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Objective. Mean lung attenuation, skewness, and kurtosis are histogram-based densitometry variables that quantify systemic sclerosis--associated interstitial lung disease (SSc-ILD) and were recently merged into a computerized integrated index (CII). Our work tested the CII in low-dose 9-slice (reduced) and standard high-resolution computed tomography (CT) scans to evaluate extensive SSc-ILD and predict mortality. Methods. CT scans from patients with SSc-ILD were assessed using the software Horos to compute standard and reduced CIIs. Extensive ILD was determined following the Goh staging system. The association between CIIs and extensive ILD was analyzed with a generalized estimating equation regression model, the predictive ability of CIIs by the area under the receiver-operation characteristic curve (AUC), and the association between CIIs and death by Kaplan-Meier analysis. Results. Among 243 patients with standard and reduced CT scans available, 157 CT scans from 119 patients with SSc-ILD constituted the derivation cohort. The validation cohort included 116 standard and 175 reduced CT scans. Both CIIs from standard (odds ratio [OR] 0.53, 95% CI 0.37-0.75; AUC 0.77, 95% CI 0.68-0.87) and reduced CT scans (OR 0.54, 95% CI 0.35-0.82; AUC 0.78, 95% CI 0.70-0.87) were significantly associated with extensive ILD. A threshold of CII ≤ -0.96 for standard CT scans and CII ≤ -1.85 for reduced CT scans detected extensive ILD with high sensitivity in both derivation and validation cohorts. Extensive ILD according to Goh staging (OR 2.94, 95% CI 1.10-7.82) and standard CII ≤ -0.96 (OR 1.78, 95% CI 1.24-2.56) significantly predicted mortality; a marginal P value was observed for reduced CII ≤ -1.85 (OR 1.27, 95% CI 0.93-1.75). Conclusion. Thresholds for both standard and reduced CII to identify extensive ILD were developed and validated, with an additional association with mortality. CIIs might help in clinical practice when radiology expertise is missing. [ABSTRACT FROM AUTHOR]

Objectives The first objective of this study was to implement and assess the performance and reliability of a vision transformer (ViT)-based deep-learning model, an 'off-the-shelf' artificial intelligence solution, for identifying distinct signs of microangiopathy in nailfold capilloroscopy (NFC) images of patients with SSc. The second objective was to compare the ViT's analysis performance with that of practising rheumatologists. Methods NFC images of patients prospectively enrolled in our European Scleroderma Trials and Research group (EUSTAR) and Very Early Diagnosis of Systemic Sclerosis (VEDOSS) local registries were used. The primary outcome investigated was the ViT's classification performance for identifying disease-associated changes (enlarged capillaries, giant capillaries, capillary loss, microhaemorrhages) and the presence of the scleroderma pattern in these images using a cross-fold validation setting. The secondary outcome involved a comparison of the ViT's performance vs that of rheumatologists on a reliability set, consisting of a subset of 464 NFC images with majority vote–derived ground-truth labels. Results We analysed 17 126 NFC images derived from 234 EUSTAR and 55 VEDOSS patients. The ViT had good performance in identifying the various microangiopathic changes in capillaries by NFC [area under the curve (AUC) from 81.8% to 84.5%]. In the reliability set, the rheumatologists reached a higher average accuracy, as well as a better trade-off between sensitivity and specificity compared with the ViT. However, the annotators' performance was variable, and one out of four rheumatologists showed equal or lower classification measures compared with the ViT. Conclusions The ViT is a modern, well-performing and readily available tool for assessing patterns of microangiopathy on NFC images, and it may assist rheumatologists in generating consistent and high-quality NFC reports; however, the final diagnosis of a scleroderma pattern in any individual case needs the judgement of an experienced observer. [ABSTRACT FROM AUTHOR]

Objective: To address the hypothesis that very early patients with systemic sclerosis (SSc) are a heterogeneous group with mild or early disease, we analyzed the extent of heterogeneity in clinical, epidemiological, and immunological characteristics of these patients.Methods: We performed an analysis of very early SSc patients from the Zurich cohort, who fulfilled neither the 2013 American College of Rheumatology (ACR)/European League Against Rheumatism nor the 1980 ACR classification criteria, but had a clinical expert diagnosis of SSc with Raynaud phenomenon (RP) and additional features of SSc (puffy fingers, SSc-specific antibodies, SSc pattern on nailfold capillaroscopy, or any organ involvement characteristic for SSc). Disease duration was defined from first RP symptom.Results: One hundred and two patients fulfilled the inclusion criteria and were analyzed. Their clinical presentation was heterogeneous with the large majority presenting with RP, antinuclear antibodies, and nailfold capillaroscopy changes, but with varying presentations of other features such as SSc-specific antibodies and early signs of organ involvement. While 54.1% (52/96) of patients had a disease duration of < 5 years, as many as 29.1% (28/96) of patients had a disease duration of > 10 years, indicating long-standing mild disease. Patients with very early, potentially progressive disease did not differ from patients with long-standing mild disease in terms of their clinical features at first presentation.Conclusion: This study showed that patients with very early SSc are a mixture with mild or early disease. This needs to be considered in clinical practice for risk stratification and for the study design of patients considered as early SSc. [ABSTRACT FROM AUTHOR]

Objectives: To identify the predictive clinical characteristics and establish a prediction model for the progression of mild interstitial lung disease (ILD) in patients with systemic sclerosis (SSc).Methods: Patients with SSc from two independent prospective cohorts were included in this observational study. All patients fulfilled the 2013 American College of Rheumatology/European League Against Rheumatism criteria, had mild ILD at baseline diagnosed by High-Resolution Computed Tomography (HRCT), available baseline and ≥1 annual follow-up pulmonary function tests and no concomitant pulmonary hypertension or airflow obstruction. ILD progression was defined as a relative decrease in forced vital capacity (FVC)%≥15%, or FVC%≥10% combined with diffusing capacity for carbon monoxide %≥15% at 1-year follow-up. Candidate predictors for multivariate logistic regression were selected by expert opinion based on clinical significance. A prediction model for ILD progression was established in the derivation cohort and validated in the multinational validation cohort.Results: A total of 25/98 and 25/117 patients with SSc showed ILD progression in the derivation cohort and the validation cohort, respectively. Lower SpO2 after 6 min walk test (6MWT) and arthritis ever were identified as independent predictors for ILD progression in both cohorts. The optimal cut-off value of SpO2 after 6MWT for predicting ILD progression was determined as 94% by receiver operating characteristic curve analysis. The derived SPAR model combining both predictors (SPO2 and ARthritis) increased the prediction rate from 25.5% to 91.7% with an area under the curve (95% CI) of 0.83 (0.73 to 0.93).Conclusions: The evidence-based SPAR prediction model developed in our study might be helpful for the risk stratification of patients with mild SSc-ILD in clinical practice and cohort enrichment for future clinical trial design. [ABSTRACT FROM AUTHOR]