This study investigated the distribution of disease severity in patients with systemic lupus erythematosus (SLE), explored its impact on health-care resource utilization and costs, and evaluated SLE severity transitions over time.
SLE patients between 2015 and 2017 in Taiwan's National Health Insurance Research Database were retrospectively analyzed, and classified into the mild, moderate, or severe SLE groups. Annual health-care resource utilization, associated costs, lupus flare events, and severity transitions were analyzed.
A total of 20 181 patients with SLE were included, 29.3% of whom had mild, 60.7% had moderate, and 9.9% had severe SLE. Severity was correlated with health-care utilization and related costs, as well as the frequency and severity of flares. Patients with severe and moderate SLE incurred 2.18 and 1.42 times greater annual medical costs than those with mild SLE, respectively. Over the 2-year follow-up, the severe group experienced 13.23 flares, including 1.2 severe flares, significantly higher than those experienced by the moderate (9.38 flares, 0.34 severe flares) and mild (3.61 flares, 0.04 severe flares) SLE groups. Despite consistent severity distribution over 2 years, severity transitions occurred. Approximately 60% of severe patients improved within 1 year, 13% becoming mild. Conversely, fewer mild (2.8%–3.4%) and moderate (4.5%–5.4%) patients progressed to severe status annually.
This study offers insights into the distribution and transition patterns of SLE severity, and the correlation with health-care utilization, costs, and flare severity/frequency within a nationwide cohort. These findings highlight the importance of targeted interventions for controlling disease severity in SLE management.
Autoimmune diseases (ADs) are characterized by a loss of immune tolerance to self-antigens. Maternal ADs are associated with pregnancy complications and are related to various diseases among their offspring. Existing studies have reported that maternal ADs increase the risk of certain types of malignancies in children. We aimed to investigate the association between maternal ADs and childhood cancer in Taiwan.
We established a population-based cohort study using the Taiwan Maternal and Child Health Database, which links parents to their children, and connected it to the Taiwan Cancer Registry to identify cases of childhood cancer. Cox proportional hazard modeling was used to examine the association between maternal ADs and childhood cancer. Further analysis of exposure to overall maternal ADs and incident individual childhood cancer types was performed and stratified by child sex.
From 2004 to 2015, there were a total of 2 385 071 live births in Taiwan; 97 138 were exposed to maternal ADs. Certain types of organ-specific maternal ADs, including gastrointestinal and skin ADs, were weakly associated with overall childhood cancers. Compared to their counterparts, offspring exposed to maternal ADs were at risk of developing CNS tumors and neuroblastoma. Exposed female offspring had a higher risk of medulloblastoma and hepatoblastoma.
Our study implicates the association between maternal ADs and childhood cancer. Future research is required to elucidate the pathogenesis of the increased risk of childhood cancer among exposed offspring.
L. Gupta, K. Ansa Ashari, and G. Harifi, “APLAR Young Rheumatology's Vision for Holistic Professional Development in Rheumatology,” International Journal of Rheumatic Diseases 28, no. 11 (2025): e70454. https://doi.org/10.1111/1756-185x.70454.
An abstract was added to the above article in error. We have removed the abstract in the online article.
We apologize for this error.
Sjögren's syndrome (SS) is a chronic autoimmune disorder characterized by heterogeneous manifestations and often delayed diagnosis due to its nonspecific symptoms and reliance on invasive tests. This review highlights the transformative role of artificial intelligence (AI), particularly machine learning (ML), and deep learning (DL), in enhancing SS diagnostic accuracy across diverse clinical settings. Artificial intelligence–driven models have demonstrated remarkable performance in histopathological analysis, automating the evaluation of salivary gland biopsies and minimizing observer variability. Imaging diagnostics, such as salivary gland ultrasonography and computed tomography (CT), have benefited from DL models that outperform inexperienced radiologists in detecting glandular abnormalities. When paired with ML classifiers, noninvasive approaches using Raman spectroscopy and tongue imaging offer promising alternatives to traditional diagnostics. Moreover, AI applications in genomic and metabolomic profiling have unveiled novel biomarkers and molecular signatures for SS. In primary-care, ML models trained on electronic health records (EHRs) show potential in early case identification and reducing referral delays. These innovations collectively illustrate AI's capability to unify disparate data sources from visual and molecular to clinical and support timely, personalized diagnostics. The inference drawn from this review is that AI integration across multiple diagnostic modalities can bridge existing gaps in SS detection, making diagnosis faster, more objective, and accessible even in resource-limited settings.
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