Chest computed tomography of suspected COVID-19 pneumonia in the Emergency Department: comparative analysis between patients with different vaccination status.

Purpose: To identify differences in chest computed tomography (CT) of the symptomatic coronavirus disease 2019 (COVID-19) population according to the patients' severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccination status (non-vaccinated, vaccinated with incomplete or complete vaccination cycle). Material and methods: CT examinations performed in the Emergency Department (ED) in May-November 2021 for suspected COVID-19 pneumonia with a positive SARS-CoV-2 test were retrospectively included. Personal data were compared for vaccination status. One 13-year experienced radiologist and two 4th-year radiology residents independently evaluated chest CT scans according to CO-RADS and ACR COVID classifications. In possible COVID-19 pneumonia cases, defined as CO-RADS 3 to 5 (ACR indeterminate and typical) by each reader, high involvement CT score (≥ 25%) and CT patterns (presence of ground glass opacities, consolidations, crazy paving areas) were compared for vaccination status. Results: 184 patients with known vaccination status were included in the analysis: 111 non-vaccinated (60%) for SARS-CoV-2 infection, 21 (11%) with an incomplete vaccination cycle, and 52 (28%) with a complete vaccination cycle (6 different vaccine types). Multivariate logistic regression showed that the only factor predicting the absence of pneumonia (CO-RADS 1 and ACR negative cases) for the 3 readers was a complete vaccination cycle (OR = 12.8-13.1compared to non-vaccinated patients, p ≤ 0.032). Neither CT score nor CT patterns of possible COVID-19 pneumonia showed any statistically significant correlation with vaccination status for the 3 readers. Conclusions: Symptomatic SARS-CoV-2-infected patients with a complete vaccination cycle had much higher odds of showing a negative CT chest examination in ED compared to non-vaccinated patients. Neither CT involvement nor CT patterns of interstitial pneumonia showed differences across different vaccination status.

Impact of COVID-19 Pandemic on the Workload of Diagnostic Radiology: A 2-Year Observational Study in a Tertiary Referral Hospital.

RATIONALE AND OBJECTIVES: To evaluate the impact of COVID-19 pandemic on diagnostic imaging workload in a tertiary referral hospital. MATERIALS AND METHODS: Radiological examinations performed in pre-pandemic period (2015-2019) and in pandemic period (2020-2021) were retrospectively included. Based on epidemiological data and restriction measures, four pandemic waves were identified. For each of them, the relative change (RC) in workload was calculated and compared to the 5-year averaged workload in the corresponding pre-COVID-19 periods. Workload variations were also assessed according to technique (radiographs, CT, MRI, ultrasounds), body district (chest, abdomen, breast, musculoskeletal, head/neck, brain/spine, cardiovascular) and care setting (inpatient, outpatient, emergency imaging, pre-admission imaging). RESULTS: A total of 1384380 examinations were included. In 2020 imaging workload decreased (RC = -11%) compared to the average of the previous 5 years, while in 2021 only a minimal variation (RC = +1%) was observed. During first wave, workload was reduced for all modalities, body regions and types of care setting (RC from -86% to -10%), except for CT (RC = +3%). In subsequent waves, workload increased only for CT (mean RC = +18%) and, regarding body districts, for breast (mean RC = +23%) and cardiovascular imaging (mean RC = +23%). For all other categories, a workload comparable to pre-pandemic period was almost only restored in the fourth wave. In all pandemics periods workload decrease was mainly due to reduced outpatient activity (p < 0.001), while inpatient and emergency imaging was increased (p < 0.001). CONCLUSION: Evaluating imaging workload changes throughout COVID-19 pandemic helps to understand the response dynamics of radiological services and to improve institutional preparedness to face extreme contingency.

Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model.

Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet's AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging.

Chest x-ray severity score in COVID-19 patients on emergency department admission: a two-centre study.

BACKGROUND: Integration of imaging and clinical parameters could improve the stratification of COVID-19 patients on emergency department (ED) admission. We aimed to assess the extent of COVID-19 pulmonary abnormalities on chest x-ray (CXR) using a semiquantitative severity score, correlating it with clinical data and testing its interobserver agreement. METHODS: From February 22 to April 8, 2020, 926 consecutive patients referring to ED of two institutions in Northern Italy for suspected SARS-CoV-2 infection were reviewed. Patients with reverse transcriptase-polymerase chain reaction positive for SARS-CoV-2 and CXR images on ED admission were included (295 patients, median age 69 years, 199 males). Five readers independently and blindly reviewed all CXRs, rating pulmonary parenchymal involvement using a 0-3 semiquantitative score in 1-point increments on 6 lung zones (range 0-18). Interobserver agreement was assessed with weighted Cohen's κ, correlations between median CXR score and clinical data with Spearman's ρ, and the Mann-Whitney U test. RESULTS: Median score showed negative correlation with SpO2 (ρ = -0.242, p < 0.001), positive correlation with white cell count (ρ = 0.277, p < 0.001), lactate dehydrogenase (ρ = 0.308, p < 0.001), and C-reactive protein (ρ = 0.367, p < 0.001), being significantly higher in subsequently dead patients (p = 0.003). Considering overall scores, readers' pairings yielded moderate (κ = 0.449, p < 0.001) to almost perfect interobserver agreement (κ = 0.872, p < 0.001), with better interobserver agreement between readers of centre 2 (up to κ = 0.872, p < 0.001) than centre 1 (κ = 0.764, p < 0.001). CONCLUSIONS: Proposed CXR pulmonary severity score in COVID-19 showed moderate to almost perfect interobserver agreement and significant but weak correlations with clinical parameters, potentially furthering CXR integration in patients' stratification.

Chest x-ray in the COVID-19 pandemic: Radiologists' real-world reader performance.

PURPOSE: To report real-world diagnostic performance of chest x-ray (CXR) readings during the COVID-19 pandemic. METHODS: In this retrospective observational study we enrolled all patients presenting to the emergency department of a Milan-based university hospital from February 24th to April 8th 2020 who underwent nasopharyngeal swab for reverse transcriptase-polymerase chain reaction (RT-PCR) and anteroposterior bedside CXR within 12 h. A composite reference standard combining RT-PCR results with phone-call-based anamnesis was obtained. Radiologists were grouped by CXR reading experience (Group-1, >10 years; Group-2, <10 years), diagnostic performance indexes were calculated for each radiologist and for the two groups. RESULTS: Group-1 read 435 CXRs (77.0 % disease prevalence): sensitivity was 89.0 %, specificity 66.0 %, accuracy 83.7 %. Group-2 read 100 CXRs (73.0 % prevalence): sensitivity was 89.0 %, specificity 40.7 %, accuracy 76.0 %. During the first half of the outbreak (195 CXRs, 66.7 % disease prevalence), overall sensitivity was 80.8 %, specificity 67.7 %, accuracy 76.4 %, Group-1 sensitivity being similar to Group-2 (80.6 % versus 81.5 %, respectively) but higher specificity (74.0 % versus 46.7 %) and accuracy (78.4 % versus 69.0 %). During the second half (340 CXRs, 81.8 % prevalence), overall sensitivity increased to 92.8 %, specificity dropped to 53.2 %, accuracy increased to 85.6 %, this pattern mirrored in both groups, with decreased specificity (Group-1, 58.0 %; Group-2, 33.3 %) but increased sensitivity (92.7 % and 93.5 %) and accuracy (86.5 % and 81.0 %, respectively). CONCLUSIONS: Real-world CXR diagnostic performance during the COVID-19 pandemic showed overall high sensitivity with higher specificity for more experienced radiologists. The increase in accuracy over time strengthens CXR role as a first line examination in suspected COVID-19 patients.