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.

Optimal Timing in Cervical Spinal Cord Injury: A Comprehensive Meta-Analysis of Ultra-Early Surgical Intervention Within Five Hours.

The optimal timing of surgery for cervical spinal cord injuries (SCI) and its impact on neurological recovery continue to be subjects of debate. This systematic review and meta-analysis aims to consolidate and assess the existing evidence regarding the efficacy of ultra-early decompression surgery in improving clinical outcomes after cervical SCI. A search was conducted in PubMed, Embase, Cochrane, and CINAHL databases from inception until September 18, 2023, focusing on human studies. The groups were categorized into ultra-early decompression (decompression surgery ≤ 5 hours post-injury) and a control group (decompression surgery between 5-24 hours post-injury). A random effects meta-analysis was performed on all studies using R Studio. Outcomes were reported as effect size (OR, treatment effect, and 95% CI. Of the 140 patients, 63 (45%) underwent decompression ≤ 5 hours, while 77 (55%) underwent decompression > 5 hours post-injury. Analysis using the OR model showed no statistically significant difference in the odds of neurological improvement between the ultra-early group and the early group (OR = 1.33, 95% CI: 0.22-8.18, p = 0.761). This study did not observe significant neurological improvement among cervical SCI patients who underwent decompression within five hours. Due to the scarcity of literature on the ultra-early decompression of cervical SCI, this study underscores the necessity for additional investigation into the potential benefits of earlier interventions for cervical SCI to enhance patient outcomes.

Primary care contact, clinical management and suicide risk following discharge from inpatient mental health care.

BACKGROUND: Evidence is sparse regarding service usage and the clinical management of people recently discharged from inpatient psychiatric care who die by suicide. AIM: To improve understanding of how people discharged from inpatient mental health care are supported by primary care during this high-risk transition. DESIGN & SETTING: A nested case-control study utilising interlinked primary and secondary care records in England for people who died within a year of discharge between 2001 and 2019, matched on age, sex, practice-level deprivation and region with up to 20 living discharged people. METHOD: We described patterns of consultation, prescription of psychotropic medication and continuity of care for people who died by suicide and those who survived. Mutually adjusted relative risk estimates were generated for a range of primary care and clinical variables. RESULTS: Over 40% of patients who died within 2 weeks and 80% who died later had at least one primary care consultation. Evidence of discharge communication from hospital was infrequent. Within-practice continuity of care was relatively high. Those who died by suicide were less likely to consult within two weeks of discharge, AOR 0.61 (0.42-0.89), more likely to consult in the week before death, AOR 1.71 (1.36-2.15), to be prescribed multiple types of psychotropic medication, (AOR 1.73, 1.28-2.33), to experience readmission and have a diagnosis outside of the 'Severe Mental Illness' definition. CONCLUSION: Primary care clinicians have opportunities to intervene and should prioritise patients experiencing transition from inpatient care. Clear communication and liaison between services is essential to provide timely support.

The role of accelerometer-derived sleep traits on glycated haemoglobin and glucose levels: a Mendelian randomization study.

Self-reported shorter/longer sleep duration, insomnia, and evening preference are associated with hyperglycaemia in observational analyses, with similar observations in small studies using accelerometer-derived sleep traits. Mendelian randomization (MR) studies support an effect of self-reported insomnia, but not others, on glycated haemoglobin (HbA1c). To explore potential effects, we used MR methods to assess effects of accelerometer-derived sleep traits (duration, mid-point least active 5-h, mid-point most active 10-h, sleep fragmentation, and efficiency) on HbA1c/glucose in European adults from the UK Biobank (UKB) (n = 73,797) and the MAGIC consortium (n = 146,806). Cross-trait linkage disequilibrium score regression was applied to determine genetic correlations across accelerometer-derived, self-reported sleep traits, and HbA1c/glucose. We found no causal effect of any accelerometer-derived sleep trait on HbA1c or glucose. Similar MR results for self-reported sleep traits in the UKB sub-sample with accelerometer-derived measures suggested our results were not explained by selection bias. Phenotypic and genetic correlation analyses suggested complex relationships between self-reported and accelerometer-derived traits indicating that they may reflect different types of exposure. These findings suggested accelerometer-derived sleep traits do not affect HbA1c. Accelerometer-derived measures of sleep duration and quality might not simply be 'objective' measures of self-reported sleep duration and insomnia, but rather captured different sleep characteristics.

Socio-demographic variation in diagnosis of and prescribing for common mental illnesses among children and young people during the COVID-19 pandemic: time series analysis of primary care electronic health records.

BACKGROUND: The impact of the COVID-19 pandemic on the mental health of children and young people (CYP) has been widely reported. Primary care electronic health records were utilised to examine trends in the diagnosing, recording and treating of these common mental disorders by ethnicity and social deprivation in Greater Manchester, England. METHODS: Time-series analyses conducted using Greater Manchester Care Record (GMCR) data examined all diagnosed episodes of anxiety disorders and depression and prescribing of anxiolytics and antidepressants among patients aged 6-24 years. The 41-month observation period was split into three epochs: Pre-pandemic (1/2019-2/2020); Pandemic Phase 1 (3/2020-6/2021); Pandemic Phase 2 (7/2021-5/2022). Rate ratios for all CYP specific to sex, age, ethnicity, and neighbourhood-level Indices of Multiple Deprivation (IMD) quintile were modelled using negative binomial regression. RESULTS: Depression and anxiety disorder rates were highest in females, CYP aged 19-24, and White and 'Other' ethnic groups. During Pandemic Phase 1, rates for these diagnoses fell in all demographic subgroups and then rose to similar levels as those recorded pre-pandemic. In Pandemic Phase 2, rates in Black and Mixed-ethnicity females rose to a significantly greater degree (by 54% and 62%, respectively) than those in White females. Prescribing rates increased throughout the study period, with significantly greater rises observed in non-White females and males. The temporal trends were mostly homogeneous across deprivation quintiles. CONCLUSION: The observed fluctuations in frequency of recorded common mental illness diagnoses likely reflect service accessibility and patients' differential propensities to consult as well as changing levels of distress and psychopathology in the population. However, psychotropic medication prescribing increased throughout the observation period, possibly indicating a sustained decline in mental health among CYP, and also clinicians' responses to problems presented. The comparatively greater increases in frequencies of diagnosis recording and medication prescribing among ethnic minority groups warrants further investigation.

Bibliometric and trend analysis of the top 100 most-cited articles on lateral interbody fusion (LIF).

PURPOSE: Lateral interbody fusion (LIF) is an increasingly popular minimally-invasive spine procedure. This study identifies notable trends in LIF literature and provides a detailed review of the bibliometric aspects of the top 100 most-cited articles. METHODS: Articles were queried from the Web of Science database. Inclusion criteria consisted of peer-reviewed articles, full-text availability, and LIF focus. Network analysis including co-authorship mapping and bibliographic coupling were complemented by trend analysis to determine prominent contributors and themes. Analyses were conducted using VOSviewer and Bibliometrix (RStudio). RESULTS: There has been a rapid increase in LIF publication and citation count since 1998. Leading journals were Spine (n = 24), Journal of Neurosurgery Spine (n = 22), and European Spine Journal (n = 12). NuVasive funded the most publications (n = 17), followed by DePuy Synthes Spine (n = 4). The United States was the most represented country (n = 81); however, trend analysis suggests a steadily growing international contribution. The most prolific author was J.S. Uribe (n = 16), followed by a tie in second place by E. Dakwar and L. Pimenta (n = 8). The most frequent keywords, "complication" (n = 34), "surgery" (n = 30), and "outcomes" (n = 24), demonstrated a patient-centric theme. CONCLUSIONS: This bibliometric analysis provides in-depth insights into the evolution and trends of LIF over the last two decades. The trends and themes identified demonstrate the innovative, collaborative, and patient-focused characteristics of this subfield. Future researchers can use this as a foundation for understanding the past and present state of LIF research while designing investigations.

Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.

Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.

Indirect effects of the COVID-19 pandemic on diagnosing, monitoring, and prescribing in people with diabetes and strategies for diabetes service recovery internationally.

The COVID-19 pandemic has caused major disruptions in clinical services for people with chronic long-term conditions. In this narrative review, we assess the indirect impacts of the COVID-19 pandemic on diabetes services globally and the resulting adverse effects on rates of diagnosing, monitoring, and prescribing in people with type 2 diabetes. We summarise potential practical approaches that could address these issues and improve clinical services and outcomes for people living with diabetes during the recovery phase of the pandemic.

Machine learning models on a web application to predict short-term postoperative outcomes following anterior cervical discectomy and fusion.

BACKGROUND: The frequency of anterior cervical discectomy and fusion (ACDF) has increased up to 400% since 2011, underscoring the need to preoperatively anticipate adverse postoperative outcomes given the procedure's expanding use. Our study aims to accomplish two goals: firstly, to develop a suite of explainable machine learning (ML) models capable of predicting adverse postoperative outcomes following ACDF surgery, and secondly, to embed these models in a user-friendly web application, demonstrating their potential utility. METHODS: We utilized data from the National Surgical Quality Improvement Program database to identify patients who underwent ACDF surgery. The outcomes of interest were four short-term postoperative adverse outcomes: prolonged length of stay (LOS), non-home discharges, 30-day readmissions, and major complications. We utilized five ML algorithms - TabPFN, TabNET, XGBoost, LightGBM, and Random Forest - coupled with the Optuna optimization library for hyperparameter tuning. To bolster the interpretability of our models, we employed SHapley Additive exPlanations (SHAP) for evaluating predictor variables' relative importance and used partial dependence plots to illustrate the impact of individual variables on the predictions generated by our top-performing models. We visualized model performance using receiver operating characteristic (ROC) curves and precision-recall curves (PRC). Quantitative metrics calculated were the area under the ROC curve (AUROC), balanced accuracy, weighted area under the PRC (AUPRC), weighted precision, and weighted recall. Models with the highest AUROC values were selected for inclusion in a web application. RESULTS: The analysis included 57,760 patients for prolonged LOS [11.1% with prolonged LOS], 57,780 for non-home discharges [3.3% non-home discharges], 57,790 for 30-day readmissions [2.9% readmitted], and 57,800 for major complications [1.4% with major complications]. The top-performing models, which were the ones built with the Random Forest algorithm, yielded mean AUROCs of 0.776, 0.846, 0.775, and 0.747 for predicting prolonged LOS, non-home discharges, readmissions, and complications, respectively. CONCLUSIONS: Our study employs advanced ML methodologies to enhance the prediction of adverse postoperative outcomes following ACDF. We designed an accessible web application to integrate these models into clinical practice. Our findings affirm that ML tools serve as vital supplements in risk stratification, facilitating the prediction of diverse outcomes and enhancing patient counseling for ACDF.

An Interpretable Machine Learning Approach to Predict Survival Outcomes in Spinal and Sacropelvic Chordomas.

STUDY DESIGN: Retrospective, population-based cohort study. OBJECTIVE: This study aimed to develop machine learning (ML) models to predict five-year and 10-year mortality in spinal and sacropelvic chordoma patients and integrate them into a web application for enhanced prognostication. SUMMARY OF BACKGROUND DATA: Past research has uncovered factors influencing survival in spinal chordoma patients. While identifying individual predictors is important, personalized survival predictions are equally vital. Though prior efforts have resulted in nomograms aiming to serve this purpose, they cannot capture complex interactions within data and rely on statistical assumptions that may not fit real-world data. METHODS: Adult spinal and sacropelvic chordoma patients were identified from the National Cancer Database. Sociodemographic, clinicopathologic, diagnostic, and treatment-related variables were utilized as predictive features. Five supervised ML algorithms (TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest) were implemented to predict mortality at five and 10 years postdiagnosis. Model performance was primarily evaluated using the area under the receiver operating characteristic (AUROC). SHapley Additive exPlanations (SHAP) values and partial dependence plots provided feature importance and interpretability. The top models were integrated into a web application. RESULTS: From the NCDB, 1206 adult patients diagnosed with histologically confirmed spinal and sacropelvic chordomas were retrieved for the five-year mortality outcome [423 (35.1%) with five-year mortality] and 801 patients for the 10-year mortality outcome [588 (73.4%) with 10-year mortality]. Top-performing models for both of the outcomes were the models created with the CatBoost algorithm. The CatBoost model for five-year mortality predictions displayed a mean AUROC of 0.801, and the CatBoost model predicting 10-year mortality yielded a mean AUROC of 0.814. CONCLUSIONS: This study developed ML models that can accurately predict five-year to 10-year survival probabilities in spinal chordoma patients. Integrating these interpretable, personalized prognostic models into a web application provides quantitative survival estimates for a given patient. The local interpretability enables transparency into how predictions are influenced. Further external validation is warranted to support generalizability and clinical utility.

Multiple adverse outcomes associated with antipsychotic use in people with dementia: population based matched cohort study.

OBJECTIVE: To investigate risks of multiple adverse outcomes associated with use of antipsychotics in people with dementia. DESIGN: Population based matched cohort study. SETTING: Linked primary care, hospital and mortality data from Clinical Practice Research Datalink (CPRD), England. POPULATION: Adults (≥50 years) with a diagnosis of dementia between 1 January 1998 and 31 May 2018 (n=173 910, 63.0% women). Each new antipsychotic user (n=35 339, 62.5% women) was matched with up to 15 non-users using incidence density sampling. MAIN OUTCOME MEASURES: The main outcomes were stroke, venous thromboembolism, myocardial infarction, heart failure, ventricular arrhythmia, fracture, pneumonia, and acute kidney injury, stratified by periods of antipsychotic use, with absolute risks calculated using cumulative incidence in antipsychotic users versus matched comparators. An unrelated (negative control) outcome of appendicitis and cholecystitis combined was also investigated to detect potential unmeasured confounding. RESULTS: Compared with non-use, any antipsychotic use was associated with increased risks of all outcomes, except ventricular arrhythmia. Current use (90 days after a prescription) was associated with elevated risks of pneumonia (hazard ratio 2.19, 95% confidence interval (CI) 2.10 to 2.28), acute kidney injury (1.72, 1.61 to 1.84), venous thromboembolism (1.62, 1.46 to 1.80), stroke (1.61, 1.52 to 1.71), fracture (1.43, 1.35 to 1.52), myocardial infarction (1.28, 1.15 to 1.42), and heart failure (1.27, 1.18 to 1.37). No increased risks were observed for the negative control outcome (appendicitis and cholecystitis). In the 90 days after drug initiation, the cumulative incidence of pneumonia among antipsychotic users was 4.48% (4.26% to 4.71%) versus 1.49% (1.45% to 1.53%) in the matched cohort of non-users (difference 2.99%, 95% CI 2.77% to 3.22%). CONCLUSIONS: Antipsychotic use compared with non-use in adults with dementia was associated with increased risks of stroke, venous thromboembolism, myocardial infarction, heart failure, fracture, pneumonia, and acute kidney injury, but not ventricular arrhythmia. The range of adverse outcomes was wider than previously highlighted in regulatory alerts, with the highest risks soon after initiation of treatment.

Intraspinal cervicothoracic junction chondrosarcoma: illustrative case.

BACKGROUND: Chondrosarcoma is an uncommon spinal tumor that can present as an extraskeletal mass. Rarely, these tumors present as dumbbell tumors through the neural foramina, mimicking schwannomas or neurofibromas. OBSERVATIONS: A 46-year-old female presented with 2 years of worsening right-arm radiculopathy. Magnetic resonance imaging of the thoracic spine revealed a peripherally enhancing extramedullary mass through the right T1 foramen and compressing the spinal cord. Computed tomography showed the mass to be partially calcified. She underwent C7-T2 laminectomy and C6-T3 posterior instrumented fusion with gross-total resection of an extradural mass. Pathology revealed a grade I chondrosarcoma. Her symptoms improved postoperatively, with some residual right-arm radicular pain. LESSONS: Intraspinal extradural dumbbell conventional chondrosarcoma is rare, with only 9 cases, including ours, reported. Patient ages range from 16 to 72 years old, and male sex is more common in these cases. The most common location is the thoracic spine, and our case is the only reported one in the cervicothoracic junction. These tumors often mimic schwannomas on imaging, but chondrosarcoma should remain in the differential diagnosis, because management of these tumors differs. Chondrosarcoma may benefit from more aggressive resection, including en bloc resection, and may require adjuvant radiotherapy.

Ultra-Early (<5 Hours) Decompression for Thoracolumbar Spinal Cord Injury: A Case Series.

Early surgical decompression within 24 hours for traumatic spinal cord injury (SCI) is associated with improved neurological recovery. However, the ideal timing of decompression is still up for debate. The objective of this study was to utilize our retrospective single-institution series of ultra-early (<5 hours) decompression to determine if ultra-early decompression led to improved neurological outcomes and was a feasible target over previously defined early decompression targets. Retrospective data on patients with SCI who underwent ultra-early (<5 hours) decompression at a level one metropolitan trauma center were extracted and collected from 2015-2018. American Spinal Injury Association (ASIA) Impairment Scale (AIS) grade improvement was the primary outcome, with ASIA Motor score improvement and complication rate as secondary outcomes. Four individuals met the criteria for inclusion in this case series. All four suffered thoracolumbar SCI. All patients improved neurologically by AIS grade, and there were no complications directly related to ultra-early surgery. Given the small sample size, there was no statistically significant difference in outcomes compared to a control group who underwent early (5-24 hour) decompression in the same period. Ultra-early decompression is a feasible and safe target for thoracolumbar SCI and may lead to improved neurological outcomes without increased risk of complications. This case series can help create the foundation for future, larger studies that may definitively show the benefit of ultra-early decompression.

Survival Outcomes in Older Women with Oestrogen-Receptor-Positive Early-Stage Breast Cancer: Primary Endocrine Therapy vs. Surgery by Comorbidity and Frailty Levels.

Primary endocrine therapy (PET) offers non-surgical treatment for older women with early-stage breast cancer who are unsuitable for surgery due to frailty or comorbidity. This research assessed all-cause and breast cancer-specific mortality of PET vs. surgery in older women (≥70 years) with oestrogen-receptor-positive early-stage breast cancer by frailty and comorbidity levels. This study used UK secondary data to analyse older female patients from 2000 to 2016. Patients were censored until 31 May 2019 and grouped by the Charlson comorbidity index (CCI) and hospital frailty risk score (HFRS). Cox regression models compared all-cause and breast cancer-specific mortality between PET and surgery within each group, adjusting for patient preferences and covariates. Sensitivity analyses accounted for competing risks. There were 23,109 patients included. The hazard ratio (HR) comparing PET to surgery for overall survival decreased significantly from 2.1 (95%CI: 2.0, 2.2) to 1.2 (95%CI: 1.1, 1.5) with increasing HFRS and from 2.1 (95%CI: 2.0, 2.2) to 1.4 (95%CI 1.2, 1.7) with rising CCI. However, there was no difference in BCSM for frail older women (HR: 1.2; 0.9, 1.9). There were no differences in competing risk profiles between other causes of death and breast cancer-specific mortality with PET versus surgery, with a subdistribution hazard ratio of 1.1 (0.9, 1.4) for high-level HFRS (p = 0.261) and CCI (p = 0.093). Given limited survival gains from surgery for older patients, PET shows potential as an effective option for frail older women with early-stage breast cancer. Despite surgery outperforming PET, surgery loses its edge as frailty increases, with negligible differences in the very frail.

Exploring the Implementation of GaAsBi Alloys as Strain-Reducing Layers in InAs/GaAs Quantum Dots.

This paper investigates the effect of GaAsBi strain reduction layers (SRLs) on InAs QDs with different Bi fluxes to achieve nanostructures with improved temperature stability. The SRLs are grown at a lower temperature (370 °C) than the usual capping temperature for InAs QDs (510 °C). The study finds that GaAs capping at low temperatures reduces QD decomposition and leads to larger pyramidal dots but also increases the threading dislocation (TD) density. When adding Bi to the capping layer, a significant reduction in TD density is observed, but unexpected structural changes also occur. Increasing the Bi flux does not increase the Bi content but rather the layer thickness. The maximum Bi content for all layers is 2.4%. A higher Bi flux causes earlier Bi incorporation, along with the formation of an additional InGaAs layer above the GaAsBi layer due to In segregation from QD erosion. Additionally, the implementation of GaAsBi SRLs results in smaller dots due to enhanced QD decomposition, which is contrary to the expected function of an SRL. No droplets were detected on the surface of any sample, but we did observe regions of horizontal nanowires within the epilayers for the Bi-rich samples, indicating nanoparticle formation.

Development and internal validation of machine learning models for personalized survival predictions in spinal cord glioma patients.

BACKGROUND CONTEXT: Numerous factors have been associated with the survival outcomes in patients with spinal cord gliomas (SCG). Recognizing these specific determinants is crucial, yet it is also vital to establish a reliable and precise prognostic model for estimating individual survival outcomes. OBJECTIVE: The objectives of this study are twofold: first, to create an array of interpretable machine learning (ML) models developed for predicting survival outcomes among SCG patients; and second, to integrate these models into an easily navigable online calculator to showcase their prospective clinical applicability. STUDY DESIGN: This was a retrospective, population-based cohort study aiming to predict the outcomes of interest, which were binary categorical variables, in SCG patients with ML models. PATIENT SAMPLE: The National Cancer Database (NCDB) was utilized to identify adults aged 18 years or older who were diagnosed with histologically confirmed SCGs between 2010 and 2019. OUTCOME MEASURES: The outcomes of interest were survival outcomes at three specific time points postdiagnosis: 1, 3, and 5 years. These outcomes were formed by combining the "Vital Status" and "Last Contact or Death (Months from Diagnosis)" variables. Model performance was evaluated visually and numerically. The visual evaluation utilized receiver operating characteristic (ROC) curves, precision-recall curves (PRCs), and calibration curves. The numerical evaluation involved metrics such as sensitivity, specificity, accuracy, area under the PRC (AUPRC), area under the ROC curve (AUROC), and Brier Score. METHODS: We employed five ML algorithms-TabPFN, CatBoost, XGBoost, LightGBM, and Random Forest-along with the Optuna library for hyperparameter optimization. The models that yielded the highest AUROC values were chosen for integration into the online calculator. To enhance the explicability of our models, we utilized SHapley Additive exPlanations (SHAP) for assessing the relative significance of predictor variables and incorporated partial dependence plots (PDPs) to delineate the influence of singular variables on the predictions made by the top performing models. RESULTS: For the 1-year survival analysis, 4,913 patients [5.6% with 1-year mortality]; for the 3-year survival analysis, 4,027 patients (11.5% with 3-year mortality]; and for the 5-year survival analysis, 2,854 patients (20.4% with 5-year mortality) were included. The top models achieved AUROCs of 0.938 for 1-year mortality (TabPFN), 0.907 for 3-year mortality (LightGBM), and 0.902 for 5-year mortality (Random Forest). Global SHAP analyses across survival outcomes at different time points identified histology, tumor grade, age, surgery, radiotherapy, and tumor size as the most significant predictor variables for the top-performing models. CONCLUSIONS: This study demonstrates ML techniques can develop highly accurate prognostic models for SCG patients with excellent discriminatory ability. The interactive online calculator provides a tool for assessment by physicians (https://huggingface.co/spaces/MSHS-Neurosurgery-Research/NCDB-SCG). Local interpretability informs prediction influences for a given individual. External validation across diverse datasets could further substantiate potential utility and generalizability. This robust, interpretable methodology aligns with the goals of precision medicine, establishing a foundation for continued research leveraging ML's predictive power to enhance patient counseling.

From Text to Insight: A Natural Language Processing-Based Analysis of Topics and Trends in Neurosurgery.

BACKGROUND AND OBJECTIVES: Neurosurgical research is a rapidly evolving field, with new research topics emerging continually. To provide a clearer understanding of the evolving research landscape, our study aimed to identify and analyze the prevalent research topics and trends in Neurosurgery. METHODS: We used BERTopic, an advanced natural language processing-based topic modeling approach, to analyze papers published in the journal Neurosurgery . Using this method, topics were identified based on unique sets of keywords that encapsulated the core themes of each article. Linear regression models were then trained on the topic probabilities to identify trends over time, allowing us to identify "hot" (growing in prominence) and "cold" (decreasing in prominence) topics. We also performed a focused analysis of the trends in the current decade. RESULTS: Our analysis led to the categorization of 12 438 documents into 49 distinct topics. The topics covered a wide range of themes, with the most commonly identified topics being "Spinal Neurosurgery" and "Treatment of Cerebral Ischemia." The hottest topics of the current decade were "Peripheral Nerve Surgery," "Unruptured Aneurysms," and "Endovascular Treatments" while the cold topics were "Chiari Malformations," "Thromboembolism Prophylaxis," and "Infections." CONCLUSION: Our study underscores the dynamic nature of neurosurgical research and the evolving focus of the field. The insights derived from the analysis can guide future research directions, inform policy decisions, and identify emerging areas of interest. The use of natural language processing in synthesizing and analyzing large volumes of academic literature demonstrates the potential of advanced analytical techniques in understanding the research landscape, paving the way for similar analyses across other medical disciplines.

Clinical Considerations and Outcomes for Spine Surgery Patients with a History of Transplant: A Systematic Scoping Review.

OBJECTIVE: The objective of this study was to investigate the perioperative management and outcomes of patients with a prior history of successful transplantation undergoing spine surgery. METHODS: We searched Medline, Embase, and Cochrane Central Register of Controlled Trials for matching reports in July 2021. We included case reports, cohort studies, and retrospective analyses, including terms for various transplant types and an exhaustive list of key words for various forms of spine surgery. RESULTS: We included 45 studies consisting of 34 case reports (published 1982-2021), 3 cohort analyses (published 2005-2006), and 8 retrospective analyses (published 2006-2020). The total number of patients included in the case reports, cohort studies, and retrospective analysis was 35, 48, and 9695, respectively. The mean 1-year mortality rate from retrospective analyses was 4.6% ± 1.93%, while the prevalence of perioperative complications was 24%. Cohort studies demonstrated an 8.5% ± 12.03% 30-day readmission rate. The most common procedure performed was laminectomy (38.9%) among the case reports. Mortality after spine surgery was noted for 4 of 35 case report patients (11.4%). CONCLUSIONS: This is the first systematic scoping review examining the population of transplant patients with subsequent unrelated spine surgery. There is significant heterogeneity in the outcomes of post-transplant spine surgery patients. Given the inherent complexity of managing this group and elevated mortality and complications compared to the general spine surgery population, further investigation into their clinical care is warranted.