James A. Prior

Associations between cardiovascular disease severity, osteoarthritis co-morbidity and physical health: a population-based study

OBJECTIVE The aim of this study was to investigate the interaction between cardiovascular disease severity and OA co-morbidity on physical health. METHODS A baseline questionnaire was mailed to 9676 patients aged ≥40 years from UK family practices. A priori exclusive morbidity groups were constructed as follows, based on records 3 years before baseline: (i) reference group--neither cardiovascular disease nor OA; (ii) cardiovascular disease severity index groups--with hypertension, ischaemic heart disease or heart failure without OA; (iii) OA index group without cardiovascular disease and (iv) co-morbid severity groups with hypertension, ischaemic heart disease or heart failure with OA. Adjusted associations between morbidity groups and physical health [mean physical component summary (PCS) score based on the 12-item Short Form Health Survey (SF-12)] compared with the reference group were assessed using linear regression methods. RESULTS A total of 5426 patients responded to the baseline questionnaire (56% response). The adjusted mean difference in PCS score between the reference group and the cardiovascular disease index were -2.4 (95% CI -3.4, -1.4) for hypertension, -5.3 (-6.3, -4.3) for ischaemic heart disease and -11.8 (-13.6, -9.9) for heart failure. The difference in the score for the index OA group was -5.6 (-6.5, -4.6). Estimates for co-morbid OA groups were -6.8 (-7.9, -5.7) for hypertension, -9.1 (-10.6, -7.6) for ischaemic heart disease and -12.8 (-16.0, -9.7) for heart failure. CONCLUSION In cardiovascular populations with differing severity, the co-morbid addition of OA was associated with incrementally poorer physical health, but such interactions were less than additive.

Comorbidity Cohort (2C) study: cardiovascular disease severity and comorbid osteoarthritis in primary care.

BackgroundTwo of the commonest chronic diseases experienced by older people in the general population are cardiovascular diseases and osteoarthritis. These conditions also commonly co-occur, which is only partly explained by age. Yet, there have been few studies investigating specific a priori hypotheses in testing the comorbid interaction between two chronic diseases and related health and healthcare outcomes. It is also unknown whether the stage or severity of the chronic disease influences the comorbidity impact. The overall plan is to investigate the interaction between cardiovascular severity groups (hypertension, ischaemic heart disease and heart failure) and osteoarthritis comorbidity, and their longitudinal impact on health and healthcare outcomes relative to either condition alone.MethodsFrom ten general practices participating in a research network, adults aged 40 years and over were sampled to construct eight exclusive cohort groups (n = 9,676). Baseline groups were defined on the basis of computer clinical diagnostic data in a 3-year time-period (between 2006 and 2009) as: (i) without cardiovascular disease or osteoarthritis (reference group), (ii) index cardiovascular disease groups (hypertension, ischaemic heart disease and heart failure) without osteoarthritis, (iii) index osteoarthritis group without cardiovascular disease, and (vi) index cardiovascular disease groups comorbid with osteoarthritis. There were three main phases to longitudinal follow-up. The first (survey population) was to invite cohorts to complete a baseline postal health questionnaire, with 10 monthly brief interval health questionnaires, and a final 12-month follow-up questionnaire. The second phase (linkage population) was to link the collected survey data to patient clinical records with consent for the 3-year time-period before baseline, during the 12-month survey period and the 12 months after final questionnaire (total 5 years). The third phase (denominator population) was to construct an anonymised clinical data archive for the study five year period for the total baseline cohorts, linking clinical information such as diagnosis, prescriptions and referrals.DiscussionThe outcomes of the study will result in the determination of the specific interaction between cardiovascular severity and osteoarthritis comorbidity on the change and progression of physical health status in individuals and on the linked and associated clinical-decision making process in primary care.

Gout, anxiety, and depression in primary care: a matched retrospective cohort study

Co-morbidity is frequently experienced by gout patients (1) but psychological morbidity associated with gout remains poorly understood. Given the established association between musculoskeletal pain and mental health problems (2), it seems plausible that gout is associated with psychological co-morbidity, such as anxiety or depression. However, the incidence of these specific psychological co-morbidities in gout populations remains unknown. We conducted a matched retrospective cohort study to examine the association between gout and subsequent incident consultation for anxiety or depression in UK primary care. Consultation data were available from nine general practices in North Staffordshire, UK between 2000 and 2011 (3). Consulters aged > 18 years who had been diagnosed with gout between 2000 and 2008 were identified using Read codes and were frequency matched by age, gender, year of consultation, and general practice to four controls without gout. The outcome of interest was the first consultation with a diagnostic Read code for anxiety or depression subsequent to the first diagnostic Read code for gout. Gout patients or controls with a diagnosis of anxiety or depression before the matching dates were excluded. Key characteristics of the gout sample and the matched controls were examined. These included factors upon which the cohorts were matched and the neighbourhood level of deprivation, determined using the Indices of Multiple Deprivation (IMD) (4). Incident consultation rates (per 1000 person-years) for anxiety or depression were calculated for gout and non-gout controls. The proportions consulting for other selected co-morbidities were also described. Cox regression was used to model the time to first anxiety or depression consultation, adjusting initially for the matched factors and then further adjusting to include deprivation status and co-morbidities. Outcomes are reported as hazard ratios (HRs) with 95% confidence intervals (CIs) for gout cases vs. matched controls. In total, 1689 gout patients were identified and matched successfully to 6756 patients without gout. The mean age of gout patients was 63 years (sd 16); 76% were male. Deprivation status was similar between those with and without gout, gout patients were more likely to have all co-morbidities, other than smoking, than controls. The incidence rate of depression was 10.8 (95% CI 10.0–11.7) per 1000 person-years for gout cases and 10.0 (95% CI 8.5–11.8) for matched controls. The incidence rate of anxiety was 15.2 (95% CI 14.2–16.2) per 1000 person-years for the gout cases and 16.2 (95% CI 14.3–18.3) for matched controls (Table 1). Unadjusted HRs in the Cox regression model found no association between gout and time to consultation for either anxiety (1.09, 95% CI 0.95–1.25) or depression (0.92, 95% CI 0.77–1.10) compared to matched controls. Adjustment had little effect on these associations for anxiety (1.01, 95% CI 0.87–1.16) or depression (0.87, 95% CI 0.73–1.05) (Table 1). Table 1. Incidence rates and hazard ratios (HRs) for the risk of anxiety and depression in gout cases vs. matched controls. This is the first study of consultation incidence of psychological co-morbidities in gout. Using a large UK primary care consultation database, incident gout was not associated with subsequent consultation for anxiety or depression. Possible explanations are that the psychological burden of pain is lessened during the prolonged asymptomatic inter-critical periods between gout attacks in some patients, or that whereas gout predominantly affects men, women are more likely to consult primary care for anxiety or depression (5). As such, our study may simply reflect population norms of consulting behaviour (6). This study provides new evidence of a lack of an association between common mental health problems and gout. This was achieved through the use of a large primary care database with proven validity and used previously to examine gout patients (3). There are several limitations of our study: anxiety and depression are typically under-reported in primary care (7) and limitations of coding by general practitioners (GPs) are also highlighted in the low prevalence of other co-morbidities, such as smoking. However, there is no reason to suspect that any under-recording of any of these conditions would differ between those with and without gout. In conclusion, the rates of consultation for anxiety or depression in UK primary care gout patients were equivalent to their matched counterparts. Further research is needed using more patient-reported measures of anxiety and depression and considering gout severity and disease characteristics.

Cardiovascular disease and musculoskeletal disorder labels in family practice acted as markers of physical health severity

OBJECTIVE Family practitioner diagnostic labels applied in consultation provide a signpost for treatment and management. Yet, it is unknown whether each label reflects the health of the respective patient group. STUDY DESIGN AND SETTING Consultation records of 7,799 patients aged 50 years and older from six family practices were linked to a cross-sectional baseline health survey. Associations between six mutually exclusive cardiovascular disease and nine mutually exclusive musculoskeletal disorder categories, and physical health severity as measured by the Short Form-12 questionnaire were examined. RESULTS There were 2,447 (31.4%) cardiovascular disease and 3,321 (42.6%) musculoskeletal disorder consulters. The mean physical health scores ranged from 38.38 (95% confidence interval [CI]: 37.8-39.0) for hypertension to the poorest score of health 28.98 (95% CI: 27.5-30.5) for consulters with heart failure, whereas in the musculoskeletal disorder group, scores ranged from 44.85 (95% CI: 42.2-47.5) for soft tissue disorder to 28.79 (95% CI: 26.8-30.8) for consulters with inflammatory polyarthropathy (trend P<0.001). This trend in the association between diagnostic categories and physical health severity within both spectrums remained after adjustment for confounders. CONCLUSION Specific diagnostic labels for selected chronic illness indicate the severity of physical health for the corresponding consulting population.

What is the impact of giant cell arteritis on patients' lives? A UK qualitative study

Objectives Clinical management of giant cell arteritis (GCA) involves balancing the risks and burdens arising from the disease with those arising from treatment, but there is little research on the nature of those burdens. We aimed to explore the impact of giant cell arteritis (GCA) and its treatment on patients’ lives. Methods UK patients with GCA participated in semi-structured telephone interviews. Inductive thematic analysis was employed. Results 24 participants were recruited (age: 65–92 years, time since diagnosis: 2 months to >6 years). The overarching themes from analysis were: ongoing symptoms of the disease and its treatment; and ‘life-changing’ impacts. The overall impact of GCA on patients’ lives arose from a changing combination of symptoms, side effects, adaptations to everyday life and impacts on sense of normality. Important factors contributing to loss of normality were glucocorticoid-related treatment burdens and fear about possible future loss of vision. Conclusions The impact of GCA in patients’ everyday lives can be substantial, multifaceted and ongoing despite apparent control of disease activity. The findings of this study will help doctors better understand patient priorities, legitimise patients’ experiences of GCA and work with patients to set realistic treatment goals and plan adaptations to their everyday lives.

Chest pain and shortness of breath in cardiovascular disease: a prospective cohort study in UK primary care

Objective To determine characteristics associated with monthly chest pain and shortness of breath (SoB) during activity in cardiovascular disease (CVD) and trajectories of these symptoms over 10 months. Study design and setting Baseline questionnaire was sent to patients aged ≥40 years from 10 UK general practices. Responders were sent monthly questionnaires for 10 months. For patients with CVD (ischaemic heart disease and heart failure), the association of sociodemographic characteristics, pain elsewhere and anxiety and depression with monthly reports of chest pain and SoB during activity were determined using multilevel, multinomial logistic regression. Common symptom trajectories were determined using dual trajectory latent class growth analysis. Results 661 patients with CVD completed at least 5 monthly questionnaires. Multiple other pain sites (relative risk ratio: 4.03; 95% CI 1.64 to 9.91) and anxiety or depression (relative risk ratio: 3.31; 95% CI 1.89 to 5.79) were associated with reporting weekly chest pain. Anxiety or depression (relative risk ratio: 4.10; 95% CI 2.72 to 6.17), obesity (relative risk ratio: 2.53; 95% CI 1.49 to 4.30), older age (80+: relative risk ratio: 2.51; 95% CI 1.19 to 5.26), increasing number of pain sites (4+: relative risk ratio: 4.64; 95% CI 2.35 to 9.18) and female gender (relative risk ratio: 1.81; 95% CI 1.20 to 2.75) were associated with reporting weekly SoB. Eight symptom trajectories were identified, with SoB symptoms more common than chest pain. Conclusions Potentially modifiable characteristics are associated with the experience of chest pain and SoB. Identified symptom trajectories may facilitate tailored care to improve outcomes in patients with CVD.

Challenges of diagnosis and management of giant cell arteritis in general practice: a multimethods study

Background In the UK, general practitioners (GPs) are usually the first medical contact for patients with suspected giant cell arteritis (GCA). While rare, it is critical not to miss, as delayed treatment can lead to significant complications including permanent visual loss. To date, little is known about the approach and challenges to diagnosis and management of GCA by GPs. Objective To investigate the diagnosis and management of patients with suspected GCA in UK general practice. Design and participants A multimethods approach was taken, comprising a postal survey of 5000 randomly selected UK GPs and semistructured telephone interviews of 24 GPs from across the UK. Setting UK general practice. Results 1249 questionnaires were returned. 879 responders (70%) indicated that they had diagnosed and managed a patient with GCA. A variety of clinical features were used to identify GCA. 21.9% suggested that they would exclude GCA as a diagnosis if headache was absent and around one-third do not routinely initiate glucocorticoid treatment prior to referral. Significant regional variations in referral pathways were reported. Thematic analysis of interview transcripts highlighted fears relating to a missed diagnosis of GCA and the non-specific nature of early GCA presentation. Accessing specialist care was highlighted as challenging by many GPs and that a national standard fast-track pathway is lacking to support this patient group. Additionally there were significant concerns regarding potential adverse effects relating to long-term treatment with glucocorticoids. Conclusion GPs appear to over-rely on headache to identify GCA and marked geographical differences in management, with conflicting referral pathways and difficulties in accessing appropriate services exist in the UK. A national standard for fast-tracking patients with suspected GCA to relevant specialists would be beneficial to improve care and outcomes for patients with GCA.

AB0212 Diagnostic delay for rheumatoid arthritis: a systematic review

Background Rheumatoid arthritis (RA) is a common inflammatory condition, affecting 1% of the population and causing pain, stiffness and swelling, leading to significant disability and loss of function[.1 Delays in the diagnosis and treatment of RA can lead to worsened joint damage and disability, in addition to a reduced rate of disease-modifying antirheumatic drugs(DMARD)-free remission. Current (2016) EULAR guidelines specify that combination DMARD treatment be initiated within 3 months of the onset of persistent RA symptoms[.2 Unfortunately, this target is not always achieved due to delays between symptom onset to treatment initiation. Objectives The aim of this systematic review, was to determine the extent of delay that occurs at different points in the patient’s journey from RA symptom onset to treatment initiation, providing benchmarks of delay. Methods Embase and Medline were searched for articles examining diagnostic and treatment delay of RA. To be included, articles had to report a time-period of delay in an adult RA population. Papers were screened by three authors (CAH, JAP, IS). The primary outcome was the reported time-period of delay at any point from RA symptom onset to treatment. Due to skewed delay data, medians (with Interquartile range (IQR)) were selected and reported using narrative synthesis. Different time-periods of delay were categorised to facilitate comparison. Results Of 4925 returned articles, 1501 duplicates were removed. The remaining articles were then screened by title, abstract and full text, leaving 26 from which we extracted data. Delay periods were categorised as 1) symptom onset to initiation of DMARDs (n=9), 2) symptom onset to diagnosis (n=14), 3) symptom onset to 1st healthcare professional (HCP) appointment (n=15), 4) 1 ST HCP appointment to rheumatology referral (n=4) and 5) 1 ST HCP appointment to diagnosis (n=4). Time-periods of delay were typically skewed to the right. The total delay from symptom onset to receiving DMARDs has dropped since the 1980’s (429 weeks before 1987) and by 2014 data indicates an average delay of 23 (IQR 14, 43) weeks. Within this total delay period, delay from symptom onset to diagnosis is at a minimum 16(7,55 weeks and delay from symptom onset to first contact with a HCP predominantly ranges from 2 (1,8) to 10(4,24 weeks in data from 2010 onwards. Delay between 1st HCP appointment and Rheumatology referral can be as quick as 2 (1,5) weeks and is within 12(2,48 weeks across all data points. Delay acquired between 1st HCP appointment and receiving a diagnosis has decreased overtime, most recently, delay was reported as 21 weeks. Conclusions Time from RA symptom onset to receiving treatment has reduced considerably in recent decades. However, despite current guidelines and research indicating an optimal treatment window for RA of twelve weeks from symptom onset, this remains unmet, with this delay approximately twice the recommended period. Continued effort is required in reducing delay across all areas of the RA patients’ journey to the early treatment needed to improve outcome. References [1] Scott DGI. What is RA? https://www.nras.org.uk/: National Rheumatoid Arthritis Society; 2014. Available from https://www.nras.org.uk/what-is-ra-article [2] Combe B, et al. 2016 update of the EULAR recommendations for the management of early arthritis. Annals of the rheumatic diseases 2017. Disclosure of Interest None declared

AB0547 Diagnostic Delay for Giant Cell Arteritis (GCA). A Systematic Review and Meta-Analysis

Background The diagnosis of giant cell arteritis (GCA) remains difficult, with patients often presenting with non-specific and atypical symptoms. Such ambiguity can lead to delays in the diagnosis of GCA, which in-turn may result in the patient experiencing preventable and life-altering outcomes, including blindness. Despite the seriousness of such diagnostic delay, the extent of this, though frequently highlighted, has seldom been the primary focus of GCA research. Without a clear understanding of the extent to which GCA diagnosis is delayed, it is difficult to quantify the current problem and how this may then impact on patient outcomes. Objectives To determine the average time-period between the onset of GCA symptoms and receiving a GCA diagnosis. Methods We conducted a systematic review and meta-analysis to identify research literature which has examined diagnostic delay of GCA and to determine the extent of this delay. Literature searches were conducted in the following bibliometric databases; MEDLINE, EMBASE, CINAHL, PsycInfo and ISI web of knowledge. A single reviewer (HR) initially performed a title screen; abstracts were then reviewed by two reviewers (HR & JP) and finally, two reviewers (JP & CM) assessed the remaining articles in full. Final article selection was based on pre-specified inclusion criteria and from these data on a multitude of factors was extracted. The primary outcome of interest was the “average number of weeks between onset of GCA symptoms and GCA diagnosis”, with other extracted data including; lead author, year of publication, sample size, gender, age, country, healthcare setting, method of GCA diagnosis and the definition of diagnostic delay. Where diagnostic delay was reported as “days” or “months”, data were converted to “weeks” to provide a standardised dataset for analysis. Standard deviations were also converted to standard errors (SE) for use in a meta-analysis. Random-effects meta-analysis was used to report the mean number of weeks (95% confidence interval (CI)) between symptom onset and GCA diagnosis. Results 4,128 articles were initially identified, 185 were reviewed in full and 34 articles were included in the final systematic review. Of these, the average age ranged from 65.2 to 81.6 years and GCA samples from 31 articles were recruited from secondary care. GCA diagnosis was defined by a positive temporal artery biopsy in 25 articles, using the 1990 ACR criteria in 4 articles, with the remaining articles either using clinical judgement or not providing a definition. Delay was determined by the article reporting “how many days, weeks or months had occurred between GCA symptom onset and receiving a diagnosis of GCA”. 16 articles were included in the meta-analysis, resulting in a mean number of weeks between symptom onset and GCA diagnosis of 8.87 (95% CI 6.4 to 11.3) (I2 =95.8%, p<0.001). Conclusions On average, patients experience approximately a 9-week delay between the onset of their symptoms and receiving a diagnosis of GCA. The reasons for this are yet to be understood, but could provide important insight and inform future strategies to improve outcomes for patients. Our research provides the current benchmark for diagnostic delay of GCA for which future efforts to reduce this problem can be measured against. Disclosure of Interest None declared