Temporal trends in hospitalisation for opportunistic infections in lupus patients in Western Australia

Abstract

Background Lupus patients often require aggressive immunosuppressive therapy, which increases the risk for infections. We studied the temporal rates for opportunistic infections (OI) and associated mortality in lupus patients hospitalised in Western Australia. Methods All patients hospitalized in the period 1985–2015 with ≥2 ICD based diagnostic codes for SLE were included. OI was defined as a microbiologically confirmed mycobacterial, fungal, or viral infection. Descriptive data are given as median (IQR) and frequency (%) with incidence rates (IR) calculated per 1000 person years and IR trend rates analysed across 10-year periods by least square regression (R2). Results The study cohort (n = 1408) contained 85.3% females with age at entry 35 years (IQR 22–51). During median follow-up of 21.1 years (IQR 17.5–29.6) hospitalisation for OI occurred in 121 (8.6%) patients with recurrent or multiple OI observed in 42 (34.7%) patients. During 29.771 thousand person years, a total of 295 OI were diagnosed for an overall IR rate of 9.91 (CI 8.82–11.09)/1000 person years which did not decrease significantly over time (R2 0.14). Significant decreases were however seen in the IR for tuberculosis (R2 0.88), cryptococcal (R2 0.98) and pneumocystis (R2 0.98) infections, with increasing IR observed for other mycobacteria (R2 0.99) and aspergillosis (R2 0.55) and little change seen for H Zoster (R2 0.18) and Varicella (R2 0.10) infections. In-hospital death during OI admission occurred in 9/121 patients (7.4%). There was no significant gender difference in IR or outcome of OI. Conclusions Hospitalization rates for OI in lupus patients have not changed significantly over time, but there has been a clear shift in the underlying OI. The decrease in mycobacterial and pneumocystis infections suggest successful prophylaxis but the increase in viral and mycotic infections indicate a sustained need to improve prevention of these OI in lupus patients.

Cancer development in patients hospitalized with systemic lupus erythematosus: A population‐level data linkage study

“…However, after further adjustment for confounding risk factors the increased risk of cancer was attenuated, which aligned with other studies 23,24 . Our lower than expected SLE prevalence in female patients (85.1% vs. >90%) has been seen in other hospitalized SLE cohorts from Argentina (83.3%) 25 and Western Europe (87%), 26 and in another Western Australian population‐level study with a stricter definition of SLE (≥2 ICD‐10 codes at least 30 days apart), 27 and might reflect male patients having higher healthcare resource utilization for SLE‐specific or comorbidity reasons, rather than a selection bias 28 . Thus, the difference between our multivariate adjusted data and the meta‐analyses is explainable by the lack of adjustment for confounding factors like age, sex, environmental triggers, ethnicity, alcohol use, viral inducers, and smoking status in their aggregated results 2 …”

“…Western Australian population-level study with a stricter definition of SLE (≥2 ICD-10 codes at least 30 days apart), 27 and might reflect male patients having higher healthcare resource utilization for SLEspecific or comorbidity reasons, rather than a selection bias. 28 Thus, the difference between our multivariate adjusted data and the metaanalyses is explainable by the lack of adjustment for confounding factors like age, sex, environmental triggers, ethnicity, alcohol use, viral inducers, and smoking status in their aggregated results.…”

Long-term incidence, risk factors and complications for venous thromboembolism in patients with systemic lupus erythematosus

“…8,9 Case selection WARDER contains health data for patients with rheumatic diseases and similar data for a control cohort free of inflammatory rheumatic disease. 10,11 SLE patients were identified among WARDER participants by a first discharge diagnosis code (ICD9-CM: 710.0 or ICD-10-AM: M32.0-9). A single diagnostic discharge code for SLE has an 80%-96% positive predictive value for correctly identifying SLE patients in administrative health data.…”