Mortality in hypotensive combat casualties who require emergent laparotomy in the forward deployed environment.

INTRODUCTION: Mortality rates among hypotensive civilian patients requiring emergent laparotomy exceed 40%. Damage control (DCR) principles were incorporated into the military's Clinical Practice Guidelines (CPG) in 2008. We examined combat casualties requiring emergent laparotomy to characterize how mortality rates compare to hypotensive civilian trauma patients. METHODS: The DoD Trauma Registry (2004-2020) was queried for adults who underwent combat laparotomy. Patients who were hypotensive were compared to normotensive patients. Mortality was the outcome of interest. Mortality rates before (2004-2007) and after (2009-2020) DCR CPG implementation were analyzed. RESULTS: 1051 patients were studied. Overall mortality was 6.5% for normotensive casualties and 28.7% for hypotensive casualties. Mortality decreased in normotensive patients but remained unchanged in hypotensive patients following the implementation of the DCR CPG. CONCLUSION: Hypotensive combat casualties undergoing emergent laparotomy demonstrated a mortality rate of 29.5%. Despite many advances, mortality rates remain high in hypotensive patients requiring emergent laparotomy.

The use of mobile thermal imaging and machine learning technology for the detection of early surgical site infections.

BACKGROUND: Surgical Site Infections (SSI) yield subtle, early signs that are not readily identifiable. This study sought to develop a machine learning algorithm that could identify early SSIs based on thermal images. METHODS: Images were taken of surgical incisions on 193 patients who underwent a variety of surgical procedures. Two neural network models were generated to detect SSIs, one using RGB images, and one incorporating thermal images. Accuracy and Jaccard Index were the primary metrics by which models were evaluated. RESULTS: Only 5 patients in our cohort developed SSIs (2.8%). Models were instead generated to demarcate the wound site. The models had 89-92% accuracy in predicting pixel class. The Jaccard indices for the RGB and RGB â€‹+ â€‹Thermal models were 66% and 64%, respectively. CONCLUSIONS: Although the low infection rate precluded the ability of our models to identify surgical site infections, we were able to generate two models to successfully segment wounds. This proof-of-concept study demonstrates that computer vision has the potential to support future surgical applications.