Clinical risk factors and inflammatory biomarkers of post-traumatic acute kidney injury in combat patients

A clinically-relevant mouse model that displays hemorrhage exacerbates tourniquet-induced acute kidney injury

Hemorrhage is a leading cause of death in trauma. Tourniquets are effective at controlling extremity hemorrhage and have saved lives. However, tourniquets can cause ischemia reperfusion injury of limbs, leading to systemic inflammation and other adverse effects, which results in secondary damage to the kidney, lung, and liver. A clinically relevant animal model is critical to understanding the pathophysiology of this process and developing therapeutic interventions. Despite the importance of animal models, tourniquet-induced lower limb ischemia/reperfusion (TILLIR) models to date lack a hemorrhage component. We sought to develop a new TILLIR model that included hemorrhage and analyze the subsequent impact on kidney, lung and liver injuries. Four groups of mice were examined: group 1) control, group 2) hemorrhage, group 3) tourniquet application, and group 4) hemorrhage and tourniquet application. The hemorrhagic injury consisted of the removal of 15% of blood volume through the submandibular vein. The tourniquet injury consisted of orthodontic rubber bands applied to the inguinal area bilaterally for 80 min. Mice were then placed in metabolic cages individually for 22 h to collect urine. Hemorrhage alone did not significantly affect transcutaneous glomerular filtration rate (tGFR), blood urea nitrogen (BUN) or urinary kidney injury molecule-1 (KIM-1) levels. Without hemorrhage, TILLIR decreased tGFR by 46%, increased BUN by 162%, and increased KIM-1 by 27% (p < 0.05 for all). With hemorrhage, TILLIR decreased the tGFR by 72%, increased BUN by 395%, and increased urinary KIM-1 by 37% (p < 0.05 for all). These differences were statistically significant (p < 0.05). While hemorrhage had no significant effect on TILLIR-induced renal tubular degeneration and necrosis, it significantly increased TILLIR-induced lung total injury scores and congestion, and fatty liver. In conclusion, hemorrhage exacerbates TILLIR-induced acute kidney injury and structural damage in the lung and liver.

Effects and mechanisms of extremely cold environment on body response after trauma

With the outbreak of the Ukrainian crisis, extremely cold environment warfare has once again become the focus of international attention. People exposed to extremely cold environments may suffer from cold damage, further aggravate trauma, trigger high disability and mortality rates, and even cause serious sequelae. To declare the effects and mechanisms of the extremely cold environment on the body after trauma, this paper reviews, firstly, physiological reaction of human body in an extremely cold environment. Then, the post-traumatic body response in an extremely cold environment was introduced, and finally, the sequelae of trauma in extremely cold environment was further summarized in the paper. The results indicated that extremely cold environment can cause a series of damage to the body, especially the body after trauma. The extremely cold factor is a double-edged sword, showing a favorable and unfavorable side in different aspects. Moreover, in addition to the trauma suffered by the body, the subsequent sequelae such as cognitive dysfunction, anxiety, depression and even post-traumatic stress disorder may also be induced. The paper summarizes the human body's physiological response in an extremely cold environment, and declares the effects and mechanisms of the extremely cold environment on the body after trauma, which may provide a theoretical basis for effectively improving the level of combat trauma treatment in extremely cold regions.

A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 2-Data From Nonwearables

OBJECTIVES

Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.

METHODS

We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics.

RESULTS

We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%).

CONCLUSIONS

The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

Multidimensional machine learning models predicting outcomes after trauma

BACKGROUND

An emerging body of literature supports the role of individualized prognostic tools to guide the management of patients after trauma. The aim of this study was to develop advanced modeling tools from multidimensional data sources, including immunological analytes and clinical and administrative data, to predict outcomes in trauma patients.

METHODS

This was a prospective study of trauma patients at Level 1 centers from 2015 to 2019. Clinical, flow cytometry, and serum cytokine data were collected within 48 hours of admission. Sparse logistic regression models were developed, jointly selecting predictors and estimating the risk of ventilator-associated pneumonia, acute kidney injury, complicated disposition (death, rehabilitation, or nursing facility), and return to the operating room. Model parameters (regularization controlling model sparsity) and performance estimation were obtained via nested leave-one-out cross-validation.

RESULTS

A total of 179 patients were included. The incidences of ventilator-associated pneumonia, acute kidney injury, complicated disposition, and return to the operating room were 17.7%, 28.8%, 22.5%, and 12.3%, respectively. Regarding extensive resource use, 30.7% of patients had prolonged intensive care unit stay, 73.2% had prolonged length of stay, and 23.5% had need for prolonged ventilatory support. The models were developed and cross-validated for ventilator-associated pneumonia, acute kidney injury, complicated dispositions, and return to the operating room, yielding predictive areas under the curve from 0.70 to 0.91. Each model derived its optimal predictive value by combining clinical, administrative, and immunological analyte data.

CONCLUSION

Clinical, immunological, and administrative data can be combined to predict post-traumatic outcomes and resource use. Multidimensional machine learning modeling can identify trauma patients with complicated clinical trajectories and high resource needs.

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.

Tourniquet-induced lower limb ischemia/reperfusion reduces mitochondrial function by decreasing mitochondrial biogenesis in acute kidney injury in mice

The mechanisms by which lower limb ischemia/reperfusion induces acute kidney injury (AKI) remain largely uncharacterized. We hypothesized that tourniquet-induced lower limb ischemia/reperfusion (TILLIR) would inhibit mitochondrial function in the renal cortex. We used a murine model to show that TILLIR of the high thigh regions inflicted time-dependent AKI as determined by renal function and histology. This effect was associated with decreased activities of mitochondrial complexes I, II, V and citrate synthase in the kidney cortex. Moreover, TILLIR reduced mRNA levels of a master regulator of mitochondrial biogenesis PGC-1α, and its downstream genes NDUFS1 and ATP5o in the renal cortex. TILLIR also increased serum corticosterone concentrations. TILLIR did not significantly affect protein levels of the critical regulators of mitophagy PINK1 and PARK2, mitochondrial transport proteins Tom20 and Tom70, or heat-shock protein 27. TILLIR had no significant effect on mitochondrial oxidative stress as determined by mitochondrial ability to generate reactive oxygen species, protein carbonylation, or protein levels of MnSOD and peroxiredoxin1. However, TILLIR inhibited classic autophagic flux by increasing p62 protein abundance and preventing the conversion of LC3-I to LC3-II. TILLIR increased phosphorylation of cytosolic and mitochondrial ERK1/2 and mitochondrial AKT1, as well as mitochondrial SGK1 activity. In conclusion, lower limb ischemia/reperfusion induces distal AKI by inhibiting mitochondrial function through reducing mitochondrial biogenesis. This AKI occurs without significantly affecting PINK1-PARK2-mediated mitophagy or mitochondrial oxidative stress in the kidney cortex.