David Fiorentino

Objective: To better characterize the clinical phenotypes and heterogeneity of anti‐synthetase syndrome (ASyS), we performed unsupervised cluster analyses using clinical and serological data from the Classification Criteria for Anti‐Synthetase Syndrome (CLASS) project database. Methods: The CLASS database comprised 2,035 ASyS cases and 2,140 comparators with mimicking conditions, namely systemic autoimmune rheumatic diseases and/or interstitial lung disease (ILD), contributed by 103 centers worldwide. We applied multiple correspondence analysis followed by hierarchical clustering using 19 clinical and serological variables to classify ASyS and comparators in the entire CLASS cohort into distinct subgroups. We then repeated the cluster analysis in the ASyS cohort alone to identify phenotypic subgroups of ASyS. Results: Cluster analysis of the entire cohort identified three subgroups. Whereas Cluster 1 (n = 1,311) and Cluster 3 (n = 1,468) predominantly comprised ASyS and comparators, respectively, Cluster 2 (n = 1,105) included both groups. Interstitial pneumonia with autoimmune features (without anti‐synthetase autoantibodies) and systemic sclerosis were more prevalent in Cluster 2, whereas dermatomyositis and polymyositis were predominantly represented in Cluster 3. Cluster analysis of the ASyS cohort identified three distinct subgroups: Cluster 1 (anti‐Jo‐1‐positive, “full‐spectrum” ASyS with myositis, ILD, and arthritis; n = 1,034), Cluster 2 (ILD‐predominant; n = 581), and Cluster 3 (without ILD; n = 248). Conclusion: Cluster analyses identified ASyS as a distinct and heterogeneous clinical entity. Three distinct phenotypic clusters emerged among patients with ASyS, with ILD‐predominant cases representing the second largest subgroup, underscoring the need for classification criteria that adequately capture patients presenting with isolated ILD. image

Primary cutaneous gamma‐delta T‐cell lymphoma (PCGD‐TCL) is a rare cytotoxic lymphoma with key oncogenic drivers in the JAK/STAT pathway. Also primarily involving the subcutaneous adipose tissue, subcutaneous panniculitis‐like T‐cell lymphoma (SPTCL) is more frequently encountered in scenarios of autoimmune disorders. SPTCL shares clinicopathologic overlap with lupus panniculitis. However, the link between autoimmunity and PCGD‐TCL is much less established, particularly in the setting of long‐standing, immunosuppressed dermatomyositis (DM). We report two cases of PCGD‐TCL arising in women with chronic anti‐TIF1‐γ DM following years of immunosuppressive therapy. Case 1 is a 47‐year‐old woman with a 19‐year history of DM on azathioprine/prednisone who developed rapidly progressive, painful subcutaneous nodules. Incisional biopsy confirmed a TCR‐delta+, CD8+ cytotoxic T‐cell lymphoproliferative disorder (TCLPD) compatible with PCGD‐TCL. She achieved complete remission following pralatrexate and subsequent allogeneic hematopoietic stem cell transplant. Case 2 is a 27‐year‐old woman with DM on mycophenolate/rituximab who developed subcutaneous nodules with an indolent course and some spontaneous regression. A biopsy revealed a similar panniculitic infiltrate with an atypical TCR‐delta+, CD8+ phenotype. Notably, both cases were negative for high‐risk JAK/STAT pathway mutations. These cases identify PCGD‐TCLPD/TCL as a potential complication of chronic, immunosuppressed DM. The shared, atypical CD8+ immunophenotype and absence of canonical driver mutations suggest a distinct pathogenic mechanism possibly linked to long‐term immune modulation. Unlike classic PCGD‐TCL, which is characterized by an aggressive course and < 2‐year median survival, the clinical courses in these two cases were variable, with one requiring transplant and the other showing indolent behavior and responsiveness to therapy.

Objective: To evaluate the long-term safety and efficacy of dazukibart, a novel antiinterferon β monoclonal antibody, in adults with moderate-to-severe dermatomyositis (DM). Methods: Participants from 2 cohorts of DM (skin-predominant [SP] and muscle-predominant [MP]) completing a 24-week phase II, double-blind study, were enrolled into a 52-week phase IIb, open-label extension (OLE) study, with a 16-week follow-up post treatment (combined study duration: 92 weeks). The pooled skin analysis set included all SP and MP DM participants with baseline Cutaneous Dermatomyositis Disease Area and Severity Index–Activity (CDASI-A) scores ≥ 14. The primary endpoint was incidence of treatment-emergent adverse events (TEAEs) from week 24 through week 92. Key secondary endpoints were change in CDASI-A and Total Improvement Score (TIS) from week 0 through week 92. Results: Participants who received ≥ 1 dose of dazukibart (600 mg) during the OLE study (comprising 9, 15, and 16 participants from the SP cohort, MP cohort, and pooled skin analysis set, respectively) were analyzed (median age 49.5 years, 83% female, 96% White). TEAEs were mild (55%), moderate (38%), or severe (7%), with no treatment-related serious AEs or treatment discontinuations. At week 76, CDASI-A scores improved, with a median change from baseline of −24.0 (range −33 to −12), −9.0 (range −32 to 1), and −23.5 (range −33 to −12) for participants from the SP cohort, MP cohort, and pooled skin analysis set, respectively. In the MP cohort, TIS (median 65.0, range 2.5-95.0) and associated core set measures, including patient-reported outcomes, improved at week 76. Overall, the observed improvements in outcomes, as indicated by the slight reduction in outcome measures, persisted during the 16-week posttreatment follow-up. Conclusion: The safety and efficacy profiles of dazukibart observed at 24 weeks were sustained through 92 weeks. ( ClinicalTrials.gov : NCT03181893 , NCT05192200 )

Background: Lung ultrasound (LUS) is a sensitive, low‐cost, and radiation‐free modality for ILD detection. We previously developed and validated LUS interpretation criteria in systemic sclerosis (SSc) and idiopathic inflammatory myopathy (IIM) showing excellent diagnostic performance and correlations with ILD severity. In this study, we applied deep learning to evaluate whether convolutional neural networks (CNNs) can accurately detect ILD and its severity on LUS. Methods: Patients with SSc or IIM ± ILD, and paired LUS and chest computed tomography (CT) were included. LUS images were labeled using CT results and human LUS‐ILD‐24 interpretation. Three pretrained CNN architectures (InceptionV3, ResNet‐50, VGG‐16) were fine‐tuned via transfer learning, and a de novo lightweight architecture (LUS‐Net) was developed. Model performance for ILD detection was assessed at image and patient‐levels using AUC, sensitivity, specificity, and agreement with expert interpretation. CNN outputs were correlated with pulmonary function tests (PFTs) and CT‐based CALIPER indices. Grad‐CAM visualized regions driving predictions. Results: A total of 140 patients representing 3,920 LUS images were included and split into development (74) and independent test sets (66). VGG‐16 achieved the best patient‐level performance (AUC = 0.972; sensitivity = 97.4%; specificity = 92.6%) showing strong correlations with PFTs and CT severity. Grad‐CAM highlighted pleural features as the primary regions influencing model predictions. CNN performance matched or exceeded LUS‐ILD‐24 interpretation. Conclusion: Deep learning applied to LUS enables accurate ILD detection in connective tissue disease and can enhance expert interpretation. Explainable AI suggests pleural features, even when B‐lines are infrequent, are sufficient for reliable ILD recognition.

Objective: Studying rare diseases like dermatomyositis (DM) in single‐center cohorts is challenging due to small sample sizes and limited generalizability. This study develops and evaluates case identification algorithms for DM to enable coordinated analysis across multiple data sources. Methods: Case identification algorithms were developed to identify adult patients with DM within 11 independent electronic health record or claims databases, totaling over 800 million patients, using the Observational Medical Outcomes Partnership Common Data Model. Algorithm performance was assessed through manual chart review and using Observational Health Data Sciences and Informatics open‐source tools (CohortDiagnostics and PheValuator), which quantify incidence rates and performance metrics such as sensitivity and positive predictive value (PPV). Results: Eight DM case identification algorithms were evaluated across 11 databases, revealing significant variability in performance, with sensitivity and PPV differing by more than 30% between some databases. Overall, we identified one incidence algorithm and one prevalence algorithm with good performance, demonstrated by sensitivity rates of 42% and 49% and PPV values of 83% and 84%, respectively. PheValuator quantified algorithm performance within each database, allowing for direct comparison of different criteria. Additionally, CohortDiagnostics generated incidence rates stratified by age decile and sex, aligning with previous epidemiologic data. Conclusion: We developed and validated multiple DM case identification algorithms across diverse databases, demonstrating their accuracy through multiple evaluation methods. This approach enables more generalizable, reproducible research using real‐world data and can be applied to other rheumatic diseases.

Objective: Interstitial lung disease (ILD) has a high prevalence in patients with systemic sclerosis (SSc) and inflammatory myopathy (IM), and early identification reduces associated morbidity and mortality. We previously developed lung ultrasound (LUS) interpretation criteria for ILD detection in 2020 (LUS‐ILD‐20) showing excellent sensitivity and specificity in patients with SSc‐ILD; herein, we sought to validate a revised LUS‐ILD‐24 in a large cohort with SSc and IM. Methods: Patients meeting criteria for SSc and IM, with planned computed tomography (CT) chest imaging underwent LUS imaging interpreted with LUS‐ILD‐24 by three blinded readers. The sensitivity and specificity for LUS‐ILD detection as noted on CT was analyzed for subgroups with SSc, IM, and possible incident ILD. Inter‐ and intrarater agreements were calculated. Correlations between LUS‐ILD‐24 severity, CT imaging severity, and pulmonary function tests were assessed. Results: Ninety‐five patients were included in the analyses. Sensitivity and specificity for ILD detection ranged from 92.4% to 95.5% and 82.8% to 86.2% across readers with similar accuracy in all subgroups. Inter‐ and intrareader reliability showed near perfect agreement (κ = 0.92 and κ = 0.90 to 1, respectively). LUS severity correlated with CT imaging severity and inversely correlated with diffusion capacity for carbon monoxide and forced vital capacity. Conclusion: We validated our revised LUS‐ILD‐24 in cohorts with SSc and IM and found excellent sensitivity, specificity, and reliability for detection of ILD identified on CT imaging. LUS severity correlated with CT and pulmonary function test markers of ILD severity. Validation of the revised LUS‐ILD‐24 supports the implementation of LUS imaging in screening algorithms for ILD in patients with SSc and IM.

Objective: Anti‐synthetase syndrome (ASSD) is a rare systemic autoimmune rheumatic disease (SARD) with significant heterogeneity and no shared classification criteria. We aimed to identify clinical and serological features associated with ASSD that may be suitable for inclusion in the data‐driven classification criteria for ASSD.Methods: We used a large, international, multicenter “Classification Criteria for Anti‐synthetase Syndrome” (CLASS) project database, which includes both patients with ASSD and controls with mimicking conditions, namely, SARDs and/or interstitial lung disease (ILD). The local diagnoses of ASSD and controls were confirmed by project team members. We employed univariable logistic regression and multivariable Ridge regression to evaluate clinical and serological features associated with an ASSD diagnosis in a randomly selected subset of the cohort.Results: Our analysis included 948 patients with ASSD and 1,077 controls. Joint, muscle, lung, skin, and cardiac involvement were more prevalent in patients with ASSD than in controls. Specific variables associated with ASSD included arthritis, diffuse myalgia, muscle weakness, muscle enzyme elevation, ILD, mechanic's hands, secondary pulmonary hypertension due to ILD, Raynaud phenomenon, and unexplained fever. In terms of serological variables, Jo‐1 and non–Jo‐1 anti‐synthetase autoantibodies, antinuclear antibodies with cytoplasmic pattern, and anti‐Ro52 autoantibodies were associated with ASSD. In contrast, isolated arthralgia, dysphagia, electromyography/magnetic resonance imaging/muscle biopsy findings suggestive of myopathy, inflammatory rashes, myocarditis, and pulmonary arterial hypertension did not differentiate between patients with ASSD and controls or were inversely associated with ASSD.Conclusion: We identified key clinical and serological variables associated with ASSD, which will help clinicians and offer insights into the development of data‐driven classification criteria for ASSD. image