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To examine the prevalence and incidence of cardiovascular risk factors (CVRFs) including hypertension (HTN), hyperlipidemia (HL), diabetes mellitus (DM), and obesity among patients with psoriatic arthritis (PsA) and rheumatoid arthritis (RA) compared to the general population, and to examine the treatment of incident CVRFs in PsA and RA compared to controls.
A cohort study was conducted within The Health Improvement Network, a medical record database in the United Kingdom, using data from 1994 to 2014. Patients age 18-89 with PsA or RA were matched to controls on practice and start date. The prevalence and incidence of CVRFs identified by diagnostic codes were calculated. Cox proportional hazards models were used to examine the relative incidence of these cardiovascular risk factors. Finally, pharmacologic therapies for incident CVRFs were examined.
Study subjects included patients with PsA (N=12,548), RA (N=53,215), and controls (N=389,269). The prevalence of all CVRFs was significantly elevated in PsA. Only the prevalence of DM and obesity was increased in RA. Incidence of HTN, HL, and DM was elevated in PsA and RA. Receipt of therapy within one year following incident diagnosis of CVRFs was not substantially different between the groups; approximately 85%, 65%, and 45% of patients received prescriptions for HTN, HL, and DM, respectively.
Patients with PsA have an increased prevalence of CVRFs, and both patients with PsA and RA have increased incidence of new diagnosis of CVRFs. Pharmacologic treatment of CVRFs in patients with PsA and RA was similar to controls in the UK.
Patients with psoriatic arthritis (PsA) and rheumatoid arthritis (RA) have an increased risk for cardiovascular disease (1-3). Previous studies have demonstrated that cardiovascular risk factors such as hypertension, hyperlipidemia, and obesity are highly prevalent, underdiagnosed, and poorly controlled among patients with inflammatory arthritis (4-10). However, relatively little is known from a population-based perspective about the screening and management of cardiovascular risk factors in these patients, particularly among patients with PsA.
Systemic inflammation in PsA extends beyond the skin and joints, and recent research highlights the increased risk of major adverse cardiovascular events (MACE; combined endpoint of myocardial infarction, stroke, and cardiovascular death) in patients with PsA (2, 11). Interestingly, attenuating systemic inflammation in PsA appears relevant to reducing both the burden of cardiovascular disease and PsA disease activity. For instance, studies by DiMinno et al. and Eder et al. have demonstrated an association between control of metabolic comorbidities, such as obesity, and reduced disease activity in PsA (12-14). Since cardiovascular risk factors are inextricably linked to the morbidity and mortality of PsA patients, it is critical to develop a better understanding of their screening and management in current clinical practice. Data regarding prevalence and adequacy of risk factor management could then potentially facilitate the development of strategies to improve cardiovascular preventive care in patients with PsA.
The objectives of our study were 1) to determine the prevalence of medical diagnoses of hypertension, hyperlipidemia, obesity, and diabetes among patients with PsA and RA compared to the general population, 2) to assess the rate of incident CVD risk factors after inflammatory disease diagnosis, and 3) to examine the treatment of hypertension, hyperlipidemia, and diabetes among patients with PsA and RA compared to the general population.
Study Design: Cross-sectional and longitudinal studies were performed among patients with PsA, RA, and randomly selected matched controls.
The Health Improvement Network (THIN) is a medical record database in the United Kingdom that includes over 12 million patients and 73 million person-years of follow-up. As this is a general practitioners' database, this is the ideal source in which to address the outlined objectives.
All patients with PsA or RA were included in the study. For each case, up to 5 controls from the general population were matched for general practice and start date in the practice (the latest of registration with the practice or “Vision” computerization date for that practice). This matching strategy, described in previous publications, was used to enroll patients followed by similar physicians over the same time period (2). PsA and RA were defined by previously validated diagnosis codes (15-17). The positive predictive value (PPV) for PsA was 85% (15) and the PPV for RA was 81%-93%.(16) Patients who had a code for both RA and PsA were included in the PsA group.
For the assessment of incident cardiovascular risk factors, cohort time began at the latest of the following three dates: first diagnosis code for PsA or RA (or an assigned diagnosis date for controls based on the matched disease patient's diagnosis date), 180 days after registration with the practice, or implementation of software in the practice. Cohort time ended at the first diagnosis of the outcome of interest (hypertension, hyperlipidemia, or diabetes), when the patient left the practice, the practice stopped contributing to THIN, or the end of the study in January 2014.
The presence of at least one diagnosis code for hypertension, hyperlipidemia, and diabetes mellitus (type I, type II, or complications of diabetes) was used to define having that disease (these definitions have been used in other studies). Hypertension, hyperlipidemia, and diabetes have been extensively studied in The Health Improvement Network.(18-21) Additionally, hypertension and diabetes are important diagnoses in the UK Quality Outcomes Framework and are part of pay-for-performance metrics, likely contributing to the validity of the diagnosis codes.(22) Obesity was defined by a body mass index >30. Prevalence of cardiovascular risk factors was defined if the patient had such a code at any point during their time in the database. Incident cardiovascular risk factors were defined as those that occurred after the diagnosis of PsA or RA and after excluding patients with a previous diagnosis or a previous prescription for the diagnosis of interest (e.g., patients with a diagnosis code for HTN prior to diagnosis of PsA were considered to have “prevalent” HTN, and similarly, patients with a prescription for hydrochlorothiazide prior to PsA were considered to have “prevalent” HTN rather than “incident” HTN).
Among patients with incident cardiovascular risk factor diagnoses, we examined the proportion of patients who received pharmacologic therapy commensurate with that diagnosis within one year following the incident cardiovascular risk factor diagnosis. An incident diagnosis was defined as a case in which the first diagnosis code for a cardiovascular risk factor being studied (hypertension, hyperlipidemia, or diabetes mellitus) occurred after registration with the practice, software implementation, and diagnosis of PsA or RA (or the assigned diagnosis date for controls). The outcomes of interest were prescriptions for an antihypertensive medication, lipid-lowering medication, or diabetes medication, respectively. Antihypertensive medications included thiazide diuretics, loop diuretics, ACE inhibitors, ARBs, calcium channel blockers, beta blockers, vasodilators (hydralazine, isosorbide mononitrate/dinitrate), and clonidine. Lipid-lowering medications included statins and fibrates. Diabetes medications included insulin, metformin, sulfonylureas (glipizide, glimepiride), and thiazolidinediones (pioglitazone, rosiglitazone).
Demographics of the subjects were descriptively reported. The prevalence of each of the cardiovascular risk factors in each disease group was reported as the number of cases divided by the total patients in that group. Logistic regression models were generated to report the age/sex-adjusted prevalence of each of the cardiovascular risk factors in PsA compared to controls and RA compared to controls. Next, after excluding those with the diagnosis of interest (HTN, HL, or DM) at baseline, the incidence of each outcome was calculated for each exposure group as the number of new cases over the total person time in each group per 10,000 patient years. Cox proportional hazard models were used to calculate the age/sex-adjusted hazard ratios. We then formed multivariable models using covariates selected a priori including age, sex, other cardiovascular risk factors (HTN, HL, DM, smoking, body mass index), heart disease (stroke, myocardial infarction, atrial fibrillation, heart failure), Charlson comorbidity index (we removed the one point generally assigned for RA to not bias the differences between the disease groups), and health care utilization (number of visits to the general practitioner in THIN prior to the start date) in the model (23, 24). Appropriate therapy interventions within the 365 days following the incident outcome diagnosis were then calculated as the number of patients receiving therapy divided by the total number of incident cases. All statistical analyses were performed using Stata version 13.0 (College Station, TX).
The following sensitivity analyses were performed to determine the robustness of our results: 1) we restricted the cohort to patients seen by the GP at least once yearly during their time in the cohort; 2) we restricted patients with at least one year of follow up prior to the start date to ensure adequate covariate capture, 3) we examined the proportion of patients receiving therapy at 180 days after the incident diagnosis, 4) we excluded patients with PsA with a previous diagnosis of RA, and 5) we examined the impact of stratifying by age in decade and sex on receipt of therapy.
Prior to the start of the study, ethics approval was obtained from the University of Pennsylvania Institutional Review Board and the Cegedim Scientific Review Committee. All data were de-identified to the investigators and thus written consent from patients was not obtained (25).
The baseline characteristics of the study population are shown in Table 1. The mean age of the RA cohort was 60.7, whereas the mean ages of the other groups were around 50 years. The RA cohort was predominantly female (70%), whereas the other two groups were approximately 50% female. The mean BMI for the three cohorts was between 26.4 and 28.0. Disease-modifying anti-rheumatic drugs used by patients during the baseline period are reported in Supplemental Table 1.
Table 2 shows the observed prevalence of HTN, HL, DM, and obesity in PsA, RA, and general population controls and the age/sex-adjusted odds ratios for prevalence compared to the controls. After adjustment, the age/sex-adjusted prevalence of HTN, HL, DM, and obesity was significantly increased in PsA patients compared to controls. In the PsA cohort, the age/sex-adjusted odds ratio (OR) was 1.31 (1.26-1.37) for the prevalence of HTN; 1.23 (1.18-1.29) for the prevalence of HL; 1.38 (1.31-1.45) for the prevalence of DM; and 1.69 (1.62-1.75) for the prevalence of obesity. In contrast, in patients with RA, only the age/sex-adjusted prevalence of DM (OR 1.07 (1.04-1.10)) and obesity (OR 1.03 (1.01-1.06)) was significantly increased.
We next examined the incidence of cardiovascular risk factors among patients with PsA, RA, and controls who did not have these CVD risk factors at the time of diagnosis with their inflammatory disease (Table 3). The age/sex-adjusted hazard ratios (HR) and fully adjusted HR (accounting for age, sex, other CVD risk factors, and comorbidities) for all of the cardiovascular risk factors under study were significantly elevated in both PsA and RA compared to controls. However, the point estimates were higher among patients with PsA. The results were also stratified by 10-year age categories (Supplemental Figures 1 and 2). The prevalence and incidence of comorbidities in PsA remained higher, in general, than in RA and controls. However, the age-stratified hazard ratios show that this risk was highest in the youngest age groups (Supplemental Table 2). Sensitivity analyses in which we restricted patients to those with at least one year of follow up and separately only those followed at least once yearly during their time in the cohort did not significantly change the results. Similarly, restricting the PsA cohort to those without a previous RA diagnosis did not significantly change the results (Supplemental Table 3).
We did not observe any decrease in the frequency of receipt of therapy among patients with incident diagnoses of HTN, HL, and DM when comparing the PsA, RA, and control cohorts (Table 4). Patients with RA or PsA were more likely to receive antihypertensive treatment, and those with RA also were more likely to receive lipid-lowering therapy compared to the control cohort. For patients with newly diagnosed HTN, the frequency of receipt of antihypertensive therapy was approximately 85% within one year following the diagnosis code in each cohort. For HL, the frequency of receipt of lipid-lowering therapy was about 65%, and for DM, the frequency of receipt of diabetic medications (including both oral medications and insulin) was approximately 45%. The proportion of patients receiving therapy and the age- and sex-adjusted odds ratios for receipt of therapy at 180 days was not significantly different from receipt of therapy at one year (Supplemental Table 3). Additionally, stratification of the cohort by age in decade and sex did not reveal significant differences by age in proportion receiving therapy (Supplemental Figure 3).
Using a similar cohort, we previously demonstrated that patients with these inflammatory diseases had an increased risk for major adverse cardiovascular events (2). One reason for conducting the current study was to determine the burden of cardiovascular risk factors and whether these patients were receiving pharmacologic therapy for their known cardiovascular risk factors. As previously demonstrated in clinic-based studies, patients with PsA had an elevated prevalence of all of the studied modifiable cardiovascular risk factors (HTN, HL, DM, and obesity) (4). In contrast, after adjusting for age and sex, only the adjusted prevalence of DM and obesity was significantly increased among patients with RA compared to controls. For both RA and PsA, however, the adjusted hazard ratios for development of incident HTN, HL, and DM were significantly elevated compared to population controls. Interestingly, while these patients had an elevated prevalence and incidence of CVD risk factors, they were treated at similar or higher rates than the general population.
The present study has several strengths. There is a paucity of population-based studies that address identification and management of cardiovascular risk factors among patients with inflammatory diseases, especially PsA (4, 11). We used a large population-based medical record database in the United Kingdom, THIN, which contains data for over 12,000 patients with PsA. The codes for PsA and RA have been previously validated in THIN. Furthermore, THIN is a general practitioners' medical record and is therefore an excellent resource for examining primary care practice patterns including the management of cardiovascular risk factors in this patient population.
Important considerations in the interpretation of the data should also be acknowledged. The cardiovascular risk factors in this study represent “diagnosed” diseases. Underdiagnosis of cardiovascular risk factors is still possible. One US study suggested 29% lower hypertension diagnosis in RA patients versus matched peers who met the same blood pressure criteria, although another US managed care study did not identify a gap (9, 26). Additionally, it may be possible that “diagnosis” was more common among patients with inflammatory diseases because of more frequent visits or increased health care utilization. To address this concern, we included baseline health care utilization in the Cox models and performed a sensitivity analysis in which we restricted patients to those followed at least once per year throughout their time in the study. Next, when examining treatment rates, we searched for medications but not non-pharmacologic interventions such as lifestyle modifications as these are not easily identifiable in the coded database. Furthermore, actual control of cardiovascular risk factors with the prescribed treatments was not assessed in this study and remains an important open area for future research (27). Finally, THIN is a primary care database in the United Kingdom, and as a result, there may be some limitation in the generalizability of the conclusions to other health care systems.
Few studies in the literature have examined treatment of these cardiovascular risk factors compared to the general population. Most (85%) of each cohort received pharmacologic treatment for HTN, while only 45% received pharmacologic treatment for DM (as noted above, we did not include lifestyle changes as a “treatment”). It is not surprising that the rate of prescriptions was highest for HTN given that blood pressure is a vital sign that is checked at nearly every office visit in primary care and does not require additional laboratory testing or risk calculation for diagnosis or treatment. Still, US hypertension treatment rates are approximately 58-75% nationally, suggesting that initiation of hypertension treatment may vary by healthcare delivery system (28). For instance, the “gatekeeper” system in the UK may force more patients to follow up with their GP, whereas in the US, 73% of annual ambulatory visits for RA patients occurred in specialty care (29). Relatively high treatment rates across populations in this UK study may also reflect nationwide pay-for-performance incentives and that since 2010 EULAR guidelines have highlighted the need for increased CVD preventive care in inflammatory disease populations (30).
Although more aggressive treatment of cardiovascular risk factors may be beneficial in mitigating CVD risk in patients with inflammatory diseases, robust prospective data to support this is currently lacking. Inflammatory arthritis was not included in the 2013 cardiovascular risk calculator published by the American College of Cardiology and the American Heart Association given an absence of sufficient data to make clear recommendations (31). Nevertheless, discussion of cardiovascular risk should be incorporated into routine health care maintenance addressed by both primary care physicians and rheumatologists on a periodic basis. Rheumatologists have the unique ability to address this issue in the context of their long-term relationships with their patients. Previous studies have suggested that in the US, patients with RA often view their rheumatologists as their main providers (32). Although there are time constraints during each office visit, this is a topic that significantly influences the morbidity and mortality of our patient population.
In summary, this study highlights the increased prevalence and incidence of modifiable cardiovascular risk factors in patients with inflammatory arthritis and is one of the first population-based studies to examine cardiovascular risk factors in PsA. Additionally, it demonstrates that patients with these inflammatory diseases are being treated for their cardiovascular risk factors at similar rates to patients in the general population. This further underscores the link between cardiovascular disease and systemic inflammation in inflammatory diseases. Future studies are needed to determine the optimal management strategies to mitigate cardiovascular risk in these patient populations, whether through increased screening for CVD risk factors, more aggressive treatment of CVD risk factors, or focusing on treatment of the underlying inflammatory disease (33).
We thank Yihui Jiang, BA, for administrative support.
Funding: This study was funded by the Rheumatology Research Foundation Ephraim P. Engleman Endowed Resident Research Preceptorship Award (PI: Jafri) and the Rheumatology Investigator Award (PI: Ogdie). Dr. Ogdie was supported by NIH K23AR063764. Dr. Bartels was supported by NIH K23AR062381. Dr. Gelfand is supported by NIH/NIAMS K24 AR064310. This research and the researchers were completely independent from the funders. The funders had no role in the study design, analysis, interpretation or decision to submit for publication.
Contributions: The study was designed and carried out by KJ, CB, and AO. KJ, AO, and DS had direct access to the data. DS created the analytic dataset. KJ and AO performed the data analysis. All authors assisted with data interpretation and suggestions for additional analyses. KJ drafted the manuscript and all authors have participated in editing the manuscript. All authors have read and approved the final version of the manuscript.
Data Sharing: While we are unable to share the primary datasets, code lists and statistical coding are available upon request.
Transparency Declaration: The authors affirm that the manuscript is an honest, accurate and transparent account of the study being reported, no aspects have been omitted, and discrepancies from the study as planned did not occur.
Competing Interests: Kashif Jafri has no financial disclosures. Christie Bartels receives institutional grant funding unrelated to this study from Independent Grants for Learning and Change (Pfizer). Daniel Shin has no financial disclosures. In the previous 12 months, Joel Gelfand served as a consultant for Abbvie, Astrazeneca, Celgene Corp, Coherus, Eli Lilly, Janssen Biologics (formerly Centocor), Sanofi, Merck, Novartis Corp, Endo, Valeant, and Pfizer Inc., receiving honoraria; and receives research grants (to the Trustees of the University of Pennsylvania) from Abbvie, Amgen, Eli Lilly, Janssen, Novartis Corp, Regeneron, and Pfizer Inc.; and received payment for continuing medical education work related to psoriasis. Alexis Ogdie has consulted for Novartis and received grant funding from Pfizer.
Kashif Jafri, Division of Rheumatology, University of California, San Francisco, UCSF Box 0633, 533 Parnassus Avenue, San Francisco, CA 94143.
Christie M. Bartels, University of Wisconsin School of Medicine & Public Health, 1685 Highland Ave 4132, Madison, WI 53705 USA.
Daniel Shin, Department of Biostatistics and Epidemiology, Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Dulles Building, Room 1104, 3400 Spruce St, Philadelphia, PA 19104 USA.
Joel M. Gelfand, Perelman School of Medicine, University of Pennsylvania, Dulles Building, Room 1104, 3400 Spruce St, Philadelphia, PA 19104 USA.
Alexis Ogdie, Perelman School of Medicine, University of Pennsylvania, White Building, Room 5024, 3400 Spruce St, Philadelphia, PA 19104 USA.