Thara Tunthanathip, Kanutpon Khocharoen and Nakornchai Phuenpathom
In the ongoing conflict in southern Thailand, the improvised explosive device (IED) has been a common cause of blast-induced traumatic brain injury (bTBI). The authors investigated the particular characteristics of bTBI and the factors associated with its clinical outcome.
A retrospective cohort study was conducted on all patients who had sustained bTBI between 2009 and 2017. Collected data included clinical characteristics, intracranial injuries, and outcomes. Factors analysis was conducted using a forest plot.
During the study period, 70 patients met the inclusion criteria. Fifty individuals (71.4%) were military personnel. One-third of the patients (32.9%) suffered moderate to severe bTBI, and the rate of intracerebral injuries on brain CT was 65.7%. Coup contusion was the most common finding, and primary blast injury was the most common mechanism of blast injury. Seventeen individuals had an unfavorable outcome (Glasgow Outcome Scale score 1–3), and the overall mortality rate for bTBI was 11.4%. In the univariate analysis, factors associated with an unfavorable outcome were preoperative coagulopathy, midline shift of the brain ≥ 5 mm, basal cistern effacement, moderate to severe TBI, hypotension, fixed and dilated pupils, surgical site infection, hematocrit < 30% on admission, coup contusion, and subdural hematoma. In the multivariable analysis, midline shift ≥ 5 mm (OR 29.1, 95% CI 2.5–328.1) and coagulopathy (OR 28.7, 95% CI 4.5–180.3) were the only factors predicting a poor outcome of bTBI.
bTBIs range from mild to severe. Midline shift and coagulopathy are treatable factors associated with an unfavorable outcome. Hence, in cases of bTBI, reversing an abnormal coagulogram is required as soon as possible to improve clinical outcomes. The management of brain shift needs further study.
Thara Tunthanathip, Sakchai Sae-heng, Thakul Oearsakul, Ittichai Sakarunchai, Anukoon Kaewborisutsakul and Chin Taweesomboonyat
Surgical 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.
A 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.
Data 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%.
The 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.