Dataset Information

Multimorbidity and co-morbidity in atrial fibrillation and effects on survival: findings from UK Biobank cohort.

ABSTRACT: Aims:To examine the number and type of co-morbid long-term health conditions (LTCs) and their associations with all-cause mortality in an atrial fibrillation (AF) population. Methods and results:Community cohort participants (UK Biobank n?=?502?637) aged 37-73?years were recruited between 2006 and 2010. Self-reported LTCs (n?=?42) identified in people with AF at baseline. All-cause mortality was available for a median follow-up of 7?years (interquartile range 76-93?months). Hazard ratios (HRs) examined associations between number and type of co-morbid LTC and all-cause mortality, adjusting for age, sex, socio-economic status, smoking, and anticoagulation status. Three thousand six hundred fifty-one participants (0.7% of the study population) reported AF; mean age was 61.9?years. The all-cause mortality rate was 6.7% (248 participants) at 7?years. Atrial fibrillation participants with??4 co-morbidities had a six-fold higher risk of mortality compared to participants without any LTC. Co-morbid heart failure was associated with higher risk of mortality [HR 2.96, 95% confidence interval (CI) 1.83-4.80], whereas the presence of co-morbid stroke did not have a significant association. Among non-cardiometabolic conditions, presence of chronic obstructive pulmonary disease (HR 3.31, 95% CI 2.14-5.11) and osteoporosis (HR 3.13, 95% CI 1.63-6.01) was associated with a higher risk of mortality. Conclusion:Survival in middle-aged to older individuals with self-reported AF is strongly correlated with level of multimorbidity. This group should be targeted for interventions to optimize their management, which in turn may potentially reduce the impact of their co-morbidities on survival. Future AF clinical guidelines need to place greater emphasis on the issue of co-morbidity.

SUBMITTER: Jani BD

PROVIDER: S-EPMC6277149 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Multimorbidity and co-morbidity in atrial fibrillation and effects on survival: findings from UK Biobank cohort.

Jani Bhautesh Dinesh BD Nicholl Barbara I BI McQueenie Ross R Connelly Derek T DT Hanlon Peter P Gallacher Katie I KI Lee Duncan D Mair Frances S FS

Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology 20181101 FI_3

<h4>Aims</h4>To examine the number and type of co-morbid long-term health conditions (LTCs) and their associations with all-cause mortality in an atrial fibrillation (AF) population.<h4>Methods and results</h4>Community cohort participants (UK Biobank n = 502 637) aged 37-73 years were recruited between 2006 and 2010. Self-reported LTCs (n = 42) identified in people with AF at baseline. All-cause mortality was available for a median follow-up of 7 years (interquartile range 76-93 months). Hazard ...[more]

PMID: 29112751

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Project description:BackgroundMultimorbidity is associated with higher mortality, but the relationship with cancer and cardiovascular mortality is unclear. The influence of demographics and type of condition on the relationship of multimorbidity with mortality remains unknown. We examine the relationship between multimorbidity (number/type) and cause of mortality and the impact of demographic factors on this relationship.MethodsData source: the UK Biobank; 500,769 participants; 37-73 years; 53.7% female. Exposure variables: number and type of long-term conditions (LTCs) (N = 43) at baseline, modelled separately. Cox regression models were used to study the impact of LTCs on all-cause/vascular/cancer mortality during median 7-year follow-up. All-cause mortality regression models were stratified by age/sex/socioeconomic status.ResultsAll-cause mortality is 2.9% (14,348 participants). Of all deaths, 8350 (58.2%) were cancer deaths and 2985 (20.8%) vascular deaths. Dose-response relationship is observed between the increasing number of LTCs and all-cause/cancer/vascular mortality. A strong association is observed between cardiometabolic multimorbidity and all three clinical outcomes; non-cardiometabolic multimorbidity (excluding cancer) is associated with all-cause/vascular mortality. All-cause mortality risk for those with ≥ 4 LTCs was nearly 3 times higher than those with no LTCs (HR 2.79, CI 2.61-2.98); for ≥ 4 cardiometabolic conditions, it was > 3 times higher (HR 3.20, CI 2.56-4.00); and for ≥ 4 non-cardiometabolic conditions (excluding cancer), it was 50% more (HR 1.50, CI 1.36-1.67). For those with ≥ 4 LTCs, morbidity combinations that included cardiometabolic conditions, chronic kidney disease, cancer, epilepsy, chronic obstructive pulmonary disease, depression, osteoporosis and connective tissue disorders had the greatest impact on all-cause mortality. In the stratified model by age/sex, absolute all-cause mortality was higher among the 60-73 age group with an increasing number of LTCs; however, the relative effect size of the increasing number of LTCs on higher mortality risk was larger among those 37-49 years, especially men. While socioeconomic status was a significant predictor of all-cause mortality, mortality risk with increasing number of LTCs remained constant across different socioeconomic gradients.ConclusionsMultimorbidity is associated with higher all-cause/cancer/vascular mortality. Type, as opposed to number, of LTCs may have an important role in understanding the relationship between multimorbidity and mortality. Multimorbidity had a greater relative impact on all-cause mortality in middle-aged as opposed to older populations, particularly males, which deserves exploration.

Project description:BackgroundIt is now well recognised that the risk of severe COVID-19 increases with some long-term conditions (LTCs). However, prior research primarily focuses on individual LTCs and there is a lack of data on the influence of multimorbidity (≥2 LTCs) on the risk of COVID-19. Given the high prevalence of multimorbidity, more detailed understanding of the associations with multimorbidity and COVID-19 would improve risk stratification and help protect those most vulnerable to severe COVID-19. Here we examine the relationships between multimorbidity, polypharmacy (a proxy of multimorbidity), and COVID-19; and how these differ by sociodemographic, lifestyle, and physiological prognostic factors.Methods and findingsWe studied data from UK Biobank (428,199 participants; aged 37-73; recruited 2006-2010) on self-reported LTCs, medications, sociodemographic, lifestyle, and physiological measures which were linked to COVID-19 test data. Poisson regression models examined risk of COVID-19 by multimorbidity/polypharmacy and effect modification by COVID-19 prognostic factors (age/sex/ethnicity/socioeconomic status/smoking/physical activity/BMI/systolic blood pressure/renal function). 4,498 (1.05%) participants were tested; 1,324 (0.31%) tested positive for COVID-19. Compared with no LTCs, relative risk (RR) of COVID-19 in those with 1 LTC was no higher (RR 1.12 (CI 0.96-1.30)), whereas those with ≥2 LTCs had 48% higher risk; RR 1.48 (1.28-1.71). Compared with no cardiometabolic LTCs, having 1 and ≥2 cardiometabolic LTCs had a higher risk of COVID-19; RR 1.28 (1.12-1.46) and 1.77 (1.46-2.15), respectively. Polypharmacy was associated with a dose response higher risk of COVID-19. All prognostic factors were associated with a higher risk of COVID-19 infection in multimorbidity; being non-white, most socioeconomically deprived, BMI ≥40 kg/m2, and reduced renal function were associated with the highest risk of COVID-19 infection: RR 2.81 (2.09-3.78); 2.79 (2.00-3.90); 2.66 (1.88-3.76); 2.13 (1.46-3.12), respectively. No multiplicative interaction between multimorbidity and prognostic factors was identified. Important limitations include the low proportion of UK Biobank participants with COVID-19 test data (1.05%) and UK Biobank participants being more affluent, healthier and less ethnically diverse than the general population.ConclusionsIncreasing multimorbidity, especially cardiometabolic multimorbidity, and polypharmacy are associated with a higher risk of developing COVID-19. Those with multimorbidity and additional factors, such as non-white ethnicity, are at heightened risk of COVID-19.

Project description:Later chronotype (i.e. evening preference) and later timing of sleep have been associated with greater morbidity, including higher rates of metabolic dysfunction and cardiovascular disease (CVD). However, no one has examined whether chronotype is associated with mortality risk to date. Our objective was to test the hypothesis that being an evening type is associated with increased mortality in a large cohort study, the UK Biobank. Our analysis included 433 268 adults aged 38-73 at the time of enrolment and an average 6.5-year follow-up. The primary exposure was chronotype, as assessed through a single self-reported question-defining participants as definite morning types, moderate morning types, moderate evening types or definite evening types. The primary outcomes were all-cause mortality and mortality due to CVD. Prevalent disease was also compared among the chronotype groups. Analyses were adjusted for age, sex, ethnicity, smoking, body mass index, sleep duration, socioeconomic status and comorbidities. Greater eveningness, particularly being a definite evening type, was significantly associated with a higher prevalence of all comorbidities. Comparing definite evening type to definite morning type, the associations were strongest for psychological disorders (OR 1.94, 95% CI 1.86-2.02, p = < 0.001), followed by diabetes (OR 1.30, 95% CI 1.24-1.36, p = < 0.001), neurological disorders (OR 1.25, 95% CI 1.20-1.30, p = < 0.001), gastrointestinal/abdominal disorders (OR 1.23, 95% CI 1.19-1.27, p = < 0.001) and respiratory disorders (OR 1.22, 95% CI 1.18-1.26, p = < 0.001). The total number of deaths was 10 534, out of which 2127 were due to CVD. Greater eveningness, based on chronotype as an ordinal variable, was associated with a small increased risk of all-cause mortality (HR 1.02, 95% CI 1.004-1.05, p = 0.017) and CVD mortality (HR 1.04, 95% CI 1.00-1.09, p = 0.06). Compared to definite morning types, definite evening types had significantly increased risk of all-cause mortality (HR 1.10, 95% CI 1.02-1.18, p = 0.012). This first report of increased mortality in evening types is consistent with previous reports of increased levels of cardiometabolic risk factors in this group. Mortality risk in evening types may be due to behavioural, psychological and physiological risk factors, many of which may be attributable to chronic misalignment between internal physiological timing and externally imposed timing of work and social activities. These findings suggest the need for researching possible interventions aimed at either modifying circadian rhythms in individuals or at allowing evening types greater working hour flexibility.

Project description:BackgroundPatients with multiple conditions present a growing challenge for healthcare provision. Measures of multimorbidity may support clinical management, healthcare resource allocation and accounting for the health of participants in purpose-designed cohorts. The recently developed Cambridge Multimorbidity scores (CMS) have the potential to achieve these aims using primary care records, however, they have not yet been validated outside of their development cohort.MethodsThe CMS, developed in the Clinical Research Practice Dataset (CPRD), were validated in UK Biobank participants whose data is not available in CPRD (the cohort used for CMS development) with available primary care records (n = 111,898). This required mapping of the 37 pre-existing conditions used in the CMS to the coding frameworks used by UK Biobank data providers. We used calibration plots and measures of discrimination to validate the CMS for two of the three outcomes used in the development study (death and primary care consultation rate) and explored variation by age and sex. We also examined the predictive ability of the CMS for the outcome of cancer diagnosis. The results were compared to an unweighted count score of the 37 pre-existing conditions.ResultsFor all three outcomes considered, the CMS were poorly calibrated in UK Biobank. We observed a similar discriminative ability for the outcome of primary care consultation rate to that reported in the development study (C-index: 0.67 (95%CI:0.66-0.68) for both, 5-year follow-up); however, we report lower discrimination for the outcome of death than the development study (0.69 (0.68-0.70) and 0.89 (0.88-0.90) respectively). Discrimination for cancer diagnosis was adequate (0.64 (0.63-0.65)). The CMS performs favourably to the unweighted count score for death, but not for the outcomes of primary care consultation rate or cancer diagnosis.ConclusionsIn the UK Biobank, CMS discriminates reasonably for the outcomes of death, primary care consultation rate and cancer diagnosis and may be a valuable resource for clinicians, public health professionals and data scientists. However, recalibration will be required to make accurate predictions when cohort composition and risk levels differ substantially from the development cohort. The generated resources (including codelists for the conditions and code for CMS implementation in UK Biobank) are available online.

Dataset Information

Multimorbidity and co-morbidity in atrial fibrillation and effects on survival: findings from UK Biobank cohort.

Publications

Multimorbidity and co-morbidity in atrial fibrillation and effects on survival: findings from UK Biobank cohort.

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