Equated hospitalization rate of patients with inflammatory rheumatic diseases as compared to normal population in 2°wave SARSCoV2 infection.

OBJECTIVE: To investigate the clinical manifestations and outcome of COVID-19 in patients with inflammatory rheumatic and musculoskeletal disease (iRMD) as compared with the general population. METHODS: This is a case-control study of patients selected from the South-Tyrol public health service-Italy with and without iRMD affected by COVID-19. We included patients ≥18 years and with a positive SARS-CoV-2 PCR test between 1.10.2020 and 01.03.2021. Cases were identified by linking the diagnosis of a rheumatic disease with PCR test positivity; these were matched in a 1:1.8 (planned 1:2) ratio for age, sex, and date of COVID-19 diagnosis with people from the general population. The outcomes of primary interest were hospitalization and severe course (intensive care unit, mechanical ventilation/extracorporeal membrane oxygenation, death). RESULTS: The study population consisted of 561 COVID-19 patients, of which 201 (mean age 60.4 years; 65.2% female) were patients with iRMD and 360 were controls from the general population (59.8 years; 64.7% female). The majority of iRMD patients (88.6%) received an immunosuppressive drug at time of COVID-19 diagnosis, 36.3% were under glucocorticoids. COVID-19 related hospitalization (12.4% vs 10.6%, p= 0.49), severe course (5.0% vs 5.3%, p= 1.00), and mortality (3.5% vs 4.4%, p= 0.66) were similar between groups. Among hospitalized patients, mechanic ventilation was more common in iRMD patients than in controls [n = 5 (20.0%) vs n = 1 (2.6%), p= 0.035]. CONCLUSIONS: Our study indicates similar rates for admission, severe course and mortality between patients with iRMD and controls affected by COVID-19. Among hospitalized patients, mechanical ventilation was more frequently required in the iRMD group.

Deep learning segmentation of Primary Sjögren's syndrome affected salivary glands from ultrasonography images.

Salivary gland ultrasonography (SGUS) has proven to be a promising tool for diagnosing various diseases manifesting with abnormalities in salivary glands (SGs), including primary Sjögren's syndrome (pSS). At present, the major obstacle for establishing SUGS as a standardized tool for pSS diagnosis is its low inter/intra observer reliability. The aim of this study was to address this problem by proposing a robust deep learning-based solution for the automated segmentation of SGUS images. For these purposes, four architectures were considered: a fully convolutional neural network, fully convolutional "DenseNets" (FCN-DenseNet) network, U-Net, and LinkNet. During the course of the study, the growing HarmonicSS cohort included 1184 annotated SGUS images. Accordingly, the algorithms were trained using a transfer learning approach. With regard to the intersection-over-union (IoU), the top-performing FCN-DenseNet (IoU = 0.85) network showed a considerable margin above the inter-observer agreement (IoU = 0.76) and slightly above the intra-observer agreement (IoU = 0.84) between clinical experts. Considering its accuracy and speed (24.5 frames per second), it was concluded that the FCN-DenseNet could have wider applications in clinical practice. Further work on the topic will consider the integration of methods for pSS scoring, with the end goal of establishing SGUS as an effective noninvasive pSS diagnostic tool. To aid this progress, we created inference (frozen models) files for the developed models, and made them publicly available.

Sjögren's Syndrome: The Clinical Spectrum of Male Patients.

BACKGROUND: To compare the clinical, serological and histologic features between male and female patients with Sjögren's syndrome (SS) and explore the potential effect of gender on lymphoma development. METHODS: From a multicenter population (Universities of Udine, Pisa and Athens, Harokopion and Ioannina (UPAHI)) consisting of consecutive SS patients fulfilling the 2016 ACR/EULAR criteria, male patients were identified, matched and compared with female controls. Data-driven multivariable logistic regression analysis was applied to identify independent lymphoma-associated factors. RESULTS: From 1987 consecutive SS patients, 96 males and 192 matched female controls were identified and compared. Males had a higher frequency of lymphoma compared to females (18% vs. 5.2%, OR = 3.89, 95% CI: 1.66 to 8.67; p = 0.0014) and an increased prevalence of serum anti-La/SSB antibodies (50% vs. 34%, OR = 1.953, 95% CI: 1.19 to 3.25; p = 0.0128). No differences were observed in the frequencies of lymphoma predictors between the two genders. Data-driven multivariable logistic regression analysis revealed negative association of the female gender with lymphoma and positive association with lymphadenopathy. CONCLUSION: Male SS patients carry an increased risk of lymphoma development. Although statistics showed no difference in classical lymphoma predictors compared to females, data-driven analysis revealed gender and lymphadenopathy as independent lymphoma-associated features.