Anti-melanoma differentiation-associated protein 5 (MDA5)-positive rapidly progressive interstitial lung disease (RP-ILD) is associated with poor prognosis, and the most effective therapeutic intervention has not been established. Herein we report a case of a 45-year-old female patient who presented with myalgia, Gottron’s papules with ulceration, and dyspnea on exertion which became aggravated within weeks. Laboratory examination and electromyography confirmed myopathy changes, and a survey of myositis-specific antibodies was strongly positive for anti-MDA5 antibody. High-resolution chest tomography suggested organizing pneumonia with rapidly progressive changes within the first month after diagnosis of the disease. Anti-MDA5-associated dermatomyositis with RP-ILD was diagnosed. Following combination therapy with rituximab, tofacitinib and pirfenidone, clinical symptoms, including cutaneous manifestation, respiratory conditions and radiographic changes, showed significant and sustainable improvement. To our knowledge, this is the first reported case of anti-MDA5-associated dermatomyositis with RP-ILD successfully treated with the combination of rituximab, tofacitinib, and pirfenidone.

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (κ) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

Objective: The objective of this study was to assess the incidence and risk factors of major adverse cardiovascular events (MACEs) in patients with systemic sclerosis (SSc). Methods: We conducted a nationwide, population-based, cohort study using Taiwan’s National Health Insurance Research Database. We performed propensity score matching (PSM) using a 1:2 ratio, resulting in inclusion of 1379 patients with SSc and 2758 non-SSc individuals in the analysis. We assessed the association between SSc and MACEs, using the multivariable Cox proportional hazard regression model with adjustment of time-dependent covariates, and investigated risk factors for MACEs in patients with SSc, shown as adjusted hazard ratios (aHRs) with 95% CIs. Results: SSc was not significantly associated with the risk of MACEs (aHR 1.04; 95% CI 0.77–1.42). Nevertheless, SSc was associated with increased risk of myocardial infarction [incidence rate ratio (IRR) 1.76; 95% CI 1.08–2.86] and peripheral arterial occlusion disease (IRR 3.67; 95% CI 2.84–4.74) but not of ischaemic stroke (IRR 0.89; 95% CI 0.61–1.29). Factors independently associated with MACEs in SSc patients included age (aHR 1.02), male gender (aHR 2.01), living in a suburban area (aHR 2.09), living in a rural area (aHR 3.00), valvular heart disease (aHR 4.26), RA (aHR 2.14), use of clopidogrel (aHR 26.65), and use of aspirin (aHR 5.31). Conclusions: The risk of MACEs was not significantly increased in Taiwanese patients with SSc, and our investigation effectively identified the factors independently associated with MACEs in SSc patients. Additionally, patients with SSc exhibited higher risks of myocardial infarction and peripheral arterial occlusion disease but not of ischaemic stroke.