Tracing topics and trends in drug-resistant epilepsy research using a natural language processing-based topic modeling approach.

Epilepsy is a common neurological disorder affecting over 70 million people worldwide. Although many patients achieve seizure control with anti-epileptic drugs (AEDs), 30%-40% develop drug-resistant epilepsy (DRE), where seizures persist despite adequate trials of AEDs. DRE is associated with reduced quality of life, increased mortality and morbidity, and greater socioeconomic challenges. The continued intractability of DRE has fueled exponential growth in research that aims to understand and treat this serious condition. However, synthesizing this vast and continuously expanding DRE literature to derive insights poses considerable difficulties for investigators and clinicians. Conventional review methods are often prolonged, hampering the timely application of findings. More-efficient approaches to analyze the voluminous research are needed. In this study, we utilize a natural language processing (NLP)-based topic modeling approach to examine the DRE publication landscape, uncovering key topics and trends. Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic. This technique employs transformer models like BERT (Bidirectional Encoder Representations from Transformers) for contextual understanding, thereby enabling accurate topic categorization. Analysis revealed 18 distinct topics spanning various DRE research areas. The 10 most common topics, including "AEDs," "Neuromodulation Therapy," and "Genomics," were examined further. "Cannabidiol," "Functional Brain Mapping," and "Autoimmune Encephalitis" emerged as the hottest topics of the current decade, and were examined further. This NLP methodology provided valuable insights into the evolving DRE research landscape, revealing shifting priorities and declining interests. Moreover, we demonstrate an efficient approach to synthesizing and visualizing patterns within extensive literature that could be applied to other research fields.

Comprehensive assessment of atypical and anaplastic pediatric meningiomas utilizing national cancer database: a retrospective cohort study.

PURPOSE: To examine demographic and clinical characteristics and their association with survival in grade 2 and 3 pediatric meningiomas in a large cohort using the National Cancer Database (NCDB). METHODS: We conducted a comprehensive analysis using data from NCDB between 2004 to 2018. Tumor-specific data included tumor grade and size. Treatment details, including surgical resection, extent of resection, and radiotherapy, were gathered. Our analytic approach incorporated logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. RESULTS: Among the included 239 patients aged 0-21 years, age category distribution was significantly different between grade 2 and grade 3 tumors (p = 0.018). For grade 2 meningiomas, 51.5% of patients were female, and 76.7% were white. 85.3% of patients with grade 2 meningiomas underwent surgical resection, of which 67% underwent gross total resection. Overall survival (OS) was significantly different between resected and non-resected patients (p = 0.048). Uninsured patients were over seven times as likely to have prolonged length of stay (LOS) versus those with private insurance (OR = 7.663, p = 0.014). For grade 3 meningiomas, 51.4% of patients were male, and 82.9% were white. 91.4% of patients with grade 3 meningiomas underwent surgical resection, of which 53.3% underwent subtotal resection. OS was not significantly different between resected and non-resected patients (p = 0.659). CONCLUSION: In summary, there were significant differences in age, maximum tumor dimension, unplanned readmission, radiotherapy, and treatment combinations between grade 2 and 3 meningiomas. These findings highlight the intricacies of managing pediatric meningiomas and emphasize the necessity for tailored therapeutic approaches to enhance outcomes in the future.

Atypical and anaplastic meningiomas in the later decades of life: A national cancer database analysis.

PURPOSE: We conducted a National Cancer Database (NCDB) study to investigate the epidemiological characteristics and identify predictors of outcomes associated with geriatric meningiomas. METHODS: The NCDB was queried for adults aged 60-89 years diagnosed between 2010 and 2017 with grade 2 and 3 meningiomas. The patients were classified into three age groups based on their age: 60-69 (hexagenarians), 70-79 (septuagenarians), and 80-89 (octogenarians). The log-rank test was utilized to compare the differences in overall survival (OS). Univariate and multivariate Cox proportional hazards regressions were used to evaluate the mortality risk associated with various patient and disease parameters. RESULTS: A total of 6585 patients were identified. Hexagenerians were the most common age group (49.8%), with the majority of meningiomas being classified as grade 2 (89.5%). The incidence of high-grade meningiomas increased in all age groups during the study period. Advanced age, male sex, black race, lower socioeconomic status, Charlson-Deyo score ≥ 2, and higher tumor grade were independent factors of poor survival. Among the modes of treatment, the extent of surgical resection, adjuvant radiotherapy, and treatment at a noncommunity cancer program were linked with better outcomes. CONCLUSION: In geriatric patients with high-grade meningiomas, the greater extent of surgical resection and radiotherapy are associated with improved survival. However, the management and outcome of geriatric patients with higher-grade meningiomas are also associated with several socioeconomic factors.

Advancing personalized prognosis in atypical and anaplastic meningiomas through interpretable machine learning models.

PURPOSE: The primary purpose of this study was to utilize machine learning (ML) models to create a web application that can predict survival outcomes for patients diagnosed with atypical and anaplastic meningiomas. METHODS: In this retrospective cohort study, patients diagnosed with WHO grade II and III meningiomas were selected from the National Cancer Database (NCDB) to analyze survival outcomes at 12, 36, and 60 months. Five machine learning algorithms - TabPFN, TabNet, XGBoost, LightGBM, and Random Forest were employed and optimized using the Optuna library for hyperparameter tuning. The top-performing models were then deployed into our web-based application. RESULTS: From the NCDB, 12,197 adult patients diagnosed with histologically confirmed WHO grade II and III meningiomas were retrieved. The mean age was 61 (± 20), and 6,847 (56.1%) of these were females. Performance evaluation indicated that the top-performing models for each outcome were the models built with the TabPFN algorithm. The TabPFN models yielded area under the receiver operating characteristic (AUROC) values of 0.805, 0.781, and 0.815 in predicting 12-, 36-, and 60-month mortality, respectively. CONCLUSION: With the continuous growth of neuro-oncology data, ML algorithms act as key tools in predicting survival outcomes for WHO grade II and III meningioma patients. By incorporating these interpretable models into a web application, we can practically utilize them to improve risk evaluation and prognosis for meningioma patients.

Cervical laminoplasty versus laminectomy and fusion: An umbrella review of postoperative outcomes.

While multiple studies exist comparing cervical laminoplasty (CLP) and posterior cervical laminectomy with fusion (PCF), no clear consensus exists on which intervention is better. An umbrella review helps provide an overall assessment by analyzing a given condition's multiple interventions and outcomes. It integrates all available information on a topic and allows a consensus to be reached on the intervention of choice. A literature search was conducted using specific search criteria in PubMed, Scopus, and Web of Science databases. Titles and abstracts were screened based on inclusion criteria. A full-text review of articles that passed the initial inclusion criteria was performed. Nine meta-analyses were deemed eligible for the umbrella review. Data was extracted on reported variables from these meta-analyses. Subsequent quality assessment using AMSTAR2 and data analysis using the R package metaumbrella were used to determine the significance of postoperative outcomes. When the meta-analyses were pooled, statistically significant differences between CLP and PCF were found for postoperative overall complications rate and postoperative JOA score. PCF was associated with a lower overall complication rate and a higher postoperative JOA score, both supported by a weak level of evidence (class IV). Data regarding all other outcomes were non-significant. Our umbrella review investigates CLP and PCF by providing a comprehensive overview of existing evidence and evaluating inconsistencies within the literature. This umbrella review revealed that PCF had better outcomes for overall complications rate and postoperative JOA than CLP, but they were classified as being of weak significance.

The Predictive Abilities of Machine Learning Algorithms in Patients with Thoracolumbar Spinal Cord Injuries.

OBJECTIVES: The goal of this study is to implement machine learning (ML) algorithms to predict mortality, non-home discharge, prolonged length of stay (LOS), prolonged length of intensive care unit stay (ICU-LOS), and major complications in patients diagnosed with thoracolumbar spinal cord injury, while creating a publicly accessible online tool. METHODS: The American College of Surgeons Trauma Quality Program database was used to identify patients with thoracolumbar spinal cord injury. Feature selection was performed with the Least Absolute Shrinkage and Selection Operator algorithm. Five ML algorithms, including TabPFN, TabNet, XGBoost, LightGBM, and Random Forest, were used along with the Optuna optimization library for hyperparameter tuning. RESULTS: A total of 147,819 patients were included in the analysis. For each outcome, we determined the best model for deployment in our web application based on the area under the receiver operating characteristic (AUROC) values. The top performing algorithms were as follows: LightGBM for mortality with an AUROC of 0.885, TabPFN for non-home discharge with an AUROC of 0.801, LightGBM for prolonged LOS with an AUROC of 0.673, Random Forest for prolonged ICU-LOS with an AUROC of 0.664, and LightGBM for major complications with an AUROC of 0.73. CONCLUSIONS: ML models demonstrate good predictive ability for in-hospital mortality and non-home discharge, fair predictive ability for major complications and prolonged ICU-LOS, but poor predictive ability for prolonged LOS. We have developed a web application that allows these models to be accessed.

Comparative Effectiveness of Open Versus Minimally Invasive Transforaminal Lumbar Interbody Fusion: An Umbrella Review of Meta-Analyses.

STUDY DESIGN: Umbrella review of meta-analyses. OBJECTIVE: To compile existing meta-analyses to provide analysis of the multiple postoperative outcomes in a comparison of open-transforaminal lumbar interbody fusions (O-TLIFs) versus minimally invasive transforaminal interbody fusions (MI-TLIFs). SUMMARY OF BACKGROUND DATA: TLIF is the standard surgical intervention for spinal fusion in degenerative spinal diseases. The comparative effectiveness of MI-TLIFs and O-TLIFs remains controversial. METHODS: A literature search was conducted in the PubMed, Scopus, and Web of Science databases. Titles and abstracts were initially screened, followed by a full-text review based on the inclusion criteria. Twenty articles were deemed eligible for the umbrella review. Data extraction and quality assessment using A Measurement Tool to Assess Systematic Reviews were performed. Effect sizes of the outcomes of interest from primary studies included in the meta-analyses were repooled. Repooling and stratification of the credibility of the evidence were performed using the R package metaumbrella . The pooled effect sizes were compared and interpreted using equivalent Hedges' g values. RESULTS: When the meta-analyses were pooled, MI-TLIF was found to have a shorter length of stay, less blood loss, and a higher radiation exposure time, with a highly suggestive level of evidence. Data regarding less postoperative drainage, infections, and Oswestry disability index for MI-TLIF were supported by weak evidence. Conversely, data regarding other postoperative outcomes were nonsignificant to draw any conclusions. CONCLUSION: Our umbrella review provides a comprehensive overview of the relevant strengths and weaknesses of each surgical technique. This overview revealed that MI-TLIF had better outcomes in terms of length of stay, blood loss, postoperative drainage, infections, and Oswestry disability index when compared with those of O-TLIF. However, O-TLIF had a better outcome for radiation exposure when compared with MI-TLIF.

Personalized Prognosis with Machine Learning Models for Predicting In-Hospital Outcomes Following Intracranial Meningioma Resections.

BACKGROUND: Meningiomas display diverse biological traits and clinical behaviors, complicating patient outcome prediction. This heterogeneity, along with varying prognoses, underscores the need for a precise, personalized evaluation of postoperative outcomes. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent intracranial meningioma resections from 2014 to 2020. We focused on 5 outcomes: prolonged LOS, nonhome discharges, 30-day readmissions, unplanned reoperations, and major complications. Six machine learning algorithms, including TabPFN, TabNet, XGBoost, LightGBM, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations were used to evaluate the importance of predictor variables. RESULTS: Our analysis included 7000 patients. Of these patients, 1658 (23.7%) had prolonged LOS, 1266 (18.1%) had nonhome discharges, 573 (8.2%) had 30-day readmission, 253 (3.6%) had unplanned reoperation, and 888 (12.7%) had major complications. Performance evaluation indicated that the top-performing models for each outcome were the models built with LightGBM and Random Forest algorithms. The LightGBM models yielded AUROCs of 0.842 and 0.846 in predicting prolonged LOS and nonhome discharges, respectively. The Random Forest models yielded AUROCs of 0.717, 0.76, and 0.805 in predicting 30-day readmissions, unplanned reoperations, and major complications, respectively. CONCLUSIONS: The study successfully demonstrated the potential of machine learning models in predicting short-term adverse postoperative outcomes after meningioma resections. This approach represents a significant step forward in personalizing the information provided to meningioma patients.

Predicting 30-Day Non-Seizure Outcomes Following Temporal Lobectomy with Personalized Machine Learning Models.

BACKGROUND: Temporal lobe epilepsy is the most common reason behind drug-resistant seizures and temporal lobectomy (TL) is performed after all other efforts have been taken for a Temporal lobe epilepsy. Our study aims to develop multiple machine learning (ML) models capable of predicting postoperative outcomes following TL surgery. METHODS: Data from the American College of Surgeons National Surgical Quality Improvement Program database identified patients who underwent TL surgery. We focused on 3 outcomes: prolonged length of stay (LOS), nonhome discharges, and 30-day readmissions. Six ML algorithms, TabPFN, XGBoost, LightGBM, Support Vector Machine, Random Forest, and Logistic Regression, coupled with the Optuna optimization library for hyperparameter tuning, were tested. Models with the highest area under the receiver operating characteristic (AUROC) values were included in the web application. SHapley Additive exPlanations was used to evaluate importance of predictor variables. RESULTS: Our analysis included 423 patients. Of these patients, 111 (26.2%) experienced prolonged LOS, 33 (7.8%) had nonhome discharges, and 29 (6.9%) encountered 30-day readmissions. The top-performing models for each outcome were those built with the Random Forest algorithm. The Random Forest models yielded AUROCs of 0.868, 0.804, and 0.742 in predicting prolonged LOS, nonhome discharges, and 30-day readmissions, respectively. CONCLUSIONS: Our study uses ML to forecast adverse postoperative outcomes following TL. We developed accessible predictive models that enhance prognosis prediction for TL surgery. Making ML models available for this purpose represents a significant advancement in shifting toward a more patient-centric, data-driven paradigm.

Enhancing Surgical Task Adherence Through a Rewards-Driven Mobile Application: A Single-Arm Intervention Feasibility Study.

INTRODUCTION: Ensuring patients follow preoperative and postoperative instructions is vital for maximizing surgical success. This pilot study investigates the feasibility of using monetary incentives through a nudge engine application-based model of omnichannel communication to prompt adherence to preoperative and postoperative instructions. METHODS: Over a six-month period, we conducted a longitudinal study employing the TheraPay® Rewards app at Maimonides Medical Center in Brooklyn, United States. Our recruitment efforts targeted English and Spanish-speaking patients with smartphones through in-person visits and phone calls. Participants received a $15 credit on a gift card for each completed task. The tasks included preoperative validations such as obtaining primary care physician clearance, completing preoperative assessments, undergoing preoperative scans with accompanying compact disks (CDs), and discontinuing specific medications. Postoperative validations included attending postoperative visits, proper incision care, discontinuation of narcotics at three weeks, and initiation of the first physical therapy session. RESULTS: We enrolled 16 patients with a mean age of 59.5 years (SD 11.68), the majority being male (n = 10, 62.5%). Preoperatively, task completion rates ranged from 83% to 100%. Postoperatively, rates varied from 20% to 100%. Preoperative task adherence averaged at 98.7% (SD 2.2%), while postoperative adherence averaged 60% (SD 21%). CONCLUSION: Our study indicates that financial incentives delivered through a gamified approach effectively encourage patients to complete essential preoperative tasks, suggesting a promise for enhancing adherence. Nonetheless, the decrease in postoperative task adherence highlights the necessity for careful implementation. Future investigations should compare cancellation rates and outcome measures to gain deeper insights into the effectiveness of app-based incentives in improving surgical outcomes and patient adherence.

Risk Analysis Index Predicts Non-home Discharge following Resection of Cavernous Malformations.

OBJECTIVE: Intracranial cavernous malformations (CMs) are benign vascular lesions associated with hemorrhage, seizures, and corresponding neurological deficits. Recent evidence shows that frailty predicts neurosurgical adverse outcomes with superior discrimination compared to greater patient age. Therefore, we utilized the Risk Analysis Index (RAI) to predict adverse outcomes following cavernous malformations resection (CMR). METHODS: This retrospective study utilized the Nationwide Inpatient Sample (NIS) to identify patients who underwent craniotomy for CMR (2019-2020). Multivariate analysis used RAI to assess the ability of frailty to predict non-home discharge (NHD), extended length of stay (eLOS) and postoperative adverse outcomes. Receiver operating characteristic (ROC) curve analysis evaluated the discriminatory accuracy of RAI for prediction of NHD. RESULTS: 1200 CMR patients were identified. Mean patient age was 38 ± 1.2 years, 53.3% (N = 640) were female, and 58.3% (N = 700) had private insurance. Patients were stratified into four frailty tiers based on RAI scores, "robust" (0-20, R): N = 905 (80.8%), "normal" (21-30, N), N = 110 (9.8%), "frail" (31-40, F) N= 25 (2.2%), and "very frail" (41+, VF): 80 (7.1%). Increasing frailty was associated with eLOS and higher rates of NHD (p< 0.05). The RAI demonstrated strong discriminatory accuracy (C-statistic = 0.722) for prediction of NHD following CMR in AUROC. CONCLUSION: Preoperative frailty independently predicts adverse outcomes, including eLOS and NHD in patients undergoing resection of cranial CMs. Integrating RAI into preoperative frailty risk assessment may optimize risk stratification and improve patient selection and reallocate perioperative management resources for better patient outcomes.

Chondrosarcoma of the Mobile Spine in the Elderly: A National Cancer Database Study.

BACKGROUND: The current research on geriatric patients with spinal chondrosarcoma is limited. This study aimed to investigate the demographics, patterns of care, and survival of geriatric patients with chondrosarcoma of the mobile spine. METHODS: The National Cancer Database was queried from 2008 to 2018 for geriatric patients (60-89 years) with chondrosarcoma of the mobile spine. The primary outcome of this study was overall survival (OS). The secondary outcome was treatment utilization patterns. Survival analyses were conducted using log-rank tests and Cox proportional hazards regressions. Logistic regression models were utilized to assess correlations between baseline variables and treatment utilization. RESULTS: The database retrieved 122 patients. While 43.7% of the patients presented with tumors exceeding 5cm in size, the incidence of regional lymph node involvement or distant metastases was relatively low, affecting only 5% of the patients. Furthermore, 22.3% of the patients had tumors graded as 3-4. The five-year OS rate was 52.9% (95% confidence interval 42-66.6). The mortality risk was significantly associated with age, tumor grade and stage, and treatment plan. Most patients (79.5%) underwent surgery, while 35.9% and 4.2% were treated with radiotherapy and chemotherapy, respectively. Age, race, comorbidities, geographical region, tumor stage, and healthcare facility type significantly correlated with treatment utilization. CONCLUSION: Surgical resection significantly lowered the mortality risk in geriatric patients with spinal chondrosarcomas. Demographic and geographical factors significantly dictated treatment plans. Further studies are required to assess the role of radiotherapy and chemotherapy in treating these patients in the modern era.

Mapping the Degenerative Cervical Myelopathy Research Landscape: Topic Modeling of the Literature.

STUDY DESIGN: Topic modeling of literature. OBJECTIVES: Our study has 2 goals: (i) to clarify key themes in degenerative cervical myelopathy (DCM) research, and (ii) to evaluate the current trends in the popularity or decline of these topics. Additionally, we aim to highlight the potential of natural language processing (NLP) in facilitating research syntheses. METHODS: Documents were retrieved from Scopus, preprocessed, and modeled using BERTopic, an NLP-based topic modeling method. We specified a minimum topic size of 25 documents and 50 words per topic. After the models were trained, they generated a list of topics and corresponding representative documents. We utilized linear regression models to examine trends within the identified topics. In this context, topics exhibiting increasing linear slopes were categorized as "hot topics," while those with decreasing slopes were categorized as "cold topics". RESULTS: Our analysis retrieved 3510 documents that were classified into 21 different topics. The 3 most frequently occurring topics were "OPLL" (ossification of the posterior longitudinal ligament), "Anterior Fusion," and "Surgical Outcomes." Trend analysis revealed the hottest topics of the decade to be "Animal Models," "DCM in the Elderly," and "Posterior Decompression" while "Morphometric Analyses," "Questionnaires," and "MEP and SSEP" were identified as being the coldest topics. CONCLUSIONS: Our NLP methodology conducted a thorough and detailed analysis of DCM research, uncovering valuable insights into research trends that were otherwise difficult to discern using traditional techniques. The results provide valuable guidance for future research directions, policy considerations, and identification of emerging trends.

Exploiting Natural Language Processing to Unveil Topics and Trends of Traumatic Brain Injury Research.

Traumatic brain injury (TBI) has evolved from a topic of relative obscurity to one of widespread scientific and lay interest. The scope and focus of TBI research have shifted, and research trends have changed in response to public and scientific interest. This study has two primary goals: first, to identify the predominant themes in TBI research; and second, to delineate "hot" and "cold" areas of interest by evaluating the current popularity or decline of these topics. Hot topics may be dwarfed in absolute numbers by other, larger TBI research areas but are rapidly gaining interest. Likewise, cold topics may present opportunities for researchers to revisit unanswered questions. We utilized BERTopic, an advanced natural language processing (NLP)-based technique, to analyze TBI research articles published since 1990. This approach facilitated the identification of key topics by extracting sets of distinctive keywords representative of each article's core themes. Using these topics' probabilities, we trained linear regression models to detect trends over time, recognizing topics that were gaining (hot) or losing (cold) relevance. Additionally, we conducted a specific analysis focusing on the trends observed in TBI research in the current decade (the 2020s). Our topic modeling analysis categorized 42,422 articles into 27 distinct topics. The 10 most frequently occurring topics were: "Rehabilitation," "Molecular Mechanisms of TBI," "Concussion," "Repetitive Head Impacts," "Surgical Interventions," "Biomarkers," "Intracranial Pressure," "Posttraumatic Neurodegeneration," "Chronic Traumatic Encephalopathy," and "Blast Induced TBI," while our trend analysis indicated that the hottest topics of the current decade were "Genomics," "Sex Hormones," and "Diffusion Tensor Imaging," while the cooling topics were "Posttraumatic Sleep," "Sensory Functions," and "Hyperosmolar Therapies." This study highlights the dynamic nature of TBI research and underscores the shifting emphasis within the field. The findings from our analysis can aid in the identification of emerging topics of interest and areas where there is little new research reported. By utilizing NLP to effectively synthesize and analyze an extensive collection of TBI-related scholarly literature, we demonstrate the potential of machine learning techniques in understanding and guiding future research prospects. This approach sets the stage for similar analyses in other medical disciplines, offering profound insights and opportunities for further exploration.

Prevalence of Acute Alcohol Use in Traumatic Brain Injury Patients During the COVID-19 Pandemic: A Retrospective Analysis From Queens, New York.

Background This study investigates the impact of New York's relaxed alcohol consumption policies during the coronavirus disease (COVID-19) pandemic on alcohol-related traumatic brain injuries (TBIs) among patients admitted to a Level 1 trauma center in Queens. Given the limited research available, this study critically explores the link between public health policies and trauma care. It aims to address a significant gap in the literature and highlight the implications of alcohol regulations during global health emergencies. Methodology A retrospective analysis was conducted among trauma patients from 2019 to 2021. The study period was divided into the following three periods: pre-lockdown (March 7, 2019, to July 31, 2019), lockdown (March 7, 2020, to July 31, 2020), and post-lockdown (March 7, 2021, to July 31, 2021). Data on demographics, injury severity, comorbidities, and outcomes were collected. The study focused on assessing the correlation between New York's alcohol policies and alcohol-related TBI admissions during these periods. Results A total of 1,074 admissions were analyzed. The study found no significant changes in alcohol-positive patients over the full calendar years of 2019, 2020, and 2021 (42.65%, 38.91%, and 31.16% respectively; p = 0.08711). Specifically, during the lockdown period, rates of alcohol-positive TBI patients remained unchanged, despite the relaxed alcohol policies. There was a decrease in alcohol-related TBI admissions in 2021 compared to 2020 during the lockdown period. Conclusions Our study concludes that New York's specific alcohol policies during the COVID-19 pandemic were not correlated with an increase in alcohol-related TBI admissions. Despite the relaxation of alcohol consumption laws, there was no increase in alcohol positivity among TBI patients. The findings suggest a complex relationship between public policies, alcohol use, and trauma during pandemic conditions, indicating that factors other than policy relaxation might influence alcohol-related trauma incidences.

Technical Optimization of Decompressive Craniectomy for Possible Conversion to Hinge Craniotomy in Traumatic Brain Injury.

Hinge craniotomy for the management of elevated intracranial pressure (ICP) in traumatic brain injury remains a technique not widely adopted. The hinged bone flap decreases the allowable intracranial volume expansion, which can lead to persistent post-operative elevated ICP and the need for salvage craniectomy. Herein, we describe the technical nuances in performing a decompressive craniectomy that, when optimized, allows for stronger consideration for hinge craniotomy as a definitive technique. To conclude, hinge craniotomy is a reasonable option in the setting of traumatic brain injury. Trauma neurosurgeons can consider the technical steps to optimize a decompressive craniectomy and perform hinge craniotomy when allowable.

Prognosis Individualized: Survival predictions for WHO grade II and III gliomas with a machine learning-based web application.

WHO grade II and III gliomas demonstrate diverse biological behaviors resulting in variable survival outcomes. In the context of glioma prognosis, machine learning (ML) approaches could facilitate the navigation through the maze of factors influencing survival, aiding clinicians in generating more precise and personalized survival predictions. Here we report the utilization of ML models in predicting survival at 12, 24, 36, and 60 months following grade II and III glioma diagnosis. From the National Cancer Database, we analyze 10,001 WHO grade II and 11,456 grade III cranial gliomas. Using the area under the receiver operating characteristic (AUROC) values, we deploy the top-performing models in a web application for individualized predictions. SHapley Additive exPlanations (SHAP) enhance the interpretability of the models. Top-performing predictive models are the ones built with LightGBM and Random Forest algorithms. For grade II gliomas, the models yield AUROC values ranging from 0.813 to 0.896 for predicting mortality across different timeframes, and for grade III gliomas, the models yield AUROCs ranging from 0.855 to 0.878. ML models provide individualized survival forecasts for grade II and III glioma patients across multiple clinically relevant time points. The user-friendly web application represents a pioneering digital tool to potentially integrate predictive analytics into neuro-oncology clinical practice, to empower prognostication and personalize clinical decision-making.