Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made.

Spinal cord injury (SCI) is responsible for approximately 18,000 trauma cases each year in the United States, often resulting in debilitating motor and autonomic disability. Neuromodulation is a rapidly growing field of interest in the neurosurgical field and has additionally shown promise in the treatment of SCI. This review characterizes all clinical trials to date studying neuromodulation for the treatment of SCI. The ClinicalTrials.gov database was queried using the search terms "neuromodulation" and "spinal cord injury" on ClinicalTrials.gov. Trials were excluded if they were not yet recruiting, suspended, terminated early, or of unknown status. In total, 33 clinical trials were included in this study. Of the 33 trials, eight were completed and one had published results. Most trials studied deficits of motor function (60%) and bladder control (37%). 14 studies (42.4%) utilized transcutaneous spinal stimulation (tSS), seven (21.2%) utilized epidural electrical stimulation (EES), and six (18.2%) utilized tibial nerve stimulation. There was an uptrend of clinical trials studying SCI indexed on PubMed, which was comparable to the increased number of publications indexed overall (Pearson correlation, p<001). Of these, only one study regarding home tibial nerve stimulation for neurogenic bladder had published data, which was performed with no adverse events. Neuromodulation in SCI studies currently assess tSS, EES and tibial nerve stimulation. There is currently one completed study suggesting feasibility of home neuromodulation techniques without adverse events. The results of trials that will be completed in the next few years will help dictate the potential of neuromodulation as a treatment for SCI.

Introduction: Hydrocephalus is a common pediatric neurosurgical pathology, typically treated with a ventricular shunt, yet approximately 30% of patients experience shunt failure within the first year after surgery. As a result, the objective of the present study was to validate a predictive model of pediatric shunt complications with data retrieved from the Healthcare Cost and Utilization Project (HCUP) National Readmissions Database (NRD). Methods: The HCUP NRD was queried from 2016 to 2017 for pediatric patients undergoing shunt placement using ICD-10 codes. Comorbidities present upon initial admission resulting in shunt placement, Johns Hopkins Adjusted Clinical Groups (JHACG) frailty-defining criteria, and Major Diagnostic Category (MDC) at admission classifications were obtained. The database was divided into training (n = 19,948), validation (n = 6,650), and testing (n = 6,650) datasets. Multivariable analysis was performed to identify significant predictors of shunt complications which were used to develop logistic regression models. Post hoc receiver operating characteristic (ROC) curves were created. Results: A total of 33,248 pediatric patients aged 6.9 ± 5.7 years were included. Number of diagnoses during primary admission (OR: 1.05, 95% CI: 1.04–1.07) and initial neurological admission diagnoses (OR: 3.83, 95% CI: 3.33–4.42) positively correlated with shunt complications. Female sex (OR: 0.87, 95% CI: 0.76–0.99) and elective admissions (OR: 0.62, 95% CI: 0.53–0.72) negatively correlated with shunt complications. ROC curve for the regression model utilizing all significant predictors of readmission demonstrated area under the curve of 0.733, suggesting these factors are possible predictors of shunt complications in pediatric hydrocephalus. Conclusion: Efficacious and safe treatment of pediatric hydrocephalus is of paramount importance. Our machine learning algorithm delineated possible variables predictive of shunt complications with good predictive value.