Development and Deployment of Web Application Using Machine Learning for Predicting Intraoperative Transfusions in Neurosurgical Operations

Background and Aim: Preoperative blood product preparation is a common practice in neurosurgical patients. However, over-requesting of blood is common and leads to the wastage of blood bank resources. Machine learning (ML) is currently one of the novel computational data analysis methods for assisting neurosurgeons in their decision-making process. The objective of the present study was to use machine learning to predict intraoperative packed red cell transfusion. Additionally, a secondary objective focused on estimating the effectiveness of blood utilization in neurosurgical operations. Methods and Materials/Patients: This was a retrospective cohort study of 3,021 patients who had previously undergone neurosurgical operations. Data from the total cohort were randomly divided into a training dataset (N=2115) and a testing dataset (N=906). The supervised ML models of various algorithms were trained and tested with test data using both classification and regression algorithms. Results: Almost all neurosurgical conditions had a cross-match to transfusion ratio of more than 2.5. Support vector machine (SVM) with linear kernel, SVM radial kernel, and random forest (RF) classification had a performance with good AUC of 0.83,0.82, and 0.82, respectively, while RF regression had the lowest root mean squared error and mean absolute error. Conclusion: In almost all neurosurgical surgeries, preoperative overpreparation of blood products was detected. The ML algorithm was proposed as a high-performance method for optimizing blood preparation and intraoperative consumption. Furthermore, ML has the potential to be incorporated into clinical practice as a calculator for the optimal cross-match to transfusion ratio

Usefulness of Robotic Stereotactic Assistance (ROSAⓇ) Device for Stereoelectroencephalography Electrode Implantation: A Systematic Review and Meta-analysis

The aim of this study was to systematically review and meta-analyze the efficiency and safety of using the Robotic Stereotactic Assistance (ROSAⓇ) device (Zimmer Biomet; Warsaw, IN, USA) for stereoelectroencephalography (SEEG) electrode implantation in patients with drug-resistant epilepsy. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a literature search was carried out. Overall, 855 nonduplicate relevant articles were determined, and 15 of them were selected for analysis. The benefits of the ROSAⓇ device use in terms of electrode placement accuracy, as well as operative time length, perioperative complications, and seizure outcomes, were evaluated. Studies that were included reported on a total of 11,257 SEEG electrode implantations. The limited number of comparative studies hindered the comprehensive evaluation of the electrode implantation accuracy. Compared with frame-based or navigation-assisted techniques, ROSAⓇ-assisted SEEG electrode implantation provided significant benefits for reduction of both overall operative time (mean difference [MD], −63.45 min; 95% confidence interval [CI] from −88.73 to −38.17 min; P < 0.00001) and operative time per implanted electrode (MD, −8.79 min; 95% CI from −14.37 to −3.21 min; P = 0.002). No significant differences existed in perioperative complications and seizure outcomes after the application of the ROSAⓇ device and other techniques for electrode implantation. To conclude, the available evidence shows that the ROSAⓇ device is an effective and safe surgical tool for trajectory-guided SEEG electrode implantation in patients with drug-resistant epilepsy, offering benefits for saving operative time and neither increasing the risk of perioperative complications nor negatively impacting seizure outcomes

Multiple, Primary Brain Tumors with Diverse Origins and Different Localizations: Case Series and Review of the Literature

Background: Multiple, primary brain tumors with different histological types occurring in the same patient are extremely rare. Several hypotheses have been proposed, and the pathophysiology of coexisting tumors has long been debated; however, due to low incidence, standard practices for this scenario are still inconclusive. Case Description: The authors describe 6 cases of coexisting tumors. By conducting a literature research focused on the computed tomography (CT) era and patients without prior radiation or phakomatosis. Sixty-five such reported cases were identified. In addition, the authors summarize their experience in 6 patients including histopathological features, chronological presentations, outcomes, mortality, and management from their series as well as from previous cases from the reported literature. Conclusion: The coexistence of multiple, primary brain tumors is an interesting condition. Surgical management remains the major treatment; malignant histology has a poor prognostic factor