Partial Resuscitative Endovascular Balloon Occlusion of the Aorta Aids in the Successful Non-Operative Management of a Life-Threatening Penetrating Liver Injury.

In recent years, isolated non-operative management of penetrating liver injuries has become the standard of care for the hemodynamically stable patient. However, when the patient becomes hemodynamically unstable, adjuncts such as resuscitative endovascular balloon occlusion of the aorta (REBOA) deployed in Zone 1 can be used to achieve complete aortic occlusion from the celiac axis down. Unfortunately, hemorrhage control through REBOA comes at the risk of deadly intra-abdominal ischemia. Partial REBOA (pREBOA) introduces the opportunity to make targeted changes in volume and thus titrate the amount of aortic occlusion in real-time to adequately manage hemorrhage while allowing some distal blood flow. This is a novel approach and one which may give providers more time to gain definitive hemorrhage control while minimizing the morbidity of ischemia. Here, we present a case of life-threatening penetrating liver injury that was successfully managed non-operatively with the assistance of p-REBOA.

Use of Kaolin-Impregnated Gauze Aids in Hemostasis and Blood Loss Mitigation in a Penetrating Injury to the Bladder and Small Bowel.

Hemorrhage control can be technically challenging in penetrating injuries to the pelvis. In an era of decreased availability of blood, rapid hemostasis is critical to minimize blood loss, limit transfusions, and control contamination from hollow viscus injuries. QuikClot Control+® 12x12 Hemostatic Device(C+) (Teleflex Medical OEM, Plymouth, MN), a form of kaolin-impregnated gauze, maybe a helpful adjunct to ebb the flow of hemorrhage from large surface area wounds. We present a case in which C+ was utilized in the preperitoneal packing of a gunshot wound to the pelvis and aided in obtaining hemostasis while simultaneously allowing the team time to complete the remainder of the case. Though further large randomized control trials are required to identify the role of C+ in trauma laparotomy, it remains a tool in the surgeon's armamentarium when dealing with hemorrhage.

A computer vision system for deep learning-based detection of patient mobilization activities in the ICU.

Early and frequent patient mobilization substantially mitigates risk for post-intensive care syndrome and long-term functional impairment. We developed and tested computer vision algorithms to detect patient mobilization activities occurring in an adult ICU. Mobility activities were defined as moving the patient into and out of bed, and moving the patient into and out of a chair. A data set of privacy-safe-depth-video images was collected in the Intermountain LDS Hospital ICU, comprising 563 instances of mobility activities and 98,801 total frames of video data from seven wall-mounted depth sensors. In all, 67% of the mobility activity instances were used to train algorithms to detect mobility activity occurrence and duration, and the number of healthcare personnel involved in each activity. The remaining 33% of the mobility instances were used for algorithm evaluation. The algorithm for detecting mobility activities attained a mean specificity of 89.2% and sensitivity of 87.2% over the four activities; the algorithm for quantifying the number of personnel involved attained a mean accuracy of 68.8%.