Jackson, Amanda

Abstract Background Radiotherapy treatment planning requires knowledge of a patient's anatomical surface for use in dose calculations, collision checks, or surface‐guided radiation therapy (SGRT). This information is usually obtained from a computed tomography (CT) scan which may encounter field‐of‐view (FOV) limitations or require additional radiation dose beyond the treatment area to capture the full patient habitus. Purpose This study demonstrates an affordable alternative solution to obtain a patient's surface information without CT or room mounted surface‐guidance imaging cameras for the purpose of detecting patient collisions and providing additional skin surface information needed for appropriate depth calculations of clipped FOV scans. Methods Three‐dimensional (3D) surface models of five patients were acquired immediately prior to CT simulation using an iPad Air 2 equipped with a Structure Sensor Pro and Monocle SS application. The 3D surfaces were manually registered to the CT images and nearest distances between CT surface and 3D surface were calculated. Nearest distance calculations were also performed with a 2 mm CT rind structure that excluded select regions not relevant to surface detection. To demonstrate the advantage of having additional scan length for clearance checks, a collision simulated plan was evaluated with a shortened CT scan length versus with additional 3D surface information. Results CT rind to 3D scan surface mean distances for each patient were 1.65 mm, 0.99 mm, −0.93 mm, −0.79 mm, and −1.53 mm with standard deviations of 6.51 mm, 6.71 mm, 3.38 mm, 2.84 mm, and 2.69 mm. Conclusion This study demonstrates the feasibility of an accessible low‐cost 3D surface scanning solution to supplement clipped field‐of‐view CT scans and reduce CT scan lengths by obtaining important patient surface data for improved calculation accuracy, treatment clearance checks, and SGRT.

Background Coronavirus disease 2019 (COVID-19) is associated with a prothrombotic state; leading to multiple sequelae. We sought to detect whether thromboelastography (TEG) parameters would be able to detect thromboembolic events in patients hospitalized with COVID-19. Methods We performed a retrospective multicenter case–control study of the Collaborative Research to Understand the Sequelae of Harm in COVID (CRUSH COVID) registry of 8 tertiary care level hospitals in the United States (US). This registry contains adult patients with COVID-19 hospitalized between March 2020 and September 2020. Results A total of 277 hospitalized COVID-19 patients were analyzed to determine whether conventional coagulation TEG parameters were associated with venous thromboembolic (VTE) and thrombotic events during hospitalization. A clotting index (CI) >3 was present in 45.8% of the population, consistent with a hypercoagulable state. Eighty-three percent of the patients had clot lysis at 30 min (LY30) = 0, consistent with fibrinolysis shutdown, with a median of 0.1%. We did not find TEG parameters (LY30 area under the receiver operating characteristic [ROC] curve [AUC] = 0.55, 95% CI: 0.44-0.65, P value = .32; alpha angle [α] AUC = 0.58, 95% CI: 0.47-0.69, P value = .17; K time AUC = 0.58, 95% CI: 0.46-0.69, P value = .67; maximum amplitude (MA) AUC = 0.54, 95% CI: 0.44-0.64, P value = .47; reaction time [R time] AUC = 0.53, 95% CI: 0.42-0.65, P value = .70) to be a good discriminator for VTE. We also did not find TEG parameters (LY30 AUC = 0.51, 95% CI: 0.42-0.60, P value = .84; R time AUC = 0.57, 95%CI: 0.48-0.67, P value .07; α AUC = 0.59, 95%CI: 0.51-0.68, P value = .02; K time AUC = 0.62, 95% CI: 0.53-0.70, P value = .07; MA AUC = 0.65, 95% CI: 0.57-0.74, P value < .01) to be a good discriminator for thrombotic events. Conclusions In this retrospective multicenter cohort study, TEG in COVID-19 hospitalized patients may indicate a hypercoagulable state, however, its use in detecting VTE or thrombotic events is limited in this population.