OBJECTIVE: To develop evidence-based recommendations for the non-pharmacological and pharmacological management of Raynaud\'s phenomenon (RP) and digital ulcers (DUs) in patients with systemic sclerosis and other immune-mediated connective tissue diseases (CTDs).
METHODS: A task force comprising 21 rheumatologists, two surgeons (vascular and plastic), two nurses, and one patient representative was established. Following a systematic literature review performed to inform the recommendations, statements were formulated and discussed during two meetings (one online and one in-person). Levels of evidence, grades of recommendation (GoR), and level of agreement (LoA) were determined.
RESULTS: Five overarching principles and 13 recommendations were developed. GoR ranged from A to D. The mean ± standard difference (SD) LoA with the overarching principles and recommendations ranged from 7.8±2.1 to 9.8±0.4. Briefly, the management of RP and DUs in patients with CTDs should be coordinated by a multidisciplinary team and based on shared decisions with patients. Nifedipine should be used as first-line therapy for RP and/or DUs. Sildenafil, tadalafil, and/or iloprost IV are second-line options for severe and/or refractory patients with RP and/or DUs. Sildenafil, tadalafil and/or Iloprost IV, should be prescribed for healing and prevention (also including bosentan) of DUs. In patients with RP and/or DUs, non-pharmacological interventions might be considered as add-ons, but there is limited quality and quantity of scientific evidence supporting their use.
CONCLUSIONS: These recommendations will inform rheumatologists, specialist nurses, other healthcare professionals, and patients about a comprehensive and personalized management of RP and DUs. A research agenda was developed to address unmet needs, particularly for non-pharmacologic interventions.

Objective To observe the expression of anti-β2 glycoprotein I (β2GPI) autoantibody in connective tissue diseases and its relationship with the degree of inflammation and immune function. Methods Patients with broad connective tissue diseases including connective tissue disease (CTD), rheumatoid arthritis (RA), Sjogren\'s syndrome (SS), and systemic lupus erythematosus (SLE) were observed. β2GPI was quantified by chemiluminescence, ESR was measured by Weil\'s method, and C-reactive protein (CRP), rheumatoid factor (RF), anti-cyclic citrullinated polypeptide (CCP) antibody were measured by automatic biochemical analyzer. Results β2GPI and their subtypes were significantly higher in RA patients compared with CTD, SS, and SLE patients. CRP was positively associated with anti-β2GPI antibody and anti-β2GPI antibody IgM in patients with connective tissue disease. ESR was positively associated with anti-β2GPI antibody. Anti-β2GPI antibody and anti-β2GPI antibody IgM were elevated in the abnormal CRP group compared with the normal CRP group. Compared with the ESR normal group, anti-β2GPI antibody and anti-β2GPI antibody IgG were elevated in the ESR abnormal group. Anti-β2GPI antibody was positively correlated with ESR and anti-CCP antibody in RA patients. Anti-β2GPI antibody IgG was positively correlated with RF. Conclusion β2GPI can be used as a predictor of the degree of inflammation and assessment of immune disorders in CTD.

UNASSIGNED: Clinical guidance on the identification and management of connective tissue disease-associated interstitial lung disease (CTD-ILD) is needed for optimal clinical practice. We aimed to develop clinical algorithms for identifying and managing three common CTD-ILDs: those associated with systemic sclerosis (SSc-ILD), rheumatoid arthritis (RA-ILD), and polymyositis/dermatomyositis (PM/DM-ILD).
UNASSIGNED: Meetings were held October - November 2023 to create consensus-based algorithms for identifying and managing SSc-ILD, RA-ILD, and PM/DM-ILD in clinical practice, based on expert consensus statements for identification and management of CTD-ILD previously derived from a Delphi process.
UNASSIGNED: We developed clinical algorithms for SSc-ILD, RA-ILD, and PM/DM-ILD that highlight both commonalities and differences in the identification and management of these CTD-ILDs. Importantly, ILD should be suspected in patients with SSc, RA, or PM/DM who have respiratory symptoms. Chest high-resolution computed tomography has utility for screening, diagnosis and assessment of severity. Furthermore, regular follow-up and multidisciplinary management are important. Disease-specific considerations include unique risk factors such as anti-topoisomerase I antibodies in SSc-ILD, high-titer cyclic citrullinated peptide antibodies in RA, anti-aminoacyl tRNA synthetase antibodies in PM/DM, and anti-melanoma differentiation-associated gene 5 antibody in DM.
UNASSIGNED: These algorithms may help physicians to identify and manage patients with SSc-ILD, RA-ILD, or PM/DM-ILD.

Interstitial lung disease (ILD) complicates connective tissue disease (CTD) with variable incidence and is a leading cause of death in these patients. To improve CTD-ILD outcomes, early recognition and management of ILD is critical. Blood-based and radiologic biomarkers that assist in the diagnosis CTD-ILD have long been studied. Recent studies, including -omic investigations, have also begun to identify biomarkers that may help prognosticate such patients. This review provides an overview of clinically relevant biomarkers in patients with CTD-ILD, highlighting recent advances to assist in the diagnosis and prognostication of CTD-ILD.

Connective tissue disease associated interstitial lung disease (CTD-ILD) is a heterogenous collection of conditions with a diverse spectrum of interstitial lung disease (ILD) manifestations. Currently, clinical practice of lung-directed immunosuppression in CTD-ILD is supported by several randomized, placebo-controlled trials (RCTs) in patients with scleroderma and several observational, retrospective studies in other autoimmune conditions. However, given the harm of immunosuppression in idiopathic pulmonary fibrosis, there is an urgent need for RCTs of immunosuppression and antifibrotic agents in fibrotic CTD-ILD populations as well as the study of intervention in patients with subclinical CTD-ILD.

The majority of connective tissue diseases (CTDs) are multisystem disorders that are often heterogeneous in their presentation and do not have a single laboratory, histologic, or radiologic feature that is defined as the gold standard to support a specific diagnosis. Given this challenging situation, the diagnosis of CTD is a process that requires the synthesis of multidisciplinary data which may include patient clinical symptoms, serologic evaluation, laboratory testing, and imaging. Pulmonary manifestations of connective tissue disease include interstitial lung disease as well as multicompartmental manifestations. These CT imaging patterns and features of specific diseases will be discussed in this article.

Pulmonary hypertension (PH), a syndrome characterized by elevated pulmonary pressures, commonly complicates connective tissue disease (CTD) and is associated with increased morbidity and mortality. The incidence of PH varies widely between CTDs; patients with systemic sclerosis are most likely to develop PH. Several different types of PH can present in CTD, including PH related to left heart disease and respiratory disease. Importantly, CTD patients are at risk for developing pulmonary arterial hypertension, a rare form of PH that is associated with high morbidity and mortality. Future therapies targeting pulmonary vascular remodeling may improve outcomes for patients with this devastating disease.

BACKGROUND: There is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients.
METHODS: This was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model.
RESULTS: The included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-β-D-glucan test (G test).
CONCLUSIONS: We successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.

BACKGROUND: Characteristics, practice patterns, and clinical outcomes of secondary spontaneous pneumothorax (SSP) with heritable connective tissue disorders (Marfan, Ehlers-Danlos, and Birt-Hogg-Dubé syndromes) are unclear.
METHODS: A nationwide Japanese inpatient database that included data of 524 patients with SSP (884 hospitalizations) and 137,821 with primary spontaneous pneumothorax between July 2010 and March 2020 was used in this study. Hospitalization for SSP (n = 884) was categorized into surgical (n = 459) and nonsurgical (n = 425) groups, and the patient characteristics, treatment, and outcomes were compared between the groups. Multivariable analyses were performed to evaluate risk factors for pneumothorax recurrence. We also compared the characteristics of patients with different underlying heritable connective tissue disorders.
RESULTS: Compared with the nonsurgical group, the surgical group had less frequent readmission for pneumothorax (26% vs 44%; hazard ratio, 0.47; 95% CI, 0.38-0.58). Young patients (2.46; 1.83-3.32) or those with Birt-Hogg-Dubé syndrome (2.53; 1.77-3.63) had a high risk of recurrence. Pneumothorax occurred frequently in teenagers with Marfan syndrome, in those aged 20 to 39 years with Ehlers-Danlos syndrome, and in those aged ≥40 years with Birt-Hogg-Dubé syndrome.
CONCLUSIONS: Detailed information on the characteristics and clinical course of SSP in heritable connective tissue disorders will aid in the clinical decision-making process.

Calcinosis cutis is a condition that is commonly associated with autoimmune connective tissue diseases. It is characterized by the deposition of insoluble calcium salts in the skin and subcutaneous tissue, which can cause pain, impair function, and have significant impacts on quality of life. Calcinosis cutis is difficult to manage because there is no generally accepted treatment: evidence supporting treatments is mostly comprised of case reports and case series, sometimes yielding mixed findings. Both pharmacologic and procedural interventions have been proposed to improve calcinosis cutis, and each may be suited to different clinical scenarios. This review summarizes current treatment options for calcinosis cutis, with discussion of recommendations based on patient-specific factors and disease severity.