Journal of Trauma and Acute Care Surgery最新文献

Background: Acute care surgery (ACS) involves rapid, high-stakes decisions with limited opportunity for preoperative planning. While machine learning (ML) may improve risk prediction and decision making in this setting, its development, validation, and implementation in ACS remain understudied. We therefore evaluated the techniques, predictor features, and outcomes used in ML-driven risk prediction models in ACS and generated recommendations to inform future research and support clinically meaningful implementation.

Methods: A systematic review of ML-driven predictive models in ACS (emergency general surgery, surgical critical care, trauma) was conducted. Models were analyzed by predictor features, outcomes, algorithms, and performance. The best-performing models for the most commonly predicted outcome were identified.

Results: Of 52 studies, 57.7% focused on trauma populations. Most models used registry data (76.8%), fewer used electronic health records (28.8%), and only five studies performed external validation after model development. Common algorithms included logistic regression (44.2%), random forest (34.6%), and decision trees (26.9%). Mortality (59.6%), complications (30.8%), and triage/severity (15.4%) were the most frequent outcomes; patient-centered/reported outcomes were absent. Features commonly included demographics, physiologic scores, and vital signs, while imaging and intraoperative data were underused. Natural language processing was used in four studies. Model performance was typically assessed using area under the receiver operating characteristic curve (88.5%), with support vector machines demonstrating the highest performance. Machine learning models generally outperformed conventional risk scores among 11 comparative studies.

Conclusion: Machine learning-driven predictive models in ACS show promising performance but are constrained by limited methodological rigor, real-world validation, and substantial heterogeneity in features, outcomes, and algorithms, challenging systematic adoption and oversight. A grounded understanding of ACS decision making workflows and their postimplementation impact may ensure clinically relevant, seamless, and safe integration of ML-based risk prediction.

Level of evidence: Systematic Review Without Meta-analysis; Level IV.

Background: CAQK (cysteine-alanine-lysine glutamine) is a homing peptide shown to selectively target injured regions of the brain. This study evaluated CAQK in a rodent model of spinal cord injury (SCI) to determine its localization capacity, dose-response characteristics, and temporal binding properties.

Methods: This is a preclinical pharmacology study of a nanotherapeutic drug delivery system. Twenty-four adult rats underwent C6 right-sided spinal cord hemicontusion. Animals received a tail vein injection of cyanine5-labeled CAQK (CAQK-Cy5) at low (0.5 mg/kg), medium (1.0 mg/kg), or high dose (2.5 mg/kg) or matched doses of free Cy5 dye as a control (n = 3 per group). Localization was monitored via in vivo fluorescence imaging at 1 and 24 hours in all animals and up to 7 days in an additional cohort of high-dose CAQK-Cy5 and Cy5 animals. Spinal cords were harvested for ex vivo imaging and histological confirmation of CAQK-Cy5 accumulation.

Results: In vivo imaging demonstrated CAQK-Cy5 signal at the SCI site within 1-hour postinjection, which persisted up to 7 days in the high-dose group. Signal intensity was dose dependent and declined over time. No significant localization was observed in control animals or uninjured spinal regions.

Conclusion: CAQK rapidly and selectively localizes to injured spinal cord tissue in a dose-responsive manner, with peak accumulation observed within 24 hours. These findings support its potential as a targeted delivery vector for injectable therapeutics in acute SCI.

Abstract: Popliteal artery injuries are rare even in busy urban trauma centers. The vast majority result from penetrating mechanisms of injury. These injuries are uncommon; therefore, few trauma surgeons and trauma centers have developed significant experience with their management. Experiences from both military and urban arenas of warfare consistently report the highest complications and amputation rates of all vascular injuries secondary to popliteal artery injuries. The popliteal artery is an end artery. Injuries cause significant ischemia, which threaten limb viability. From a surgical standpoint, they are difficult to expose and require excellent surgical technique to repair and restore blood flow in a timely fashion, prioritizing operative efficiency to decrease ischemia. This is of the utmost importance to obtain excellent results. Past and recent military conflicts have provided trauma surgeons with excellent experiences to develop a framework to manage these injuries, specifically the Vietnam War. If there are any lessons to be learned from the recent conflicts in Iraq, Afghanistan, and, currently, Ukraine, it is that trauma surgeons must be prepared to effectively and rapidly operate on these injuries.

Level of evidence: Therapeutic Study; Level III.

Background: Acute lung injury (ALI) after trauma is associated with alveolar dysfunction that causes significant morbidity with limited treatment options. We have shown that AR-R17779, a small molecule agonist of the α7 nicotinic acetylcholine receptor highly expressed on macrophages, prevents lung edema in preclinical models of injury. The mechanism by which AR-R17779 attenuates trauma-induced ALI is unknown. We hypothesized that AR-R17779 decreases trauma-induced ALI by limiting macrophage activation and preventing lung epithelial glycocalyx breakdown.

Methods: C57BL/6 mice underwent a polytrauma model consisting of lung contusion and liver crush injury with cohorts treated with an intraperitoneal dose of AR-R17779 (25 mg/kg) immediately following polytrauma. Lungs were harvested 24 hours postinjury for evaluation of ALI using histology and immunofluorescence of heparan sulfate (HS), a key component of the glycocalyx. In vitro coculture studies were performed to assess the impact of AR-R17779 on the activation of macrophages by lipopolysaccharide (LPS) and its effects on the lung epithelial glycocalyx by immunoblotting, enzyme-linked immunosorbent assay, and HS expression.

Results: AR-R17779 attenuated polytrauma-induced histological ALI and loss of HS from the lung epithelial glycocalyx. In vitro, AR-R17779 dose-dependently suppressed signal transducer and activator of transcription 3 phosphorylation and tumor necrosis factor release from LPS-treated macrophages. In coculture studies, treating LPS-stimulated macrophages with AR-R17779 decreased epithelial glycocalyx degradation compared with macrophages treated with LPS alone.

Conclusion: AR-R17779 attenuated trauma-induced ALI by preventing lung epithelial glycocalyx breakdown, which may result from the suppression of macrophage proinflammatory activity. Future research should test AR-R17779 as an adjunct to resuscitation aimed at limiting dysregulated macrophage activation and ALI after trauma.