes, the cut-point at which optimal sensitivity and specificity occurred was for an FI > 0.40. Specificity was higher than sensitivity with positive predictive values of 7-52% and negative predictive values of 88-98%. FI-AC was not significantly associated with readmissions to hospital. Conclusions: the interRAI-AC can be used to derive a single score that predicts multiple adverse outcomes in older inpatients. A score of ≤0.40 can well discriminate patients who are unlikely to die or experience a geriatric syndrome. Whether the FI-AC can result in management decisions that improve outcomes requires further study.

Clinical meaningfulness of Alzheimer's Disease Assessment Scale-Cognitive subscale change in relation to goal attainment in patients on cholinesterase inhibitors.

INTRODUCTION: The clinical meaningfulness of Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) subscale change is disputed. We compared 2- to 4-point ADAS-Cog changes with changes in Goal Attainment Scaling (GAS) and everyday function across initial ADAS-Cog scores and treatment responses. METHODS: This exploratory analysis evaluated mild-moderate Alzheimer's disease patients treated with donepezil (12 months) or galantamine (8 months). Clinical meaningfulness was defined as concomitant ADAS-Cog and GAS changes of ±3 points and/or functional improvement. RESULTS: Patients with ≥3-point ADAS-Cog improvement significantly improved on GAS but not on standard tests of everyday function. ADAS-Cog "no change" (≤±3 points) was seen with mean GAS improvement. Initial ADAS-Cog improvement made endpoint improvement (ADAS-Cog 3 points and GAS 1 point) more likely (odds ratio = 6.9; 95% confidence interval = 2.5-19.5). In contrast, initial deterioration made endpoint improvement unlikely (0.33; 0.14-0.64). DISCUSSION: ADAS-Cog improvement and no change were each associated with GAS improvement. Initial ADAS-Cog worsening was unlikely to result in later improvement. CLINICAL TRIAL REGISTRATION NUMBER: ISRCTN26167328.

The rate of aging: the rate of deficit accumulation does not change over the adult life span.

People age at different rates. We have proposed that rates of aging can be quantified by the rate at which individuals accumulate health deficits. Earlier estimates, using cross-sectional analyses suggested that deficits accumulated exponentially, at an annual rate of 3.5%. Here, we estimate the rate of deficit accumulation using longitudinal data from the Canadian National Population Health Survey. By analyzing age-specific trajectories of deficit accumulation in people aged 20 years and over (n = 13,668) followed biannually for 16 years, we found that the longitudinal average annual rate of deficit accumulation was 4.5% (±0.75%). This estimate was notably stable during the adult life span. The corresponding average doubling time in the number of deficits was 15.4 (95% CI 14.82-16.03) years, roughly 30% less than we had reported from the cross-sectional analysis. Earlier work also established that the average number of deficits accumulated by individuals (N), equals the product of the intensity of environmental stresses (λ) causing damage to the organism, by the average recovery time (W). At the individual level, changes in deficit accumulation can be attributed to both changes in environmental stresses and changes in recovery time. By contrast, at the population level, changes in the number of deficits are proportional to the changes in recovery time. In consequence, we propose here that the average recovery time, W doubles approximately every 15.4 years, independently of age. Such changes quantify the increase of vulnerability to stressors as people age that gives rise to increasing risk of frailty, disability and death. That deficit accumulation will, on average, double twice between ages 50 and 80 highlights the importance of health in middle age on late life outcomes.

No evidence that frailty modifies the positive impact of antihypertensive treatment in very elderly people: an investigation of the impact of frailty upon treatment effect in the HYpertension in the Very Elderly Trial (HYVET) study, a double-blind, placebo-controlled study of antihypertensives in people with hypertension aged 80 and over.

BACKGROUND: Treatment for hypertension with antihypertensive medication has been shown to reduce stroke, cardiovascular events, and mortality in older adults, but there is concern that such treatment may not be appropriate in frailer older adults. To investigate whether there is an interaction between effect of treatment for hypertension and frailty in older adults, we calculated the frailty index (FI) for all available participants from the HYpertension in the Very Elderly Trial (HYVET) study, a double-blind, placebo-controlled study of antihypertensives in people with hypertension aged 80 and over, and obtained frailty adjusted estimates of the effect of treatment with antihypertensive medication on risk of stroke, cardiovascular events, and mortality. METHODS: Participants in HYVET were randomised 1:1 to active treatment with indapamide sustained release 1.5 mg ± perindopril 2 to 4 mg or to matching placebo. Data relating to blood pressure, comorbidities, cognitive function, depression, and quality of life were collected at entry into the study and at subsequent follow-up visits. The FI was calculated at entry, based on 60 potential deficits. The distribution of FI was similar to that seen in population studies of adults aged 80 years and above (median FI, 0.17; IQR, 0.11-0.24). Cox regression was used to assess the impact of FI at entry to the study on subsequent risk of stroke, total mortality, and cardiovascular events. Models were stratified by region of recruitment and adjusted for sex and age at entry. Extending these models to include a term for a possible interaction between treatment for hypertension and FI provided a formula for the treatment effect as a function of FI. For all three models, the point estimates of the hazard ratios for the treatment effect decreased as FI increased, although to varying degrees and with varying certainty. RESULTS: We found no evidence of an interaction between effect of treatment for hypertension and frailty as measured by the FI. Both the frailer and the fitter older adults with hypertension appeared to gain from treatment. CONCLUSIONS: Further work to examine whether antihypertensive treatment modifies frailty as measured by the FI should be explored. TRIAL REGISTRATION: ClinicalTrials.gov NCT00122811 (July 2005).

Age-related frailty and its association with biological markers of ageing.

BACKGROUND: The relationship between age-related frailty and the underlying processes that drive changes in health is currently unclear. Considered individually, most blood biomarkers show only weak relationships with frailty and ageing. Here, we examined whether a biomarker-based frailty index (FI-B) allowed examination of their collective effect in predicting mortality compared with individual biomarkers, a clinical deficits frailty index (FI-CD), and the Fried frailty phenotype. METHODS: We analyzed baseline data and up to 7-year mortality in the Newcastle 85+ Study (n = 845; mean age 85.5). The FI-B combined 40 biomarkers of cellular ageing, inflammation, haematology, and immunosenescence. The Kaplan-Meier estimator was used to stratify participants into FI-B risk strata. Stability of the risk estimates for the FI-B was assessed using iterative, random subsampling of the 40 FI-B items. Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under receiver operating characteristic (ROC) curves. RESULTS: The mean FI-B was 0.35 (SD, 0.08), higher than the mean FI-CD (0.22; SD, 0.12); no participant had an FI-B score <0.12. Higher values of each FI were associated with higher mortality risk. In a sex-adjusted model, each one percent increase in the FI-B increased the hazard ratio by 5.4 % (HR, 1.05; CI, 1.04-1.06). The FI-B was more powerful for mortality prediction than any individual biomarker and was robust to biomarker substitution. The ROC analysis showed moderate discriminative ability for 7-year mortality (AUC for FI-CD = 0.71 and AUC for FI-B = 0.66). No individual biomarker's AUC exceeded 0.61. The AUC for combined FI-CD/FI-B was 0.75. CONCLUSIONS: Many biological processes are implicated in ageing. The systemic effects of these processes can be elucidated using the frailty index approach, which showed here that subclinical deficits increased the risk of death. In the future, blood biomarkers may indicate the nature of the underlying causal deficits leading to age-related frailty, thereby helping to expose targets for early preventative interventions.

Neuropsychiatric symptom clusters targeted for treatment at earlier versus later stages of dementia.

OBJECTIVE: To characterize clusters of neuropsychiatric symptoms targeted for tracking the disease course in people with dementia, in relation to stage. METHODS: Baseline symptoms from 2922 subjects from two datasets (one clinic based, one online) were aggregated. Common neuropsychiatric symptoms identified by patients/carers as targets of treatment using a dementia SymptomGuide™ were selected. The Global Deterioration Scale was used for clinic staging, and an artificial neural network algorithm, for staging online subjects. Symptom clusters were detected using multiple correspondence analysis and connectivity graph analysis based on relative risk (RR). In a connectivity graph, each pair of nodes (representing symptoms) is connected if their co-occurrence is statistically significant; direction is indicated as positive if RR > 1 and negative otherwise. RESULTS: Neuropsychiatric symptoms were targeted for treatment in 1072 patients (37%). Agitation (37%) and sleep disturbances (28%) were most common symptoms. One cluster (in people with cognitive impairment, no dementia (CIND) or mild dementia) showed significant co-occurrence of anxiety and restlessness; decreased initiative was chiefly seen in isolation. A second cluster (in moderate/severe dementia) was defined by significant co-occurrence of delusions and hallucinations with sleep disturbances; in these subjects, decreased initiative was related to aggression. CONCLUSIONS: Two analytical methods identified neuropsychiatric symptom clusters targeted to track the disease course. In CIND/mild dementia, a profile of decreased initiative distinct from depression suggests possible executive dysfunction. In moderate/severe dementia, targets more reflected psychotic symptoms. Visual data displays allow the relationships between multiple symptoms to be considered simultaneously, which commonly is how they present in patients.

What are frailty instruments for?

Frailty is measured to understand its nature and biology, to aid diagnosis and care planning, to measure outcomes and to stratify risk. Such goals oblige two types of frailty measures - for screening and for assessment - and recognition that not all measures will serve all purposes. When the goal is broad identification of people at risk, a dichotomised approach (frailty is present or absent ) is appropriate. If, however, the degree of risk varies, strategies to test grades of frailty will be required. Frailty measures should be implemented and evaluated in relation to the goal for their use.

The validation of a care partner-derived frailty index based upon comprehensive geriatric assessment (CP-FI-CGA) in emergency medical services and geriatric ambulatory care.

BACKGROUND: The derivation of a frailty index (FI) based on deficit accumulation from a Comprehensive Geriatric Assessment (CGA) has been criticised as cumbersome. To improve feasibility, we developed a questionnaire based on a CGA that can be completed by care partners (CP-FI-CGA) and assessed its validity. METHODS: We enrolled a convenience sample of patients aged 70 or older (n=203) presenting to emergency medical services (EMS) or geriatric ambulatory care (GAC). To test construct validity, we evaluated the shape of the CP-FI-CGA distribution, including its maximum value, relationship with age and gender. Criterion validity was evaluated by survival analysis and by the correlation between the CP-FI-CGA and specialist-completed FI-CGA. RESULTS: The mean age was 82.2±5.9 years. Most patients were women (62.1%), unmarried (widowed, divorced and single) (59.6%) and lived in their own home or apartment (78.3%). The mean CP-FI-CGA was 0.41±0.15 and was higher in the EMS group (0.45±0.15) than in GAC (0.37±0.14) (P<0.001). The CP-FI-CGA correlated well with the specialist-completed FI-CGA (0.7; P<0.05). People who died had a higher CP-FI-CGA than did survivors (0.48±0.13 versus 0.38±0.15). Each 0.01 increase in the FI was associated with a higher risk of death (HR 1.04; 95% CI 1.02-1.06). CONCLUSION: The CP-FI-CGA has properties that resemble other published FIs and may be useful in busy clinical practice for grading degrees of frailty. It efficiently integrates information from care partners so that it can help guide decision-making.

Social vulnerability and survival across levels of frailty in the Honolulu-Asia Aging Study.

PURPOSE: we evaluated mortality risk in relation to social vulnerability across levels of frailty among a cohort of older Japanese-American men. METHODS: in secondary analysis of the Honolulu-Asia Aging Study (HAAS), participants (n = 3,271) were aged 72-93 years at baseline. A frailty index (FI) created using 58 potential health deficits to quantify participants' frailty level at baseline, with four frailty strata: 0.0 < FI ≤ 0.1 (n = 1,074); 0.1 < FI ≤ 0.20 (n = 1,549); 0.2 < FI ≤ 0.30 (n = 472); FI > 0.3 (n = 176). Similarly, a social vulnerability index was created using 19 self-reported social deficits. Cox proportional hazard modelling was employed to estimate the impact of social vulnerability across the four levels of frailty, accounting for age, smoking, alcohol use and variation in health deficits within each frailty level. RESULTS: for the fittest participants, social vulnerability was associated with mortality (hazards ratio (HR) = 1.04, 95% confidence interval (CI) = 1.01, 1.07; P value = 0.008). Similarly, for those considered at risk for frailty, each social deficit was associated with a 5% increased risk of mortality. For frail individuals, the Cox regression analyses indicated that social vulnerability was not significantly associated with mortality (0.2 < FI ≤ 0.3: HR = 1.016, 95% CI = 0.98, 1.06; P value = 0.442; FI > 0.3: HR = 0.98, 95% CI = 0.93, 1.04). CONCLUSIONS: for the fittest and at-risk HAAS participants, the accumulation of social deficits was associated with significant increases in mortality risk. For frail individuals (FI > 0.20), the estimation of mortality risk may depend more so on intrinsic factors related to their health.

Derivation of a frailty index from the interRAI acute care instrument.

BACKGROUND: A better understanding of the health status of older inpatients could underpin the delivery of more individualised, appropriate health care. METHODS: 1418 patients aged ≥ 70 years admitted to 11 hospitals in Australia were evaluated at admission using the interRAI assessment system for Acute Care. This instrument surveys a large number of domains, including cognition, communication, mood and behaviour, activities of daily living, continence, nutrition, skin condition, falls, and medical diagnosis. RESULTS: Variables across multiple domains were selected as health deficits. Dichotomous data were coded as symptom absent (0 deficit) or present (1 deficit). Ordinal scales were recoded as 0, 0.5 or 1 deficit based on face validity and the distribution of data. Individual deficit scores were summed and divided by the total number considered (56) to yield a Frailty index (FI-AC) with theoretical range 0-1. The index was normally distributed, with a mean score of 0.32 (±0.14), interquartile range 0.22 to 0.41. The 99% limit to deficit accumulation was 0.69, below the theoretical maximum of 1.0. In logistic regression analysis including age, gender and FI-AC as covariates, each 0.1 increase in the FI-AC increased the likelihood of inpatient mortality twofold (OR: 2.05 [95% CI 1.70-2.48]). CONCLUSIONS: Quantification of frailty status at hospital admission can be incorporated into an existing assessment system, which serves other clinical and administrative purposes. This could optimise clinical utility and minimise costs. The variables used to derive the FI-AC are common to all interRAI instruments, and could be used to precisely measure frailty across the spectrum of health care.

Standard laboratory tests to identify older adults at increased risk of death.

BACKGROUND: Older adults are at an increased risk of death, but not all people of the same age have the same risk. Many methods identify frail people (that is, those at increased risk) but these often require time-consuming interactions with health care providers. We evaluated whether standard laboratory tests on their own, or added to a clinical frailty index (FI), could improve identification of older adults at increased risk of death. METHODS: This is a secondary analysis of a prospective cohort study, where community dwelling and institutionalized participants in the Canadian Study of Health and Aging who also volunteered for blood collection (n = 1,013) were followed for up to six years. A standard FI (FI-CSHA) was constructed from data obtained during the clinical evaluation and a second, novel FI was constructed from laboratory data plus systolic and diastolic blood pressure measurements (FI-LAB). A combined FI included all items from each index. Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under receiver operating characteristic (ROC) curves. RESULTS: Of 1,013 participants, 51.3% had died by six years. The mean baseline value of the FI-LAB was 0.27 (standard deviation 0.11; range 0.05 to 0.63), the FI-CSHA was 0.25 (0.11; 0.02 to 0.72), and the combined FI was 0.26 (0.09; 0.06 to 0.59). In an age- and sex-adjusted model, with each increment in the FI-LAB, the hazard ratios increased by 2.8% (95% confidence interval 1.02 to 1.04). The hazard ratios for the FI-CSHA and the combined FI were 1.02 (1.01 to 1.03) and 1.04 (1.03 to 1.05), respectively. The FI-LAB and FI-CSHA remained independently associated with death in the face of the other. The areas under the ROC curves were 0.72 for FI-LAB, 0.73 for FI-CSHA and 0.74 for the combined FI. CONCLUSIONS: An FI based on routine laboratory data can identify older adults at increased risk of death. Additional evaluation of this approach in clinical settings is warranted.

The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment.

BACKGROUND: prognostication for frail older adults is complex, especially when they become seriously ill. OBJECTIVES: to test the measurement properties, especially the predictive validity, of a frailty index based on a comprehensive geriatric assessment (FI-CGA) in an acute care setting in relation to the risk of death, length of stay and discharge destination. DESIGN AND SETTING: prospective cohort study. Inpatient medical units in a teaching, acute care hospital. SUBJECTS: individuals on inpatient medical units in a hospital, n = 752, aged 75+ years, were evaluated on their first hospital day; to test reliability, a subsample (n = 231) was seen again on Day 3. MEASUREMENTS: all frailty data collected routinely as part of a CGA were used to create the FI-CGA. Mortality data were reviewed from hospital records, claims data, Social Security Death Index and interviews with Discharge Managers. RESULTS: thirty-day mortality was 93 (12.4%; 95% confidence interval (CI) = 10-15%) of whom 52 died in hospital. The risk of dying increased with each 0.01 increment in the FI-CGA: hazard ratio (HR) = 1.05, (95% CI = 1.04-1.07). People who were discharged home had the lowest admitting mean FI-CGA = 0.38 (±standard deviation 0.11) compared with those who died, FI-CGA = 0.51 (±0.12) or were discharged to nursing home, FI-CGA = 0.49 (±0.11). Likewise, increasing FI-CGA values on admission were significantly associated with a longer length of hospital stay. CONCLUSIONS: frailty, measured by the FI-CGA, was independently associated with a higher risk of death and other adverse outcomes in older people admitted to an acute care hospital.

Comparison of alternate scoring of variables on the performance of the frailty index.

BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.