Disparities in severe infection risk associated with systemic lupus erythematosus: evidence from a causal forest analysis

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2026
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Abstract
Abstract OBJECTIVES Severe infections are a primary cause of morbidity and premature mortality in patients with Systemic Lupus Erythematosus (SLE). Although SLE patients are known to have an elevated average risk of severe infection, little is known about how this excess risk varies by patient characteristics. Population-average estimates may mask important clinical heterogeneity. Identifying factors that place certain patients at disproportionately higher risk is essential for personalized risk assessment. The aim of this study was to quantify the heterogeneity in the excess severe infection rate due to SLE and to identify the key patient-level clinical and demographic risk modifiers that drive this heterogeneity. METHODS We conducted a population-based matched cohort study using health administrative data from British Columbia, Canada (1990–2024). We identified 10,517 incident SLE patients using a validated algorithm and matched them 1:5 to 52,585 non-SLE controls based on birth year, sex, and index year. To measure the heterogeneity of infection risk, we employed Causal Forests to estimate personalized excess infection risk for each SLE patient. Generalized Additive Models (GAMs) were used to characterize the potentially non-linear relationships between patient profiles and the estimated excess risk of severe infection. All inferences, opinions, and conclusions drawn in this research are those of the authors, and do not reflect the opinions or policies of the Data Steward(s). No personal identifying information was made available as part of this study. Procedures used were in compliance with British Columbia’s Freedom of Information and Privacy Protection Act. Ethical approval was obtained from the University of British Columbia’s Behavioral Research Ethics Board (H15-00887). RESULTS Using causal forests, the estimated individual-level excess risk of severe infection among SLE patients averaged 21.12 per 1,000 person-years (SE = 3.14), with substantial variation across individuals (SD = 19.73 per 1,000 person-years). Variable importance analyses highlight baseline number of outpatient visits, number of hospitalizations, Charlson comorbidity index, age, rural residence, hypertension, and income as key effect modifiers. GAMs analyses reveal higher excess risk is associated with prior infection, rural residence, hypertension, cardiovascular medication, congestive heart failure, glucocorticoid use and depression. Higher income and oral contraceptive pill/hormone replacement therapy are associated with lower excess infection risk. CONCLUSION Significant disparities exist in the excess infection risk associated with SLE. Focusing only on population-average risk can obscure vulnerable subgroups with disproportionately high infection rates. Modern causal machine learning methods can support personalized risk stratification, helping clinicians move beyond average risk estimates toward targeted surveillance and prevention for high-risk SLE patients.
Reference Key
openalex_W7168024875 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Kai Zhao, Aosen Cheer, Hui Xie
Journal Lara D. Veeken
Year 2026
DOI
10.1093/rheumatology/keag362
URL
https://doi.org/10.1093/rheumatology/keag362
Keywords Keywords not found

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