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Clinical and epidemiological research
Extended report
Risk of mortality in patients with psoriatic arthritis, rheumatoid arthritis and psoriasis: a longitudinal cohort study
  1. Alexis Ogdie1,
  2. Kevin Haynes2,
  3. Andrea B Troxel2,
  4. Thorvardur Jon Love3,
  5. Sean Hennessy2,
  6. Hyon Choi4,
  7. Joel M Gelfand5
  1. 1Division of Rheumatology, Center for Clinical Epidemiology and Biostatistics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
  2. 2Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
  3. 3Division of Rheumatology, Landspitali University Hospital, Reykjavik, Iceland
  4. 4Section of Rheumatology and the Clinical Epidemiology Unit, Boston University School of Medicine, Boston, Massachusetts, USA
  5. 5Department of Dermatology, Center for Clinical Epidemiology and Biostatistics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
  1. Correspondence to Dr Alexis Ogdie, Division of Rheumatology, Center for Clinical Epidemiology and Biostatistics, Center for Pharmacoepidemiology Research and Training, Perelman School of Medicine at the University of Pennsylvania, 8 Penn Tower, 1 Convention Ave, Philadelphia, PA 19104, USA; alexis.ogdie{at}uphs.upenn.edu

Abstract

Background There are conflicting reports in the literature of the mortality risk among patients with psoriatic arthritis (PsA). The objective of this study was to examine the risk of mortality in patients with PsA compared with matched controls, patients with psoriasis and those with rheumatoid arthritis (RA).

Methods A longitudinal cohort study was performed in a large UK medical record database, The Health Improvement Network, among patients with PsA, rheumatoid arthritis (RA) or psoriasis with data from 1994 to 2010. Unexposed controls were matched on practice and start date within the practice for each patient with PsA. Cox proportional hazards models were used to calculate the relative hazards for death.

Results Patients with PsA (N=8706), RA (N=41 752), psoriasis (N=138 424) and unexposed controls (N=82 258) were identified; 1 442 357 person-years were observed during which 21 825 deaths occurred. Compared with population controls, patients with PsA did not have an increased risk of mortality after adjusting for age and sex (disease-modifying antirheumatic drug (DMARD) users: HR 0.94, 95% CI 0.80 to 1.10; DMARD non-users: HR 1.06, 95% CI 0.94 to 1.19) whereas patients with RA had increased mortality (DMARD users: HR 1.59, 95% CI 1.52 to 1.66; DMARD non-users: HR 1.54, 95% CI 1.47 to 1.60). Patients with psoriasis who had not been prescribed a DMARD had a small increased risk of mortality (HR 1.08, 95% CI 1.04 to 1.12) while those who had been prescribed a DMARD, indicating severe psoriasis, were at increased risk (HR 1.75, 95% CI 1.56 to 1.95).

Conclusions Patients with RA and psoriasis have increased mortality compared with the general population but patients with PsA do not have a significantly increased risk of mortality.

  • Psoriatic Arthritis
  • Rheumatoid Arthritis
  • Epidemiology

https://doi.org/10.1136/annrheumdis-2012-202424

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    Introduction

    Psoriatic arthritis (PsA) is a chronic progressive inflammatory disease that affects over 500 000 Americans and can cause permanent joint damage and severe disability.1 However, it is unclear whether PsA confers an increased risk of mortality as, to date, observational studies have yielded conflicting evidence.2 Such studies have been limited by small sample size with few events and the potential for selection bias in clinic-based studies. Furthermore, very little is known about the risk of mortality in PsA from a population-based perspective.

    Given that previous studies have demonstrated increased mortality among patients with psoriasis and rheumatoid arthritis (RA),3 ,4 we hypothesised that patients with PsA would similarly have increased rates of mortality compared with the general population. The objective of this study was to measure the risk of all-cause mortality among patients with PsA compared with the general population and with patients with psoriasis only (without a diagnosis of PsA) and RA.

    Methods

    Study design and data source

    A cohort study was undertaken using data from The Health Improvement Network (THIN) in the UK between 1994 and 2010. The UK is an ideal health system for capturing medical record data as the general practitioner (GP) is the primary contact for all aspects of the patient's care.5 In the UK, 95% of patients are registered with a GP.6 Participating GPs record data as part of routine patient care (eg, demographics, medical diagnoses, laboratory data and prescriptions) in the electronic medical record including recommendations made by specialists in secondary and tertiary care. (However, some treatments may initially be prescribed by a specialist and often the GP will thereafter assume prescribing the medication. Tumour necrosis factor α prescriptions are not generally recorded in THIN as these are exclusively prescribed by the specialist.) The data are anonymised and collected by THIN, assessed for quality and made available for research use.7 THIN draws patients from 514 general practices and is representative of the UK population in terms of age, sex, geography and medical conditions.8–11

    Study population

    All patients with PsA, psoriasis or RA between the ages of 18 and 89 at the start date were included if they had observation time in THIN after Vision software implementation. Patients were excluded if they died or transferred out of the practice prior to the implementation of Vision software. Up to 10 unexposed controls from the general population were randomly selected for each patient with PsA and were matched on practice and start date within the practice. Unexposed controls were assigned a ‘diagnosis’ date within 6 months of the diagnosis date of the patient with PsA. This algorithm was designed to minimise bias by ensuring that patients with PsA and unexposed controls are followed by similar doctors during similar time periods. Patients were not eligible to serve as unexposed controls if they had PsA, psoriasis or RA or were using disease-modifying antirheumatic drugs (DMARDs).

    Person-time calculation

    Cohort entry was defined as the latest of diagnosis, 6 months after initial registration with the practice, DMARD initiation, implementation of Vision software in the patient's practice or a practice acceptable mortality reporting (the first three are patient-level factors and last two are practice-level factors).7 ,12 The rationale for choosing these elements is further described in the online supplementary methods section. Censoring occurred when the patient died, left the practice, the practice stopped participating in THIN or the study ended in September 2010.

    Exposure definitions

    PsA, psoriasis and RA were defined by the presence of at least one READ code consistent with these diseases. READ codes are a comprehensive hierarchical alphanumerical clinical language developed in the UK to record diagnoses, symptoms and tests, similar to International Classification of Diseases codes.13 READ codes for psoriasis and RA have been previously validated within the same or analogous large medical record databases.14–16 READ codes for PsA have a positive predictive value of 85% as determined by a survey of 100 randomly selected GPs caring for patients with PsA.17 We have also used this definition of PsA in other studies.18

    Outcome definition

    The primary outcome, death, was defined by specific codes noting death and/or codes indicating the patient was transferred out of the practice because of his/her death.19 An algorithm recommended by Cegedim, the administrators of THIN, was used and identifies death codes from within the patient, medical and administration files. This algorithm has been used in other studies and has a sensitivity of 99.7%.19

    Covariates of interest

    All covariates of interest were measured prior to cohort entry. The following potential confounders were measured: Charlson comorbidity score,20 smoking, body mass index, blood pressure at baseline, depression, prior hospitalisation in the baseline follow-up period, year of cohort entry, socioeconomic status (via Townsend deprivation score), urban versus rural living environment, chronic kidney disease, heart disease, atrial fibrillation, diabetes, hypertension, history of cancer, asthma, chronic obstructive pulmonary disease and liver disease. Furthermore, a priori we hypothesised a statistical interaction between disease status and DMARD use. DMARDs included methotrexate, sulfasalazine, azathioprine, leflunamide, cyclosporine, mycophenolate, hydroxychloroquine and biologic disease-modifying agents including adalimumab, etanercept and infliximab. In the UK, these medications can be prescribed by consultants (specialists) but should be captured by GP records with the exception of the biologic medications which are rarely recorded.17

    Statistical analysis

    Power calculations prior to the start of the study revealed that, with 7000 patients with PsA and 35 000 unexposed patients, we would have 96% power to detect a HR as small as 1.05 for patients with PsA with an average of 5 years of follow-up per patient. Descriptive statistics were used to examine age, sex, person-time and covariate distribution between the four groups. We fit a Cox proportional hazards regression model, adjusting for age and sex, to determine the overall HR for each group compared with the unexposed group. We then tested the hypothesised statistical interaction and fit models with the hypothesised confounders using a purposeful selection modelling approach.21 More detail with regard to the modelling approach can be found in the online supplementary methods. Log-log survival plots were generated to assess the assumption of proportionality. All statistical analysis was performed using STATA 12.0 (College Station, Texas, USA).

    Sensitivity analyses

    The following sensitivity analyses were conducted: (1) multiple imputation was performed for body mass index and smoking status where missing and models containing these variables were retested; (2) DMARDs were included as a time-varying covariate rather than a stratification variable; (3) we restricted patients to only those followed for at least 1 year prior to the start date to ensure capture of comorbidities; (4) to examine whether missed DMARD prescriptions would have an impact on the results, we imputed additional DMARD users by first creating a propensity score for DMARD use and then assigning 25% of the non-DMARD users with the highest propensity scores to DMARD use; (5) we restricted the cohort to only those with incident disease defined as patients with at least 1 year of follow-up prior to the first diagnosis code; and (6) we conducted an unmeasured confounder analysis to test the assumption that there is a confounder that we are unable to measure that may skew the results had we been able to measure this confounder. More detail with regard to the methods used in the sensitivity analyses can be found in the online supplementary methods.

    Results

    Between 1994 and 2010, 8706 patients with PsA, 41 752 with RA and 138 424 patients with psoriasis met the inclusion criteria and 82 258 unexposed patients were randomly selected using the described criteria. Baseline characteristics are shown in table 1. Patients with RA were significantly older and were predominantly female, while the male: female ratio was closer to 1:1 in the other three groups. Mean person-time contributed was 5.3 years. Median year of cohort entry was 1 year earlier for patients with RA compared with the other three groups. DMARDs were prescribed to 48%, 52% and 3% of patients with PsA, RA and psoriasis, respectively. The prevalence of comorbidities was highest among patients with RA in nearly all categories.

    View this table:
    Table 1

    Demographic data of study subjects

    Among the 271 140 patients, 21 825 deaths were observed over 1 442 238 person-years (table 2). The unadjusted incidence of mortality was highest in patients with RA (table 3). The interaction between disease status and history of DMARD use was statistically significant and therefore results stratified by DMARD use are presented. The addition of hypothesised confounders to the model did not change the model substantially (all of the covariates listed in the Methods section were tested but individual p values and HRs are not provided due to space restrictions). Therefore, the final model is the age- and sex-adjusted model demonstrating increased mortality risk among patients with RA (DMARD users: HR 1.59, 95% CI 1.52 to 1.66; DMARD non-users: HR 1.54, 95% CI 1.47 to 1.60) and psoriasis (DMARD users: HR 1.75, 95% CI 1.56 to 1.95; DMARD non-users: HR 1.08, 95% CI 1.04 to 1.12) but no increased mortality risk among patients with PsA (DMARD users: HR 0.94, 95% CI 0.80 to 1.10; DMARD non-users: HR 1.06, 95% CI 0.94 to 1.19).

    View this table:
    Table 2

    Incidence of death

    View this table:
    Table 3

    Cox proportional hazards models

    Reasons for censoring (patient leaving the practice, practice stopped contributing to THIN and end of study in September 2010) were not significantly different among the groups (see online supplementary table 1). The five sensitivity analyses conducted did not change the results of the final model (see online supplementary table 2). An unmeasured confounder analysis showed that even if the unmeasured confounder has a HR of 10.0 and a prevalence of 60% among patients with PsA, the mortality risk for patients with PsA would not significantly change (see online supplementary table 3).

    Discussion

    In this large population-based study we found that patients with PsA did not have a statistically significant increase in mortality compared with the general population. Furthermore, patients with RA and those with severe psoriasis (DMARD users) had significantly higher mortality than the general population. The increased all-cause mortality risk in patients with psoriasis not using DMARDs (8% increase over the general population) is small and statistically similar to the PsA groups based on the 95% CIs. These results are consistent with the findings of other population-based studies3 ,4 ,22–29 and were robust to multiple sensitivity analyses. This is the first study to compare directly the risk of all-cause mortality in PsA with an unexposed population rather than using standardised mortality ratios (SMR) to compare with census statistics. Internal controls are generally felt to provide a better approximation of the true effect than SMRs.30

    Most previous studies of mortality in PsA have been performed within specialty clinics. These studies have had mixed results ranging from no difference in mortality compared with local census statistics to an SMR of 1.62 (95% CI 1.21 to 2.12).31 (Of note, the SMR was 1.36 (95% CI 1.12 to 1.64) in the same cohort 10 years later32). Clinic- and hospital-based studies demonstrating higher mortality could be capturing a larger proportion of patients with severe disease reflecting selection bias. In the general population a larger proportion of patients are likely to have mild disease. It is possible that severity of disease is a driver of mortality risk. We were unable to test this hypothesis in THIN, however a sensitivity analysis demonstrated that the effect size of this unmeasured confounder must be substantial (HR=10) in order to change the results. Finally, it is possible that mortality has declined over time in the PsA population.32 However, adjusting the regression model for start year in the cohort did not change the results of the final model.

    Population-based studies using large medical record databases have tremendous advantages in examining mortality. Consideration must be given to the choice of the unexposed or control population in such studies as control patients may not have regular contact with their physicians and new diagnoses or events may not be captured. It was for this reason that we chose to include only patients who had contact with their GP around the time the matched patient with PsA was diagnosed. Selecting patients in this way may increase the ‘illness level’ of patients in the unexposed group and reduce the HR for mortality in patients in the exposed groups. It is therefore possible that there is a minimally increased risk of mortality in patients with PsA. On the other hand, we included patients with prevalent diagnoses in this study. Studies including patients with prevalent diagnoses are generally thought to have higher death rates than those with incident disease.33 However, a sensitivity analysis including only cases with at least 1 year prior to the first diagnosis code (a commonly used definition of incident disease) did not substantially change the results.

    Potential limitations of this study include misclassification of diagnoses and missing information regarding DMARD use. Our previous validation study showed limited misclassification of patients with a PsA diagnosis (ie, a high positive predictive value for the diagnostic code).17 In fact, there was an even higher positive predictive value (93%) for patients with a diagnostic code for PsA patients with a DMARD prescription, further decreasing the likelihood that misclassification could be the reason for the null result (unpublished data). While we are confident in the diagnostic code for PsA, there may be patients with PsA categorised as psoriasis only (eg, undiagnosed cases of PsA among patients with psoriasis).34 It is not possible to identify the frequency of this phenomenon in this population-based study. Population-based studies such as those performed in THIN often lack information on disease activity measures and disease characteristics. However, the goal of this study was to examine patients with already diagnosed PsA, a more specific cohort than all patients with possible PsA. Misclassification of the outcome is not a major concern as mortality is unlikely to be subject to surveillance bias because it has near complete ascertainment.19 This population-based study draws from a larger cohort, THIN, which is felt to be representative of the UK but may over-represent more affluent areas.11 However, adjusting for socioeconomic status (via Townsend deprivation score) did not change the results. Finally, use of DMARDs is difficult to model in an observational study and can be performed in numerous ways, though all are limited by confounding by indication.35 ,36 We chose to classify patients as ‘ever’ or ‘never’ users because we were using DMARDs as a stratification variable, and we hypothesised that patients prescribed treatment may be different from those who had not been prescribed treatment. There may be under-reporting of DMARD use in cases where the consultant is the primary prescriber, despite the fact that this may be recorded by the GP in the medical record, and this could result in misclassification. However, increasing the number of DMARD users by 25% did not significantly change the results.

    In conclusion, we present the results of a large population-based study demonstrating increased mortality in patients with severe psoriasis or RA but no statistically significant increase in mortality among patients with PsA. The lack of higher mortality in patients with PsA has been previously demonstrated in smaller population-based cohorts. Despite a lack of higher mortality, there is still significant morbidity in patients with PsA including concomitant illnesses and impaired quality of life. Future research efforts are needed to identify mechanisms by which we can improve comorbidities and quality of life for patients with PsA.

    Acknowledgments

    We thank Dr Peter Merkel for helpful discussions regarding this paper.

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    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

      Files in this Data Supplement:

    Footnotes

    • Handling editor Tore K Kvien

    • Contributors AO and JMG conceptualised and designed the study with input from KH, SH, ABT, TJL and HC, and these authors were integral in interpretation of the results. AO performed the programming, statistical analysis, preparation of the data and the first draft of the manuscript. KH performed data abstraction from The Health Improvement Network and assisted in programming. All authors were involved in critical review of the data as well as drafting and revision of the manuscript, and all have approved the final version of the paper to be published.

    • Funding This project was funded by the American College of Rheumatology. Data from The Health Improvement Network is supported by the Clinical and Translational Science Award at the University of Pennsylvania (8UL1TR000003from the National Center for Research Resources). AO was supported by NIH T32 GM075766-05 and the American College of Rheumatology Research and Education Foundation; SH was supported by R01AG025152; JMG was supported by R01HL111293; and TJL was supported by The Icelandic Research Fund #120433021.

    • Competing interests JMG serves as a consultant to Amgen, Abbott, Centocor, Celgene, Novartis and Pfizer and has received honoraria. He has received grants from Amgen, Abbott, Pfizer, Novartis and Genentech. The remaining authors have no competing interests. Cegedim Strategic Data (CSD) Medical Research UK is an expert in UK anonymous patient data for the healthcare industry. CSD is a commercial organisation that supplies data and trains and supports researchers in the use of primary care patient data. Data are available for use in medical research in the academic setting as well as in industry for a fee which varies depending on the type of data requested. Aside from undergoing ethical review by the Scientific Review Committee at Cegedim, independent academic groups who voluntarily act as an ethical review body, this protocol was not in any way discussed with Cegedim nor were any changes made by the company. No financial support or other forms of computational or analytical support were received from Cegedim/THIN. The data were collected by Cegedim and the general practitioners without knowledge of the study objectives and hypotheses.

    • Ethical approval This study was approved by the University of Pennsylvania Institutional Review Board and Cegedim's Scientific Review Committee. All data in the study were anonymous to the investigators.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Data sharing statement Data from this study will be shared upon request. Please email the corresponding author for such inquires.