Objectives: Nailfold videocapillaroscopy (NVC) is the gold standard for diagnosing SSc and differentiating primary from secondary RP. The CAPI-Score algorithm, designed for simplicity, classifies capillaroscopy scleroderma patterns (CSPs) using a limited number of capillary variables. This study aims to develop a more advanced machine learning (ML) model to improve CSP identification by integrating a broader range of statistical variables while minimizing examiner-related bias. Methods: A total of 1780 capillaroscopies were randomly and blindly analysed by three to four trained observers. Consensus was defined as agreement among all but one observer (partial consensus) or unanimous agreement (full consensus). Capillaroscopies with at least partial consensus were used to train ML-based classification models using CatBoost software, incorporating 24 capillary architecture-related variables extracted via automated NVC analysis. Validation sets were employed to assess model performance. Results: Of the 1490 capillaroscopies classified with consensus, 515 achieved full consensus. The model, evaluated on partial and full consensus datasets, achieved 0.912, 0.812 and 0.746 accuracy for distinguishing SSc from non-SSc, among SSc patterns, and between normal and non-specific patterns, respectively. When evaluated on full consensus only, accuracy improved to 0.910, 0.925 and 0.933. CAPI-Detect outperformed CAPI-Score, revealing novel capillary variables critical to ML-based classification. Conclusions: CAPI-Detect, an ML-based model, provides an unbiased, quantitative analysis of capillary structure, shape, size and density, significantly improving capillaroscopic pattern identification.

Although several biomarkers have shown correlation to prognosis in COVID-19 patients, their clinical value is limited because of lack of specificity, suboptimal sensibility or poor dynamic behavior. We hypothesized that circulating soluble ST2 (sST2) could be associated to a worse outcome in COVID-19. In total, 152 patients admitted for confirmed COVID-19 were included in a prospective non-interventional, observational study. Blood samples were drawn at admission, 48–72 h later and at discharge. sST2 concentrations and routine blood laboratory were analyzed. Primary endpoints were admission at intensive care unit (ICU) and mortality. Median age was 57.5 years [Standard Deviation (SD: 12.8)], 60.4% males. 10% of patients (n = 15) were derived to ICU and/or died during admission. Median (IQR) sST2 serum concentration (ng/mL) rose to 53.1 (30.9) at admission, peaked at 48–72 h (79.5(64)) and returned to admission levels at discharge (44.9[36.7]). A concentration of sST2 above 58.9 ng/mL was identified patients progressing to ICU admission or death. Results remained significant after multivariable analysis. The area under the receiver operating characteristics curve (AUC) of sST2 for endpoints was 0.776 (p = 0.001). In patients admitted for COVID-19 infection, early measurement of sST2 was able to identify patients at risk of severe complications or death.

The European Alliance of Associations for Rheumatology (EULAR) supports the use of nailfold videocapillaroscopy (NVC) to identify disease patterns (DPs) associated with systemic sclerosis (SSc) and Raynaud’s phenomenon (RP). Recently, EULAR proposed an easy-to-manage procedure, a so-called Fast Track algorithm, to differentiate SSc from non-SSc patterns in NVC specimens. However, subjectivity among capillaroscopists remains a limitation. Our aim was to perform a software-based analysis of NVC peculiarities in a cohort of samples from SSc and RP patients and, subsequently, build a Fast Track-inspired algorithm to identify DPs without the constraint of interobserver variability. NVCs were examined by 9 capillaroscopists. Those NVCs whose DPs were consensually agreed (≥2 out of 3 interobservers) were subsequently analysed with an in-house developed software. Each variable’s results were grouped according to the consensually agreed DPs in order to identify useful hallmarks to categorise them. Eight-hundred and fifty-one NVCs (21 957 images) whose DPs had been consensually agreed were software-analysed. Appropriate cut-offs set in capillary density and percentage of abnormal and giant capillaries, tortuosities and hemorrhages allowed DP categorization and the development of the CAPI-Score algorithm. This consisted of 4 rules: Rule 1, SSc vs non-SSc, accuracy 0.88; Rules 2 and 3, SSc-early vs SSc-active vs SSc-late, accuracy 0.82; Rule 4, non-SSc normal vs non-SSc non-specific, accuracy 0.73. Accuracy improved when the analysis was limited to NVCs whose DPs had achieved full consensus among interobservers. The CAPI-Score algorithm may become a useful tool to assign DPs by overcoming the limitations of subjectivity.

Full Papers; Authors: B. Gracia Tello, E. Ramos Ibañez, P. Fanlo Mateo, L. Sáez Cómet, E. Martínez Robles, J.J. Ríos Blanco, B. Marí Alfonso, G. Espinosa Garriga, J. Todolí Parra, N. Ortego Centeno, J.L. Callejas Rubio, M. Freire Dapena, A. Marín Ballvé, A. Selva-O'callaghan, A. Guillén Del Castillo, C.P. Simeón Aznar, V. Fonollosa Pla

To analyze the clinical characteristics and outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients with sarcoidosis from a large multicenter cohort from Southern Europe and to identify the risk factors associated with a more complicated infection. We searched for patients with sarcoidosis presenting with SARS-CoV-2 infection (defined according to the European Centre for Disease Prevention and Control guidelines) among those included in the SarcoGEAS Registry, a nationwide, multicenter registry of patients fulfilling the American Thoracic Society/European Respiratory Society/World Association of Sarcoidosis and Other Granulomatous Disorders 1999 classification criteria for sarcoidosis. A 2:1 age-sex-matched subset of patients with sarcoidosis without SARS-CoV-2 infection was selected as control population. Forty-five patients with SARS-CoV-2 infection were identified (28 women, mean age 55 years). Thirty-six patients presented a symptomatic SARS-CoV-2 infection and 14 were hospitalized (12 required supplemental oxygen, 2 intensive care unit admission and 1 mechanical ventilation). Four patients died due to progressive respiratory failure. Patients who required hospital admission had an older mean age (64.9 vs. 51.0 years, p = 0.006), a higher frequency of baseline comorbidities including cardiovascular disease (64% vs. 23%, p = 0.016), diabetes mellitus (43% vs. 13%, p = 0.049) and chronic liver/kidney diseases (36% vs. 0%, p = 0.002) and presented more frequently fever (79% vs. 35%, p = 0.011) and dyspnea (50% vs. 3%, p = 0.001) in comparison with patients managed at home. Age- and sex-adjusted multivariate analysis identified the age at diagnosis of SARS-Cov-2 infection as the only independent variable associated with hospitalization (adjusted odds ratio 1.18, 95% conficence interval 1.04–1.35). A baseline moderate/severe pulmonary impairment in function tests was associated with a higher rate of hospitalization but the difference was not statistically significant (50% vs. 23%, p = 0.219). A close monitoring of SARS-CoV-2 infection in elderly patients with sarcoidosis, especially in those with baseline cardiopulmonary diseases and chronic liver or renal failure, is recommended. The low frequency of severe pulmonary involvement in patients with sarcoidosis from Southern Europe may explain the weak prognostic role of baseline lung impairment in our study, in contrast to studies from other geographical areas.

Full Papers; Authors: B.C. Gracia Tello, E. Ramos Ibañez, L. Saez Comet, A. Guillén Del Castillo, C.P. Simeón Aznar, A. Selva O'callaghan, G. Espinosa, G. Lledó, M. Freire Dapena, E. Martinez Robles, J.J. Ríos, J.A. Todolí Parra, B. Marí Alfonso, N. Ortego Centeno, A. Marín Ballvé, J.L. Callejas Rubio, V. Fonollosa Plá, P. Fanlo