Acute Pulmonary Embolism in COVID-19: A Potential Connection between Venous Congestion and Thrombus Distribution.

Background: Vascular abnormalities, including venous congestion (VC) and pulmonary embolism (PE), have been recognized as frequent COVID-19 imaging patterns and proposed as severity markers. However, the underlying pathophysiological mechanisms remain unclear. In this study, we aimed to characterize the relationship between VC, PE distribution, and alveolar opacities (AO). Methods: This multicenter observational registry (clinicaltrials.gov identifier NCT04824313) included 268 patients diagnosed with SARS-CoV-2 infection and subjected to contrast-enhanced CT between March and June 2020. Acute PE was diagnosed in 61 (22.8%) patients, including 17 females (27.9%), at a mean age of 61.7 ± 14.2 years. Demographic, laboratory, and outcome data were retrieved. We analyzed CT images at the segmental level regarding VC (qualitatively and quantitatively [diameter]), AO (semi-quantitatively as absent, <50%, or >50% involvement), clot location, and distribution related to VC and AO. Segments with vs. without PE were compared. Results: Out of 411 emboli, 82 (20%) were lobar or more proximal and 329 (80%) were segmental or subsegmental. Venous diameters were significantly higher in segments with AO (p = 0.031), unlike arteries (p = 0.138). At the segmental level, 77% of emboli were associated with VC. Overall, PE occurred in 28.2% of segments with AO vs. 21.8% without (p = 0.047). In the absence of VC, however, AO did not affect PE rates (p = 0.94). Conclusions: Vascular changes predominantly affected veins, and most PEs were located in segments with VC. In the absence of VC, AOs were not associated with the PE rate. VC might result from increased flow supported by the hypothesis of pulmonary arteriovenous anastomosis dysregulation as a relevant contributing factor.

Non-invasive evaluation of new-onset atrial fibrillation after cardiac surgery: a protocol for the BigMap study.

AIMS: New-onset atrial fibrillation (NOAF) is the most common complication after cardiac surgery, occurring in 25-50% of patients. It is associated with post-operative stroke, increased mortality, prolonged hospital length of stay, and higher treatment costs. Previous small observational studies have identified the left atrium as a source of the electrical rotors and foci maintaining NOAF, but confirmation by a large prospective clinical study is still missing. The aim of the proposed study is to investigate whether the source of NOAF lies in the left atrium. The correct identification of NOAF-maintaining structures in cardiac surgical patients might offer potential therapeutic targets for prophylactic perioperative ablation strategies. METHODS AND RESULTS: This is a prospective single-centre observational study of patients developing NOAF after cardiac surgery. The primary outcome is the description of NOAF-maintaining structures within the atria. Key secondary outcomes include overall mortality, intensive care unit length of stay, hospital-ventilator-free days, and proportion of persistent NOAF. In NOAF patients, the non-invasive electrophysiological mapping will be conducted using a 252-electrode electrocardiogram vest. After mapping, a low-dose computed tomography scan of the chest will be performed to integrate the electrophysiological mapping results into a 3D picture of the heart. The study will include approximately 570 patients, of whom 30% (n = 170) are expected to develop NOAF. Sample size calculation revealed that 157 NOAF patients are necessary to assess the primary outcome. Patients will be tracked for a total of 5 years. CONCLUSIONS: This is the largest prospective study to date describing the electrophysiological mechanisms of NOAF using non-invasive mapping.

Deep learning-based automated detection of pulmonary embolism on CT pulmonary angiograms: No significant effects on report communication times and patient turnaround in the emergency department nine months after technical implementation.

OBJECTIVES: Rapid communication of CT exams positive for pulmonary embolism (PE) is crucial for timely initiation of anticoagulation and patient outcome. It is unknown if deep learning automated detection of PE on CT Pulmonary Angiograms (CTPA) in combination with worklist prioritization and an electronic notification system (ENS) can improve communication times and patient turnaround in the Emergency Department (ED). METHODS: In 01/2019, an ENS allowing direct communication between radiology and ED was installed. Starting in 10/2019, CTPAs were processed by a deep learning (DL)-powered algorithm for detection of PE. CTPAs acquired between 04/2018 and 06/2020 (n = 1808) were analysed. To assess the impact of the ENS and the DL-algorithm, radiology report reading times (RRT), radiology report communication time (RCT), time to anticoagulation (TTA), and patient turnaround times (TAT) in the ED were compared for three consecutive time periods. Performance measures of the algorithm were calculated on a per exam level (sensitivity, specificity, PPV, NPV, F1-score), with written reports and exam review as ground truth. RESULTS: Sensitivity of the algorithm was 79.6 % (95 %CI:70.8-87.2%), specificity 95.0 % (95 %CI:92.0-97.1%), PPV 82.2 % (95 %CI:73.9-88.3), and NPV 94.1 % (95 %CI:91.4-96 %). There was no statistically significant reduction of any of the observed times (RRT, RCT, TTA, TAT). CONCLUSION: DL-assisted detection of PE in CTPAs and ENS-assisted communication of results to referring physicians technically work. However, the mere clinical introduction of these tools, even if they exhibit a good performance, is not sufficient to achieve significant effects on clinical performance measures.