Life Over Limb: Why Not Both? Revisiting Tourniquet Practices Based on Lessons Learned From the War in Ukraine.

The use of tourniquets for life-threatening limb hemorrhage is standard of care in military and civilian medicine. The United States (U.S.) Department of Defense (DoD) Committee on Tactical Combat Casualty Care (CoTCCC) guidelines, as part of the Joint Trauma System, support the application of tourniquets within a structured system reliant on highly trained medics and expeditious evacuation. Current practices by entities such as the DoD and North Atlantic Treaty Organization (NATO) are supported by evidence collected in counter-insurgency operations and other conflicts in which transport times to care rarely went beyond one hour, and casualty rates and tactical situations rarely exceeded capabilities. Tourniquets cause complications when misused or utilized for prolonged durations, and in near-peer or peer-peer conflicts, contested airspace and the impact of high-attrition warfare may increase time to definitive care and limit training resources. We present a series of cases from the war in Ukraine that suggest tourniquet practices are contributing to complications such as limb amputation, overall morbidity and mortality, and increased burden on the medical system. We discuss factors that contribute to this phenomenon and propose interventions for use in current and future similar contexts, with the ultimate goal of reducing morbidity and mortality.

Defining High-risk Emergency Chief Complaints: Data-driven Triage for Low- and Middle-income Countries.

OBJECTIVES: Emergency medicine in low- and middle-income countries (LMICs) is hindered by lack of research into patient outcomes. Chief complaints (CCs) are fundamental to emergency care but have only recently been uniquely codified for an LMIC setting in Uganda. It is not known whether CCs independently predict emergency unit patient outcomes. METHODS: Patient data collected in a Ugandan emergency unit between 2009 and 2018 were randomized into validation and derivation data sets. A recursive partitioning algorithm stratified CCs by 3-day mortality risk in each group. The process was repeated in 10,000 bootstrap samples to create an averaged risk ranking. Based on this ranking, CCs were categorized as "high-risk" (>2× baseline mortality), "medium-risk" (between 2 and 0.5× baseline mortality), and "low-risk" (<0.5× baseline mortality). Risk categories were then included in a logistic regression model to determine if CCs independently predicted 3-day mortality. RESULTS: Overall, the derivation data set included 21,953 individuals with 7,313 in the validation data set. In total, 43 complaints were categorized, and 12 CCs were identified as high-risk. When controlled for triage data including age, sex, HIV status, vital signs, level of consciousness, and number of complaints, high-risk CCs significantly increased 3-day mortality odds ratio (OR = 2.39, 95% confidence interval [CI] = 1.95 to 2.93, p < 0.001) while low-risk CCs significantly decreased 3-day mortality odds (OR = 0.16, 95% CI = 0.09 to 0.29, p < 0.001). CONCLUSIONS: High-risk CCs were identified and found to predict increased 3-day mortality independent of vital signs and other data available at triage. This list can be used to expand local triage systems and inform emergency training programs. The methodology can be reproduced in other LMIC settings to reflect their local disease patterns.

Anxiety, depression, and regret of donation in living kidney donors.

BACKGROUND: Previous studies have reported a wide range of prevalence of post-donation anxiety, depression, and regret in living kidney donors (LKDs). It is also unclear what risk factors are associated with these outcomes. METHODS: We screened 825 LKDs for anxiety and depression using 2-item GAD-2 and PHQ-2 scales and asked about regret. RESULTS: Overall, 5.5% screened positive for anxiety, 4.2% for depression, and 2.1% reported regretting their donation. While there was moderate correlation between positive anxiety and depression screens (r = 0.52), there was no correlation between regret and positive screens (r < 0.1 for both). A positive anxiety screen was more likely in LKDs with a positive depression screen (adjusted relative risk [aRR] 13.72, 95% confidence interval [CI] 6.78-27.74, p < 0.001). Similarly, a positive depression screen was more likely in LKDs with a positive anxiety screen (aRR 19.50, 95% CI 6.94-54.81, p < 0.001), as well as in those whose recipients experienced graft loss (aRR 5.38, 95% CI 1.29-22.32, p = 0.02). Regret was more likely in LKDs with a positive anxiety screen (aRR 5.68, 95% CI 1.20-26.90, p = 0.03). This was a single center cross-sectional study which may limit generalizability and examination of causal effects. Also, due to the low prevalence of adverse psychosocial outcomes, we may lack power to detect some associations between donor characteristics and anxiety, depression, or regret. CONCLUSIONS: Although there is a low prevalence of anxiety, depression, and regret of donation among LKDs, these are interrelated conditions and a positive screen for one condition should prompt evaluation for other conditions.

MELD as a metric for survival benefit of liver transplantation.

Currently, there is debate among the liver transplant community regarding the most appropriate mechanism for organ allocation: urgency-based (MELD) versus utility-based (survival benefit). We hypothesize that MELD and survival benefit are closely associated, and therefore, our current MELD-based allocation already reflects utility-based allocation. We used generalized gamma parametric models to quantify survival benefit of LT across MELD categories among 74 196 adult liver-only active candidates between 2006 and 2016 in the United States. We calculated time ratios (TR) of relative life expectancy with transplantation versus without and calculated expected life years gained after LT. LT extended life expectancy (TR > 1) for patients with MELD > 10. The highest MELD was associated with the longest relative life expectancy (TR = 1.05 1.201.37 for MELD 11-15, 2.29 2.492.70 for MELD 16-20, 5.30 5.726.16 for MELD 21-25, 15.12 16.3517.67 for MELD 26-30; 39.26 43.2147.55 for MELD 31-34; 120.04 128.25137.02 for MELD 35-40). As a result, candidates with the highest MELD gained the most life years after LT: 0.2, 1.5, 3.5, 5.8, 6.9, 7.2 years for MELD 11-15, 16-20, 21-25, 26-30, 31-34, 35-40, respectively. Therefore, prioritizing candidates by MELD remains a simple, effective strategy for prioritizing candidates with a higher transplant survival benefit over those with lower survival benefit.