A dual-lumen extracorporeal membrane oxygenation cannulation technique using a mobile x-ray device

PURPOSE Dual-lumen extracorporeal membrane oxygenation (ECMO) cannulation is considered technically challenging and harbors the risk of potential life-threatening complications during cannulation. Dual-lumen cannula insertion is performed under either ultrasound or fluoroscopy guidance. Both techniques have significant disadvantages, such as examiner dependence or the necessity for transportation of the patient from the intensive care unit to the operating room. DESCRIPTION Digital, mobile x-ray devices provide a novel, examiner-independent imaging modality for bedside dual-lumen ECMO cannulation. EVALUATION From November 2019 to November 2021, 23 dual-lumen cannulations were performed in 20 patients at the Department of Thoracic Surgery, Medical University of Vienna. Twelve of 23 (52.2%) were inserted in the intensive care unit using a mobile x-ray device. The remaining patients (47.8%) were cannulated in the operating room with conventional fluoroscopy guidance. In none of the procedures did cardiovascular injuries occur. Insertion site bleeding was the most common ECMO-related complication (n = 2). CONCLUSIONS Dual-lumen cannulation using sequential x-rays can be performed safely. Especially for infectious patients or patients who require an awake ECMO, this technique overcomes disadvantages of established imaging modalities

A Dual-Lumen Extracorporeal Membrane Oxygenation Cannulation Technique Using a Mobile X-Ray Device

PURPOSE Dual-lumen extracorporeal membrane oxygenation (ECMO) cannulation is considered technically challenging and harbors the risk of potential life-threatening complications during cannulation. Dual-lumen cannula insertion is performed under either ultrasound or fluoroscopy guidance. Both techniques have significant disadvantages, such as examiner dependence or the necessity for transportation of the patient from the intensive care unit to the operating room. DESCRIPTION Digital, mobile x-ray devices provide a novel, examiner-independent imaging modality for bedside dual-lumen ECMO cannulation. EVALUATION From November 2019 to November 2021, 23 dual-lumen cannulations were performed in 20 patients at the Department of Thoracic Surgery, Medical University of Vienna. Twelve of 23 (52.2%) were inserted in the intensive care unit using a mobile x-ray device. The remaining patients (47.8%) were cannulated in the operating room with conventional fluoroscopy guidance. In none of the procedures did cardiovascular injuries occur. Insertion site bleeding was the most common ECMO-related complication (n = 2). CONCLUSIONS Dual-lumen cannulation using sequential x-rays can be performed safely. Especially for infectious patients or patients who require an awake ECMO, this technique overcomes disadvantages of established imaging modalities

Lung transplantation for acute respiratory distress syndrome:A multicenter experience

Acute respiratory distress syndrome (ARDS) is a rapidly progressive lung disease with a high mortality rate. Although lung transplantation (LTx) is a well-established treatment for a variety of chronic pulmonary diseases, LTx for acute lung failure (due to ARDS) remains controversial. We reviewed posttransplant outcome of ARDS patients from three high-volume European transplant centers. Demographics and clinical data were collected and analyzed. Viral infection was the main reason for ARDS (n = 7/13, 53.8%). All patients were admitted to ICU and required mechanical ventilation, 11/13 were supported with ECMO at the time of listing. They were granted a median LAS of 76 (IQR 50-85) and waited for a median of 3 days (IQR 1.5-14). Postoperatively, median length of mechanical ventilation was 33 days (IQR 17-52.5), median length of ICU and hospital stay were 39 days (IQR 19.5-58.5) and 54 days (IQR 43.5-127). Prolongation of peripheral postoperative ECMO was required in 7/13 (53.8%) patients with a median duration of 2 days (IQR 2-7). 30-day mortality was 7.7%, 1 and 5-year survival rates were calculated as 71.6% and 54.2%, respectively. Given the lack of alternative treatment options, the herein presented results support the concept of offering live-saving LTx to carefully selected ARDS patients

LUNG TRANSPLANTATION FOR ACUTE RESPIRATORY DISTRESS SYNDROME:A MULTICENTER EXPERIENCE

Acute respiratory distress syndrome (ARDS) is a rapidly progressive lung disease with a high mortality rate. Although lung transplantation (LTx) is a well-established treatment for a variety of chronic pulmonary diseases, LTx for acute lung failure (due to ARDS) remains controversial. We reviewed posttransplant outcome of ARDS patients from three high-volume European transplant centers. Demographics and clinical data were collected and analyzed. Viral infection was the main reason for ARDS (n = 7/13, 53.8%). All patients were admitted to ICU and required mechanical ventilation, 11/13 were supported with ECMO at the time of listing. They were granted a median LAS of 76 (IQR 50-85) and waited for a median of 3 days (IQR 1.5-14). Postoperatively, median length of mechanical ventilation was 33 days (IQR 17-52.5), median length of ICU and hospital stay were 39 days (IQR 19.5-58.5) and 54 days (IQR 43.5-127). Prolongation of peripheral postoperative ECMO was required in 7/13 (53.8%) patients with a median duration of 2 days (IQR 2-7). 30-day mortality was 7.7%, 1 and 5-year survival rates were calculated as 71.6% and 54.2%, respectively. Given the lack of alternative treatment options, the herein presented results support the concept of offering live-saving LTx to carefully selected ARDS patients

Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence.

We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 18F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only 18F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body 18F-FDG PET/CT images with high accuracy