Your browser doesn't support javascript.
loading
: 20 | 50 | 100
1 - 6 de 6
2.
Sarcoidosis Vasc Diffuse Lung Dis ; 40(2): e2023023, 2023 Jun 29.
Article En | MEDLINE | ID: mdl-37382068

Sarcoidosis is a multisystemic granulomatous disease of unknown origin. It has been argued that the skin is one of the entry doors of the possible antigen that causes sarcoidosis and after entering the skin, the causal agent may progress to the underlying bone. We report four cases with development of sarcoidosis in old scars located on the forehead, and contiguous bone involvement of the frontal bone. In most cases scar sarcoidosis was the first manifestation of the disease, and in most cases it was asymptomatic. Two patients never required treatment, and in all cases the frontal problem improved or remained stable spontaneously or under sarcoidosis treatment. Scar sarcoidosis in the frontal area may have contiguous bone damage. This bone involvement does not seem to be associated with neurological extension.

4.
Respir Res ; 24(1): 151, 2023 Jun 08.
Article En | MEDLINE | ID: mdl-37291562

OBJECTIVE: To investigate the association of air pollution exposure with the severity of interstitial lung disease (ILD) at diagnosis and ILD progression among patients with systemic sclerosis (SSc)-associated ILD. METHODS: We conducted a retrospective two-center study of patients with SSc-associated ILD diagnosed between 2006 and 2019. Exposure to the air pollutants particulate matter of up to 10 and 2.5 µm in diameter (PM10, PM2.5), nitrogen dioxide (NO2), and ozone (O3) was assessed at the geolocalization coordinates of the patients' residential address. Logistic regression models were used to evaluate the association between air pollution and severity at diagnosis according to the Goh staging algorithm, and progression at 12 and 24 months. RESULTS: We included 181 patients, 80% of whom were women; 44% had diffuse cutaneous scleroderma, and 56% had anti-topoisomerase I antibodies. ILD was extensive, according to the Goh staging algorithm, in 29% of patients. O3 exposure was associated with the presence of extensive ILD at diagnosis (adjusted OR: 1.12, 95% CI 1.05-1.21; p value = 0.002). At 12 and 24 months, progression was noted in 27/105 (26%) and 48/113 (43%) patients, respectively. O3 exposure was associated with progression at 24 months (adjusted OR: 1.10, 95% CI 1.02-1.19; p value = 0.02). We found no association between exposure to other air pollutants and severity at diagnosis and progression. CONCLUSION: Our findings suggest that high levels of O3 exposure are associated with more severe SSc-associated ILD at diagnosis, and progression at 24 months.


Air Pollutants , Air Pollution , Lung Diseases, Interstitial , Ozone , Scleroderma, Systemic , Humans , Female , Male , Retrospective Studies , Air Pollution/adverse effects , Lung Diseases, Interstitial/diagnosis , Lung Diseases, Interstitial/epidemiology , Lung Diseases, Interstitial/etiology , Air Pollutants/adverse effects , Air Pollutants/analysis , Ozone/adverse effects , Particulate Matter/analysis , Scleroderma, Systemic/diagnosis , Scleroderma, Systemic/epidemiology , Scleroderma, Systemic/complications , Environmental Exposure/adverse effects
5.
Eur Radiol ; 33(8): 5540-5548, 2023 Aug.
Article En | MEDLINE | ID: mdl-36826504

OBJECTIVES: The objective was to define a safe strategy to exclude pulmonary embolism (PE) in COVID-19 outpatients, without performing CT pulmonary angiogram (CTPA). METHODS: COVID-19 outpatients from 15 university hospitals who underwent a CTPA were retrospectively evaluated. D-Dimers, variables of the revised Geneva and Wells scores, as well as laboratory findings and clinical characteristics related to COVID-19 pneumonia, were collected. CTPA reports were reviewed for the presence of PE and the extent of COVID-19 disease. PE rule-out strategies were based solely on D-Dimer tests using different thresholds, the revised Geneva and Wells scores, and a COVID-19 PE prediction model built on our dataset were compared. The area under the receiver operating characteristics curve (AUC), failure rate, and efficiency were calculated. RESULTS: In total, 1369 patients were included of whom 124 were PE positive (9.1%). Failure rate and efficiency of D-Dimer > 500 µg/l were 0.9% (95%CI, 0.2-4.8%) and 10.1% (8.5-11.9%), respectively, increasing to 1.0% (0.2-5.3%) and 16.4% (14.4-18.7%), respectively, for an age-adjusted D-Dimer level. D-dimer > 1000 µg/l led to an unacceptable failure rate to 8.1% (4.4-14.5%). The best performances of the revised Geneva and Wells scores were obtained using the age-adjusted D-Dimer level. They had the same failure rate of 1.0% (0.2-5.3%) for efficiency of 16.8% (14.7-19.1%), and 16.9% (14.8-19.2%) respectively. The developed COVID-19 PE prediction model had an AUC of 0.609 (0.594-0.623) with an efficiency of 20.5% (18.4-22.8%) when its failure was set to 0.8%. CONCLUSIONS: The strategy to safely exclude PE in COVID-19 outpatients should not differ from that used in non-COVID-19 patients. The added value of the COVID-19 PE prediction model is minor. KEY POINTS: • D-dimer level remains the most important predictor of pulmonary embolism in COVID-19 patients. • The AUCs of the revised Geneva and Wells scores using an age-adjusted D-dimer threshold were 0.587 (95%CI, 0.572 to 0.603) and 0.588 (95%CI, 0.572 to 0.603). • The AUC of COVID-19-specific strategy to rule out pulmonary embolism ranged from 0.513 (95%CI: 0.503 to 0.522) to 0.609 (95%CI: 0.594 to 0.623).


COVID-19 , Pulmonary Embolism , Humans , Retrospective Studies , Outpatients , ROC Curve
6.
Med Image Anal ; 67: 101860, 2021 01.
Article En | MEDLINE | ID: mdl-33171345

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.


Artificial Intelligence , COVID-19/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Biomarkers/analysis , Disease Progression , Humans , Neural Networks, Computer , Prognosis , Radiographic Image Interpretation, Computer-Assisted , SARS-CoV-2 , Triage
...