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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).
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COVID-19 , Embolia Pulmonar , Humanos , Estudios Retrospectivos , Pacientes Ambulatorios , Curva ROCRESUMEN
BACKGROUND: In patients with tracheobronchial involvement, the differential diagnosis between granulomatosis with polyangiitis (GPA) and relapsing polychondritis (RP) can be challenging. The aim of this study was to describe the characteristics of airway abnormalities on chest computed tomography (CT) in patients with GPA or RP and to determine whether specific imaging criteria could be used to differentiate them. METHODS: GPA and RP patients with tracheobronchial involvement referred to a national referral center from 2008 to 2020 were evaluated. Their chest CT images were reviewed by two radiologists who were blinded to the final diagnosis in order to analyze the characteristics of airway involvement. The association between imaging features and a diagnosis of GPA rather than RP was analyzed using a generalized linear regression model. RESULTS: Chest CTs from 26 GPA and 19 RP patients were analyzed. Involvement of the subglottic trachea (odds ratio for GPA=28.56 [95% CI: 3.17; 847.63]; P=0.001) and extensive airway involvement (odds ratio for GPA=0.02 [95% CI: 0.00; 0.43]; P=0.008) were the two independent CT features that differentiated GPA from RP in multivariate analysis. Tracheal thickening sparing the posterior membrane was significantly associated to RP (odds ratio for GPA=0.09 [95% CI: 0.02; 0.39]; P=0.003) but only in the univariate analysis and suffered from only moderate interobserver agreement (kappa=0.55). Tracheal calcifications were also associated with RP only in the univariate analysis (odds ratio for GPA=0.21 [95% CI: 0.05; 0.78]; P=0.045). CONCLUSION: The presence of subglottic involvement and diffuse airway involvement are the two most relevant criteria in differentiating between GPA and RP on chest CT. Although generally considered to be a highly suggestive sign of RP, posterior tracheal membrane sparing is a nonspecific and an overly subjective sign.
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Granulomatosis con Poliangitis , Policondritis Recurrente , Humanos , Policondritis Recurrente/complicaciones , Policondritis Recurrente/diagnóstico por imagen , Granulomatosis con Poliangitis/diagnóstico , Granulomatosis con Poliangitis/diagnóstico por imagen , Estudios de Cohortes , Tomografía Computarizada por Rayos X , Sistema RespiratorioRESUMEN
It is unknown what role chest ultrasound plays in distinguishing the various usual interstitial pneumonia (UIP) patterns of high-resolution chest tomography (HRCT). The purpose of this study was to see if there was a link between the results of chest ultrasound (u/s) and HRCT in patients with idiopathic pulmonary fibrosis (IPF). We performed chest u/s in 16 patients with UIP and probable UIP patterns to indeterminate UIP and alternative diagnosis patterns in this single center prospective study to determine any possible relationship with the HRCT findings. A chest radiologist reviewed each HRCT to determine the pattern in accordance with the American Thoracic Society (ATS) / European Respiratory Society (ERS) Guidelines. The local multidisciplinary committee validated the patients' diagnoses before they were included. When compared to the indeterminate for UIP or alternative diagnosis pattern group, there was a trend (p=0.07) toward the presence of more B lines in UIP or probable UIP patterns. There was no statistically significant difference in the presence of small, large, white lung, or pleural line thickening >5mm. Subgroup analysis revealed that patients with honeycombing were more likely to have a fragmented pleural line (p=0.04). To summarize, in our pilot study, chest u/s appears unable to differentiate UIP and probable UIP patterns from indeterminate UIP and alternative diagnosis patterns. However, it appears that this technique can be used to recognize the honeycombing pattern.
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Fibrosis Pulmonar Idiopática , Pulmón , Humanos , Pulmón/diagnóstico por imagen , Proyectos Piloto , Estudios Prospectivos , Fibrosis Pulmonar Idiopática/diagnóstico por imagen , Probabilidad , Estudios RetrospectivosRESUMEN
PURPOSE: To evaluate the feasibility of a chest CT-based body composition analysis in idiopathic pulmonary fibrosis (IPF), and to investigate the respective contribution of lung and muscle CT quantitative analyses to the prognosis of IPF. METHOD: A total of 71 IPF patients were recruited at diagnosis. All patients underwent a standard chest CT-scan and a bioelectrical impedance analysis considered as reference standard for estimating malnutrition through the use of the fat-free mass index (FFMI). The skeletal muscle index (SMI) was measured on chest-CT at the level of the first lumbar vertebra by two radiologists. Lung fibrosis extent was quantified by three radiologists in consensus. The extent of emphysema, the pulmonary artery to aorta (PA/AO) diameter ratio and lymph node enlargement were also reported. Mortality and hospitalization over a 14-month follow-up were recorded. RESULTS: A low FFMI defining malnutrition was identified in 26.8% of patients. SMI was significantly lower in these patients (p<0.001) and was correlated with FFMI (r=0.637, p<0.001). Interobserver agreement of SMI measurement was very good (ICC=0.91). For diagnosing malnutrition, SMI showed a 0.79 sensitivity, a 0.69 specificity, a 0.48 PPV and a 0.90 NPV. In univariate analysis, fibrosis extent was significantly associated with death, while SMI did not reach significance. In multivariate analysis, fibrosis extent and PA/AO ratio were independently associated with hospitalization. CONCLUSIONS: SMI measured on chest CT could be a reliable tool to exclude malnutrition in IPF. A quantitative analysis of both fibrosis and skeletal muscle may allow holistic management of IPF patients.
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Fibrosis Pulmonar Idiopática , Desnutrición , Composición Corporal , Humanos , Fibrosis Pulmonar Idiopática/diagnóstico por imagen , Proyectos Piloto , Estudios Retrospectivos , Tomografía Computarizada por Rayos XRESUMEN
Background There are conflicting data regarding the diagnostic performance of chest CT for COVID-19 pneumonia. Disease extent at CT has been reported to influence prognosis. Purpose To create a large publicly available data set and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter, observational, retrospective cohort study involved 20 French university hospitals. Eligible patients presented at the emergency departments of the hospitals involved between March 1 and April 30th, 2020, and underwent both thoracic CT and reverse transcription-polymerase chain reaction (RT-PCR) testing for suspected COVID-19 pneumonia. CT images were read blinded to initial reports, RT-PCR, demographic characteristics, clinical symptoms, and outcome. Readers classified CT scans as either positive or negative for COVID-19 based on criteria published by the French Society of Radiology. Multivariable logistic regression was used to develop a model predicting severe outcome (intubation or death) at 1-month follow-up in patients positive for both RT-PCR and CT, using clinical and radiologic features. Results Among 10 930 patients screened for eligibility, 10 735 (median age, 65 years; interquartile range, 51-77 years; 6147 men) were included and 6448 (60%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity of CT were 80.2% (95% CI: 79.3, 81.2) and 79.7% (95% CI: 78.5, 80.9), respectively, with strong agreement between junior and senior radiologists (Gwet AC1 coefficient, 0.79). Of all the variables analyzed, the extent of pneumonia at CT (odds ratio, 3.25; 95% CI: 2.71, 3.89) was the best predictor of severe outcome at 1 month. A score based solely on clinical variables predicted a severe outcome with an area under the curve of 0.64 (95% CI: 0.62, 0.66), improving to 0.69 (95% CI: 0.6, 0.71) when it also included the extent of pneumonia and coronary calcium score at CT. Conclusion Using predefined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at 1 month. ClinicalTrials.gov identifier: NCT04355507 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Rubin in this issue.