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Table 1.  Characteristics of Patients Before and After 1:1 Propensity Score Matching
Characteristics of Patients Before and After 1:1 Propensity Score Matching
Table 2.  Rate of Newly Recorded Inflammatory Arthritis, Before and After 1:1 Propensity Score Matching
Rate of Newly Recorded Inflammatory Arthritis, Before and After 1:1 Propensity Score Matching
1.
Rondags  A, Arends  S, Wink  FR, Horváth  B, Spoorenberg  A.  High prevalence of hidradenitis suppurativa symptoms in axial spondyloarthritis patients: a possible new extra-articular manifestation.  Semin Arthritis Rheum. 2019;48(4):611-617. doi:10.1016/j.semarthrit.2018.03.010PubMedGoogle ScholarCrossref
2.
Kim  GE, Shlyankevich  J, Kimball  AB.  The validity of the diagnostic code for hidradenitis suppurativa in an electronic database.  Br J Dermatol. 2014;171(2):338-342. doi:10.1111/bjd.13041PubMedGoogle ScholarCrossref
3.
Curtis  JR, Harrold  LR, Asgari  MM,  et al.  Diagnostic prevalence of ankylosing spondylitis using computerized health care data, 1996 to 2009: underrecognition in a US health care setting.  Perm J. 2016;20(4):15-151. doi:10.7812/TPP/15-151PubMedGoogle Scholar
4.
Hanly  JG, Thompson  K, Skedgel  C.  The use of administrative health care databases to identify patients with rheumatoid arthritis.  Open Access Rheumatol. 2015;7:69-75. doi:10.2147/OARRR.S92630PubMedGoogle ScholarCrossref
5.
Gagne  JJ, Glynn  RJ, Avorn  J, Levin  R, Schneeweiss  S.  A combined comorbidity score predicted mortality in elderly patients better than existing scores.  J Clin Epidemiol. 2011;64(7):749-759. doi:10.1016/j.jclinepi.2010.10.004PubMedGoogle ScholarCrossref
6.
Rosenbaum  PR, Rubin  DB.  The central role of the propensity score in observational studies for causal effects.  Biometrika. 1983;70:41-55. doi:10.1093/biomet/70.1.41Google ScholarCrossref
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    Research Letter
    January 22, 2020

    Risk of Inflammatory Arthritis After a New Diagnosis of Hidradenitis Suppurativa

    Author Affiliations
    • 1Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
    • 2Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
    • 3Division of Rheumatology, Inflammation and Immunity, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
    • 4Department of Dermatology, Tufts Medical Center, Tufts University School of Medicine, Boston, Massachusetts
    JAMA Dermatol. 2020;156(3):342-345. doi:10.1001/jamadermatol.2019.4590

    Hidradenitis suppurativa (HS) has been associated with a high prevalence of spondyloarthritis; however, the population-based risk of developing inflammatory arthritis remains unclear.1 This population-based cohort study sought to evaluate the risk of developing inflammatory arthritis among patients with HS compared with matched patients without HS.

    Methods
    Patients

    We used longitudinal claims data from commercially insured patients, including those with Medicaid and Medicare, covering 185 million lives in the United States between January 1, 2003, and January 1, 2016. We identified patients of all ages who received a diagnosis of HS (International Classification of Diseases, Ninth Revision code 705.83 or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision code L73.2), defined as 1 diagnosis by a dermatologist or 3 diagnoses by any health care professional.2 The cohort entry date for the HS group was the first recorded diagnosis date of HS after at least 180 days of continuous enrollment (eFigure in the Supplement). For the non-HS group, we risk-set sampled 2 patients without HS from all plan enrollees who did not have a diagnosis of HS matched on the date the patient with HS entered the cohort. We excluded patients who had less than 180 days of continuous enrollment before cohort entry or had preexisting inflammatory arthritis, including ankylosing spondylitis, psoriatic arthritis, other spondyloarthritis, or rheumatoid arthritis (eAppendix in the Supplement). The Brigham and Women’s Hospital’s Institutional Review Board approved this study. All patient information was deidentified.

    Outcomes

    We used validated algorithms using hospital discharge diagnoses or physician visit claims to identify the following outcomes: ankylosing spondylitis, psoriatic arthritis, other spondyloarthritis (reactive arthropathy, spinal enthesopathy, sacroiliitis, or unspecified inflammatory spondylopathies), and rheumatoid arthritis (eTable 1 in the Supplement).3,4 Follow-up started the day after cohort entry and lasted until one of the following events occurred: outcome, death, disenrollment, or end of data stream, whichever came first (eTable 3 in the Supplement).

    Baseline Patient Characteristics

    All the characteristics of the patients were assessed during the 180 days before or on the cohort entry date: age, sex, region, number of outpatient visits, number of unique medications, use of systemic biologic or nonbiologic immunomodulatory agents, comorbidities (ie, psoriasis or inflammatory bowel disease), and combined comorbidity score (eTable 2 in the Supplement).5

    Statistical Analysis

    Statistical analysis was performed from January 1, 2003, to January 1, 2016. We estimated hazard ratios (HRs) of the outcomes with 95% CIs by fitting Cox proportional hazards regression models, adjusting for all 10 baseline characteristics. To further account for confounding, HRs of developing inflammatory arthritis were computed after 1:1 propensity score matching.6 Patients’ propensity scores were estimated using the multivariable logistic regression that included all aforementioned patient characteristics.

    Results

    We identified 70 697 patients with HS (mean [SD] age, 36.5 [14.7] years) and 141 412 risk-set sampled patients without HS (mean [SD] age, 38.3 [21.1] years). Before matching, patients with HS were more often female (78.0% vs 52.0%) and had an increased comorbidity burden (Table 1). Median follow-up was 1.5 years (interquartile range, 0.6-3.1 years), with a maximum of 11 years. Both groups were free of arthritis prior to the cohort entry. After propensity score matching, patients with HS had an increased risk for developing ankylosing spondylitis compared with those without HS (incidence rate, 0.60 vs 0.36 per 1000; HR, 1.65 [95% CI, 1.15-2.35]), psoriatic arthritis (incidence rate, 0.84 vs 0.58 per 1000; HR, 1.44 [95% CI, 1.08-1.93]), and rheumatoid arthritis (incidence rate, 4.54 vs 3.86 per 1000; HR, 1.16 [95% CI, 1.03-1.31]) (Table 2). We did not observe an increased risk of other spondyloarthritis among patients with HS vs those without HS (incidence rate, 3.07 vs 3.00 per 1000; HR, 1.02 [95% CI, 0.89-1.17]).

    Discussion

    This large cohort study provides population-based rates of newly recorded inflammatory arthritis after a diagnosis of HS. We observed increased risks of developing ankylosing spondylitis, psoriatic arthritis, and rheumatoid arthritis among patients with HS compared with those without HS. Generally, the incidence of arthritis was low, resulting in only between 2 and 6 additional cases per 10 000 patients with HS per 1.5 years. Although the data support a systematic association between HS and subsequent newly diagnosed inflammatory joint disease, the low incremental risk is reassuring. Nevertheless, physicians treating patients with HS should be aware of symptoms suggestive of inflammatory arthritis, including morning stiffness and joint pain or swelling. This study cannot prove a causal relationship between HS and inflammatory arthritis, and further work is needed to elucidate the underlying potential shared pathogenesis of these disorders.

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    Article Information

    Accepted for Publication: November 26, 2019.

    Corresponding Author: Maria C. Schneeweiss, MD, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, 1620 Tremont St, Ste 3030, Boston, MA 02120 (mschneeweiss@partners.org).

    Published Online: January 22, 2020. doi:10.1001/jamadermatol.2019.4590

    Author Contributions: Dr M.C. Schneeweiss had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Rosmarin and Merola were co–senior authors.

    Concept and design: M. C. Schneeweiss, S. Schneeweiss, Rosmarin, Merola.

    Acquisition, analysis, or interpretation of data: M. C. Schneeweiss, Kim, S. Schneeweiss, Rosmarin.

    Drafting of the manuscript: M. C. Schneeweiss, Merola.

    Critical revision of the manuscript for important intellectual content: M. C. Schneeweiss, Kim, S. Schneeweiss, Rosmarin.

    Statistical analysis: M. C. Schneeweiss, Kim.

    Administrative, technical, or material support: M. C. Schneeweiss.

    Supervision: Kim, Rosmarin, Merola.

    Conflict of Interest Disclosures: Dr Kim reported receiving research grants to the Brigham and Women’s Hospital from Pfizer, Roche, AbbVie, and Bristol-Myers Squibb for unrelated topics. Dr Rosmarin reported serving as a paid consultant for AbbVie, Celgene, Dermavant, Janssen, Lilly, Novartis, Pfizer, and Regeneron Pharmaceuticals Inc; receiving research support from AbbVie, Celgene, Dermira, Incyte, Janssen, Lilly, Merck, Novartis, Pfizer, and Regeneron Pharmaceuticals Inc; and serving as a paid speaker for AbbVie, Celgene, Janssen, Lilly, Novartis, Pfizer, Regeneron Pharmaceuticals Inc, and Sanofi. Dr S. Schneeweiss reported being the principal investigator of investigator-initiated grants to the Brigham and Women’s Hospital from Bayer, Vertex, and Boehringer Ingelheim unrelated to the topic of this study; and serving as a consultant to WHISCON and to Aetion, a software manufacturer for which he owns equity. Dr Merola reported serving as a consultant and/or investigator for Merck Research Laboratories, AbbVie, Dermavant, Eli Lilly and Co, Novartis, Janssen, UCB, Samumed, Celgene, Sanofi Regeneron, GSK, Almirall, Sun Pharma, Biogen, Pfizer, Incyte, Aclaris, and Leo Pharma. No other disclosures were reported.

    Funding/Support: This study was supported by the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital, and the Department of Dermatology, Brigham and Women’s Hospital. This project was partially funded by the Pfizer fellowship for dermatology-pharmacoepidemiology research.

    Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Rondags  A, Arends  S, Wink  FR, Horváth  B, Spoorenberg  A.  High prevalence of hidradenitis suppurativa symptoms in axial spondyloarthritis patients: a possible new extra-articular manifestation.  Semin Arthritis Rheum. 2019;48(4):611-617. doi:10.1016/j.semarthrit.2018.03.010PubMedGoogle ScholarCrossref
    2.
    Kim  GE, Shlyankevich  J, Kimball  AB.  The validity of the diagnostic code for hidradenitis suppurativa in an electronic database.  Br J Dermatol. 2014;171(2):338-342. doi:10.1111/bjd.13041PubMedGoogle ScholarCrossref
    3.
    Curtis  JR, Harrold  LR, Asgari  MM,  et al.  Diagnostic prevalence of ankylosing spondylitis using computerized health care data, 1996 to 2009: underrecognition in a US health care setting.  Perm J. 2016;20(4):15-151. doi:10.7812/TPP/15-151PubMedGoogle Scholar
    4.
    Hanly  JG, Thompson  K, Skedgel  C.  The use of administrative health care databases to identify patients with rheumatoid arthritis.  Open Access Rheumatol. 2015;7:69-75. doi:10.2147/OARRR.S92630PubMedGoogle ScholarCrossref
    5.
    Gagne  JJ, Glynn  RJ, Avorn  J, Levin  R, Schneeweiss  S.  A combined comorbidity score predicted mortality in elderly patients better than existing scores.  J Clin Epidemiol. 2011;64(7):749-759. doi:10.1016/j.jclinepi.2010.10.004PubMedGoogle ScholarCrossref
    6.
    Rosenbaum  PR, Rubin  DB.  The central role of the propensity score in observational studies for causal effects.  Biometrika. 1983;70:41-55. doi:10.1093/biomet/70.1.41Google ScholarCrossref
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