Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury.

BACKGROUND: Traumatic brain injury (TBI) is a major cause of death and functional disability in the general population. The nomogram is a clinical prediction tool that has been researched for a wide range of medical conditions. The purpose of this study was to identify prognostic factors associated with in-hospital mortality. The secondary objective was to develop a clinical nomogram for TBI patients' in-hospital mortality based on prognostic factors. METHODS: A retrospective cohort study was conducted to analyze 14,075 TBI patients who were admitted to a tertiary hospital in southern Thailand. The total dataset was divided into the training and validation datasets. Several clinical characteristics and imaging findings were analyzed for in-hospital mortality in both univariate and multivariable analyses using the training dataset. Based on binary logistic regression, the nomogram was developed and internally validated using the final predictive model. Therefore, the predictive performances of the nomogram were estimated by the validation dataset. RESULTS: Prognostic factors associated with in-hospital mortality comprised age, hypotension, antiplatelet, Glasgow coma scale score, pupillary light reflex, basilar skull fracture, acute subdural hematoma, subarachnoid hemorrhage, midline shift, and basal cistern obliteration that were used for building nomogram. The predictive performance of the nomogram was estimated by the training dataset; the area under the receiver operating characteristic curve (AUC) was 0.981. In addition, the AUCs of bootstrapping and cross-validation methods were 0.980 and 0.981, respectively. For the temporal validation with an unseen dataset, the sensitivity, specificity, accuracy, and AUC of the nomogram were 0.90, 0.88, 0.88, and 0.89, respectively. CONCLUSION: A nomogram developed from prognostic factors had excellent performance; thus, the tool had the potential to serve as a screening tool for prognostication in TBI patients. Furthermore, future research should involve geographic validation to examine the predictive performances of the clinical prediction tool.

Comparing neuroendocrine recovery between surgical and conservative management in pituitary apoplexy patients: a propensity score-matched analysis.

Pituitary apoplexy is a rare and potentially life-threatening clinical syndrome. Patients may present with severeneuro-ophthalmologic or endocrine symptoms. Current evidence is unclear whether conservative or surgicalmanagement leads to the best neuroendocrine outcomes. This study aimed to compare neuroendocrine outcomesbetween surgical and conservative treatments in a single center. Cases of patients with pituitary apoplexy whoreceived transsphenoidal surgery or conservative management in Songklanagarind Hospital between January 1,2005 and December 31, 2022 were retrospectively reviewed. A propensity score matching method was used toadjust bias from treatment selection (surgery or conservative treatment). Differences in visual field, visual acuity,cranial nerve, and endocrine outcomes between the surgical and conservative treatment groups were analyzedusing logistic regression analysis. This study included 127 patients, with 98 and 29 patients in the surgical and theconservative treatment group, respectively. The optimal matching method was used for propensity score matching.Compared to the conservative group, the surgically treated patients had a significantly higher rate of visual fieldrecovery (odds ratio (OR): 12.89, P = 0.007). However, there were no statistical differences in the recovery rate ofpreoperative visual acuity, cranial nerve, and endocrine deficits between the groups. Transsphenoidal surgery wasassociated with a higher rate of visual field recovery when compared to the conservative treatment for pituitaryapoplexy patients. Careful selection of appropriate treatment based on the patient's presentation andneuroendocrine status will result in the best outcomes while avoiding unnecessary surgical intervention.

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

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.

Application of machine learning to predict the outcome of pediatric traumatic brain injury.

PURPOSE: Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI. METHODS: A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets. RESULTS: There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78. CONCLUSION: The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.

Traumatic cerebrovascular injury: Prevalence and risk factors.

BACKGROUND: Traumatic cerebrovascular injury (TCVI) is uncommon in traumatic brain injury (TBI). Although TCVI is a rare condition, this complication is serious. A missed or delayed diagnosis may lead to an unexpected life-threatening hemorrhagic event or persistent neurological deficit. The object of this study was to determine the prevalence and risk factors associated with TCVI. METHODS: The authors retrospectively reviewed medical records and neuroimaging studies of 5178 patients with TBI. The association of various factors was investigated using time-to-event statistical analysis. A TCVI which resulted in an occlusion, arteriovenous fistula, pseudoaneurysm or cerebral artery transection was defined as an event. RESULTS: Forty-two patients developed a TCVI after injuries with an overall prevalence of 0.8%. The risk factors for an intracranial arterial injury based on univariate analysis using the Cox proportional hazard regression were penetrating injury, severe head injury, orbitofacial injury, basilar skull fracture, subdural hematoma, and cerebral contusion. In multivariable analysis, the two variables that were independently associated with TCVI were basilar skull fracture (odds ratio [OR] 22.1, 95% confidence interval [CI] 11.5-42.2) followed by orbitofacial fracture (OR 13.6, 95% CI 6.8-27.3). CONCLUSIONS: Although TCVI is a rare complication of TBI, early investigation in high-risk patients may be necessary for early treatment before an unexpected fatal event occurs.

Traumatic cerebrovascular injury: clinical characteristics and illustrative cases.

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.

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

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.

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

OBJECTIVEIn 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.METHODSA 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.RESULTSDuring 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.CONCLUSIONSbTBIs 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.

Predictive factors of the outcome and intraventricular rupture of brain abscess.

OBJECTIVE: To identify the significant factors predicting afavorable outcome and to study clinical characteristics and identify the factors predicted by intraventricular rupture of brain abscess (IVROBA). MATERIAL AND METHOD: In the retrospective study, the computer-based medical records of patients of a tertiary care hospital between 1999 and 2013 were reviewed. Univariate and multivariate analyses were used to determine the significant factors predicting the outcomes and IVROBA. RESULTS: One hundred fourteen patients with brain abscesses were enrolled. The predictivefactor of a favorable outcome was Glasgow Coma Scale (GCS) score 13 to 15 (OR 14.64; 95% CI 2.70-79.34; p = 0.02). Conversely, the factors associated with an unfavorable outcome were fungal brain abscess (OR 40.81; 95% CI 3.57-466.49; p = 0.003) and IVROBA (OR 5.50; 95% CI 1.34-22.49; p = 0.017). Moreover greater distance of the brain abscess from the ventricle decreased the IVROBA (OR 0.62; 95% CI 0.45-0.87; p = 0.005). Abscesses with intraventricular rupture that were at less than 7 mm of a ventricle (p < 0.000) were likely to IVROBA. CONCLUSION: The outcome of a brain abscess depends on good clinical status, pathogens, and fatal complication of lVROBA. If poor prognostic factors exist, then better surgical option can be selected.

Significance of Pretreatment Hemoglobin-Albumin-Lymphocyte-Platelet Index for the Prediction of Suboptimal Surgery in Epithelial Ovarian Cancer.

Background: Epithelial ovarian cancer (EOC) is the leading cause of death in gynecological cancers in developed countries. In recent years, there has been a growing need for economical and accurate pretreatment laboratory investigations to assess the prognosis of patients with advanced EOC (AEOC). We aimed to investigate the role of the hemoglobin-albumin-lymphocyte-platelet (HALP) index in suboptimal cytoreduction and oncological outcomes. Methods: A prognostic prediction model for diagnosing suboptimal cytoreduction for patients with AEOC receiving neoadjuvant chemotherapy (NACT) was developed. Multivariate logistic regression analysis was performed to identify the independent predictors of suboptimal cytoreduction, with a P-value < 0.05, and then transformed into risk-scoring systems. Internal validation was performed using the bootstrapping procedure, and predictive cytoreduction (PSC) scores were compared using non-parametric receiver operating characteristic (ROC) regression. Survival analysis was performed using Kaplan-Meier estimation and Cox proportional regression. Results: In total, 473 patients were analyzed, and the rate of suboptimal surgery was 43%. A scoring system in predicting suboptimal cytoreduction included age, cancer antigen (CA)-125 level before surgery, performance status, cycles of chemotherapy, peritoneal cancer index, and HALP index ≤ 22.6. The model had good discriminative ability (area under the ROC (AUROC), 0.80; 95% confidence interval (CI), 0.76 - 0.84), outperforming the PSC score (AUROC, 0.75; 95% CI, 0.71 - 0.80). The score was divided into the low-risk (positive predictive value (PPV), 22.4; 95% CI, 17.8 - 27.7), moderate-risk (PPV, 65.9; 95% CI, 56.9 - 74.0), and high-risk (PPV, 90.6; 95% CI, 79.3 - 96.9) groups. The HALP index score of ≤ 22.6 was independently associated with progression-free survival (hazard ratio (HR), 2.92; 95% CI, 1.58 - 5.40) and overall survival (HR, 2.66; 95% CI, 1.57 - 4.49). Conclusion: The HALP index is a newly predicted factor for suboptimal cytoreduction and oncological outcomes in patients with AEOC after NACT.

Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses.

Background: Neuroblastoma (NB) is a common solid tumor in children, with a dismal prognosis in high-risk cases. Despite advancements in NB treatment, the clinical need for precise prognostic models remains critical, particularly to address the heterogeneity of cancer stemness which plays a pivotal role in tumor aggressiveness and patient outcomes. By utilizing machine learning (ML) techniques, we aimed to explore the cancer stemness features in NB and identify stemness-related hub genes for future investigation and potential targeted therapy. Methods: The public dataset GSE49710 was employed as the training set for acquire gene expression data and NB sample information, including age, stage, and MYCN amplification status and survival. The messenger RNA (mRNA) expression-based stemness index (mRNAsi) was calculated and patients were grouped according to their mRNAsi value. Stemness-related hub genes were identified from the differentially expressed genes (DEGs) to construct a gene signature. This was followed by evaluating the relationship between cancer stemness and the NB immune microenvironment, and the development of a predictive nomogram. We assessed the prognostic outcomes including overall survival (OS) and event-free survival, employing machine learning methods to measure predictive accuracy through concordance indices and validation in an independent cohort E-MTAB-8248. Results: Based on mRNAsi, we categorized NB patients into two groups to explore the association between varying levels of stemness and their clinical outcomes. High mRNAsi was linked to the advanced International Neuroblastoma Staging System (INSS) stage, amplified MYCN, and elder age. High mRNAsi patients had a significantly poorer prognosis than low mRNAsi cases. According to the multivariate Cox analysis, the mRNAsi was an independent risk factor of prognosis in NB patients. After least absolute shrinkage and selection operator (LASSO) regression analysis, four key genes (ERCC6L, DUXAP10, NCAN, DIRAS3) most related to mRNAsi scores were discovered and a risk model was built. Our model demonstrated a significant prognostic capacity with hazard ratios (HR) ranging from 18.96 to 41.20, P values below 0.0001, and area under the receiver operating characteristic curve (AUC) values of 0.918 in the training set, suggesting high predictive accuracy which was further confirmed by external verification. Individuals with a low four-gene signature score had a favorable outcome and better immune responses. Finally, a nomogram for clinical practice was constructed by integrating the four-gene signature and INSS stage. Conclusions: Our findings confirm the influence of CSC features in NB prognosis. The newly developed NB stemness-related four-gene signature prognostic signature could facilitate the prognostic prediction, and the identified hub genes may serve as promising targets for individualized treatments.

Comparison of intracranial pressure prediction in hydrocephalus patients among linear, non-linear, and machine learning regression models in Thailand.

BACKGROUND: Hydrocephalus (HCP) is one of the most significant concerns in neurosurgical patients because it can cause increased intracranial pressure (ICP), resulting in mortality and morbidity. To date, machine learning (ML) has been helpful in predicting continuous outcomes. The primary objective of the present study was to identify the factors correlated with ICP, while the secondary objective was to compare the predictive performances among linear, non-linear, and ML regression models for ICP prediction. METHODS: A total of 412 patients with various types of HCP who had undergone ventriculostomy was retrospectively included in the present study, and intraoperative ICP was recorded following ventricular catheter insertion. Several clinical factors and imaging parameters were analyzed for the relationship with ICP by linear correlation. The predictive performance of ICP was compared among linear, non-linear, and ML regression models. RESULTS: Optic nerve sheath diameter (ONSD) had a moderately positive correlation with ICP (r=0.530, P<0.001), while several ventricular indexes were not statistically significant in correlation with ICP. For prediction of ICP, random forest (RF) and extreme gradient boosting (XGBoost) algorithms had low mean absolute error and root mean square error values and high R2 values compared to linear and non-linear regression when the predictive model included ONSD and ventricular indexes. CONCLUSIONS: The XGBoost and RF algorithms are advantageous for predicting preoperative ICP and establishing prognoses for HCP patients. Furthermore, ML-based prediction could be used as a non-invasive method.

Machine learning-based classification of pineal germinoma from magnetic resonance imaging.

Introduction: Surgical approaches for tissue diagnosis of pineal tumors have been associated with morbidity and mortality. The classification of images by machine learning (ML) may assist physicians in determining the extent of resection and treatment plans for a specific patient. Therefore, the present study aimed to evaluate the diagnostic performances of the ML-based models for distinguishing between pure and non-germinoma of the pineal area. In addition, the secondary objective was to compare diagnostic performances among feature extraction methods. Methods: This is a retrospective cohort study of patients diagnosed with pineal tumors. We used the RGB feature extraction, histogram of oriented gradients (HOG), and local binary pattern methods from magnetic resonance imaging (MRI) scans; therefore, we trained an ML model from various algorithms to classify pineal germinoma. Diagnostic performances were calculated from a test dataset with several diagnostic indices. Results: MRI scans from 38 patients with pineal tumors were collected and extracted features. As a result, the k-nearest neighbors (KNN) algorithm with HOG had the highest sensitivity of 0.81 (95% CI 0.78-0.84), while the random forest (RF) algorithm with HOG had the highest sensitivity of 0.82 (95% CI 0.79-0.85). Moreover, the KNN model with HOG had the highest AUC, at 0.845. Additionally, the AUCs of the artificial neural network and RF algorithms with HOG were 0.770 and 0.713, respectively. Conclusions: The classification of images using ML is a viable way for developing a diagnostic tool to differentiate between germinoma and non-germinoma that will aid neurosurgeons in treatment planning in the future.

Deep learning for image classification between primary central nervous system lymphoma and glioblastoma in corpus callosal tumors.

Objectives: It can be challenging in some situations to distinguish primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM) based on magnetic resonance imaging (MRI) scans, especially those involving the corpus callosum. The objective of this study was to assess the diagnostic performance of deep learning (DL) models between PCNSLs and GBMs in corpus callosal tumors. Materials and Methods: The axial T1-weighted gadolinium-enhanced MRI scans of 274 individuals with pathologically confirmed PCNSL (n = 94) and GBM (n = 180) were examined. After image pooling, pre-operative MRI scans were randomly split with an 80/20 procedure into a training dataset (n = 709) and a testing dataset (n = 177) for DL model development. Therefore, the DL model was deployed as a web application and validated with the unseen images (n = 114) and area under the receiver operating characteristic curve (AUC); other outcomes were calculated to assess the discrimination performance. Results: The first baseline DL model had an AUC of 0.77 for PCNSL when evaluated with unseen images. The 2nd model with ridge regression regularization and the 3rd model with drop-out regularization increased an AUC of 0.83 and 0.84. In addition, the last model with data augmentation yielded an AUC of 0.57. Conclusion: DL with regularization may provide useful diagnostic information to help doctors distinguish PCNSL from GBM.

Malignant transformation in low-grade astrocytoma for long-term monitoring.

Background: Malignant transformation (MT) of low-grade astrocytoma (LGA) produces a poor prognosis in benign tumors. Currently, variables linked with MT of LGA have proven equivocal. The present study aims to evaluate the risk variables, indicating that LGA gradually differentiates to malignant astrocytoma. Methods: Retrospective cohort analysis of LGA patients was performed. Both univariate and multivariate studies were used to discover variables connected to MT using the Cox regression method. As a result, the cumulative incidence of MT for each covariate survival curve was built after the final model. Results: In the current study, 115 individuals with LGA were included in the analysis, and MT was found in 16.5% of cases. In the case of MT, 68.4% of patients progressed to glioblastoma, whereas 31.6% progressed to anaplastic astrocytoma. Significant factors included supratentorial tumor (hazard ratio (HR) 3.41, 95% CI 1.18-12.10), midline shift > 5 mm (HR 7.15, 95% CI 2.28-34.33), and non-total resection as follows: subtotal resection (HR 5.09, 95% CI 0.07-24.02), partial resection (HR 1.61, 95% CI 1.09-24.11), and biopsy (HR 2.80, 95% CI 1.18-32.52). Conclusion: In individuals with LGA, MT dramatically altered the disease's natural history to a poor prognosis. The present study's analysis of the clinical features of patients indicated supratentorial LGA, a midline shift greater than 5 mm, and the degree of resection as risk factors for MT. The more extensive the resection, the greater the reduction in tumor load and MT. In addition, more molecular study is necessary to elucidate the pathophysiology of MT.

Development and internal validation of a nomogram for predicting outcomes in children with traumatic subdural hematoma.

BACKGROUND: A subdural hematoma (SDH) following a traumatic brain injury (TBI) in children can lead to unexpected death or disability. The nomogram is a clinical prediction tool used by physicians to provide prognosis advice to parents for making decisions regarding treatment. In the present study, a nomogram for predicting outcomes was developed and validated. In addition, the predictors associated with outcomes in children with traumatic SDH were determined. METHODS: In this retrospective study, 103 children with SDH after TBI were evaluated. According to the King's Outcome Scale for Childhood Head Injury classification, the functional outcomes were assessed at hospital discharge and categorized into favorable and unfavorable. The predictors associated with the unfavorable outcomes were analyzed using binary logistic regression. Subsequently, a two-dimensional nomogram was developed for presentation of the predictive model. RESULTS: The predictive model with the lowest level of Akaike information criterion consisted of hypotension (odds ratio [OR], 9.4; 95% confidence interval [CI], 2.0-42.9), Glasgow coma scale scores of 3-8 (OR, 8.2; 95% CI, 1.7-38.9), fixed pupil in one eye (OR, 4.8; 95% CI, 2.6-8.8), and fixed pupils in both eyes (OR, 3.5; 95% CI, 1.6-7.1). A midline shift ≥5 mm (OR, 1.1; 95% CI, 0.62-10.73) and co-existing intraventricular hemorrhage (OR, 6.5; 95% CI, 0.003-26.1) were also included. CONCLUSIONS: SDH in pediatric TBI can lead to mortality and disability. The predictability level of the nomogram in the present study was excellent, and external validation should be conducted to confirm the performance of the clinical prediction tool.

Development of a nomogram to predict the outcome of moderate or severe pediatric traumatic brain injury.

OBJECTIVES: Traumatic brain injury (TBI) in children has become the major cause of mortality and morbidity in Thailand that has had an impact with economic consequences. This study aimed to develop and internally validate a nomogram for a 6-month follow-up outcome prediction in moderate or severe pediatric TBI. METHODS: This retrospective cohort study involved 104 children with moderate or severe TBI. Various clinical variables were reviewed. The functional outcome was assessed at the hospital discharge and at a 6-month follow-up based on the King's Outcome Scale for Childhood Head Injury classification. Predictors associated with the 6-month follow-up outcome were developed from the predictive model using multivariable binary logistic regression to estimate the performance and internal validation. A nomogram was developed and presented as a predictive model. RESULTS: The mean age of the samples was 99.75 months (standard deviation 59.65). Road traffic accidents were the highest injury mechanism at 84.6%. The predictive model comprised Glasgow Coma Scale of 3-8 (odds ratio [OR]: 16.07; 95% confidence interval [CI]: 1.27-202.42), pupillary response in one eye (OR 7.74; 95% CI 1.26-47.29), pupillary nonresponse in both eyes (OR: 57.74; 95% CI: 2.28-145.81), hypotension (OR: 19.54; 95%: CI 3.23-117.96), and subarachnoid hemorrhage (OR: 9.01, 95% CI: 1.33-60.80). The concordance statistic index (C-index) of the model's discrimination was 0.931, while the C-index following the bootstrapping and 5-cross validation were 0.920 and 0.924, respectively. CONCLUSIONS: The performance of a clinical nomogram for predicting 6-month follow-up outcomes in pediatric TBI patients was assessed at an excellent level. However, further external validation would be required for the confirmation of the tool's performance.

Risk Factors Associated with Malignant Transformation of Astrocytoma: Competing Risk Regression Analysis.

Background Malignant transformation (MT) of low-grade astrocytoma (LGA) triggers a poor prognosis in benign tumors. Currently, factors associated with MT of LGA have been inconclusive. The present study aims to explore the risk factors predicting LGA progressively differentiated to malignant astrocytoma. Methods The study design was a retrospective cohort study of medical record reviews of patients with LGA. Using the Fire and Gray method, the competing risk regression analysis was performed to identify factors associated with MT, using both univariate and multivariable analyses. Hence, the survival curves of the cumulative incidence of MT of each covariate were constructed following the final model. Results Ninety patients with LGA were included in the analysis, and MT was observed in 14.4% of cases in the present study. For MT, 53.8% of patients with MT transformed to glioblastoma, while 46.2% differentiated to anaplastic astrocytoma. Factors associated with MT included supratentorial tumor (subdistribution hazard ratio [SHR] 4.54, 95% confidence interval [CI] 1.08-19.10), midline shift > 1 cm (SHR 8.25, 95% CI 2.18-31.21), and nontotal resection as follows: subtotal resection (SHR 5.35, 95% CI 1.07-26.82), partial resection (SHR 10.90, 95% CI 3.13-37.90), and biopsy (SHR 11.10, 95% CI 2.88-42.52). Conclusion MT in patients with LGA significantly changed the natural history of the disease to an unfavorable prognosis. Analysis of patients' clinical characteristics from the present study identified supratentorial LGA, a midline shift more than 1 cm, and extent of resection as risk factors associated with MT. The more extent of resection would significantly help to decrease tumor burden and MT. In addition, future molecular research efforts are warranted to explain the pathogenesis of MT.

Economic impact of a machine learning-based strategy for preparation of blood products in brain tumor surgery.

BACKGROUND: Globally, blood donation has been disturbed due to the pandemic. Consequently, the optimization of preoperative blood preparation should be a point of concern. Machine learning (ML) is one of the modern approaches that have been applied by physicians to help decision-making. The main objective of this study was to identify the cost differences of the ML-based strategy compared with other strategies in preoperative blood products preparation. A secondary objective was to compare the effectiveness indexes of blood products preparation among strategies. METHODS: The study utilized a retrospective cohort design conducted on brain tumor patients who had undergone surgery between January 2014 and December 2021. Overall data were divided into two cohorts. The first cohort was used for the development and deployment of the ML-based web application, while validation, comparison of the effectiveness indexes, and economic evaluation were performed using the second cohort. Therefore, the effectiveness indexes of blood preparation and cost difference were compared among the ML-based strategy, clinical trial-based strategy, and routine-based strategy. RESULTS: Over a 2-year period, the crossmatch to transfusion (C/T) ratio, transfusion probability (Tp), and transfusion index (Ti) of the ML-based strategy were 1.10, 57.0%, and 1.62, respectively, while the routine-based strategy had a C/T ratio of 4.67%, Tp of 27.9%%, and Ti of 0.79. The overall costs of blood products preparation among the ML-based strategy, clinical trial-based strategy, and routine-based strategy were 30, 061.56$, 57,313.92$, and 136,292.94$, respectively. From the cost difference between the ML-based strategy and routine-based strategy, we observed cost savings of 92,519.97$ (67.88%) for the 2-year period. CONCLUSION: The ML-based strategy is one of the most effective strategies to balance the unnecessary workloads at blood banks and reduce the cost of unnecessary blood products preparation from low C/T ratio as well as high Tp and Ti. Further studies should be performed to confirm the generalizability and applicability of the ML-based strategy.

Risk Factors for Intraoperative Hypotension During Decompressive Craniectomy in traumatic Brain Injury Patients.

BACKGROUND: Decompressive craniectomy (DC) is an important therapy for treating intracranial pressure elevation following traumatic brain injury (TBI). During this procedure, about one-third of patients become complicated with intraoperative hypotension (IH), which is associated with abruptly decreasing sympathetic activity resulting from brain decompression. This study aimed to identify factors associated with IH during DC procedures and the mortality rate in these patients. METHODS: The records of adult TBI patients aged 18 years and older who underwent DC at Songklanagarind Hospital between January 2014 and January 2021 were retrospectively reviewed. Using logistic regression analysis, various factors were analyzed for their associations with IH during the DC procedures. RESULTS: This study included 83 patients. The incidence of IH was 54%. Multivariate analysis showed that Glasgow Coma Scale motor response (GCS-M) 1-3 (vs. 4-6), higher preoperative heart rate (PHR), and larger amount of intraoperative blood loss were significantly associated with IH (P = 0.013, P < 0.001, and P < 0.001, respectively). Patients with GCS-M 1-3 and PHR ≥ 75 bpm had the highest chance of IH (77%), while patients with neither of these risk factors had the lowest chance (29%). The in-hospital mortality rate in the IH and non-IH groups was 44% and 26%, respectively (P = 0.138). CONCLUSIONS: GCS-M 1-3, higher PHR, and larger amount of intraoperative blood loss were the risk factors associated with IH during DC procedure in TBI patients. Patients who have these risk factors should be closely monitored and the attending physician be ready to apply prompt resuscitation and treatment for IH.