Physical frailty characteristics have a differential impact on symptoms as measured by the CAT score: an observational study

Medina-Mirapeix, Françesc; Bernabeu-Mora, Roberto; Giménez-Giménez, Luz María; Escolar-Reina, Pilar; Gacto-Sánchez, Mariano; Oliveira-Sousa, Silvana Loana de

doi:10.1186/s12955-018-0969-9

Cited by 15 publications

(25 citation statements)

References 37 publications

(43 reference statements)

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“…Furthermore, while some studies have demonstrated an association between frailty and both severity of airflow limitation [16][17][18] and mortality in people with COPD, [19][20][21] these findings have been inconsistent. [22][23][24][25] It is also not clear if the relationship between frailty and adverse outcomes in COPD is independent of the severity of COPD assessed by airflow limitation.…”

Section: What This Study Addsmentioning

confidence: 99%

Frailty in COPD: an analysis of prevalence and clinical impact using UK Biobank

et al. 2022

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BackgroundFrailty, a state of reduced physiological reserve, is common in people with chronic obstructive pulmonary disease (COPD). Frailty can occur at any age; however, the implications in younger people (eg, aged <65 years) with COPD are unclear. We assessed the prevalence of frailty in UK Biobank participants with COPD; explored relationships between frailty and forced expiratory volume in 1 second (FEV1) and quantified the association between frailty and adverse outcomes.MethodsUK Biobank participants (n=3132, recruited 2006–2010) with COPD aged 40–70 years were analysed comparing two frailty measures (frailty phenotype and frailty index) at baseline. Relationship with FEV1 was assessed for each measure. Outcomes were mortality, major adverse cardiovascular event (MACE), all-cause hospitalisation, hospitalisation with COPD exacerbation and community COPD exacerbation over 8 years of follow-up.ResultsFrailty was common by both definitions (17% frail using frailty phenotype, 28% moderate and 4% severely frail using frailty index). The frailty phenotype, but not the frailty index, was associated with lower FEV1. Frailty phenotype (frail vs robust) was associated with mortality (HR 2.33; 95% CI 1.84 to 2.96), MACE (2.73; 1.66 to 4.49), hospitalisation (incidence rate ratio 3.39; 2.77 to 4.14) hospitalised exacerbation (5.19; 3.80 to 7.09) and community exacerbation (2.15; 1.81 to 2.54), as was frailty index (severe vs robust) (mortality (2.65; 95% CI 1.75 to 4.02), MACE (6.76; 2.68 to 17.04), hospitalisation (3.69; 2.52 to 5.42), hospitalised exacerbation (4.26; 2.37 to 7.68) and community exacerbation (2.39; 1.74 to 3.28)). These relationships were similar before and after adjustment for FEV1.ConclusionFrailty, regardless of age or measure, identifies people with COPD at risk of adverse clinical outcomes. Frailty assessment may aid risk stratification and guide-targeted intervention in COPD and should not be limited to people aged >65 years.

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Section: What This Study Addsmentioning

confidence: 99%

Frailty in COPD: an analysis of prevalence and clinical impact using UK Biobank

et al. 2022

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“…According to Medina-Mirapeix et al, 24 in addition to functional capacity, participants classified as frail presented worse clinical outcomes, such as a higher incidence of comorbidities, as compared to non-frail ones. In this sense, the complementary data obtained in the present study demonstrate that older adults classified as frail tended to consume more medications compared to those classified as non-frail and pre-frail, although the former presented higher HDL values and higher age.…”

Section: Discussionmentioning

confidence: 99%

“…Other previous studies have presented similar results, describing the relationship between FS and LTCIs residence, 21 increased risk of falls, 22 and low functional capacity. 23,24 It is important to understand that the literature presents several approaches regarding the definition of frailty, so that depending on the type of approach used, these prevalence values could change significantly. Among the variables tested, only GS and STS scores emerged as significant predictors of FS.…”

Section: Discussionmentioning

confidence: 99%

“…13,28 Additionally, low functional capacity, represented by poor performance in the GS and STS tests, greatly increases the risk of falls and vulnerability, besides being a clinical indicator of frailty. 13,23,24 In this sense, a study on the association between frailty and quality of life of older adults according to their frailty status, considering functional abilities such as GS and STS performance, level of physical activity, HGS performance, and exhaustion showed that the non-frail group presented better indexes of functionality and quality of life compared to the pre-frail and frail groups. 10 Another study 12 in 1 327 older adults, suggests that higher age and worse GS performance were associated with a greater risk of FS.…”

Section: Discussionmentioning

confidence: 99%

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Frailty syndrome in older adults from the community and long-term care institutions: an exploratory analysis

Cunha¹,

Baião²,

Santos³

et al. 2019

Geriatr., Gerontol. Aging

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OBJECTIVES:To investigate the association between Frailty syndrome, lipid profile, anthropometric variables, and the functional capacity of older adults; and to analyze an explanatory model of variables with higher predictive capacity for Frailty syndrome. METHODS: This cross-sectional study included 36 and 86 older adults residing in long-term care facilities and in their households, respectively. Anamnesis was followed by evaluation of anthropometric data, risk of falls, functional tests, and biochemical tests. Frailty syndrome was determined according to the criteria suggested by Fried et al. RESULTS: Geriatric patients classified as frail were older; had higher medication consumption; and presented lower performance in handgrip strength, sit-to-stand, and gait speed tests as compared to pre-and non-frail older adults. CONCLUSION: Gait speed and sit-to-stand tests were significant predictors of Frailty syndrome. Specifically, a good performance in these tests represents a protection factor against Frailty syndrome. Furthermore, gait speed performance was explained by age, handgrip strength performance, and frailty status, while sit-to-stand performance was explained by risk of falls and muscular strength. KEYWORDS: frail older adults; aging; health of the elderly. RESUMOOBJETIVOS: Investigar a associação entre síndrome da fragilidade, perfil lipídico, variáveis antropométricas e capacidade funcional de idosos; e analisar um modelo explicativo de variáveis com maior capacidade preditiva para síndrome da fragilidade. MÉTODOS: Este estudo transversal incluiu 36 e 86 idosos residentes em instituições de longa permanência e em suas residências, respectivamente. A anamnese foi seguida pela avaliação dos dados antropométricos, risco de quedas, testes funcionais e testes bioquímicos. A síndrome da fragilidade foi determinada de acordo com os critérios sugeridos por Fried et al. RESULTADOS: Os idosos classificados como frágeis eram mais idosos; tiveram maior consumo de medicação; e apresentaram menor desempenho nos testes de força de preensão palmar, levantar e sentar e velocidade da marcha quando comparados aos idosos pré e não frágeis. CONCLUSÕES: Os testes velocidade da marcha e levantar e sentar foram preditores significativos de síndrome da fragilidade. Especificamente, um bom desempenho nesses testes representa um fator de proteção contra a síndrome da fragilidade. Além disso, o desempenho da velocidade da marcha foi explicado pela idade, desempenho da força de preensão palmar e estado de fragilidade, enquanto o desempenho do levantar e sentar foi explicado pelo risco de quedas e força muscular.

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“…Studying the determinants of health status in COPD is of interest, because determinants can reveal potentially modifiable factors that might minimize the impact of the disease, and thus, improve the patient’s ability to cope with the consequences of this disease [7]. Previous studies have shown a cross-transversal association between poor health status (CAT ≥ 10) and various pulmonary and non-pulmonary factors, such as forced expiratory volume in 1 s (FEV1), history of smoking, dyspnea [8,9] and exercise ability, among others [10]. Nevertheless, predictors of poor health status might differ from factors that predict recovery from poor health status in patients with COPD.…”

Section: Introductionmentioning

confidence: 99%

Patterns and Predictors of Recovery from Poor Health Status Measured with the Chronic Obstructive Pulmonary Disease (COPD) Assessment Test in Patients with Stable COPD: A Longitudinal Study

Medina-Mirapeix

Bernabeu-Mora

Sánchez-Martínez

et al. 2019

JCM

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Recent recommendations for chronic obstructive pulmonary disease (COPD) suggest that evaluation and management should focus on patient health status. Despite the frequency of poor health status and its negative impact on patients with COPD, little is known about how poor or non-poor health status persists and/or remits over time or what factors might predict recovery from a poor health status. The aim was to determine the likelihood of transitioning between poor and non-poor health status in patients with stable COPD followed for 2 years and to investigate factors that might predict recovery from poor health status. We prospectively included 137 patients with stable COPD (mean age, 66.9 years ± 8.3). Health status was measured at baseline and after 1 and 2 years with the COPD assessment test (CAT). Higher scores indicated worse health status, and 10 was the cut-off score for discriminating between non-poor and poor health status. The likelihoods of annual transitions to new episodes and recovery were calculated. We evaluated demographic, non-respiratory, and respiratory variables as potential predictors with generalized estimating equations. At baseline, 37 patients (27%) reported non-poor health status. Within the group of patients displaying poor health status at the beginning of the year, 176 annual transitions were identified during the study period: 15.9% were transitions to recovery from poor health status. In contrast, of the 70 transitions from a starting non-poor health status, 32.4% worsened. Predictors of transitions to recovery were: current non-smoker status (odds ratio (OR) = 3.88; 95% confidence interval (CI): 0.64–5.54) and handgrip strength (OR = 1.08; 95% CI: 1.00–1.16). This study suggests that self-reported health status, measured with the CAT, has a dynamic nature in patients with COPD. Annual transitions towards recovery from poor health status are most likely among current non-smoking patients and those with high handgrip strength.

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Physical frailty characteristics have a differential impact on symptoms as measured by the CAT score: an observational study

Cited by 15 publications

References 37 publications

Frailty in COPD: an analysis of prevalence and clinical impact using UK Biobank

Frailty in COPD: an analysis of prevalence and clinical impact using UK Biobank

Frailty syndrome in older adults from the community and long-term care institutions: an exploratory analysis

Patterns and Predictors of Recovery from Poor Health Status Measured with the Chronic Obstructive Pulmonary Disease (COPD) Assessment Test in Patients with Stable COPD: A Longitudinal Study

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