OBJECTIVESurgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The purpose of this study was to compare the performance of various ML models for predicting surgical infection after neurosurgical operations.METHODSA retrospective cohort study was conducted on patients who had undergone neurosurgical operations at tertiary care hospitals between 2010 and 2017. Supervised ML algorithms, which included decision tree, naive Bayes with Laplace correction, k-nearest neighbors, and artificial neural networks, were trained and tested as binary classifiers (infection or no infection). To evaluate the ML models from the testing data set, their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as their accuracy, receiver operating characteristic curve, and area under the receiver operating characteristic curve (AUC) were analyzed.RESULTSData were available for 1471 patients in the study period. The SSI rate was 4.6%, and the type of SSI was superficial, deep, and organ/space in 1.2%, 0.8%, and 2.6% of cases, respectively. Using the backward stepwise method, the authors determined that the significant predictors of SSI in the multivariable Cox regression analysis were postoperative CSF leakage/subgaleal collection (HR 4.24, p < 0.001) and postoperative fever (HR 1.67, p = 0.04). Compared with other ML algorithms, the naive Bayes had the highest performance with sensitivity at 63%, specificity at 87%, PPV at 29%, NPV at 96%, and AUC at 76%.CONCLUSIONSThe naive Bayes algorithm is highlighted as an accurate ML method for predicting SSI after neurosurgical operations because of its reasonable accuracy. Thus, it can be used to effectively predict SSI in individual neurosurgical patients. Therefore, close monitoring and allocation of treatment strategies can be informed by ML predictions in general practice.

Hemangiomas have rarely been found in the spinal cord. A few cases of spinal capillary hemangioma have been reported since 1987. The authors reported the two cases of capillary hemangioma including the tumor at conus medullaris and the another mimicked von Hippel-Lindau disease. A 15-year-old man was presented with coccydynia and left leg pain. A magnetic resonance imaging (MRI) revealed an intradural extramedullary enhancing mass at conus medullaris. Another case, a 31-year-old man was presented with a history of familial history of brain tumor, retinal hemangioma both eyes, multiple pancreatic cyst and syringobulbia with syringohydromyelia. On MRI, a well-circumscribed intramedullary nodule was detected at C5-6 level and multiple subpial nodule along cervicothoracic spinal cord. All patients underwent surgery, and the histological diagnosis confirmed capillary hemangioma. Although rare and indistinguishable from other tumors, capillary hemangioma should be in the differential diagnosis of the spinal cord tumor.

Background:Motor vehicle is a major transportation in Southern Thailand as the result of road traffic injury and death. Consequently, severe disability and mortality in pediatric traumatic brain injury (TBI) were observed from traffic accident, particularly motorcycle accident. To identify the risk of intracranial injury in children, the association of treatment outcome with various factors including mechanisms of injury, clinical characteristics, and intracranial pathology can be assessed.Materials and Methods:This was a retrospective study conducted on children, who were younger than 15 years old with TBI and were enrolled from 2004 to 2015. Several clinically relevant issues were reviewed and statistically analyzed.Results:A total of 948 casualties were enrolled. Compared with falling down, the motorcycle accident was significantly associated with intracranial injury (odds ratio 1.73, 95% confidence interval [CI] 1.08–2.76). Other factors associated with intracranial injury were hemiparesis (odds ratio 5.69, 95% CI 1.44–22.36), positive of basal skull fracture signs (odds ratio 15.66, 95% CI 3.44-71.28), and fixed reaction to light of both pupils (odds ratio 5.74, 95% CI 1.71–19.23). Mortality found in thirty cases (3.2%). Furthermore, the risk of death correlated with motorcycle accident (P = 0.02) and severe head injury (P < 0.001). Neurosurgical intervention was not associated with outcome, but severe head injury, hemorrhagic shock, epidural, and subdural hematoma were impact factors.Conclusion:The findings demonstrate road traffic injury, especially motorcycle accident leading to brain injury and death. Prevention program is a necessary key to decrease mortality and disability in pediatric TBI.

Background Early posttraumatic seizure (PTS) is a significant cause of unfavorable outcomes in traumatic brain injury (TBI). This study was aimed to investigate the incidence and determine a predictive model for early PTS. Materials and Methods A prospective cohort study of 484 TBI patients was conducted. All patients were evaluated for seizure activities within 7 days after the injury. Risk factors for early PTS were identified using univariate analysis. The candidate risk factors with p < 0.1 were selected into multivariable logistic regression analysis to identify predictors of early PTS. The fitting model and the power of discrimination with the area under the receiver operating characteristic (AUROC) curve were demonstrated. The nomogram for prediction of early PTS was developed for individuals. Results There were 27 patients (5.6%) with early PTS in this study. The final model illustrated chronic alcohol use (odds ratio [OR]: 4.06, 95% confidence interval [CI]: 1.64–10.07), epidural hematoma (OR: 3.98, 95% CI: 1.70–9.33), and Glasgow Coma Scale score 3–8 (OR: 3.78, 95% CI: 1.53–9.35) as predictors of early PTS. The AUROC curve was 0.77 (95% CI: 0.66–0.87). Conclusions The significant predictors for early PTS were chronic alcohol use, epidural hematoma, and severe TBI. Our nomogram was considered as a reliable source for prediction.