Coronary computed tomography angiography in acute chest pain: A sustainable model with remote support.

PURPOSE: To propose a sustainable model of coronary computed tomography angiography (CCTA) use in acute coronary syndrome (ACS) in emergency department (ED) using a partially based teleradiology reporting model. We also analyzed impact of the protocol on short- and long-term patient's outcome. METHODS: During a 12-month period, 104 consecutive patients admitted to the ED for acute chest pain (ACP) with low-to-intermediate risk of ACS were selected and underwent CCTA. Medical reporting was based on a model combining on-site physician and a remote radiologist supported by a web client-based teleradiology system, covering a 24/7 service. CCTA findings were correlated with the incidence of major adverse cardiovascular events (MACEs) over a 5-year follow-up. RESULTS: CCTA ruled-out CAD in 76 patients (73.1%). Moderate (7.7%) to severe (19.2%) CAD was identified in 28 patients who were directly referred to functional tests or invasive angiography. The mean discharge time was 10.8 ± 5.8 h; patients with absent to mild disease were safely and quickly discharged. Remote reporting using a teleradiology platform was performed in 82/104 cases (78.9%), with slight impact on patient's discharge time (10.4 ± 5.6 vs. 12.1 ± 6.1 h, p: 0.24). MACEs at 6-month and at 5-year follow-up were 0.96% (n = 1/104) and 15.5% (n = 14/90). CONCLUSION: CCTA assessment of patients with ACP enables to quickly rule-out ACS, avoiding waste of time and resources, to identify patients with obstructive CAD which should be referred to subsequent tests and to stratify the risk of MACEs at short and long time. A partial teleradiology based 24/7 CCTA service is feasible and sustainable, even in small ED.

Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.

BACKGROUND: Chest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software. METHODS: This double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software: 3DSlicer, CT Lung Density Analysis, and CT Pulmo 3D. RESULTS: The agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73-0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90-0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer "LungCTAnalyzer" and the median of the visual score (0.75 with a CI 0.67-82 and with a median value of 22% of disease extension for the software and 25% for the visual values). CONCLUSIONS: This study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.