Heart Rate Variability Can Detect Blunt Traumatic Brain Injury Within the First Hour.

INTRODUCTION: In patients with multi-organ system trauma, the diagnosis of coinciding traumatic brain injury can be difficult due to injuries from the hemorrhagic shock that confound clinical and radiographic signs of traumatic brain injury. In this study, a novel technique using heart rate variability was developed in a porcine model to detect traumatic brain injury early in the setting of hemorrhagic shock without the need for radiographic imaging or clinical exam. METHODS: A porcine model of hemorrhagic shock was used with an arm of swine receiving hemorrhagic shock alone and hemorrhagic shock with traumatic brain injury. High-resolution heart rate frequencies were collected at different time intervals using waveforms based on voltage delivered from the heart rate monitor. Waveforms were analyzed to assess statistically significant differences between heart rate variability parameters in those with hemorrhagic shock and traumatic brain injury versus those with only hemorrhagic shock. Stochastic analysis was used to assess the validity of results and create a model by machine learning to better assess the presence of traumatic brain injury. RESULTS: Significant differences were found in several heart rate variability parameters between the two groups. Additionally, significant differences in heart rate variability parameters were found in swine within 1 hour of inducing hemorrhage in those with traumatic brain injury versus those without. These results were confirmed with stochastic analysis and machine learning was used to generate a model which determined the presence of traumatic brain injury in the setting of hemorrhage shock with 91.6% accuracy. CONCLUSIONS:  Heart rate variability represents a promising diagnostic tool to aid in the diagnosis of traumatic brain injury within 1 hour of injury.

Kidney Injury in Critically Ill Patients Treated with Vancomycin and Zosyn or an Alternative: A Systematic Review and Meta-Analysis.

Background: Zosyn® (piperacillin-tazobactam; Pfizer Medical, New York, NY), a valuable antibiotic against gram-negative bacteria, combined with vancomycin (Z+V) is known for its high incidence of acute kidney injury (AKI), particularly in the intensive care unit (ICU), leading to the frequent use of alternatives for gram-negative coverage (Alt+V). Because there are limited data describing AKI on these alternative antibiotic agents, a systematic review and meta-analysis was conducted to determine if these regimens were indeed associated with decreased rates of AKI. Patients and Methods: A literature review was performed electronically from its inception to November 1, 2018, screening for relevant literature by title, abstract and full text according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines within the following databases: PubMed/Medline, CINAHL, Scopus, and Cochrane Central Register of Controlled Trials. Studies were included if they contained adults who had been admitted to the ICU for treatment and had received a combination of intravenous Z + V or Alt+V as well as had AKI measured during administration of these antibiotic agents. Studies were excluded if they represented pediatric populations, did not receive care in an ICU during their hospital admission, only received monotherapy for antibiotic treatment or received antibiotic treatment for less than 48 hours. Independent extraction was performed by two reviewers. Risk of bias was assessed using the Risk of Bias in Non-randomised Studies of Interventions (ROBINS-I) methodology for retrospective studies. Random-effects models were used to calculate any differences between rates of AKI after Z + V or Alt + V. Results: Fourteen articles (totaling 30,399 patients) were included. All studies available were retrospective in design. Compared with Alt + V, Z + V was associated with a higher risk ratio of AKI (1.79; 95% confidence interval [CI], 1.46-2.19; p < 0.001). Cefepime (C + V) was the most common alternative to Zosyn, and Z + V was associated with higher rates of kidney injury compared with C + V (1.70; 95% CI, 1.36-2.12; p < 0.00001). However, there was substantial heterogeneity in the data collected as well as high risk of bias. Conclusions: Zosyn plus vancomycin is associated with more risk of AKI compared with Alt+V coverage in ICU adult populations. However, the conclusions were limited by the retrospective nature of the studies, high bias of included articles, and heterogeneity of the included studies.

Supervised Resistance Training on Functional Capacity, Muscle Strength and Vascular Function in Peripheral Artery Disease: An Updated Systematic Review and Meta-Analysis.

Supervised resistance training appears to be a promising alternative exercise modality to supervised walking in patients with peripheral artery disease (PAD). This meta-analysis examined the efficacy of supervised RT for improving walking capacity, and whether adaptations occur at the vascular and/or skeletal muscle level in PAD patients. We searched Medline, CINAHL, Scopus, and Cochrane Central Register of Controlled Trials databases for randomized controlled trials (RCTs) in PAD patients testing the effects of supervised RT for ≥4 wk. on walking capacity, vascular function, and muscle strength. Pooled effect estimates were calculated and evaluated using conventional meta-analytic procedures. Six RCTs compared supervised RT to standard care. Overall, supervised RT prolonged claudication onset distance during a 6-min walk test (6-MWT) (101.7 m (59.6, 143.8), p < 0.001) and improved total walking distance during graded treadmill walking (SMD: 0.67 (0.33, 1.01), p < 0.001) and the 6-MWT (49.4 m (3.1, 95.6), p = 0.04). Five RCTS compared supervised RT and supervised intermittent walking, where the differences in functional capacity between the two exercise modalities appear to depend on the intensity of the exercise program. The insufficient evidence on the effects of RT on vascular function and muscle strength permitted only limited exploration. We conclude that RT is effective in prolonging walking performance in PAD patients. Whether RT exerts its influence on functional capacity by promoting blood flow and/or enhancing skeletal muscle strength remains unclear.

Novel Technique for Laparoscopic Common Bile Duct Exploration Using Endovascular Instrumentation.

Treatment of choledocholithiasis is sometimes a two-step process involving both surgeons and gastroenterologists. Common bile duct (CBD) exploration can be performed at the same time as cholecystectomy but often requires the use of rigid tools, increasing the risk of CBD damage. Here, we report the case of a 64-year-old man who presented with epigastric pain and a positive Murphy's sign. Ultrasonography revealed cholecystitis with cholelithiasis. Gangrenous cholecystitis was visualized upon surgical exploration, and an intraoperative cholangiogram diagnosed likely choledocholithiasis. Cholecystectomy was completed, and CBD exploration was performed by the manipulation of endovascular equipment using a trans-cystic approach through to the ampulla of Vater, and the patient made a complete recovery without complications. The substantial flexibility, gentleness, and durability of endovascular instruments allow for minimal tension on structures during the removal of gallstones from the CBD, providing safe, definitive treatment for choledocholithiasis during cholecystectomy.

Machine learning applications for the differentiation of primary central nervous system lymphoma from glioblastoma on imaging: a systematic review and meta-analysis.

OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered attention for their potential to aid in the diagnostic assessment of a variety of pathologies. Several ML algorithms have recently been designed to differentiate GBM from PCNSL radiologically with a high sensitivity and specificity. The objective of this systematic review and meta-analysis was to evaluate the implementation of ML algorithms in differentiating GBM and PCNSL.METHODSThe authors performed a systematic review of the literature using PubMed in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML and brain tumors. These studies were further narrowed down to focus on works published between January 2008 and May 2018 addressing the use of ML in training models to distinguish between GBM and PCNSL on radiological imaging. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).RESULTSEight studies were identified addressing use of ML in training classifiers to distinguish between GBM and PCNSL on radiological imaging. ML performed well with the lowest reported AUC being 0.878. In studies in which ML was directly compared with radiologists, ML performed better than or as well as the radiologists. However, when ML was applied to an external data set, it performed more poorly.CONCLUSIONSFew studies have applied ML to solve the problem of differentiating GBM from PCNSL using imaging alone. Of the currently published studies, ML algorithms have demonstrated promising results and certainly have the potential to aid radiologists with difficult cases, which could expedite the neurosurgical decision-making process. It is likely that ML algorithms will help to optimize neurosurgical patient outcomes as well as the cost-effectiveness of neurosurgical care if the problem of overfitting can be overcome.