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Blast-induced traumatic brain injury: the experience from a level I trauma center in southern Thailand

Thara Tunthanathip, Kanutpon Khocharoen, and Nakornchai Phuenpathom

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

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Comparison of intracranial injury predictability between machine learning algorithms and the nomogram in pediatric traumatic brain injury

Thara Tunthanathip, Jarunee Duangsuwan, Niwan Wattanakitrungroj, Sasiporn Tongman, and Nakornchai Phuenpathom

OBJECTIVE

The overuse of head CT examinations has been much discussed, especially those for minor traumatic brain injury (TBI). In the disruptive era, machine learning (ML) is one of the prediction tools that has been used and applied in various fields of neurosurgery. The objective of this study was to compare the predictive performance between ML and a nomogram, which is the other prediction tool for intracranial injury following cranial CT in children with TBI.

METHODS

Data from 964 pediatric patients with TBI were randomly divided into a training data set (75%) for hyperparameter tuning and supervised learning from 14 clinical parameters, while the remaining data (25%) were used for validation purposes. Moreover, a nomogram was developed from the training data set with similar parameters. Therefore, models from various ML algorithms and the nomogram were built and deployed via web-based application.

RESULTS

A random forest classifier (RFC) algorithm established the best performance for predicting intracranial injury following cranial CT of the brain. The area under the receiver operating characteristic curve for the performance of RFC algorithms was 0.80, with 0.34 sensitivity, 0.95 specificity, 0.73 positive predictive value, 0.80 negative predictive value, and 0.79 accuracy.

CONCLUSIONS

The ML algorithms, particularly the RFC, indicated relatively excellent predictive performance that would have the ability to support physicians in balancing the overuse of head CT scans and reducing the treatment costs of pediatric TBI in general practice.

Free access

Traumatic cerebrovascular injury: clinical characteristics and illustrative cases

Thara Tunthanathip, Nakornchai Phuenpathom, Sakchai Sae-Heng, Thakul Oearsakul, Ittichai Sakarunchai, and Anukoon Kaewborisutsakul

OBJECTIVE

Traumatic cerebrovascular injury (TCVI) is a rare and serious complication of traumatic brain injury (TBI). Various forms of TCVIs have been reported, including occlusions, arteriovenous fistulas, pseudoaneurysms, and transections. They can present at a variety of intervals after TBI and may manifest as sudden episodes, progressive symptoms, and even delayed fatal events. The purpose of this study was to analyze cases of TCVI identified at a single institution and further explore types and characteristics of these complications of TBI in order to improve recognition and treatment of these injuries.

METHODS

The authors performed a retrospective review of cases of TCVI identified at their institution between 2013 and 2016. A total of 5178 patients presented with TBI during this time period, and 42 of these patients qualified for a diagnosis of TCVI and had adequate medical and imaging records for analysis. Data from their cases were analyzed, and 3 illustrative cases are presented in detail.

RESULTS

The most common type of TCVI was arteriovenous fistula (86.4%), followed by pseudoaneurysm (11.9%), occlusion (2.4%), and transection (2.4%). The mortality rate of patients with TCVI was 7.1%.

CONCLUSIONS

The authors describe the clinical characteristics of patients with TCVI and provide data from a series of 42 cases. It is important to recognize TCVI in order to facilitate early diagnosis and treatment.

Free access

Machine learning applications for the prediction of surgical site infection in neurological operations

Thara Tunthanathip, Sakchai Sae-heng, Thakul Oearsakul, Ittichai Sakarunchai, Anukoon Kaewborisutsakul, and Chin Taweesomboonyat

OBJECTIVE

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.

METHODS

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.

RESULTS

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%.

CONCLUSIONS

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.