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BACKGROUND: Frailty, an age-related state of reduced physiological reserve, is often associated with lower socio-economic position (SEP). This systematic review synthesised observational studies assessing (i) the association between SEP and frailty prevalence; (ii) how changes in frailty status over time vary by SEP; and (iii) whether the association between frailty and clinical outcomes is modified by SEP. METHODS: We searched three electronic databases from 2001 to 2023. We included observational studies measuring early-, mid-, and late-life indicators of SEP (education, income, wealth, housing, occupation, and area-based measures of multiple deprivation) and frailty (assessed using any validated measure). Screening and extraction were performed in duplicate. Findings were synthesised using narrative synthesis. RESULTS: We included 383 studies reporting findings from 265 independent samples/cohorts across 64 countries. Lower SEP was associated with higher frailty prevalence across all indicators (childhood deprivation 7/8 studies, education 227/248, occupation 28/32, housing 8/9, income 98/108, wealth 39/44 and area-based deprivation 32/34). Lower SEP was also associated with higher frailty incidence (27/30), with greater odds of transitioning towards a more severe frailty state (35/43), lower odds of frailty reversion (7/11), and (in some studies) with more rapid accumulation of deficits (7/15). The relationship between frailty and mortality was not modified by SEP. INTERPRETATION: Preventative measures across multiple levels of individual and structural inequality are likely to be required to reduce the rising levels of frailty. Resourcing of interventions and services to support people living with frailty should be proportionate to needs in the population to avoid widening existing health inequalities.
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Background: It remains unclear how to meaningfully classify people living with multimorbidity (multiple long-term conditions (MLTCs)), beyond counting the number of conditions. This paper aims to identify clusters of MLTCs in different age groups and associated risks of adverse health outcomes and service use. Methods: Latent class analysis was used to identify MLTCs clusters in different age groups in three cohorts: Secure Anonymised Information Linkage Databank (SAIL) (n = 1,825,289), UK Biobank (n = 502,363), and the UK Household Longitudinal Study (UKHLS) (n = 49,186). Incidence rate ratios (IRR) for MLTC clusters were computed for: all-cause mortality, hospitalisations, and general practice (GP) use over 10 years, using <2 MLTCs as reference. Information on health outcomes and service use were extracted for a ten year follow up period (between 01st Jan 2010 and 31st Dec 2019 for UK Biobank and UKHLS, and between 01st Jan 2011 and 31st Dec 2020 for SAIL). Findings: Clustering MLTCs produced largely similar results across different age groups and cohorts. MLTC clusters had distinct associations with health outcomes and service use after accounting for LTC counts, in fully adjusted models. The largest associations with mortality, hospitalisations and GP use in SAIL were observed for the "Pain+" cluster in the age-group 18-36 years (mortality IRR = 4.47, hospitalisation IRR = 1.84; GP use IRR = 2.87) and the "Hypertension, Diabetes & Heart disease" cluster in the age-group 37-54 years (mortality IRR = 4.52, hospitalisation IRR = 1.53, GP use IRR = 2.36). In UK Biobank, the "Cancer, Thyroid disease & Rheumatoid arthritis" cluster in the age group 37-54 years had the largest association with mortality (IRR = 2.47). Cardiometabolic clusters across all age groups, pain/mental health clusters in younger groups, and cancer and pulmonary related clusters in older age groups had higher risk for all outcomes. In UKHLS, MLTC clusters were not significantly associated with higher risk of adverse outcomes, except for the hospitalisation in the age-group 18-36 years. Interpretation: Personalising care around MLTC clusters that have higher risk of adverse outcomes may have important implications for practice (in relation to secondary prevention), policy (with allocation of health care resources), and research (intervention development and targeting), for people living with MLTCs. Funding: This study was funded by the National Institute for Health and Care Research (NIHR; Personalised Exercise-Rehabilitation FOR people with Multiple long-term conditions (multimorbidity)-NIHR202020).
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INTRODUCTION: In trials, subgroup analyses are used to examine whether treatment effects differ by important patient characteristics. However, which subgroups are most commonly reported has not been comprehensively described. DESIGN AND SETTINGS: Using a set of trials identified from the US clinical trials register (ClinicalTrials.gov), we describe every reported subgroup for a range of conditions and drug classes. METHODS: We obtained trial characteristics from ClinicalTrials.gov via the Aggregate Analysis of ClinicalTrials.gov database. We subsequently obtained all corresponding PubMed-indexed papers and screened these for subgroup reporting. Tables and text for reported subgroups were extracted and standardised using Medical Subject Headings and WHO Anatomical Therapeutic Chemical codes. Via logistic and Poisson regression models we identified independent predictors of result reporting (any vs none) and subgroup reporting (any vs none and counts). We then summarised subgroup reporting by index condition and presented all subgroups for all trials via a web-based interactive heatmap (https://ihwph-hehta.shinyapps.io/subgroup_reporting_app/). RESULTS: Among 2235 eligible trials, 23% (524 trials) reported subgroups. Follow-up time (OR, 95%CI: 1.13, 1.04-1.24), enrolment (per 10-fold increment, 3.48, 2.25-5.47), trial starting year (1.07, 1.03-1.11) and specific index conditions (eg, hypercholesterolaemia, hypertension, taking asthma as the reference, OR ranged from 0.15 to 10.44), predicted reporting, sponsoring source and number of arms did not. Results were similar on modelling any result reporting (except number of arms, 1.42, 1.15-1.74) and the total number of subgroups. Age (51%), gender (45%), racial group (28%) were the most frequently reported subgroups. Characteristics related to the index condition (severity/duration/types etc) were frequently reported (eg, 69% of myocardial infarction trials reported on its severity/duration/types). However, reporting on comorbidity/frailty (five trials) and mental health (four trials) was rare. CONCLUSION: Other than age, sex, race ethnicity or geographic location and characteristics related to the index condition, information on variation in treatment effects is sparse. PROSPERO REGISTRATION NUMBER: CRD42018048202.
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Ensaios Clínicos como Assunto , Humanos , Doença Crônica , Estudos Epidemiológicos , Projetos de PesquisaRESUMO
Objectives: To assess whether age, sex, comorbidity count, and race and ethnic group are associated with the likelihood of trial participants not being enrolled in a trial for any reason (ie, screen failure). Design: Bayesian meta-analysis of individual participant level data. Setting: Industry funded phase 3/4 trials of chronic medical conditions. Participants: Participants were identified using individual participant level data to be in either the enrolled group or screen failure group. Data were available for 52 trials involving 72 178 screened individuals of whom 24 733 (34%) were excluded from the trial at the screening stage. Main outcome measures: For each trial, logistic regression models were constructed to assess likelihood of screen failure in people who had been invited to screening, and were regressed on age (per 10 year increment), sex (male v female), comorbidity count (per one additional comorbidity), and race or ethnic group. Trial level analyses were combined in Bayesian hierarchical models with pooling across condition. Results: In age and sex adjusted models across all trials, neither age nor sex was associated with increased odds of screen failure, although weak associations were detected after additionally adjusting for comorbidity (odds ratio of age, per 10 year increment was 1.02 (95% credibility interval 1.01 to 1.04) and male sex (0.95 (0.91 to 1.00)). Comorbidity count was weakly associated with screen failure, but in an unexpected direction (0.97 per additional comorbidity (0.94 to 1.00), adjusted for age and sex). People who self-reported as black seemed to be slightly more likely to fail screening than people reporting as white (1.04 (0.99 to 1.09)); a weak effect that seemed to persist after adjustment for age, sex, and comorbidity count (1.05 (0.98 to 1.12)). The between-trial heterogeneity was generally low, evidence of heterogeneity by sex was noted across conditions (variation in odds ratios on log scale of 0.01-0.13). Conclusions: Although the conclusions are limited by uncertainty about the completeness or accuracy of data collection among participants who were not randomised, we identified mostly weak associations with an increased likelihood of screen failure for age, sex, comorbidity count, and black race or ethnic group. Proportionate increases in screening these underserved populations may improve representation in trials. Trial registration number: PROSPERO CRD42018048202.
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Chronic kidney disease (CKD) is a major global health problem, affecting about 9.5% of the population and 850 million people worldwide. In primary care, most CKD is caused by diabetes and/or hypertension, but a substantial proportion of cases may have alternative causes. During the early stages, CKD is asymptomatic, and many people are unaware that they are living with the disease. Despite the lack of symptoms, CKD is associated with elevated risks of cardiovascular disease, progressive kidney disease, kidney failure and premature mortality. Risk reduction strategies are effective and cost-effective but require early diagnosis through testing of the estimated glomerular filtration rate and albuminuria in high-risk populations. Once diagnosed, the treatment of CKD centres around lifestyle interventions, blood pressure and glycaemic control, and preventative treatments for cardiovascular disease and kidney disease progression. Most patients with CKD should be managed with statins, renin-angiotensin-aldosterone system inhibitors and sodium-glucose cotransporter-2 inhibitors. Additional treatment options to reduce cardiorenal risk are available in patients with diabetes, including glucagon-like peptide-1 receptor agonists and non-steroidal mineralocorticoid receptor antagonists. The Kidney Failure Risk Equation is a new tool that can support the identification of patients at high risk of progressive kidney disease and kidney failure and can be used to guide referrals to nephrology. This review summarizes the latest guidance relevant to managing adults with, or at risk of, CKD and provides practical advice for managing patients with CKD in primary care.
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Frailty, social isolation, and loneliness have individually been associated with adverse health outcomes. This study examines how frailty in combination with loneliness or social isolation is associated with socioeconomic deprivation and with all-cause mortality and hospitalisation rate in a middle-aged and older population. Baseline data from 461,047 UK Biobank participants (aged 37-73) were used to assess frailty (frailty phenotype), social isolation, and loneliness. Weibull models assessed the association between frailty in combination with loneliness or social isolation and all-cause mortality adjusted for age/sex/smoking/alcohol/socioeconomic-status and number of long-term conditions. Negative binomial regression models assessed hospitalisation rate. Frailty prevalence was 3.38%, loneliness 4.75% and social isolation 9.04%. Frailty was present across all ages and increased with age. Loneliness and social isolation were more common in younger participants compared to older. Co-occurrence of frailty and loneliness or social isolation was most common in participants with high socioeconomic deprivation. Frailty was associated with increased mortality and hospitalisation regardless of social isolation/loneliness. Hazard ratios for mortality were 2.47 (2.27-2.69) with social isolation and 2.17 (2.05-2.29) without social isolation, 2.14 (1.92-2.38) with loneliness and 2.16 (2.05-2.27) without loneliness. Loneliness and social isolation were associated with mortality and hospitalisation in robust participants, but this was attenuated in the context of frailty. Frailty and loneliness/social isolation affect individuals across a wide age spectrum and disproportionately co-occur in areas of high deprivation. All were associated with adverse outcomes, but the association between loneliness and social isolation and adverse outcomes was attenuated in the context of frailty. Future interventions should target people living with frailty or loneliness/social isolation, regardless of age.
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Fragilidade , Solidão , Pessoa de Meia-Idade , Humanos , Idoso , Fragilidade/epidemiologia , Bancos de Espécimes Biológicos , Biobanco do Reino Unido , Isolamento Social , Fatores SocioeconômicosRESUMO
Both frailty (reduced physiological reserve) and social vulnerability (scarcity of adequate social connections, support, or interaction) become more common as people age and are associated with adverse consequences. Analyses of the relationships between these constructs can be limited by the wide range of measures used to assess them. In this systematic review, we synthesised 130 observational studies assessing the association between frailty and social vulnerability, the bidirectional longitudinal relationships between constructs, and their joint associations with adverse health outcomes. Frailty, across assessment type, was associated with increased loneliness and social isolation, perceived inadequacy of social support, and reduced social participation. Each of these social vulnerability components was also associated with more rapid progression of frailty and lower odds of improvement compared with the absence of that social vulnerability component (eg, more rapid frailty progression in people with social isolation vs those who were not socially isolated). Combinations of frailty and social vulnerability were associated with increased mortality, decline in physical function, and cognitive impairment. Clinical and public health measures targeting frailty or social vulnerability should, therefore, account for both frailty and social vulnerability.
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Disfunção Cognitiva , Fragilidade , Humanos , Vulnerabilidade Social , Solidão , Saúde PúblicaRESUMO
BACKGROUND AND HYPOTHESIS: People with chronic kidney disease (CKD) have increased incidence and mortality from most cancer types. We hypothesised that odds of presenting with advanced cancer may vary according to differences in eGFR, that this could contribute to increased all-cause mortality and that sex differences may exist. METHODS: Data were from Secure Anonymised Information Linkage Databank, including people with de-novo cancer diagnosis (2011-2017) and two kidney function tests within two years prior to diagnosis to determine baseline eGFR (mL/min/1.73m2). Logistic regression models determined odds of presenting with advanced cancer by baseline eGFR. Cox proportional hazards models tested associations between baseline eGFRcr and all-cause mortality. RESULTS: eGFR < 30 was associated with higher odds of presenting with advanced cancer of prostate, breast and female genital organs, but not other cancer sites. Compared to eGFR > 75-90, eGFR < 30 was associated with greater hazards of all-cause mortality in both sexes, but the association was stronger in females (female: HR 1.71, 95%CI 1.56-1.88; male versus female comparison HR 0.88, 95%CI 0.78-0.90). CONCLUSIONS: Lower or higher eGFR was not associated with substantially higher odds of presenting with advanced cancer across most cancer sites, but was associated with reduced survival. A stronger assocation with all-cause mortality in females compared to males with eGFR < 30 is concerning and warrants further scrutiny.
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BACKGROUND: There has been conflicting evidence on the association between multimorbidity and blood pressure (BP) control. This study aimed to investigate this associations in people with hypertension attending primary care in Canada, and to assess whether individual long-term conditions are associated with BP control. METHODS: This was a cross-sectional study in people with hypertension attending primary care in Toronto between January 1, 2017 and December 31, 2019. Uncontrolled BP was defined as systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg. A list of 11 a priori selected chronic conditions was used to define multimorbidity. Multimorbidity was defined as having ≥1 long-term condition in addition to hypertension. Logistic regression models were used to estimate the association between multimorbidity (or individual long-term conditions) with uncontrolled BP. RESULTS: A total of 67 385 patients with hypertension were included. They had a mean age of 70, 53.1% were female, 80.6% had multimorbidity, and 35.7% had uncontrolled BP. Patients with multimorbidity had lower odds of uncontrolled BP than those without multimorbidity (adjusted OR = 0.72, 95% CI 0.68-0.76). Among the long-term conditions, diabetes (aOR = 0.73, 95%CI 0.70-0.77), heart failure (aOR = 0.81, 95%CI 0.73-0.91), ischemic heart disease (aOR = 0.74, 95%CI 0.69-0.79), schizophrenia (aOR = 0.79, 95%CI 0.65-0.97), depression/anxiety (aOR = 0.91, 95%CI 0.86-0.95), dementia (aOR = 0.87, 95%CI 0.80-0.95), and osteoarthritis (aOR = 0.89, 95%CI 0.85-0.93) were associated with a lower likelihood of uncontrolled BP. CONCLUSION: We found that multimorbidity was associated with better BP control. Several conditions were associated with better control, including diabetes, heart failure, ischemic heart disease, schizophrenia, depression/anxiety, dementia, and osteoarthritis.
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Demência , Diabetes Mellitus , Insuficiência Cardíaca , Hipertensão , Isquemia Miocárdica , Osteoartrite , Humanos , Feminino , Masculino , Pressão Sanguínea , Multimorbidade , Estudos Transversais , Hipertensão/epidemiologia , Diabetes Mellitus/epidemiologia , Isquemia Miocárdica/epidemiologia , Insuficiência Cardíaca/epidemiologia , Atenção Primária à Saúde , Demência/epidemiologiaRESUMO
Background: People with comorbidities are under-represented in randomised controlled trials, and it is unknown whether patterns of comorbidity are similar in trials and the community. Methods: Individual-level participant data were obtained for 83 clinical trials (54,688 participants) for 16 index conditions from two trial repositories: Yale University Open Data Access (YODA) and the Centre for Global Clinical Research Data (Vivli). Community data (860,177 individuals) were extracted from the Secure Anonymised Information Linkage (SAIL) databank for the same index conditions. Comorbidities were defined using concomitant medications. For each index condition, we estimated correlations between comorbidities separately in trials and community data. For the six commonest comorbidities we estimated all pairwise correlations using Bayesian multivariate probit models, conditioning on age and sex. Correlation estimates from trials with the same index condition were combined into a single estimate. We then compared the trial and community estimates for each index condition. Results: Despite a higher prevalence of comorbidities in the community than in trials, the correlations between comorbidities were mostly similar in both settings. On comparing correlations between the community and trials, 21% of correlations were stronger in the community, 10% were stronger in the trials and 68% were similar in both. In the community, 5% of correlations were negative, 21% were null, 56% were weakly positive and 18% were strongly positive. Equivalent results for the trials were 11%, 33%, 45% and 10% respectively. Conclusions: Comorbidity correlations are generally similar in both the trials and community, providing some evidence for the reporting of comorbidity-specific findings from clinical trials.
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BACKGROUND: In the United Kingdom (UK), cancer screening invitations are based on general practice (GP) registrations. We hypothesize that GP electronic medical records (EMR) can be utilised to calculate a lung cancer risk score with good accuracy/clinical utility. METHODS: The development cohort was Secure Anonymised Information Linkage-SAIL (2.3 million GP EMR) and the validation cohort was UK Biobank-UKB (N = 211,597 with GP-EMR availability). Fast backward method was applied for variable selection and area under the curve (AUC) evaluated discrimination. RESULTS: Age 55-75 were included (SAIL: N = 574,196; UKB: N = 137,918). Six-year lung cancer incidence was 1.1% (6430) in SAIL and 0.48% (656) in UKB. The final model included 17/56 variables in SAIL for the EMR-derived score: age, sex, socioeconomic status, smoking status, family history, body mass index (BMI), BMI:smoking interaction, alcohol misuse, chronic obstructive pulmonary disease, coronary heart disease, dementia, hypertension, painful condition, stroke, peripheral vascular disease and history of previous cancer and previous pneumonia. The GP-EMR-derived score had AUC of 80.4% in SAIL and 74.4% in UKB and outperformed ever-smoked criteria (currently the first step in UK lung cancer screening pilots). DISCUSSION: A GP-EMR-derived score may have a role in UK lung cancer screening by accurately targeting high-risk individuals without requiring patient contact.
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Medicina Geral , Neoplasias Pulmonares , Humanos , Pessoa de Meia-Idade , Idoso , Registros Eletrônicos de Saúde , Detecção Precoce de Câncer , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/epidemiologia , Fatores de Risco , Medição de RiscoRESUMO
Frailty is an age-related clinical condition characterised by an increased susceptibility to stressors and an elevated risk of adverse outcomes such as mortality. In the light of global population ageing, the prevalence of frailty is expected to soar in coming decades. This narrative review provides critical insights into recent developments and emerging practices in frailty research regarding identification, management, risk factors, and prevention. We searched journals in the top two quartiles of geriatrics and gerontology (from Clarivate Journal Citation Reports) for articles published between 01 January 2018 and 20 December 2022. Several recent developments were identified, including new biomarkers and biomarker panels for frailty screening and diagnosis, using artificial intelligence to identify frailty, and investigating the altered response to medications by older adults with frailty. Other areas with novel developments included exercise (including technology-based exercise), multidimensional interventions, person-centred and integrated care, assistive technologies, analysis of frailty transitions, risk-factors, clinical guidelines, COVID-19, and potential future treatments. This review identified a strong need for the implementation and evaluation of cost-effective, community-based interventions to manage and prevent frailty. Our findings highlight the need to better identify and support older adults with frailty and involve those with frailty in shared decision-making regarding their care.
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Fragilidade , Geriatria , Publicações Periódicas como Assunto , Humanos , Idoso , Fragilidade/diagnóstico , Fragilidade/prevenção & controle , Inteligência Artificial , Gestão de Riscos , Idoso Fragilizado , Avaliação GeriátricaRESUMO
BACKGROUND: Frailty and dementia have a bidirectional relationship. However, frailty is rarely reported in clinical trials for dementia and mild cognitive impairment (MCI) which limits assessment of trial applicability. This study aimed to use a frailty index (FI)-a cumulative deficit model of frailty-to measure frailty using individual participant data (IPD) from clinical trials for MCI and dementia. Moreover, the study aimed to quantify the prevalence of frailty and its association with serious adverse events (SAEs) and trial attrition. METHODS: We analysed IPD from dementia (n = 1) and MCI (n = 2) trials. An FI comprising physical deficits was created for each trial using baseline IPD. Poisson and logistic regression were used to examine associations with SAEs and attrition, respectively. Estimates were pooled in random effects meta-analysis. Analyses were repeated using an FI incorporating cognitive as well as physical deficits, and results compared. RESULTS: Frailty could be estimated in all trial participants. The mean physical FI was 0.14 (SD 0.06) and 0.14 (SD 0.06) in the MCI trials and 0.24 (SD 0.08) in the dementia trial. Frailty prevalence (FI > 0.24) was 6.9%/7.6% in MCI trials and 48.6% in the dementia trial. After including cognitive deficits, the prevalence was similar in MCI (6.1% and 6.7%) but higher in dementia (75.4%). The 99th percentile of FI (0.31 and 0.30 in MCI, 0.44 in dementia) was lower than in most general population studies. Frailty was associated with SAEs: physical FI IRR = 1.60 [1.40, 1.82]; physical/cognitive FI IRR = 1.64 [1.42, 1.88]. In a meta-analysis of all three trials, the estimated association between frailty and trial attrition included the null (physical FI OR = 1.17 [0.92, 1.48]; physical/cognitive FI OR = 1.16 [0.92, 1.46]), although higher frailty index values were associated with attrition in the dementia trial. CONCLUSION: Measuring frailty from baseline IPD in dementia and MCI trials is feasible. Those living with more severe frailty may be under-represented. Frailty is associated with SAEs. Including only physical deficits may underestimate frailty in dementia. Frailty can and should be measured in future and existing trials for dementia and MCI, and efforts should be made to facilitate inclusion of people living with frailty.
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Transtornos Cognitivos , Disfunção Cognitiva , Demência , Fragilidade , Humanos , Fragilidade/diagnóstico , Fragilidade/epidemiologia , Prevalência , Disfunção Cognitiva/epidemiologia , Demência/epidemiologia , Ensaios Clínicos Controlados Aleatórios como AssuntoRESUMO
BACKGROUND: People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). METHODS AND FINDINGS: We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. CONCLUSIONS: Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.
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Asma , Diabetes Mellitus Tipo 2 , Rinite Alérgica , Humanos , Masculino , Comorbidade , Ensaios Clínicos Controlados Aleatórios como AssuntoRESUMO
Context: The applicability of randomised controlled trials of pharmacological agents to older people with frailty/multimorbidity is often uncertain, due to concerns that trials are not representative. However, assessing trial representativeness is challenging and complex. Objectives: We explore an approach assessing trial representativeness by comparing rates of trial Serious Adverse Events (SAEs: most of which reflect hospitalisations/deaths) to rates of hospitalisation/death in routine care (which, in a trial setting, would be SAEs be definition). Study design: Secondary analysis of trial and routine healthcare data. Dataset and population: 483 trials (n=636,267) from clinicaltrials.gov across 21 index conditions. A routine care comparison was identified from SAIL databank (n=2.3M). Instrument: SAIL data were used to derive the expected rate of hospitalisations/deaths by age, sex and index condition. Outcomes: We calculated the expected number of SAEs for each trial compared to the observed number of SAEs (observed/expected SAE ratio). We then re-calculated the observed/expected SAE ratio additionally accounting for comorbidity count in 125 trials for which we accessed individual participant data. Results: For 12/21 index conditions the observed/expected SAE ratio was <1, indicating fewer SAEs in trials than expected given community rates of hospitalisations and deaths. A further 6/21 had point estimates <1 but the 95% CI included the null. The median observed/expected SAE ratio was 0.60 (95% CI 0.56-0.65; COPD) and the interquartile range was 0.44 (0.34-0.55; Parkinson's disease) to 0.88 (0.59-1.33; IBD). Higher comorbidity count was associated with SAEs/hospitalisations and deaths across index conditions. For most trials, the observed/expected ratio was attenuated but remained <1 after additionally accounting for comorbidity count. Conclusion: Trial participants experience fewer SAEs than expected based on age/sex/condition hospitalisation and death rates in routine care, confirming the predicted lack of representativeness. This difference is only partially explained by differences in multimorbidity. Assessing observed/expected SAE may help assess applicability of trial findings to older populations in whom multimorbidity and frailty are common.
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Fragilidade , Humanos , Idoso , Ensaios Clínicos Controlados Aleatórios como AssuntoRESUMO
BACKGROUND: Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. METHODS AND FINDINGS: Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631. CONCLUSIONS: Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. TRIAL REGISTRATION: A prespecified protocol was registered on PROSPERO (CRD42018048202).
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Qualidade de Vida , Humanos , Teorema de Bayes , Doença Crônica , Inquéritos e Questionários , ComorbidadeRESUMO
This systematic review synthesised measurement and prevalence of frailty in COPD and associations between frailty and adverse health outcomes. We searched Medline, Embase and Web of Science (1 January 2001-8 September 2021) for observational studies in adults with COPD assessing frailty prevalence, trajectories, or association with health-related outcomes. We performed narrative synthesis and random-effects meta-analyses. We found 53 eligible studies using 11 different frailty measures. Most common were frailty phenotype (n = 32), frailty index (n = 5) and Kihon checklist (n = 4). Prevalence estimates varied by frailty definitions, setting, and age (2.6-80.9%). Frailty was associated with mortality (5/7 studies), COPD exacerbation (7/11), hospitalisation (3/4), airflow obstruction (11/14), dyspnoea (15/16), COPD severity (10/12), poorer quality of life (3/4) and disability (1/1). In conclusion, frailty is a common among people with COPD and associated with increased risk of adverse outcomes. Proactive identification of frailty may aid risk stratification and identify candidates for targeted intervention.
Assuntos
Fragilidade , Doença Pulmonar Obstrutiva Crônica , Humanos , Fragilidade/epidemiologia , Prevalência , Qualidade de Vida , Doença Pulmonar Obstrutiva Crônica/complicações , Doença Pulmonar Obstrutiva Crônica/epidemiologia , HospitalizaçãoRESUMO
BACKGROUND: Our understanding of the relationship between frailty and stroke, beyond the acute phase of stroke, is limited. We aimed to estimate the prevalence of frailty in stroke survivors using differing methods of assessment and describe relationships with stroke outcomes. METHODS: We used data from three international population surveys (American Health and Retirement Survey/English Longitudinal Study of Ageing/Survey for Health and Retirement in Europe) of aging. Frailty status was assessed using the Fried frailty phenotype, a 40-item frailty index (FI) and the clinical frailty scale (CFS). We created estimates of frailty prevalence and assessed association of frailty with outcomes of mortality/hospital admission/recurrent stroke at 2 years follow-up using logistic regression models adjusted for age/sex. Additional analyses explored effects of adding cognitive measures to frailty assessments and of missing grip strength data. FINDINGS: Across 9617 stroke survivors, using the frailty phenotype, 23.8% (n = 2094) identified as frail; with CFS, 30.1% (n = 2906) were moderately or severely frail; using FI, 22.7% (n = 2147) had moderate frailty and 31.9% (n = 3021) had severe frailty. Frailty was associated with increased risk of mortality/hospitalization/recurrent stroke using all three measures. Adding cognitive variables to the FI produced minimal difference in prevalence of frailty. People with physical frailty (phenotype or CFS) plus cognitive impairment had a greater risk of mortality than people with an equivalent level of frailty but no cognitive impairment. Excluding people unable to provide grip strength underestimated frailty prevalence. INTERPRETATION: Frailty is common in stroke and associated with poor outcomes, regardless of measure used. Adding cognitive variables to frailty phenotype/CFS measures identified those at greater risk of poor outcomes. Physical and cognitive impairments in stroke survivors do not preclude frailty assessment.
Assuntos
Fragilidade , Acidente Vascular Cerebral , Humanos , Idoso , Fragilidade/epidemiologia , Estudos Longitudinais , Idoso Fragilizado/psicologia , Prevalência , Avaliação Geriátrica/métodos , Acidente Vascular Cerebral/complicações , Acidente Vascular Cerebral/epidemiologia , EnvelhecimentoRESUMO
BACKGROUND: Multimorbidity (the presence of two or more chronic conditions) is common amongst people with chronic kidney disease, but it is unclear which conditions cluster together and if this changes as kidney function declines. We explored which clusters of conditions are associated with different estimated glomerular filtration rates (eGFRs) and studied associations between these clusters and adverse outcomes. METHODS: Two population-based cohort studies were used: the Stockholm Creatinine Measurements project (SCREAM, Sweden, 2006-2018) and the Secure Anonymised Information Linkage Databank (SAIL, Wales, 2006-2021). We studied participants in SCREAM (404,681 adults) and SAIL (533,362) whose eGFR declined lower than thresholds (90, 75, 60, 45, 30 and 15 mL/min/1.73m2). Clusters based on 27 chronic conditions were identified. We described the most common chronic condition(s) in each cluster and studied their association with adverse outcomes using Cox proportional hazards models (all-cause mortality (ACM) and major adverse cardiovascular events (MACE)). RESULTS: Chronic conditions became more common and clustered differently across lower eGFR categories. At eGFR 90, 75, and 60 mL/min/1.73m2, most participants were in large clusters with no prominent conditions. At eGFR 15 and 30 mL/min/1.73m2, clusters involving cardiovascular conditions were larger and were at the highest risk of adverse outcomes. At eGFR 30 mL/min/1.73m2, in the heart failure, peripheral vascular disease and diabetes cluster in SCREAM, ACM hazard ratio (HR) is 2.66 (95% confidence interval (CI) 2.31-3.07) and MACE HR is 4.18 (CI 3.65-4.78); in the heart failure and atrial fibrillation cluster in SAIL, ACM HR is 2.23 (CI 2.04 to 2.44) and MACE HR is 3.43 (CI 3.22-3.64). Chronic pain and depression were common and associated with adverse outcomes when combined with physical conditions. At eGFR 30 mL/min/1.73m2, in the chronic pain, heart failure and myocardial infarction cluster in SCREAM, ACM HR is 2.00 (CI 1.62-2.46) and MACE HR is 4.09 (CI 3.39-4.93); in the depression, chronic pain and stroke cluster in SAIL, ACM HR is 1.38 (CI 1.18-1.61) and MACE HR is 1.58 (CI 1.42-1.76). CONCLUSIONS: Patterns of multimorbidity and corresponding risk of adverse outcomes varied with declining eGFR. While diabetes and cardiovascular disease are known high-risk conditions, chronic pain and depression emerged as important conditions and associated with adverse outcomes when combined with physical conditions.