Factors predicting outcomes of endoscopic endonasal approach in craniopharyngioma patients.

Objectives: Endoscopic endonasal approach (EEA) is commonly used for resection of craniopharyngioma (CP). Treatment outcomes of EEA for CP were related to numerous factors; however, they have been evaluated in few studies. The objective of this study is to investigate factors associated with the outcomes of CP following this operation. Materials and Methods: The records of patients with CP, who underwent EEA at our institution from January 2014 to June 2022, were retrospectively reviewed. Surgical outcomes, including the extent of resection, visual recovery, and endocrinological outcomes, were reported. Clinical and radiographic factors were analyzed for their associations with treatment outcomes using logistic regression analyzes. Results: This study cohort consisted of 28 patients with CP. Gross total resection (GTR) was achieved in 12 patients (43%). Post-operative visual status improved, stabilized, and deteriorated in 89%, 6%, and 6% of the patients, respectively. There were no patients recovered from pre-operative pituitary dysfunctions, while post-operative hypoadrenalism, hypothyroidism, and hypogonadism were found in 9 (36%), 11 (42%), and 4 (22%) patients, respectively. Post-operative permanent diabetic insipidus was found in 13 patients (50%). Greater suprasellar extension of the tumor was associated with a lower rate of GTR (P = 0.011). Diabetes mellitus (DM) was associated with poor visual recovery (P = 0.022). Larger tumor size and Puget grade 2 were associated with postoperative hypoadrenalism (P = 0.01 and 0.023, respectively). In addition, Puget grade 2 was associated with post-operative hypothyroidism (P = 0.017). Conclusion: For EEA in CP, the extent of resection could be determined by suprasellar extension of the tumor. DM was a poor predicting factor for visual recovery, while larger tumors and Puget grade 2 had a higher risk of post-operative hypopituitarism.

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.

Quality of life, out-of-pocket expenditures, and indirect costs among patients with the central nervous system tumors in Thailand.

Objectives: The aim of this study was to investigate out-of-pocket (OOP) expenditures, indirect costs, and health-related quality of life (HRQoL) associated with the central nervous system (CNS) tumors in Thailand. Materials and Methods: A prospective study of CNS tumor patients who underwent first tumor resection at a tertiary care institution in Thailand was conducted. Patients were interviewed during hospitalization for undergoing first surgery. Within 6 months, they were interviewed once more if the disease continued to progress. Costs collected from a patient perspective and converted to 2019 US dollars. For dealing with these skewed data, a generalized linear model was used to investigate the effects of disease severity (malignancy, progressive disease, Karnofsky performance status score, and histology) and other factors on costs (OOP, informal care, productivity loss, and total costs). P < 0.05 was considered statistical significant for all analysis. Results: Among a total of 123 intracranial CNS tumor patients, there were 83 and 40 patients classified into benign and malignant, respectively. In the first brain surgery, there was no statistical difference in HRQoL between patients with benign and malignant tumors (P = 0.072). However, patients with progressive disease had lower HRQoL mean scores at pre-operative and progressive disease periods were 0.711 (95% confidence interval [CI]: 0.662-0.760) and 0.261 (95% CI: 0.144-0.378), respectively. Indirect expenditures were the primary cost driver, accounting for 73.81% of annual total costs. The total annual costs accounted for 59.81% of the reported patient's income in malignant tumor patients. The progressive disease was the only factor that was significantly increases in all sorts of costs, including the OOP (P = 0.001), the indirect costs (P = 0.013), and the total annual costs (P = 0.001). Conclusion: Although there was no statistical difference in HRQoL and costs between patients with benign and malignant tumor, the total costs accounted for more than half of the reported income in malignant tumor patients. The primary cause of significant increases in all costs categories was disease progression.

Prognostic factors and clinical nomogram predicting survival in high-grade glioma.

BACKGROUND: Genomic-based tools have been used to predict poor prognosis high-grade glioma (HGG). As genetic technologies are not generally available in countries with limited resources, clinical parameters may be still necessary to use in predicting the prognosis of the disease. This study aimed to identify prognostic factors associated with survival of patients with HGG. We also proposed a validated nomogram using clinical parameters to predict the survival of patients with HGG. METHODS: A multicenter retrospective study was conducted in patients who were diagnosed with anaplastic astrocytoma (WHO III) or glioblastoma (WHO IV). Collected data included clinical characteristics, neuroimaging findings, treatment, and outcomes. Prognostic factor analysis was conducted using Cox proportional hazard regression analysis. Then, we used the significant prognostic factors to develop a nomogram. A split validation of nomogram was performed. Twenty percent of the dataset was used to test the performance of the developed nomogram. RESULTS: Data from 171 patients with HGG were analyzed. Overall median survival was 12 months (interquartile range: 5). Significant independent predictors included frontal HGG (hazard ratio [HR]: 0.62; 95% confidence interval [CI]: 0.40-0.60), cerebellar HGG (HR: 4.67; 95% CI: 0.93-23.5), (HR: 1.55; 95% CI: 1.03-2.32; reference = total resection), and postoperative radiotherapy (HR: 0.18; 95% CI: 0.10-0.32). The proposed nomogram was validated using nomogram's predicted 1-year mortality rate. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve of our nomogram were 1.0, 0.50, 0.45, 1.0, 0.64, and 0.75, respectively. CONCLUSION: We developed a nomogram for individually predicting the prognosis of HGG. This nomogram had acceptable performances with high sensitivity for predicting 1-year mortality.

Predicting the Health-related Quality of Life in Patients Following Traumatic Brain Injury.

Background Traumatic brain injury (TBI) commonly causes death and disability that can result in productivity loss and economic burden. The health-related quality of life (HRQoL) has been measured in patients suffering from TBI, both in clinical and socioeconomic perspectives. The study aimed to assess the HRQoL in patients following TBI using the European quality of life measure-5 domain-5 level (EQ-5D-5L) questionnaire and develop models for predicting the EQ-5D-5L index score in patients with TBI. Method A cross-sectional study was performed with 193 TBI patients who had completed the EQ-5D-5L questionnaire. The clinical characteristics, Glasgow coma scale (GCS) score, treatment, and Glasgow outcome scale (GOS) were collected. The total data was divided into training data (80%) and testing data (20%); hence, the factors affecting the EQ-5D-5L index scores were used to develop the predictive model with linear and nonlinear regression. The performances of the predictive models were estimated with the adjusted coefficient of determination (R 2 ) and the root mean square error (RMSE). Results A good recovery was found at 96.4%, while 2.1% displayed an unfavorable outcome. Moreover, the mean EQ-5D-5L index scores were 0.91558 (standard deviation [SD] 1.09639). GCS score, pupillary light reflex, surgery, and GOS score significantly correlated with the HRQoL scores. The multiple linear regression model had a high adjusted R 2 of 0.6971 and a low RMSE of 0.06701, while the polynomial regression developed a nonlinear model that had the highest adjusted R 2 of 0.6843 and the lowest RMSE of 0.06748. Conclusions A strong positive correlation between the physician-based outcome as GOS and HRQoL was observed. Furthermore, both the linear and nonlinear regression models were acceptable approaches to predict the HRQoL of patients after TBI. There would be limitations for estimating the HRQoL in unconscious or intubated patients. The HRQoL obtained from the predictive models would be an alternative method to resolve this problem.

Prognostic Factors and Nomogram Predicting Survival in Diffuse Astrocytoma.

Background Prognosis of low-grade glioma are currently determined by genetic markers that are limited in some countries. This study aimed to use clinical parameters to develop a nomogram to predict survival of patients with diffuse astrocytoma (DA) which is the most common type of low-grade glioma. Materials and Methods Retrospective data of adult patients with DA from three university hospitals in Thailand were analyzed. Collected data included clinical characteristics, neuroimaging findings, treatment, and outcomes. Cox's regression analyses were performed to determine associated factors. Significant associated factors from the Cox regression model were subsequently used to develop a nomogram for survival prediction. Performance of the nomogram was then tested for its accuracy. Results There were 64 patients with DA with a median age of 39.5 (interquartile range [IQR] = 20.2) years. Mean follow-up time of patients was 42 months (standard deviation [SD] = 34.3). After adjusted for three significant factors associated with survival were age ≥60 years (hazard ratio [HR] = 5.8; 95% confidence interval [CI]: 2.09-15.91), motor response score of Glasgow coma scale < 6 (HR = 75.5; 95% CI: 4.15-1,369.4), and biopsy (HR = 0.45; 95% CI: 0.21-0.92). To predict 1-year mortality, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve our nomogram was 1.0, 0.50, 0.45, 1.0, 0.64, and 0.75, respectively. Conclusions This study provided a nomogram predicting prognosis of DA. The nomogram showed an acceptable performance for predicting 1-year mortality.

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.

Diagnostic Yield and Complication of Frameless Stereotactic Brain Biopsy.

BACKGROUND: With the advancement of neuronavigation technologies, frameless stereotactic brain biopsy has been developed. Previous studies proved that frameless stereotactic brain biopsy was as effective and safe as frame-based stereotactic brain biopsy. The authors aimed to find the factors associated with diagnostic yield and complication rate of frameless intracranial biopsy. MATERIALS AND METHODS: Frameless stereotactic brain biopsy procedures, between March 2009 and April 2017, were retrospectively reviewed from medical records including imaging studies. Using logistic regression analysis, various factors were analyzed for association with diagnostic yield and postoperative complications. RESULTS: Eighty-nine frameless stereotactic brain biopsy procedures were performed on 85 patients. The most common pathology was primary central nervous system lymphoma (43.8%), followed by low-grade glioma (15.7%), and high-grade glioma (15%), respectively. The diagnostic yield was 87.6%. Postoperative intracerebral hematoma occurred in 19% of cases; however, it was symptomatic in only one case. The size of the lesion was associated with both diagnostic yield and postoperative intracerebral hematoma complication. Lesions, larger than 3 cm in diameter, were associated with a higher rate of positive biopsy result (P = 0.01). Lesion 3 cm or smaller than 3 cm in diameter, and intraoperative bleeding associated with a higher percentage of postoperative intracerebral hematoma complications (P = 0.01). CONCLUSIONS: For frameless stereotactic brain biopsy, the size of the lesion is the essential factor determining diagnostic yield and postoperative intracerebral hematoma complication.

Butterfly Tumor of the Corpus Callosum: Clinical Characteristics, Diagnosis, and Survival Analysis.

BACKGROUND: The pathologies implicate the bilateral corpus callosum that builds the butterfly pattern on axial view. These tumors have seldom been investigated for both clinical manifestations and outcome. OBJECTIVE: The objective of this study was to describe the clinical characteristics and outcomes of the butterfly tumor and to identify the predictive factors associated with survival outcome. METHODS: A retrospective study of 50 butterfly tumor was conducted between 2003 and 2016. The clinical characteristics, imaging, and outcome were assessed for the purpose of descriptive analysis. Using the Kaplan-Meier method, the median overall survival of the butterfly tumor was determined. Furthermore, the Cox proportional hazard regression was the estimated hazard ratio for death. RESULTS: Diffuse large B-cell lymphoma was common of butterfly lesions. The mortality rate was 78% and overall median survival time was 16.03 months (95% confidence interval: 14.0-19.8). Using Cox proportional hazards regression, the independent prognostic factors were Karnofsky Performance Status score ≤70, splenium involvement, and butterfly glioblastoma. CONCLUSIONS: The butterfly tumor is a poor prognostic disease compared with each histology subgroup. Further molecular investigation is preferable to explore genetic variations associated with these tumors.

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.