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  1. Article ; Online: Does determining the degree of frailty help pandemic decision-making when resources are scarce?

    Samuel D Searle / Kenneth Rockwood

    The Lancet. Healthy Longevity, Vol 2, Iss 3, Pp e119-e

    2021  Volume 120

    Keywords Geriatrics ; RC952-954.6 ; Medicine ; R
    Language English
    Publishing date 2021-03-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: The responsiveness of goal attainment scaling using just one goal in controlled clinical trials

    Lisa McGarrigle / Kenneth Rockwood

    Journal of Patient-Reported Outcomes, Vol 4, Iss 1, Pp 1-

    an exploratory analysis

    2020  Volume 10

    Abstract: Abstract Background Goal Attainment Scaling (GAS) is an individualized outcome measure that allows the setting of personalized treatment goals. We compared the responsiveness of GAS when individuals set only one goal instead of the recommended three or ... ...

    Abstract Abstract Background Goal Attainment Scaling (GAS) is an individualized outcome measure that allows the setting of personalized treatment goals. We compared the responsiveness of GAS when individuals set only one goal instead of the recommended three or more goals. Methods We conducted exploratory analyses on data from two randomized controlled trials: the Video-Imaging Synthesis of Treating Alzheimer’s Disease (VISTA) (n = 130); and the Mobile Geriatric Assessment Team (MGAT) (n = 265). Independent t-tests and standardized response means (SRMs) were used to assess responsiveness of one- vs. multiple-goal GAS. Results In VISTA, clinician-rated multiple-goal GAS detected higher goal attainment in the intervention group (p = 0.01; SRM = 0.48). One-goal GAS, whether rated by patients or by clinicians, did not detect differences in goal attainment between groups (patient: p = 0.56, SRM = 0.10; clinician: p = 0.10, SRM = 0.29). In MGAT, multiple-goal GAS (outcome goals: p < .001, SRM = 1.29; total goals: p < .001, SRM = 1.52) and one-goal GAS (outcome goals: p < .001, SRM = 0.89; total goals: p < .001, SRM = 0.75), detected significantly higher goal attainment in the intervention group. Conclusion One-goal GAS detected significant change in response to a patient-centred, multi-domain care initiative. As such, in similar contexts, one-goal GAS may be an effective means of optimizing personalization and improving GAS feasibility through reduced administration time. However, it is not yet clear if one-goal GAS is responsive in the context of a pharmacological intervention and further research is recommended.
    Keywords Goal attainment ; Responsiveness ; RCT ; Frailty ; Dementia ; Assessment ; Public aspects of medicine ; RA1-1270
    Language English
    Publishing date 2020-05-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Interpretable machine learning for high-dimensional trajectories of aging health.

    Spencer Farrell / Arnold Mitnitski / Kenneth Rockwood / Andrew D Rutenberg

    PLoS Computational Biology, Vol 18, Iss 1, p e

    2022  Volume 1009746

    Abstract: 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 ... ...

    Abstract 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.
    Keywords Biology (General) ; QH301-705.5
    Subject code 006
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Fifteen years of progress in understanding frailty and health in aging

    Kenneth Rockwood / Susan E. Howlett

    BMC Medicine, Vol 16, Iss 1, Pp 1-

    2018  Volume 4

    Abstract: Abstract The notion of frailty has evolved for more than 15 years. Although there is no consensus definition, frailty reflects a state of increased vulnerability to adverse health outcomes for individuals of the same chronological age. Two commonly used ... ...

    Abstract Abstract The notion of frailty has evolved for more than 15 years. Although there is no consensus definition, frailty reflects a state of increased vulnerability to adverse health outcomes for individuals of the same chronological age. Two commonly used clinical tools, the frailty index and the frailty phenotype, both measure health-related deficits. The frailty index is a ratio of the number of deficits that an individual has accumulated divided by all deficits measured, whereas the phenotype specifies frailty as represented by poor performance in three of five criteria (i.e., weight loss, exhaustion, weakness, slowness, lack of activity). From human studies, animal models of both approaches have been developed and are beginning to shed light on mechanisms underlying frailty, the influence of frailty on disease expression, and new interventions to attenuate frailty. Currently, back-translation to humans is occurring. As we start to understand subcellular mechanisms involved in damage and repair as well as their response to treatment, we will begin to understand the molecular basis of aging and, thus, of frailty.
    Keywords Frailty index ; Frailty phenotype ; Deficit index ; Deficit accumulation ; Aging ; biomarkers ; Medicine ; R
    Language English
    Publishing date 2018-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Biomarkers to Measure Treatment Effects in Alzheimer's Disease

    Kenneth Rockwood

    International Journal of Alzheimer's Disease, Vol

    What Should We Look for?

    2011  Volume 2011

    Keywords Geriatrics ; RC952-954.6 ; Special situations and conditions ; RC952-1245 ; Internal medicine ; RC31-1245 ; Medicine ; R ; DOAJ:Internal medicine ; DOAJ:Medicine (General) ; DOAJ:Health Sciences ; Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 ; DOAJ:Neurology
    Language English
    Publishing date 2011-01-01T00:00:00Z
    Publisher Hindawi Publishing Corporation
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: The impact of cardiovascular health and frailty on mortality for males and females across the life course

    Jack Quach / Olga Theou / Judith Godin / Kenneth Rockwood / Dustin Scott Kehler

    BMC Medicine, Vol 20, Iss 1, Pp 1-

    2022  Volume 11

    Abstract: Abstract Background The effect of frailty and poor cardiovascular health on mortality for males and females is not fully elucidated. We investigated whether the combined burden of frailty and poor cardiovascular health is associated with all-cause and ... ...

    Abstract Abstract Background The effect of frailty and poor cardiovascular health on mortality for males and females is not fully elucidated. We investigated whether the combined burden of frailty and poor cardiovascular health is associated with all-cause and cardiovascular disease (CVD) mortality by sex and age. Methods We analyzed data of 35,207 non-institutionalized US residents aged 20–85 years old (mean age [standard deviation]: 46.6 [16.7 years], 51.4% female, 70.8% White, 10.3% Black, 13.2% Hispanic) from the National Health and Nutrition Examination Survey (1999–2015). Cardiovascular health was measured with the American Heart Association’s Life’s Simple 7 score (LS7). A 33-item frailty index (FI) was constructed to exclude cardiovascular health deficits. We grouped the FI into 0.1 increments (non-frail: FI < 0.10, very mildly frail: 0.1 ≤ FI < 0.20, mildly frail: 0.20 ≤ FI < 0.30, and moderately/severely frail: FI ≥ 0.30) and LS7 into tertiles (T1[poor] = 0–7, T2[intermediate] = 8-9, T3[ideal] = 10–14). All-cause and CVD mortality data were analyzed up to 16 years. All regression models were stratified by sex. Results The average FI was 0.09 (SD 0.10); 29.6% were at least very mildly frail, and the average LS7 was 7.9 (2.3). Mortality from all-causes and CVD were 8.5% (4228/35,207) and 6.1% (2917/35,207), respectively. The median length of follow-up was 8.1 years. The combined burden of frailty and poor cardiovascular health on mortality risk varied according to age in males (FI*age interaction p = 0.01; LS7*age interaction p < 0.001) but not in females. In females, poor FI and LS7 combined to predict all-cause and CVD mortality in a dose-response manner. All-cause and CVD mortality risk was greater for older males (60 and 70 years old) who were at least mildly frail and had intermediate cardiovascular health or worse (hazard ratio [lower/higher confidence interval ranges] range: all-cause mortality = 2.02–5.30 [1.20–4.04, 3.15–6.94]; CVD-related mortality = 2.22–7.16 [1.03–4.46, 4.49–11.50]) but ...
    Keywords Frailty ; Cardiovascular Health ; Mortality ; Medicine ; R
    Subject code 590 ; 310
    Language English
    Publishing date 2022-11-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Relationship of body mass index with frailty and all-cause mortality among middle-aged and older adults

    Kulapong Jayanama / Olga Theou / Judith Godin / Andrea Mayo / Leah Cahill / Kenneth Rockwood

    BMC Medicine, Vol 20, Iss 1, Pp 1-

    2022  Volume 12

    Abstract: Abstract Background Parallel to growth of aging and obese populations, the prevalence of metabolic diseases is rising. How body mass index (BMI) relates to frailty and mortality across frailty levels is controversial. We examined the associations of high ...

    Abstract Abstract Background Parallel to growth of aging and obese populations, the prevalence of metabolic diseases is rising. How body mass index (BMI) relates to frailty and mortality across frailty levels is controversial. We examined the associations of high BMI with frailty and mortality and explored the effects of percent body fat on these associations. Methods We included 29,937 participants aged ≥50 years from the 2001–2006 National Health and Nutrition Examination Survey (NHANES) cohorts (N=6062; 53.7% females) and from wave 1 (2004) of Survey of Health, Ageing and Retirement in Europe (SHARE) (N=23,875; 54% females). BMI levels were categorized as: normal: 18.5–24.9 kg/m2, overweight: 25.0–29.9, obese grade 1: 30.0–34.9, and obese grade 2 or 3: >35.0. A frailty index (FI) was constructed excluding nutrition-related items: 36 items for NHANES and 57 items for SHARE. We categorized the FI using 0.1-point increments: FI ≤ 0.1 (non-frail), 0.1 < FI ≤ 0.2 (very mildly frail), 0.2 < FI ≤ 0.3 (mildly frail), and FI > 0.3 (moderately/severely frail). Percent body fat was measured using DXA for NHANES participants. All-cause mortality data were obtained until 2015 for NHANES and 2017 for SHARE to estimate 10-year mortality risk. All analyses were adjusted for age, sex, educational, marital, employment, and smoking statuses. Results Mean age of participants was 63.3±10.2 years for NHANES and 65.0±10.0 years for SHARE. In both cohorts, BMI levels ≥25 kg/m2 were associated with higher frailty, compared to normal BMI. In SHARE, having a BMI level greater than 35 kg/m2 increased mortality risk in participants with FI≤0.1 (HR 1.31, 95%CI 1.02–1.69). Overweight participants with FI scores >0.3 were at lower risk for mortality compared to normal BMI [NHANES (0.79, 0.64–0.96); SHARE (0.71, 0.63–0.80)]. Higher percent body fat was associated with higher frailty. Percent body fat significantly mediated the relationship between BMI levels and frailty but did not mediate the relationship between BMI levels and ...
    Keywords Body mass index ; Obese ; Body composition ; Percent body fat ; Frailty ; Mortality ; Medicine ; R
    Subject code 310
    Language English
    Publishing date 2022-10-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Changes in the severity and lethality of age-related health deficit accumulation in the USA between 1999 and 2018

    Joanna M Blodgett, MSc / Kenneth Rockwood, MD / Olga Theou, PhD

    The Lancet. Healthy Longevity, Vol 2, Iss 2, Pp e96-e

    a population-based cohort study

    2021  Volume 104

    Abstract: Summary: Background: With an ageing population, the number of people with frailty is increasing. Despite this trend, the extent to which the severity and lethality of frailty have changed over time is not well understood. We aimed to investigate how ... ...

    Abstract Summary: Background: With an ageing population, the number of people with frailty is increasing. Despite this trend, the extent to which the severity and lethality of frailty have changed over time is not well understood. We aimed to investigate how frailty severity and lethality have changed over an 18-year period in the USA. Methods: In this population-based observational study, we used data from the National Health and Nutrition Examination Survey (NHANES) to identify community-dwelling individuals (aged ≥20 years) in the USA between 1999 and 2018. We analysed data from a series of ten 2-year, nationally representative, cross-sectional, prospective studies (from 1999–2000 to 2017–18) from the NHANES. Frailty was measured by use of the deficit accumulation approach (ie, a 46-item frailty index). The proportion of individuals categorised as non-frail, or living with very mild frailty, mild frailty, moderate frailty, and severe frailty were compared across cohorts. Random-effects models were used to examine the association between frailty index score and sex, age, and cohort. Mortality status as of Dec 31, 2015, was ascertained by use of National Death Index data, and 5-year mortality was available in the first six cohorts (1999–2010). Cox regression models and Kaplan-Meier curves were used to estimate the association between frailty index scores and mortality. Findings: In total, 49 004 individuals were included in our study. Associations were mainly non-linear (quadratic), with frailty increasing at a faster rate in more recent cohorts. Between 1999 and 2018, the proportion of non-frail individuals decreased by 10·4% (from 2747 [63·8%; 95% CI 61·9–65·6] of 4307 to 2884 [53·4%; 51·3–55·5] of 5399), whereas the proportion of individuals with very mild frailty increased by 2·4% (from 987 [22·9%; 21·3–24·6] to 1365 [25·3%; 23·5–27·2]), by 2·7% (from 370 [8·6%; 7·7–9·6] to 609 [11·3%; 10·1–12·5]) in those with mild frailty, by 3·1% (from 140 [3·3%; 2·7–3·9] to 347 [6·4%; 5·6–7·4]) in those with moderate frailty, ...
    Keywords Geriatrics ; RC952-954.6 ; Medicine ; R
    Subject code 310
    Language English
    Publishing date 2021-02-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Exploring the responsiveness of goal attainment scaling in relation to number of goals set in a sample of hemophilia-A patients

    Lisa McGarrigle / Jonathan C. Roberts / Michael Denne / Kenneth Rockwood

    Journal of Patient-Reported Outcomes, Vol 3, Iss 1, Pp 1-

    2019  Volume 5

    Abstract: Abstract Purpose Guidelines for the use of goal attainment scaling (GAS) recommend that the patient specify at least three goals. Even so, this may not always be feasible or align with patient preferences. Investigations into the psychometric properties ... ...

    Abstract Abstract Purpose Guidelines for the use of goal attainment scaling (GAS) recommend that the patient specify at least three goals. Even so, this may not always be feasible or align with patient preferences. Investigations into the psychometric properties of GAS using three or more goals largely support its reliability, validity, and responsiveness compared with standard measures. As evaluations of responsiveness rely on variability estimates, this metric may be impacted when GAS is based on fewer than three goals. For this reason, we investigated the responsiveness of one- and two-goal GAS. Methods Secondary analyses were conducted on data from a mixed sample of pediatric, adolescent and adult subjects with hemophilia A. The standardized response mean (SRM) and its 95% confidence intervals (CI) were used to assess responsiveness of one- and two-goal GAS at six and twelve weeks. Results Both one-goal and two-goal GAS demonstrated similar responsiveness to change at 6-week (Patient-Rated GAS: one-goal SRM [95% CI] = 0.70 [0.45–1.08], two-goal = 0.96 [0.68–1.30]; Clinician-Rated GAS: one-goal = 1.26 [0.81–1.77], two-goal = 1.01 [0.73–1.32]) and 12-week follow-up (Patient-Rated GAS: one-goal SRM [95% CI] = 1.14 [0.53–1.71], two-goal = 1.35 [0.92–1.82]; Clinician-Rated GAS: one-goal = 1.71 [1.12–2.30], two-goal = 1.48 [1.02–2.02]). Larger SRMs were observed for clinician-rated GAS, but all were within the rubric of a large effect size. Conclusions One-goal GAS is responsive to change in a clinical population. Further research is recommended in a larger sample where responsiveness of one- and multiple-goal GAS can be compared
    Keywords Patient ; Goal attainment ; Responsiveness ; Hemophilia ; Public aspects of medicine ; RA1-1270
    Language English
    Publishing date 2019-04-01T00:00:00Z
    Publisher SpringerOpen
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Frailty and mortality in patients with COVID-19

    Jai N Darvall / Rinaldo Bellomo / Paul J Young / Kenneth Rockwood / David Pilcher

    The Lancet Public Health, Vol 5, Iss 11, Pp e580- (2020)

    2020  

    Keywords Public aspects of medicine ; RA1-1270
    Language English
    Publishing date 2020-11-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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