Defining prognosis in sarcoidosis.

Sarcoidosis is a multi-systemic granulomatous disease. Affected individuals can show spontaneous healing, develop remission with drug treatment within 2 years, or become chronically ill. Our main goal was to identify features that are related to prognosis.The study consisted of 101 patients, recruited at a single center, who were already diagnosed with sarcoidosis at the start of the study or were diagnosed within 48 months. Ninety individuals were followed-up for at least 24 months and were classified according to clinical outcome status (COS 1 to 9). Those with COS 1-4 and COS 5-9 were classified as having favorable and unfavorable outcomes, respectively. Unconditional logistic regression analyses were conducted to define which variables were associated with sarcoidosis outcomes. Subsequently, we established a scoring system to help predict the likelihood of a favorable or unfavorable outcome.Of our patients, 48% developed a chronic form of the disease (COS 5-9). Three clinical features were predictive of prognosis in sarcoidosis. We built a score-based model where the absence of rheumatological markers (1 point), normal pulmonary functions (2 points), and the presence of early respiratory symptoms manifestations (2 points) were associated with a favorable prognosis. We predicted that a patient with a score of 5 had an 86% (95% confidence interval [CI] 74%-98%) probability of having a favorable prognosis, while those with scores of 4, 3, 2, 1, and 0 had probabilities of 72% (95% CI 59-85%), 52% (95% CI 40-63%), 31% (95% CI 17-44%), 15% (95% CI 2-28%), and 7% (95% CI 0-16%) of having a favorable prognosis, respectively. Thus, our easy-to-compute algorithm can help to predict prognosis of sarcoidosis patients, facilitating their management.

Changes in lung ultrasound of symptomatic healthcare professionals with COVID-19 pneumonia and their association with clinical findings.

PURPOSE: To evaluate ultrasound signs of coronavirus disease-19 (COVID-19) pneumonia in symptomatic healthcare professionals and to correlate those changes with clinical findings. METHODS: All patients underwent real-time polymerase chain reaction (RT-PCR), lung ultrasound (LUS) and clinical evaluation on the same day. In each of the 12 areas evaluated in the LUS, the LUS signs were scored to generate the aeration score. RESULTS: A total of 409 participants had positive PCR, with a median age of 41 (35-51) years. All participants had clinical symptoms, with cough in 84.1%, fever in 69.7%, and dyspnea in 36.2% of cases. In the LUS, 72.6% of participants had B-lines >2, 36.2% had coalescent B-lines, and 8.06% had subpleural consolidations. The median aeration score was 3 (2-7). The aeration score differed significantly regarding the presence of cough (P = .002), fever (P = .001), and dyspnea (P < .0001). The finding of subpleural consolidations in the LUS showed significant differences between participants with or without dyspnea (P < .0001). CONCLUSIONS: In healthcare professionals with COVID-19, LUS plays a key role in the characterization of lung involvement. Although B-lines are the most common ultrasound sign, subpleural consolidations are those that most impact the respiratory condition.

Identification of Active Sarcoidosis Using Chitotriosidase and Angiotensin-Converting Enzyme.

PURPOSE: Activity/remission differentiation is a great challenge in the follow-up and treatment of sarcoidosis patients. Angiotensin-converting enzyme (ACE) and high sensitivity C-reactive protein (hs-CRP) were proposed as sarcoidosis biomarkers. More recently, chitotriosidase (CHITO) has been described as a better alternative. This study has the aim to evaluate the association of CHITO activity, ACE, hs-CRP or a combination of these biomarkers and to construct a clinical algorithm to differentiate between sarcoidosis activity/remission status. METHODS: Forty-six patients with either active sarcoidosis or sarcoidosis in remission and 21 healthy individuals were included. ACE, hs-CRP, and CHITO were evaluated in serum samples. Comparisons of the laboratory variable means among groups were performed by linear models. The cutoff points of the biomarkers for activity/remission differentiation were calculated using the Youden's index. Biomarker cutoff points and decision tree classifier (DTC) performance were estimated by their leave-one-out cross-validation (LOOCV) accuracy (Acc), sensitivity (Se), and specificity (Sp). RESULTS: A 55% mean Se and a 100% mean Sp were found for CHITO, while an 88% Se and a 47% Sp were found for ACE, and a 66% Se and a 68% Sp for hs-CRP cutoff points for activity/remission differentiation. The DTC algorithm with CHITO, hs-CRP, and ACE information had an LOOCV mean Acc of 82%, Se of 78%, and Sp of 89% for sarcoidosis activity/remission differentiation. CONCLUSIONS: The algorithm involving CHITO, hs-CRP, and ACE could be a suitable strategy for differentiation between sarcoidosis activity/remission status.