Utilizing Precision Medicine to Estimate Timing for Surgical Closure of Traumatic Extremity Wounds

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

BACKGROUND

Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8.

METHODS

We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open.

RESULTS

Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time."

CONCLUSION

We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.

The influence of microbial colonization on inflammatory versus pro-healing trajectories in combat extremity wounds

A combination of improved body armor, medical transportation, and treatment has led to the increased survival of warfighters from combat extremity injuries predominantly caused by blasts in modern conflicts. Despite advances, a high rate of complications such as wound infections, wound failure, amputations, and a decreased quality of life exist. To study the molecular underpinnings of wound failure, wound tissue biopsies from combat extremity injuries had RNA extracted and sequenced. Wounds were classified by colonization (colonized vs. non-colonized) and outcome (healed vs. failed) status. Differences in gene expression were investigated between timepoints at a gene level, and longitudinally by multi-gene networks, inferred proportions of immune cells, and expression of healing-related functions. Differences between wound outcomes in colonized wounds were more apparent than in non-colonized wounds. Colonized/healed wounds appeared able to mount an adaptive immune response to infection and progress beyond the inflammatory stage of healing, while colonized/failed wounds did not. Although, both colonized and non-colonized failed wounds showed increasing inferred immune and inflammatory programs, non-colonized/failed wounds progressed beyond the inflammatory stage, suggesting different mechanisms of failure dependent on colonization status. Overall, these data reveal gene expression profile differences in healing wounds that may be utilized to improve clinical treatment paradigms.

Inhibition of focal adhesion kinase 2 results in a macrophage polarization shift to M2 which attenuates local and systemic inflammation and reduces heterotopic ossification after polysystem extremity trauma

Introduction

Heterotopic ossification (HO) is a complex pathology often observed in combat injured casualties who have sustained severe, high energy polytraumatic extremity injuries. Once HO has developed, prophylactic therapies are limited outside of surgical excision. Tourniquet-induced ischemia injury (IR) exacerbates trauma-mediated musculoskeletal tissue injury, inflammation, osteogenic progenitor cell development and HO formation. Others have shown that focal adhesion kinase-2 (FAK2) plays a key role in regulating early inflammatory signaling events. Therefore, we hypothesized that targeting FAK2 prophylactically would mitigate extremity trauma induced IR inflammation and HO formation.

Methods

We tested whether the continuous infusion of a FAK2 inhibitor (Defactinib, PF-573228; 6.94 µg/kg/min for 14 days) can mitigate ectopic bone formation (HO) using an established blast-related extremity injury model involving femoral fracture, quadriceps crush injury, three hours of tourniquet-induced limb ischemia, and hindlimb amputation through the fracture site. Tissue inflammation, infiltrating cells, osteogenic progenitor cell content were assessed at POD-7. Micro-computed tomography imaging was used to quantify mature HO at POD-56.

Results

In comparison to vehicle control-treated rats, FAK2 administration resulted in no marked wound healing complications or weight loss. FAK2 treatment decreased HO by 43%. At POD-7, marked reductions in tissue proinflammatory gene expression and assayable osteogenic progenitor cells were measured, albeit no significant changes in expression patterns of angiogenic, chondrogenic and osteogenic genes. At the same timepoint, injured tissue from FAK-treated rats had fewer infiltrating cells. Additionally, gene expression analyses of tissue infiltrating cells resulted in a more measurable shift from an M1 inflammatory to an M2 anti-inflammatory macrophage phenotype in the FAK2 inhibitor-treated group.

Discussion

Our findings suggest that FAK2 inhibition may be a novel strategy to dampen trauma-induced inflammation and attenuate HO in patients at high risk as a consequence of severe musculoskeletal polytrauma.

Central role for neurally dysregulated IL-17A in dynamic networks of systemic and local inflammation in combat casualties

Dynamic Network Analysis (DyNA) and Dynamic Hypergraphs (DyHyp) were used to define protein-level inflammatory networks at the local (wound effluent) and systemic circulation (serum) levels from 140 active-duty, injured service members (59 with TBI and 81 non-TBI). Interleukin (IL)-17A was the only biomarker elevated significantly in both serum and effluent in TBI vs. non-TBI casualties, and the mediator with the most DyNA connections in TBI wounds. DyNA combining serum and effluent data to define cross-compartment correlations suggested that IL-17A bridges local and systemic circulation at late time points. DyHyp suggested that systemic IL-17A upregulation in TBI patients was associated with tumor necrosis factor-α, while IL-17A downregulation in non-TBI patients was associated with interferon-γ. Correlation analysis suggested differential upregulation of pathogenic Th17 cells, non-pathogenic Th17 cells, and memory/effector T cells. This was associated with reduced procalcitonin in both effluent and serum of TBI patients, in support of an antibacterial effect of Th17 cells in TBI patients. Dysregulation of Th17 responses following TBI may drive cross-compartment inflammation following combat injury, counteracting wound infection at the cost of elevated systemic inflammation.

Transcriptomic, Proteomic, and Morphologic Characterization of Healing in Volumetric Muscle Loss

Skeletal muscle has a robust, inherent ability to regenerate in response to injury from acute to chronic. In severe trauma, however, complete regeneration is not possible, resulting in a permanent loss of skeletal muscle tissue referred to as volumetric muscle loss (VML). There are few consistently reliable therapeutic or surgical options to address VML. A major limitation in investigation of possible therapies is the absence of a well-characterized large animal model. Here, we present results of a comprehensive transcriptomic, proteomic, and morphologic characterization of wound healing following volumetric muscle loss in a novel canine model of VML which we compare to a nine-patient cohort of combat-associated VML. The canine model is translationally relevant as it provides both a regional (spatial) and temporal map of the wound healing processes that occur in human VML. Collectively, these data show the spatiotemporal transcriptomic, proteomic, and morphologic properties of canine VML healing as a framework and model system applicable to future studies investigating novel therapies for human VML.

Metagenomic features of bioburden serve as outcome indicators in combat extremity wounds

Battlefield injury management requires specialized care, and wound infection is a frequent complication. Challenges related to characterizing relevant pathogens further complicates treatment. Applying metagenomics to wounds offers a comprehensive path toward assessing microbial genomic fingerprints and could indicate prognostic variables for future decision support tools. Wound specimens from combat-injured U.S. service members, obtained during surgical debridements before delayed wound closure, were subjected to whole metagenome analysis and targeted enrichment of antimicrobial resistance genes. Results did not indicate a singular, common microbial metagenomic profile for wound failure, instead reflecting a complex microenvironment with varying bioburden diversity across outcomes. Genus-level Pseudomonas detection was associated with wound failure at all surgeries. A logistic regression model was fit to the presence and absence of antimicrobial resistance classes to assess associations with nosocomial pathogens. A. baumannii detection was associated with detection of genomic signatures for resistance to trimethoprim, aminoglycosides, bacitracin, and polymyxin. Machine learning classifiers were applied to identify wound and microbial variables associated with outcome. Feature importance rankings averaged across models indicated the variables with the largest effects on predicting wound outcome, including an increase in P. putida sequence reads. These results describe the microbial genomic determinants in combat wound bioburden and demonstrate metagenomic investigation as a comprehensive tool for providing information toward aiding treatment of combat-related injuries.

Proteomic signatures for perioperative oxygen delivery in skin after major elective surgery: mechanistic sub-study of a randomised controlled trial

BACKGROUND

Maintaining adequate oxygen delivery (DO₂) after major surgery is associated with minimising organ dysfunction. Skin is particularly vulnerable to reduced DO₂. We tested the hypothesis that reduced perioperative DO₂ fuels inflammation in metabolically compromised skin after major surgery.

METHODS

Participants undergoing elective oesophagectomy were randomised immediately after surgery to standard of care or haemodynamic therapy to achieve their individualised preoperative DO₂. Abdominal punch skin biopsies were snap-frozen before and 48 h after surgery. On-line two-dimensional liquid chromatography and ultra-high-definition label-free mass spectrometry was used to characterise the skin proteome. The primary outcome was proteomic changes compared between normal (≥preoperative value before induction of anaesthesia) and low DO₂ (<preoperative value before induction of anaesthesia) after surgery. Secondary outcomes were functional enrichment analysis of up/down-regulated proteins (Ingenuity pathway analysis; STRING Protein-Protein Interaction Networks). Immunohistochemistry and immunoblotting confirmed selected proteomic findings in skin biopsies obtained from patients after hepatic resection.

RESULTS

Paired punch skin biopsies were obtained from 35 participants (mean age: 68 yr; 31% female), of whom 17 underwent oesophagectomy. There were 14/2096 proteins associated with normal (n=10) vs low (n=7) DO₂ after oesophagectomy. Failure to maintain preoperative DO₂ was associated with upregulation of proteins counteracting oxidative stress. Normal DO₂ after surgery was associated with pathways involving leucocyte recruitment and upregulation of an antimicrobial peptidoglycan recognition protein. Immunohistochemistry (n=6 patients) and immunoblots after liver resection (n=12 patients) supported the proteomic findings.

CONCLUSIONS

Proteomic profiles in serial skin biopsies identified organ-protective mechanisms associated with normal DO₂ after major surgery.

CLINICAL TRIAL REGISTRATION

ISRCTN76894700.

Immune response profiling in patients with traumatic injuries associated with alcohol ingestion

Traumatic injuries afflict more than 5 million people globally every year. Current and past animal research has demonstrated association among alcohol, trauma, and impaired immune function, whereas human registries have shown association between alcohol and morbidity as well as mortality. The purpose of this study is to elucidate the immune interactions with alcohol in traumatically injured patients. We prospectively enrolled 379 patients after trauma at three medical centers in the Surgical Critical Care Initiative. Plasma was analyzed using Luminex for up to 35 different cytokines. Collected samples were grouped by patients with detectable plasma alcohol levels versus those without. Univariate testing determined differences in analytes between groups. We built Bayesian belief networks with multiple minimum descriptive lengths to compare the two groups. All 379 patient samples were analyzed. Two hundred eighty-two (74.4%) patients were men, and 143 (37.7%) were White. Patients had a median intensive care unit length of stay (LOS) of 5.8 days and hospital LOS of 12 days. Using single variate analyses, eight different cytokines were differentially associated with alcohol. Cytokines IL-12 and IL-6 were important nodes in both models and IL-10 was a prominent node in the nonalcohol model. This study found select immune function differed between traumatically injured patients with measurable serum alcohol levels as compared with those without. Traumatically injured patients with positive blood alcohol content appear less able to inhibit inflammatory stress. Alcohol appears to suppress pro-inflammatory IL-12 and IL-6, whereas patients without alcohol have greater levels of anti-inflammatory IL-10 expressed at injury and may better regulate anti-inflammatory pathways. Future studies should determine the relationship with these markers with clinically oriented outcomes.

Prediction of venous thromboembolism using clinical and serum biomarker data from a military cohort of trauma patients

INTRODUCTION

Venous thromboembolism (VTE) is a frequent complication of trauma associated with high mortality and morbidity. Clinicians lack appropriate tools for stratifying trauma patients for VTE, thus have yet to be able to predict when to intervene. We aimed to compare random forest (RF) and logistic regression (LR) predictive modelling for VTE using (1) clinical measures alone, (2) serum biomarkers alone and (3) clinical measures plus serum biomarkers.

METHODS

Data were collected from 73 military casualties with at least one extremity wound and prospectively enrolled in an observational study between 2007 and 2012. Clinical and serum cytokine data were collected. Modelling was performed with RF and LR based on the presence or absence of deep vein thrombosis (DVT) and/or pulmonary embolism (PE). For comparison, LR was also performed on the final variables from the RF model. Sensitivity/specificity and area under the curve (AUC) were reported.

RESULTS

Of the 73 patients (median Injury Severity Score=16), nine (12.3%) developed VTE, four (5.5%) with DVT, four (5.5%) with PE, and one (1.4%) with both DVT and PE. In all sets of predictive models, RF outperformed LR. The best RF model generated with clinical and serum biomarkers included five variables (interleukin-15, monokine induced by gamma, vascular endothelial growth factor, total blood products at resuscitation and presence of soft tissue injury) and had an AUC of 0.946, sensitivity of 0.992 and specificity of 0.838.

CONCLUSIONS

VTE may be predicted by clinical and molecular biomarkers in trauma patients. This will allow the development of clinical decision support tools which can help inform the management of high-risk patients for VTE.