OP0115\u2005GENERAL AND SEX-SPECIFIC PREDICTORS OF PSA AMONG PATIENTS WITH PSORIASIS

Abstract

Background Risk prediction models in electronic health record (EHR) databases may assist in early identification of patients with psoriasis likely to develop psoriatic arthritis (PsA).1 A better understanding of potential predictors and whether stratification by sex would be needed in building such algorithms is required.1 Objectives Examine general and sex-specific predictors of PsA in an EHR dataset among patients with psoriasis Methods A retrospective cohort study was performed within the OptumInsights EHR Database (United States) between 2006-2017. Patients with two or more ICD codes for psoriasis and ages 16-90 were identified. The outcome was PsA (defined by a single ICD code). Potential predictors, in particular comorbidities and infections, were also identified using ICD codes. Hazard ratios were calculated using Cox proportional hazards models between individual predictors and development of incident PsA in univariate models and those that were significant (p<0.1) were entered into a multivariable model. A final model was achieved using automated stepwise regression. Separate models were developed for each sex as some predictors (e.g., polycystic ovarian syndrome, prostatitis) are sex-specific. Results Among 215,386 patients with psoriasis, mean age was 50 (SD 15.6) and 55% were female. At index date (one year after date of first psoriasis code), 4.6% and 4.2% of patients had been prescribed a biologic therapy or oral therapy in the past year. Mean follow up time was 5.6 years (SD 2.8) and 4,288 patients developed incident PsA (incidence 3.5 cases/1,000 person years). Previously identified predictors were significant in univariate models (depression, fatigue, inflammatory bowel disease, uveitis, hyperlipidemia, fracture; data not shown due to space restrictions) but several new predictors were also identified (diabetes, hidradenitis suppurativa, celiac disease, irritable bowel syndrome, sepsis, post-traumatic stress disorder, anxiety, anemia) (Table). Automated regression identified subsets of these factors in multivariable models; these models differed by sex. Conclusion Predictors of developing PsA differed by sex but obesity, depression, and fatigue were statistically significant predictors in both groups. Infections were also associated with development of PsA but the type of infection differed by sex. References [1] Scher, et al. Nat Rev Rheum2019 In Press. Table. Multivariable HRs for the risk for PsA among patients with psoriasis. All* Women Men Age 0.99 (0.99-1.00) 1.00 (0.99-1.00) 0.99 (0.99-0.99) Male Sex 1.09 (1.02-1.16) Obesity 1.31 (1.16-1.48) 1.30 (1.11-1.53) 1.35 (1.12-1.64) Depression 1.19 (1.06-1.33) 1.19 (1.04-1.37) 1.23 (1.01-1.49) Fatigue 1.61 (1.43-1.81) 1.50 (1.30-1.75) 1.91 (1.59-2.29) Anemia 1.48 (1.29-1.70) 1.62 (1.37-1.92) Uveitis 2.48 (1.41-4.38) 2.90 (1.38-6.08) Sepsis 1.64 (1.07-2.52) 2.39 (1.41-4.03) Liver Disease 1.31 (1.06-1.62) 1.40 (1.04-1.88) Hiddradenitis Suppertiva 2.16 (1.16-4.02) 4.04 (1.68-9.74) Hypertension 1.16 (1.07-1.26) 1.18 (1.05-1.33) Osteomyelitis 2.17 (1.29-3.67) 2.70 (1.38-5.29) Celiac Disease 1.98 (1.10-3.58) HIV 0.24 (0.06-0.96) Any infection 1.13 (1.04-1.22) Restless Leg Syndrome 1.55 (1.06-2.28) Salmonella 9.30 (1.30-66.27) Cellulitis 1.36 (1.09-1.70) Diabetes 1.23 (1.06-1.43) Irritable Bowel Syndrome 1.62 (1.00-2.62) Venous Thromboembolism 1.58 (1.04-2.43) Encephalitis 4.40 (1.10-17.62) Gangrene 4.33 (1.05-17.85) Disclosure of Interests Alexis Ogdie Grant/research support from: (To my university) Novartis, Pfizer, Grant/research support from: Novartis, Pfizer, Grant/research support from: Novartis, Pfizer, Grant/research support from: Novartis, Pfizer, Consultant for: AbbVie, Bristol-Myers Squibb, Celgene, Corrona, Eli Lilly and Company, Novartis, Pfizer, and Takeda, Consultant for: AbbVie, Amgen, Bristol-Myers Squibb, Celgene, Corrona, Eli Lilly, Novartis, Pfizer Inc, Takeda, Consultant for: Abbvie, Amgen, BMS, Celgene, Corrona, Lilly, Novartis, Pfizer, Takeda, Consultant for: Abbvie, Amgen, BMS, Celgene, Corrona, Lilly, Novartis, Pfizer, Takeda, Jose Scher Grant/research support from: Pfizer, Novartis, Consultant for: Janssen, UCB, Novartis, Amgen, Shiyu Vanessa Wang: None declared, Daniel Shin: None declared, David Margolis Grant/research support from: Research funds from Valeant to the trustees of the university of pennsylvania, Consultant for: Data monitoring boards for Johnson and Johnson, Junko Takeshita Grant/research support from: Pfizer (to the Trustees of the University of Pennsylvania), Paid instructor for: Continuing medical education work related to psoriasis that was supported indirectly by Eli Lilly., Hyon Choi: None declared, Thorvardur Jon Love Consultant for: Received reimbursment from Celgene for speaking about guidelines for the treatment of psoriatic arthritis, Christopher T. Ritchlin Grant/research support from: AbbVie, Amgen, UCB Pharma, Consultant for: AbbVie, Amgen, Lilly, Novartis, Pfizer, UCB Pharma, Joel Gelfand Grant/research support from: Research grants (to the Trustees of the University of Pennsylvania) from Abbvie, Boehringer Ingelheim, Janssen, Novartis Corp, Celgene, Ortho Dermatologics, and Pfizer Inc., Consultant for: BMS, Boehringer Ingelheim, Janssen Biologics, Novartis Corp, UCB (DSMB), Sanofi, and Pfizer Inc., Paid instructor for: Received payment for continuing medical education work related to psoriasis that was supported indirectly by Lilly, Ortho Dermatologics and Novartis., Joseph F. Merola Consultant for: Biogen IDEC, Abbvie, Amgen, Eli Lilly and Company, Novartis, Pfizer, Janssen, UCB, Samumed, Celgene, Sanofi Regeneron, Merck, and GSK