CT features of community-acquired pneumonia at the emergency department.

BACKGROUND: Chest computed tomography (CT) was reported to improve the diagnosis of community-acquired pneumonia (CAP) as compared to chest X-ray (CXR). The aim of this study is to describe the CT-patterns of CAP in a large population visiting the emergency department and to see if some of them are more frequently missed on CXR. MATERIALS AND METHODS: This is an ancillary analysis of the prospective multicenter ESCAPED study including 319 patients. We selected the 163 definite or probable CAP based on adjudication committee classification; 147 available chest CT scans were reinterpreted by 3 chest radiologists to identify CAP patterns. These CT-patterns were correlated to epidemiological, biological and microbiological data, and compared between false negative and true positive CXR CAP. RESULTS: Six patterns were identified: lobar pneumonia (51/147, 35%), including 35 with plurifocal involvement; lobular pneumonia (43/147, 29%); unilobar infra-segmental consolidation (24/147, 16%); bronchiolitis (16/147, 11%), including 4 unilobar bronchiolitis; atelectasis and bronchial abnormalities (8/147, 5.5%); interstitial pneumonia (5/147, 3.5%). Bacteria were isolated in 41% of patients with lobar pneumonia-pattern (mostly Streptococcus pneumoniae and Mycoplasma pneumonia) versus 19% in other patients (p = 0.01). Respiratory viruses were equally distributed within all patterns. CXR was falsely negative in 46/147 (31%) patients. Lobar pneumonia was significantly less missed on CXR than other patterns (p = 0.003), especially lobular pneumonia and unilobar infra-segmental consolidation, missed in 35% and 58% of cases, respectively. CONCLUSION: Lobar and lobular pneumonias are the most frequent CT-patterns. Lobar pneumonia is appropriately detected on CXR and mainly due to Streptococcus pneumoniae or Mycoplasma pneumoniae. Chest CT is very useful to identify CAP in other CT-patterns. Prior the COVID pandemic, CAP was rarely responsible for interstitial opacities on CT.

COVID-19 in Lung Transplant Recipients: 2 Cases With Acute Respiratory Distress Syndrome Successfully Treated With High-Dose Corticosteroids.

Acute respiratory distress syndrome remains the main cause of death among people with COVID-19. Although many immunomodulatory and antiviral drug therapies have been tested, the only effective therapy against severe COVID-19 pneumonia among the general population is a regimen of high-dose corticosteroids for cases of severe associated inflammation. In solid-organ transplant recipients with long-term immunosuppression, data on disease presentation and evolution are scarce, and the benefit of high-dose corticosteroids remains uncertain for cases of severe COVID-19 pneumonia. Here, we report 2 cases of COVID-19-related acute respiratory distress syndrome that occurred in lung transplant recipients in March and April 2020, respectively. Both cases of acute respiratory distress syndrome occurred in patients with long-term azithromycin treatment prescribed to prevent chronic allograft dysfunction. Acute respiratory distress syndrome was associated with severe inflammation and was cured after early administration of high-dose corticosteroids in both cases, with progressive and complete resolution of lung lesions evidenced on thoracic computed tomography scan. Our findings support the benefit of early high-dose corticosteroids in COVID-19-related acute respiratory distress syndrome with hyperinflammation in patients with long-term immunosuppression such as lung transplant recipients.

Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia.

The purpose of our work was to assess the independent and incremental value of AI-derived quantitative determination of lung lesions extent on initial CT scan for the prediction of clinical deterioration or death in patients hospitalized with COVID-19 pneumonia. 323 consecutive patients (mean age 65 ± 15 years, 192 men), with laboratory-confirmed COVID-19 and an abnormal chest CT scan, were admitted to the hospital between March and December 2020. The extent of consolidation and all lung opacities were quantified on an initial CT scan using a 3D automatic AI-based software. The outcome was known for all these patients. 85 (26.3%) patients died or experienced clinical deterioration, defined as intensive care unit admission. In multivariate regression based on clinical, biological and CT parameters, the extent of all opacities, and extent of consolidation were independent predictors of adverse outcomes, as were diabetes, heart disease, C-reactive protein, and neutrophils/lymphocytes ratio. The association of CT-derived measures with clinical and biological parameters significantly improved the risk prediction (p = 0.049). Automated quantification of lung disease at CT in COVID-19 pneumonia is useful to predict clinical deterioration or in-hospital death. Its combination with clinical and biological data improves risk prediction.

Tocilizumab for Severe Worsening COVID-19 Pneumonia: a Propensity Score Analysis.

BACKGROUND: High levels of serum interleukin-6 (IL-6) correlate with disease severity in COVID-19. We hypothesized that tocilizumab (a recombinant humanized anti-IL-6 receptor) could improve outcomes in selected patients with severe worsening COVID-19 pneumonia and high inflammatory parameters. METHODS: The TOCICOVID study included a prospective cohort of patients aged 16-80 years with severe (requiring > 6 L/min of oxygen therapy to obtain Sp02 > 94%) rapidly deteriorating (increase by ≥ 3 L/min of oxygen flow within the previous 12 h) COVID-19 pneumonia with ≥ 5 days of symptoms and C-reactive protein levels > 40 mg/L. They entered a compassionate use program of treatment with intravenous tocilizumab (8 mg/kg with a maximum of 800 mg per infusion; and if needed a second infusion 24 to 72 h later). A control group was retrospectively selected with the same inclusion criteria. Outcomes were assessed at D28 using inverse probability of treatment weighted (IPTW) methodology. RESULTS: Among the 96 patients included (81% male, mean (SD) age: 60 (12.5) years), underlying conditions, baseline disease severity, and concomitant medications were broadly similar between the tocilizumab (n = 49) and the control (n = 47) groups. In the IPTW analysis, treatment with tocilizumab was associated with a reduced need for overall ventilatory support (49 vs. 89%, wHR: 0.39 [0.25-0.56]; p < 0.001). Albeit lacking statistical significance, there was a substantial trend towards a reduction of mechanical ventilation (31% vs. 45%; wHR: 0.58 [0.36-0.94]; p = 0.026). However, tocilizumab did not improve overall survival (wHR = 0.68 [0.31-1.748], p = 0.338). Among the 85 (89%) patients still alive at D28, patients treated with tocilizumab had a higher rate of oxygen withdrawal (82% vs. 73.5%, wHR = 1.66 [1.17-2.37], p = 0.005), with a shorter delay before being weaned of oxygen therapy (mean 11 vs. 16 days; p < 0.001). At D28, the rate of patients discharged from hospital was higher in the tocilizumab group (70% vs. 40%, wHR = 1.82 [1.22-2.75]; p = 0.003). The levels of CRP and fibrinogen post therapy (p < 0.001 for both variables) were significantly lower in the tocilizumab group (interaction test, mixed model). Rates of neutropenia (35% vs. 0%; p < 0.001) were higher in the tocilizumab group, yet rates of infections (22% vs. 38%, p = 0.089) including ventilator-acquired pneumonia (8% vs. 26%, p = 0.022) were higher in the control group. CONCLUSION: These data could be helpful for the design of future trials aiming to counter COVID-19-induced inflammation, especially before patients require admission to the intensive care unit.

COVID-19 pneumonia: high diagnostic accuracy of chest CT in patients with intermediate clinical probability.

OBJECTIVES: To assess inter-reader agreements and diagnostic accuracy of chest CT to identify COVID-19 pneumonia in patients with intermediate clinical probability during an acute disease outbreak. METHODS: From March 20 to April 8, 319 patients (mean age 62.3 years old) consecutive patients with an intermediate clinical probability of COVID-19 pneumonia underwent a chest CT scan. Two independent chest radiologists blinded to clinical information and RT-PCR results retrospectively reviewed and classified images on a 1-5 confidence level scale for COVID-19 pneumonia. Agreements between radiologists were assessed with kappa statistics. Diagnostic accuracy of chest CT compared with RT-PCR assay and patient outcomes was measured using receiver operating characteristics (ROC). Positive predictive value (PPV) and negative predictive value (NPV) for COVID-19 pneumonia were calculated. RESULTS: Inter-observer agreement for highly probable (kappa: 0.83 [p < .001]) and highly probable or probable (kappa: 0.82 [p < .001]) diagnosis of COVID-19 pneumonia was very good. RT-PCR tests performed in 307 patients were positive in 174 and negative in 133. The areas under the curve (AUC) were 0.94 and 0.92 respectively. With a disease prevalence of 61.2%, PPV were 95.9% and 94.3%, and NPV 84.4% and 77.1%. CONCLUSION: During acute COVID-19 outbreak, chest CT scan may be used for triage of patients with intermediate clinical probability with very good inter-observer agreements and diagnostic accuracy. KEY POINTS: • Concordances between two chest radiologists to diagnose or exclude a COVID-19 pneumonia in 319 consecutive patients with intermediate clinical probability were very good (kappa: 0.82; p < .001). • When compared with RT-PCR results and patient outcomes, the diagnostic accuracy of CT to identify COVID-19 pneumonia was high for both radiologists (AUC: 0.94 and 0.92). • With a disease prevalence of 61.2% in the studied population, the positive predictive values of CT for diagnosing COVID-19 pneumonia were 95.9% and 94.3% with negative predictive values of 84.4% and 77.1%.