Frailty and its association with disability and comorbidity in a community-dwelling sample of seniors in Montreal: a cross-sectional study

Wong, Chek Hooi; Weiß, Deborah; Sourial, Nadia; Karunananthan, Sathya; Quail, Jacqueline; Wolfson, Christina; Bergman, Howard

doi:10.1007/bf03324816

Cited by 154 publications

(112 citation statements)

References 23 publications

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“…It is very important for our clinical practice that frailty is independently predictive of fall incidents, comorbidities, worsening of mobility or activities of daily life, hospitalization, and death (Fairhal et al 2008;Wong et al 2010;Heuberger 2011;Jürschik et al 2012;Runzer-Colmenares et al 2014). At the end of our study, as many as 39.2 and 43.3 % of the older adult participants were found frail and pre-frail, respectively.…”

Section: Discussionmentioning

confidence: 76%

“…It is very important for our clinical practice that this frailty phenotype is independently predictive (over 3 years) of falls, worsening of mobility or activities of daily life, disability, hospitalization, and death. Additionally, some previous research results point out the association of frailty with comorbidity (Wong et al 2010;Heuberger 2011;Jürschik et al 2012). However, this association has not been considered adequately in the literature probably because the pathogenesis has not been fully discerned.…”

Section: Introductionmentioning

confidence: 99%

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Frailty prevalence and related factors in the older adult—FrailTURK Project

Eyigör

Kutsal

Duran

et al. 2015

AGE

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Frailty is one of the geriatric syndromes and has an important relationship with mortality and morbidity. The aim of this study is to present the characteristics, prevalence, and related factors of frailty in older adults in our country. The study included 1126 individuals over 65 years of age from 13 centers. Frailty was evaluated using the Fried Frailty criteria, and patients were grouped as Bfrail,^Bpre-frail,^and Bnon-frail.N utritional status was assessed with BMini Nutritional Test,^psychological status with the BCenter for Epidemiological Studies Depression Scale-CES-D,^and additional diseases with the "Charlson Comorbidity index." Approximately 66.5 % of the participants were between 65 and 74 years of age and 65.7 % were AGE (2015) women. Some 39.2 and 43.3 % of the participants were rated as frail and pre-frail, respectively. The multinomial logistic regression analysis was used to determine the factors associated with frailty. It was observed that age, female gender, low education level, being a housewife, living with the family, being sedentary, presence of an additional disease, using 4 or more drugs/day, avoiding to go outside, at least one visit to any emergency department within the past year, hospitalization within the past year, non-functional ambulation, and malnutrition increased the risk of frailty (p<0.05). Establishing the factors associated with frailty is highly important for both clinical practice and national economy. This is the first study on this subject in our country and will provide guidance in determining treatment strategies.

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Section: Discussionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Frailty prevalence and related factors in the older adult—FrailTURK Project

Eyigör

Kutsal

Duran

et al. 2015

AGE

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“…The Cardiovascular Health Study (CHS) index developed by Fried et al (2001) is an operational definition of frailty in older subjects based on the presence of any three of the following five characteristics: shrinking, weakness, poor endurance, slowness, and low physical activity. While other population-based studies confirmed original findings from the CHS of the overlaps and dissociations among frailty, disability, and comorbidity, there were important differences in the reported prevalence estimates (from 6.9% to 20.0%; Fried et al 2001;Woods et al 2005;Ottenbacher et al 2005;Bandeen-Roche et al 2006;Cesari et al 2006;Gill et al 2006;Hirsch et al 2006;Avila-Funes et al 2008;Santos-Eggimann et al 2009;Wong et al 2010).…”

Section: Introductionmentioning

confidence: 67%

“…The prevalence estimate of frailty syndrome The Italian Longitudinal Study on Aging, 2nd survey, 1995Aging, 2nd survey, -1996Aging, 2nd survey, and 3rd survey, 2000Aging, 2nd survey, -2001 ADL Activities of Daily Living, HR hazard ratios, CI confidence interval, IADL Instrumental Activities of Daily Living a HRs were adjusted for age, sex (coded 0 for women and 1 for men), and failure type as covariate in partially adjusted models b HRs were adjusted for age categories (coded 1 for 65-69, coded 2 for 70-74, coded 3 for 75-79, and coded 4 for 80-84), gender (coded 0 for women and 1 for men), education, pack-years {pack-years cigarettes [coded 0 for pack-years cigarettes = 0 (never smoking) and 1 for pack-years cigarettes ≥0.5]}, Instrumental Activities of Daily Living score, Mini Mental State Examination score, Charlson comorbidity index, serum albumin levels, in fully adjusted models for the nondemented population; MMSE and IADL scores were excluded from the models for the demented population in the ILSA was also similar to that of another Italian population-based study, the Invecchiare in Chianti Study (8.8%; Cesari et al 2006). However, several other population-based studies have applied the same definition of frailty syndrome of the CHS and the ILSA with very different prevalence estimates that varied from 6.9% to 20.0% (Fried et al 2001;Woods et al 2005;Ottenbacher et al 2005;Bandeen-Roche et al 2006;Gill et al 2006;Hirsch et al 2006;AvilaFunes et al 2008;Wong et al 2010). In these population-based studies, differences in the tools used in estimating the different components of the frailty syndrome, different races, and different age and gender distribution of the samples could be the source of this great variability.…”

Section: Discussionmentioning

confidence: 91%

Frailty syndrome and all-cause mortality in demented patients: the Italian Longitudinal Study on Aging

Solfrizzi

Scafato

Frisardi

et al. 2011

AGE

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Cognition has already been considered as a component of frailty, and it has been demonstrated that it is associated with adverse health outcomes. We estimated the prevalence of frailty syndrome in an Italian older population and its predictive role on allcause mortality and disability in nondemented subjects and in demented patients. We evaluated 2,581 individuals recruited from the Italian Longitudinal Study on Aging, a population-based sample of 5,632 subjects, aged 65-84 years old. Participants received identical baseline evaluation at the 1st survey (1992)(1993) and were followed at 2nd (1995)(1996) and 3rd survey (2000)(2001). A phenotype of frailty according to partially modified measurement of Cardiovascular Health Study criteria was operationalized. The overall prevalence of frailty syndrome in this populationbased study was 7.6% (95% confidence interval (CI) 6.55-8.57). Frail individuals noncomorbid or nondisable were 9.1% and 39.3%, respectively, confirming an overlap but not concordance in the cooccurrence among these conditions. Frailty was associated with a significantly increased risk of allcause mortality over a 3-year follow-up (hazard ratio (HR) 1.98, 95% confidence interval (CI) 1.52-2.60) and over a 7-year follow-up (HR 1.74, 95% CI 1.44-2.16), but with significant increased risk of disability only over a 3-year follow-up (HR 1.32, 95% CI 1.06-1.86 over a 3-year follow-up and HR 1.16, 95% CI 0.88-1.56 over a 7-year follow-up). Frail demented patients were at higher risk of all-cause mortality over 3-(HR 3.33, 95% CI 1.28-8.29) and 7-year follow-up periods (HR 1.89, 95% CI 1.10-3.44), but not of disability. Frailty syndrome was a short-term predictor of disability in nondemented older subjects and short-and long-term predictor of all-cause mortality in nondemented and demented patients.

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“…Frailty considerably changes care utilisation pattern due to a significant increase in risks of comorbidity [40]- [42] and adverse outcomes, such as fall, post-operative complications, disability, mortality, prolonged length-of-stay, readmission and institutionalisation. Therefore, there is a considerable benefit in identifying these patients and proactively planning their care to enable rapid control of symptoms and prioritisation of anticipated needs.…”

Section: Frailtymentioning

confidence: 99%

Predictive Risk Modelling for Integrated Care: A Structured Review

Mesgarpour

Chaussalet

Worrall

et al. 2016

2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS)

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Considerable work has been carried out in this subject area especially in the USA and the UK. This has led for instance to the development of tools such as PARR, PARR-30, and the Combined Predictive Model for prediction of emergency readmission or admission to acute care.Here we perform a structured review the academic and grey literature on predictive risk tools for social care utilisation, as well as admission and readmission to general hospitals and psychiatric hospitals. This is the first phase of a project in partnership with Docobo Ltd and funded by Innovate UK, in which we seek to develop novel predictive risk tools and dashboards to assist commissioners in Clinical Commissioning Groups with the triangulation of the intelligence available from routinely collected data to optimise integrated care and better understand the complex needs of individuals.

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Frailty and its association with disability and comorbidity in a community-dwelling sample of seniors in Montreal: a cross-sectional study

Abstract: Findings on the relationship between frailty and sociodemographic variables, morbidity and disability, support previous studies, providing further evidence that although frailty seems to be a distinct geriatric concept, it also overlaps with other concepts.

Cited by 154 publications

References 23 publications

Frailty prevalence and related factors in the older adult—FrailTURK Project

Frailty prevalence and related factors in the older adult—FrailTURK Project

Frailty syndrome and all-cause mortality in demented patients: the Italian Longitudinal Study on Aging

Predictive Risk Modelling for Integrated Care: A Structured Review

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