Prognostic value of automated assessment of interstitial lung disease on CT in systemic sclerosis.

OBJECTIVE: Stratifying the risk of death in SSc-related interstitial lung disease (SSc-ILD) is a challenging issue. The extent of lung fibrosis on high-resolution CT (HRCT) is often assessed by a visual semiquantitative method that lacks reliability. We aimed to assess the potential prognostic value of a deep-learning-based algorithm enabling automated quantification of ILD on HRCT in patients with SSc. METHODS: We correlated the extent of ILD with the occurrence of death during follow-up, and evaluated the additional value of ILD extent in predicting death based on a prognostic model including well-known risk factors in SSc. RESULTS: We included 318 patients with SSc, among whom 196 had ILD; the median follow-up was 94 months (interquartile range 73-111). The mortality rate was 1.6% at 2 years and 26.3% at 10 years. For each 1% increase in the baseline ILD extent (up to 30% of the lung), the risk of death at 10 years was increased by 4% (hazard ratio 1.04, 95% CI 1.01, 1.07, P = 0.004). We constructed a risk prediction model that showed good discrimination for 10-year mortality (c index 0.789). Adding the automated quantification of ILD significantly improved the model for 10-year survival prediction (P = 0.007). Its discrimination was only marginally improved, but it improved prediction of 2-year mortality (difference in time-dependent area under the curve 0.043, 95% CI 0.002, 0.084, P = 0.040). CONCLUSION: The deep-learning-based, computer-aided quantification of ILD extent on HRCT provides an effective tool for risk stratification in SSc. It might help identify patients at short-term risk of death.

Elastic Registration-driven Deep Learning for Longitudinal Assessment of Systemic Sclerosis Interstitial Lung Disease at CT.

Background Longitudinal follow-up of interstitial lung diseases (ILDs) at CT mainly relies on the evaluation of the extent of ILD, without accounting for lung shrinkage. Purpose To develop a deep learning-based method to depict worsening of ILD based on lung shrinkage detection from elastic registration of chest CT scans in patients with systemic sclerosis (SSc). Materials and Methods Patients with SSc evaluated between January 2009 and October 2017 who had undergone at least two unenhanced supine CT scans of the chest and pulmonary function tests (PFTs) performed within 3 months were retrospectively included. Morphologic changes on CT scans were visually assessed by two observers and categorized as showing improvement, stability, or worsening of ILD. Elastic registration between baseline and follow-up CT images was performed to obtain deformation maps of the whole lung. Jacobian determinants calculated from the deformation maps were given as input to a deep learning-based classifier to depict morphologic and functional worsening. For this purpose, the set was randomly split into training, validation, and test sets. Correlations between mean Jacobian values and changes in PFT measurements were evaluated with the Spearman correlation. Results A total of 212 patients (median age, 53 years; interquartile range, 45-62 years; 177 women) were included as follows: 138 for the training set (65%), 34 for the validation set (16%), and 40 for the test set (21%). Jacobian maps demonstrated lung parenchyma shrinkage of the posterior lung bases in patients found to have worsened ILD at visual assessment. The classifier detected morphologic and functional worsening with an accuracy of 80% (32 of 40 patients; 95% confidence interval [CI]: 64%, 91%) and 83% (33 of 40 patients; 95% CI: 67%, 93%), respectively. Jacobian values correlated with changes in forced vital capacity (R = -0.38; 95% CI: -0.25, -0.49; P < .001) and diffusing capacity for carbon monoxide (R = -0.42; 95% CI: -0.27, -0.54; P < .001). Conclusion Elastic registration of CT scans combined with a deep learning classifier aided in the diagnosis of morphologic and functional worsening of interstitial lung disease in patients with systemic sclerosis. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.

Chronic Granulomatous Disease in Patients Reaching Adulthood: A Nationwide Study in France.

Background: Although prognosis of Chronic Granulomatous Disease (CGD) has greatly improved, few studies have focused on its long-term outcome. We studied the clinical course and sequelae of CGD patients diagnosed before age 16, at various adult time points. Method: Cross-sectional French nationwide retrospective study of patients screened through the National Reference Center for Primary Immunodeficiencies (CEREDIH) registry. Results: Eighty CGD patients (71 males [88.7%], 59 X-linked [73.7%], median age 23.9 years [minimum, 16.6; maximum, 59.9]) were included, Median ages at diagnosis and last follow-up were 2.52 and 23.9 years, respectively. Seven patients underwent hematopoietic stem cell transplantation. A total of 553 infections requiring hospitalization occurred in 2017 patient-years. The most common site of infection was pulmonary (31%). Aspergillus spp. (17%) and Staphylococcus aureus (10.7%) were the commonest pathogens. A total of 224 inflammatory episodes occurred in 71 patients, mainly digestive (50%). Their characteristics as well as their annual frequency did not vary before and after age 16. Main sequelae were a small adult height and weight and mild chronic restrictive respiratory failure. At age 16, only 53% of patients were in high school. After age 30 years, 9/13 patients were working. Ten patients died during adulthood. Conclusions: Adult CGD patients displayed similar characteristics and rates of severe infections and inflammatory episodes that those of childhood. The high rate of handicap has become a matter of medical and social consideration. Careful follow-up in centers of expertise is strongly recommended and an extended indication of curative treatment by HSCT should be considered.

Employment, work disability and quality of life in patients with ANCA-associated vasculitides. The EXPOVAS study.

OBJECTIVES: Improved therapeutic strategies for ANCA-associated vasculitis (AAV) have transformed acute and life-threatening diseases into chronic ones responsible for marked morbidity that could impact employment, work disability and quality of life (QoL). We aimed to analyse work, handicaps and QoL of AAV patients and identify their determinants. METHODS: Patients with AAV were included in a cross-sectional study assessing employment, work disability and QoL. Specific and non-specific questionnaires, including SF-36, were sent to patients, and clinical-biological data that could affect QoL and their determinants were analysed. RESULTS: Questionnaires were completed by 189 patients. Among 94 working-age (<60 years) patients, 57% had jobs, consistent with their qualifications for 81%, 77% were stably employed; 23% of workers felt that their disease qualitatively limited the nature of their work, while 43% felt it limited the quantity of work they could do; 50% thought their disease had hindered their careers and 43% that it had led to a salary reduction. These results were comparable for the different vasculitides. QoL was significantly impaired for AAV patients compared to the general population (p<0.0001). Physical health determinants for our population were diagnosis of eosinophilic granulomatosis with polyangiitis (EGPA), long disease duration and its neurological involvement, whereas mental health determinants tended to be ear, nose and throat and cardiovascular involvement, and unemployment. CONCLUSIONS: Our findings showed that AAV patients' QoL was impaired compared to the general population, mainly for patients with EGPA and long-standing disease. In contrast, normal employment seemed to be preserved for the majority of the patients.