POS1172 RISK OF VENOUS THROMBOEMBOLISM AFTER GOUT FLARES

Background Several population-based cohort studies have reported an increased risk of venous thromboembolism (VTE) in gout patients. However, none these has investigated the temporal relationship between flares and VTE. Objectives To explore whether increase VTE short-term using self-controlled case series (SCCS) method. Methods We identified participants with incident from Clinical Practice Research Datalink (CPRD). Participants having less than one year registration CPRD patients a history or anticoagulant prescription more before first consultation were excluded. at least flare diagnosis selected. VTEs ascertained primary care data, hospitalisation mortality records, previously validated algorithms (positive predictive value 94% for [1] 68-95% [2,3]). SCCS method involves fitting Poisson model conditioned on number VTEs, it calculates adjusted incidence ratio (aIRR) its 95% confidence interval (95%CI) each stratum “at-risk” period as compared “baseline” (Figure 1). The analysis was age calendar season. Figure 1. Schematic description observation (“at-risk” baseline periods). (in red) defined following exposure (the flare), subdivided follows: days 0-30, 31-60 61-120 after flare. green) consisted pre-exposure post-exposure 365 each. length varied according to occurrence next timing. Panel A panel B provide schematic representation single multiple “non-overlapping” periods, respectively. In such cases, “at risk” 120 days, while Results Among 104,962 1997 2020, we 2,678 (4.0 events/1,000 person-years). There 53 (13.3 events/month) during 143 (6.0 (crude rate ratio, 1.75; 95%CI: 1.27-2.42). rates highest month then fell progressively (Table Sensitivity analyses consistent main Table Gout Number events per aIRR (95%CI ) p trend Main 0-30 17.0 2.11 (1.27-3.50) 0.01 14.0 1.86 (1.07-3.24) 61-90 11.0 1.50 (0.95-2.37) Baseline 6.0 Reference (excluding factors VTE) [4] 3.13 (1.77-5.53) 7.0 1.66 (0.76-3.61) 8.0 1.75 (0.94-3.37) 3.4 Conclusion transitory observed flares. References [1]Huerta C, et al. Risk diagnosed setting United Kingdom. Arch Intern Med. 2007;167:935-43. [2]Zheng Using natural language processing machine learning identify electronic clinical notes. Arthritis Care Res (Hoboken). 2014;66:1740-8. [3]MacFarlane LA, Liu Validation claims-based Pharmacoepidemiol Drug Saf. 2016;25:820-6. [4]Konstantinides SV, 2019 ESC Guidelines management acute pulmonary embolism developed collaboration European Respiratory Society (ERS). Eur Heart J. 2020;41:543-603. Disclosure Interests None declared