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Background The association of pulmonary embolism (PE) with deep vein thrombosis (DVT) in patients with coronavirus disease 2019 (COVID-19) remains unclear, and the diagnostic accuracy of D -dimer tests for PE is unknown. Purpose To conduct meta-analysis of the study-level incidence of PE and DVT and to evaluate the diagnostic accuracy of D -dimer tests for PE from multicenter individual patient data. Materials and Methods A systematic literature search identified studies evaluating the incidence of PE or DVT in patients with COVID-19 from January 1, 2020, to June 15, 2020. These outcomes were pooled using a random-effects model and were further evaluated using metaregression analysis. The diagnostic accuracy of D -dimer tests for PE was estimated on the basis of individual patient data using the summary receiver operating characteristic curve. Results Twenty-seven studies with 3342 patients with COVID-19 were included in the analysis. The pooled incidence rates of PE and DVT were 16.5% (95% CI: 11.6, 22.9; I 2 = 0.93) and 14.8% (95% CI: 8.5, 24.5; I 2 = 0.94), respectively. PE was more frequently found in patients who were admitted to the intensive care unit (ICU) (24.7% [95% CI: 18.6, 32.1] vs 10.5% [95% CI: 5.1, 20.2] in those not admitted to the ICU) and in studies with universal screening using CT pulmonary angiography. DVT was present in 42.4% of patients with PE. D -dimer tests had an area under the receiver operating characteristic curve of 0.737 for PE, and D -dimer levels of 500 and 1000 μg/L showed high sensitivity (96% and 91%, respectively) but low specificity (10% and 24%, respectively). Conclusion Pulmonary embolism (PE) and deep vein thrombosis (DVT) occurred in 16.5% and 14.8% of patients with coronavirus disease 2019 (COVID-19), respectively, and more than half of patients with PE lacked DVT. The cutoffs of D -dimer levels used to exclude PE in preexisting guidelines seem applicable to patients with COVID-19. © RSNA, 2020 Supplemental material is available for this article. See also the editorial by Woodard in this issue.

Highlights An algorithm for automatic Covid-19 quantification based on 2D & 3D deep convolutional neural networks is presented. A Covid-19-specific holistic, highly compact multi-omics signature integrating imaging/clinical/ biological data and associated comorbidities for automatic patient staging is presented and evaluated. Short and Long-term prognosis for clinical resources optimization offering alternative/complementary means to facilitate triage for Covid-19 Clinically-relevant quantification and staging tool validated by comparison with clinical experts is reported.