Rheumatology Advances in Practice最新文献

{"title":"Increased risk of acute myocardial infarction in Swedish patients with systemic sclerosis: a population-based study.","authors":"Majd Bairkdar, Karina Patasova, Pontus Andell, Marie Holmqvist","doi":"10.1093/rap/rkaf054","DOIUrl":"10.1093/rap/rkaf054","url":null,"abstract":"<p><strong>Objectives: </strong>To study the risk of acute myocardial infarction (AMI) in patients with SSc in a population-based cohort.</p><p><strong>Methods: </strong>Using nationwide Swedish registers, we identified patients with incident SSc 2004-19 and age- and sex-matched comparators from the general population (1:10). Our primary outcome was incident AMI or death from incident AMI. We started follow-up from SSc diagnosis until the primary outcome, death from other cause than AMI, emigration or study end (31 December 2019). We estimated crude AMI incidence rate. We used flexible parametric models to explore the relative risk of AMI over time since diagnosis. We also used age as time scale to explore how AMI risk changes over increasing age. We also studied the outcomes of AMI in SSc compared with the matched comparators.</p><p><strong>Results: </strong>We identified 1579 patients and 16 064 comparators. The incidence rate of AMI was 75.2 (95% CI 58.8-94.6) per 10 000 person-years in patients with SSc and 37.5 (95% CI 34.0-41.3) in the comparators, median follow-up was 5.2 and 6.3 years, respectively. The adjusted hazard ratio (HR) was highest during the first year after SSc diagnosis (HR 3.1, 95% CI 2.0-4.6). In patients with SSc, the risk of AMI increased more rapidly with increasing age compared with the comparators. AMI in SSc was associated with higher risk of mortality (HR 2.7, 95% CI 1.6-4.4) but not 30-day readmission (HR 1.3, 95% CI 0.7-2.0) compared with the comparators.</p><p><strong>Conclusion: </strong>In line with previous studies, SSc is associated with a 2-fold increase in AMI incidence compared with the general population.</p>","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkaf054"},"PeriodicalIF":2.1,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12106857/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144161821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anti-PCNA antibodies in psoriatic arthritis: a case-control study.","authors":"Rebecca Fitzpatrick, Jemima Albayda, Qingyuan Yang, Ning Meng, Livia Casciola-Rosen, Ana-Maria Orbai","doi":"10.1093/rap/rkaf049","DOIUrl":"10.1093/rap/rkaf049","url":null,"abstract":"<p><strong>Objectives: </strong>Psoritic arthritis (PsA) is an inflammatory arthritis with infrequent antibodies. We present a case-control study of six PsA patients with antibodies against PCNA and observed clinical associations.</p><p><strong>Methods: </strong>We conducted a study to identify autoantibodies in PsA. Sera from 81 PsA patients were assayed by immunoprecipitation from radiolabelled cell extracts. From this study, serum that immunoprecipitated a ≈32-kDa band was selected for mass spectrometry-based antibody discovery, which identified anti-PCNA antibodies. These were validated using immunoprecipitation of ³⁵S-methionine-labelled PCNA from <i>in vitro</i> transcription and translation. This assay tested 222 PsA sera for anti-PCNA antibodies. This study included sera from 39 healthy controls. Descriptive statistics were used to assess clinical associations with anti-PCNA status.</p><p><strong>Results: </strong>Anti-PCNA antibodies were identified in 6/222 PsA patients. In 4/6 patients, banked longitudinal sera were available. In patients with anti-PCNA positivity compared with negativity, the mean age was 46 years (s.d. 14) and 52 years (s.d. 13), PsA disease duration was 8 years (s.d. 7) and 7 years (s.d. 8), BMI was 27 (s.d. 4) and 31 (s.d. 7), 67% and 44% were male, 100% and 89% were White, mean clinical DAPSA was 16 (s.d. 22) and 21 (s.d. 16), psoriasis body surface area was 2% (s.d. 4) and 5% (s.d. 12), HLA-B27 positivity was 0 and 12% and sulfasalazine (SSZ) use was 83% and 10%, respectively. Of these, only SSZ use was significant (Fisher's exact test <i>P</i>-value <0.001). Among anti-PCNA-positive patients with longitudinal sera, low/absent anti-PCNA coincided with articular low disease activity/remission.</p><p><strong>Conclusion: </strong>Anti-PCNA antibodies were found in 6/222 (2.7%) PsA patients. Given the low prevalence of anti-PCNA, additional studies are needed to identify the clinical associations and significance.</p>","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkaf049"},"PeriodicalIF":2.1,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12103914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oral ulcer in systemic lupus erythematosus: a case of methotrexate-induced Epstein-Barr virus infection.","authors":"Nabihah Malik, Maymunah Malik, Shuting Guan, Sahena Haque, Richard Porter","doi":"10.1093/rap/rkaf042","DOIUrl":"10.1093/rap/rkaf042","url":null,"abstract":"","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkaf042"},"PeriodicalIF":2.1,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144111440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug induced subacute cutaneous lupus.","authors":"Richard Porter, Jennifer Bendelow, Pippa Watson, Christos Tsekos, Janice Ferguson","doi":"10.1093/rap/rkaf045","DOIUrl":"10.1093/rap/rkaf045","url":null,"abstract":"","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkaf045"},"PeriodicalIF":2.1,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12069228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving efficiency of a rheumatology outpatient clinic through classification of newly referred patients.","authors":"Sander Dijkstra, Yaël A de Man, Aleida Braaksma, Maurits de Graaf, Hein J Bernelot Moens, Richard J Boucherie","doi":"10.1093/rap/rkae155","DOIUrl":"10.1093/rap/rkae155","url":null,"abstract":"<p><strong>Objectives: </strong>Diagnosing a rheumatological disease in patients newly referred by their general practitioner requires assessment by a rheumatologist and often diagnostic tests. Ideally, these tests are performed prior to the patient's first consultation with the rheumatologist, aiming for quick diagnosis and fewer visits. We retrospectively studied whether a pre-first visit digital patient questionnaire can lead to fewer consultations and faster diagnosis.</p><p><strong>Methods: </strong>We applied machine learning-based binary classification algorithms to questionnaire data of newly referred patients to classify a patient's diagnostic class (inflammatory vs. non-inflammatory). Afterwards, we quantified the rheumatology clinic's benefit when all patients classified to be non-inflammatory are planned for specific diagnostic tests at their first visit.</p><p><strong>Results: </strong>Classification for inflammatory vs. non-inflammatory disease could be done with accuracy of 0.771, sensitivity (recall) of 0.809 and precision of 0.833. When non-inflammatory classified patients receive a blood test before first consultation, on average 0.1 in-person consultations and 0.26 teleconsultations per patient are avoided at the cost of having overdiagnostics for 44% of all newly referred patients. If, in addition to a blood test, the first consultation is also preceded by a radiology examination, on average 0.21 in-person consultations and 0.49 teleconsultations per patient are avoided, at the cost of having overdiagnostics (e.g. only the blood test or the combination of the blood test and radiology examination) for 71% of all newly referred patients.</p><p><strong>Conclusion: </strong>Classification algorithms based on pre-first visit patient questionnaires may shorten the patient journey in a rheumatology outpatient clinic and may therefore improve efficiency.</p>","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkae155"},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007594/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144045617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI for imaging evaluation in rheumatology: applications of radiomics and computer vision-current status, future prospects and potential challenges.","authors":"Lina Xu, Keno Bressem, Lisa Adams, Denis Poddubnyy, Fabian Proft","doi":"10.1093/rap/rkae147","DOIUrl":"10.1093/rap/rkae147","url":null,"abstract":"<p><p>Inflammatory rheumatic diseases, a diverse group of immune-mediated conditions, are characterized by chronic inflammation that can lead to irreversible damage to joints, bones and organs, posing a significant global health challenge. If left untreated, these conditions can severely deteriorate patients' quality of life, underscoring the importance of timely and accurate diagnosis and appropriate management. Artificial intelligence (AI), including radiomics and computer vision, presents promising advancements in improving the early diagnosis and monitoring of these diseases through the analysis of various imaging modalities such as X-rays, CT scans, MRIs and ultrasounds. This review examines the current state of AI applications in the imaging analysis of inflammatory rheumatic diseases, including RA, SpA, SS, SSc and GCA. AI has demonstrated encouraging results, achieving high sensitivity, specificity and accuracy, often on par with or exceeding expert performance. The review also highlights future opportunities for improving the diagnosis and management of rheumatic diseases, as well as the challenges associated with their clinical implementation.</p>","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkae147"},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144044304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disease burden in inflammatory arthritis: an unsupervised machine learning approach of the COVAD-2 e-survey dataset.","authors":"Vincenzo Venerito, Sergio Del Vescovo, Sergio Prieto-González, Marco Fornaro, Lorenzo Cavagna, Florenzo Iannone, Masataka Kuwana, Vishwesh Agarwal, Jessica Day, Mrudula Joshi, Sreoshy Saha, Kshitij Jagtap, Wanruchada Katchamart, Phonpen Akarawatcharangura Goo, Binit Vaidya, Tsvetelina Velikova, Parikshit Sen, Samuel Katsuyuki Shinjo, Ai Lyn Tan, Nelly Ziade, Marcin Milchert, Abraham Edgar Gracia-Ramos, Carlo V Caballero-Uribe, Hector Chinoy, Latika Gupta, Vikas Agarwal","doi":"10.1093/rap/rkaf031","DOIUrl":"10.1093/rap/rkaf031","url":null,"abstract":"<p><strong>Objectives: </strong>To comprehensively compare the disease burden among patients with RA, PsA and AS using Patient-Reported Outcome Measurement Information System (PROMIS) scores and to identify distinct patient clusters based on comorbidity profiles and PROMIS outcomes.</p><p><strong>Methods: </strong>Data from the global COVID-19 Vaccination in Autoimmune Diseases (COVAD) 2 e-survey were analysed. Patients with RA, PsA or AS undergoing treatment with DMARDs were included. PROMIS scores (global physical health, global mental health, fatigue 4a and physical function short form 10a), comorbidities and other variables were compared among the three groups, stratified by disease activity status. Unsupervised hierarchical clustering with eXtreme Gradient Boosting feature importance analysis was performed to identify patient subgroups based on comorbidity profiles and PROMIS outcomes.</p><p><strong>Results: </strong>The study included 2561 patients (1907 RA, 311 PsA, 343 AS). After adjusting for demographic factors, no significant differences in PROMIS scores were observed among the three groups, regardless of disease activity status. Clustering analysis identified four distinct patient groups: low burden, comorbid PsA/AS, low burden with depression and high-burden RA. Feature importance analysis revealed PROMIS global physical health as the strongest determinant of cluster assignment, followed by depression and diagnosis. The comorbid PsA/AS and high-burden RA clusters showed a higher prevalence of comorbidities (56.47% and 69.7%, respectively) and depression (41.18% and 41.67%, respectively), along with poorer PROMIS outcomes.</p><p><strong>Conclusion: </strong>Disease burden in inflammatory arthritis is determined by a complex interplay of factors, with physical health status and depression playing crucial roles. The identification of distinct patient clusters suggests the need for a paradigm shift towards more integrated care approaches that equally emphasize physical and mental health, regardless of the underlying diagnosis.</p>","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkaf031"},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144036755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ethical challenges and regulatory pathways for artificial intelligence in rheumatology.","authors":"Vincenzo Venerito, Latika Gupta, Saverio Mileto, Florenzo Iannone, Emre Bilgin","doi":"10.1093/rap/rkaf035","DOIUrl":"https://doi.org/10.1093/rap/rkaf035","url":null,"abstract":"","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkaf035"},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143996348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting EQ-5D full health state in systemic lupus erythematosus using machine learning algorithms.","authors":"João Botto, Nursen Cetrez, Dionysis Nikolopoulos, Malin Regardt, Emelie Heintz, Julius Lindblom, Ioannis Parodis","doi":"10.1093/rap/rkaf032","DOIUrl":"10.1093/rap/rkaf032","url":null,"abstract":"<p><strong>Objectives: </strong>To determine factors associated with reports of EuroQol 5-Dimensions (EQ-5D) full health state (FHS) before and after a trial intervention in patients with SLE, resorting to machine learning algorithms.</p><p><strong>Methods: </strong>We conducted a post hoc analysis of two phase 3 clinical trials of belimumab (BLISS-52, BLISS-76). Demographic, laboratory and clinical features were retrieved and the Monte Carlo Feature Selection algorithm was employed, then further refined upon consideration of collinearity and clinical relevance. We used support vector machine with radial basis function kernel (SVMRadial), least absolute shrinkage and selection operator (LASSO), neural network (NNet) and logistic regression (LR) to capture both linear and non-linear relationships while ensuring interpretability and robustness.</p><p><strong>Results: </strong>Among 1642 SLE patients, 12.9% reported FHS at baseline and 23.1% at week 52. Selected features were age, sex, Asian ancestry, baseline cSLEDAI-2K, SELENA-SLEDAI PGA, and urine protein:creatinine ratio (UPCR) and baseline EQ-5D 3-Levels (EQ-5D-3L) index score (week 52 models only). The models predicting FHS demonstrated comparable performance at baseline and week 52. A maximum area under the curve of 0.73 was seen for the baseline LASSO and LR models and a maximum of 0.77 for the week 52 LASSO and NNet models. Negative predictive values were high for all models (0.88-0.94). Calibration showed marginal improvement in week 52 models.</p><p><strong>Conclusion: </strong>Machine learning identified older age, female sex, non-Asian ancestry, high disease activity and low UPCR to be associated with a lack of FHS experience in SLE patients at baseline and week 52. High baseline EQ-5D-3L index scores constituted the strongest predictor of FHS at week 52.</p><p><strong>Trial registration: </strong>The BLISS-52 and BLISS-76 trials are registered at www.clinicaltrials.gov (NCT00424476 and NCT00410384, respectively).</p>","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkaf032"},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning <i>vs</i> human experts: sacroiliitis analysis from the RAPID-axSpA and C-OPTIMISE phase 3 axSpA trials.","authors":"Fabian Proft, Janis L Vahldiek, Joeri Nicolaes, Rachel Tham, Bengt Hoepken, Baran Ufuktepe, Denis Poddubnyy, Keno K Bressem","doi":"10.1093/rap/rkae118","DOIUrl":"https://doi.org/10.1093/rap/rkae118","url":null,"abstract":"<p><strong>Objective: </strong>Diagnosis of axial spondyloarthritis (axSpA) is primarily established through the identification of the presence or absence of radiographic sacroiliitis. However, the reliability of conventional radiographs (X-rays) is undermined by significant interreader variability. A machine learning tool could reduce diagnosis time, thereby minimising interreader variability. The present study aimed to evaluate the performance of a deep learning model for detecting radiographic sacroiliitis in axSpA patients from the RAPID-axSpA (NCT01087762) and C-OPTIMISE (NCT02505542) trials.</p><p><strong>Methods: </strong>Radiographs from the RAPID-axSpA and C-OPTIMISE cohorts were retrospectively used. The deep learning model was previously trained by using a transfer learning approach on non-medical data. The model's agreement with expert readers was tested on baseline X-rays using central reader data. Sensitivity, specificity, Cohen's κ, positive and negative predictive values and the area under the receiver operating characteristics curve were calculated.</p><p><strong>Results: </strong>The model's performance was evaluated in the RAPID-axSpA (<i>n</i> = 277) and C-OPTIMISE (<i>n</i> = 739) cohorts. In RAPID-axSpA, the model achieved 82% sensitivity, 81% specificity and a Cohen's κ of 0.61, closely matching central reader performance. In C-OPTIMISE, the model demonstrated 90% sensitivity, 56% specificity and a Cohen's κ of 0.48. The agreement between the model and central readers was 82% (RAPID-axSpA) and 75% (C-OPTIMISE).</p><p><strong>Conclusions: </strong>The tested deep learning model exhibited accurate radiographic sacroiliitis detection in axSpA patients from diverse clinical trials. The proposed deep learning model could expedite diagnosis, reduce healthcare resource usage and improve patient care pathways.</p>","PeriodicalId":21350,"journal":{"name":"Rheumatology Advances in Practice","volume":"9 2","pages":"rkae118"},"PeriodicalIF":2.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12007599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}