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INTRODUCTION: More evidence for patterns of healthcare utilisation and associated costs among people receiving opioid agonist therapy (OAT) is needed. We investigated primary and secondary healthcare usage and costs among methadone and buprenorphine recipients in England. METHODS: We conducted a cohort study using the Clinical Practice Research Datalink GOLD and Aurum databases of patients who were prescribed OAT between 1 January 2007 and 31 July 2019. The cohort was linked to Hospital Episode Statistics admitted patient care, outpatient and emergency department data, neighbourhood- and practice-level Index of Multiple Deprivation quintiles and mortality records. Negative binomial regression models were applied to estimate weighted rate ratios (wRR) of healthcare utilisation. Total and mean costs were calculated using Unit Costs of Health and Social Care and the National Healthcare Service Payment by Results National Tariffs. RESULTS: Among 12,639 patients observed over 39,016 person-years, we found higher rate of hospital admissions (wRR 1.18; 1.08-1.28) among methadone compared with buprenorphine recipients. The commonest hospital discharge diagnoses among methadone patients were infectious diseases (19.2%), mental and behavioural disorders (17.0%) and drug-related poisoning (16.5%); the three commonest among buprenorphine patients were mental and behavioural diseases (21.5%), endocrine (13.8%) and genitourinary system diseases (13.1%). Methadone patients had similar mean costs compared with buprenorphine patients (cost difference: £539.01; 432.11-1006.69). DISCUSSION AND CONCLUSIONS: Differences in healthcare utilisation frequency for methadone versus buprenorphine recipients were observed. The differences in associated costs were mainly driven by hospital admissions. These findings offer valuable insights for optimising care strategies and resource allocation for OAT recipients.

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Background Paediatric otorrhoea (PO) is a symptom-based diagnosis encompassing acute and chronic ear infections which cause otorrhoea in children and young people (CYP). Aim To understand the burden of PO on primary care services. Design and Setting A longitudinal population study in UK primary care. Methods Data from the Clinical Practice Research Datalink (CPRD Aurum), January 2005 to December 2019, was analysed. CYP under 17 years of age with otorrhoea were included. Standardised annual incidence and presentation rates were estimated. Poisson regression modelling was used to determine risk ratios comparing sex, age and IMD. A probabilistic simulation scaled-up estimates for the UK population. Results The cohort included 6,605,193 CYP, observed over 32,942,594 person-years. There were 80,454 incident cases and 106,318 presentations of PO during the 15-year period, equating to standardised annual incidence and presentation rates per 1000 patient-years of 2.42 (95% CI: 2.40-2.44) and 3.15 (3.13-3.17) respectively. In the UK this equates to 41,141 primary care appointments per year. Incidence was higher in males, those aged 0-2 years, and those living in the least deprived quintile. Treatment involved oral antibiotics (57.1%), no prescription (28.1%), topical antibiotics (9.7%), or combination (4.9%). The cost to NHS primary care is estimated at £1.97 million per year. Conclusions This is the first longitudinal population-based study investigating PO which demonstrates the burden on primary care. Antimicrobial prescribing predominantly follows NICE guidelines using oral amoxicillin. Aminoglycosides are the most frequently prescribed topical antibiotic despite the concern of ototoxicity.

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BACKGROUND AND OBJECTIVES: Retrieve data from the National Cancer Database (NCDB) to examine information on the epidemiological prevalence, treatment strategies, and survival outcomes of pediatric vertebral, sacral and pelvic osteosarcomas. METHODS: We reviewed NCDB data from 2008 to 2018, concentrating on vertebral, sacral, and pelvic osteosarcomas in children 0 to 21 years. Our analysis involved logistic and Poisson regression, Kaplan-Meier survival estimates, and Cox proportional hazards models. RESULTS: The study population included 207 patients. For vertebral osteosarcomas, 62.5% of patients were female, and 78.1% were white. Regional lymph node involvement predicted 80 times higher mortality hazard (p = 0.021). Distant metastasis predicted 25 times higher mortality hazard (p = 0.027). For sacral and pelvic osteosarcomas, 58.3% of patients were male, and 72% were white. Patients with residual tumor were 4 times more likely to have prolonged LOS (p = 0.031). No residual tumor (HR = 0.53, p = 0.03) and radiotherapy receipt (HR = 0.46, p = 0.034) were associated with lower mortality hazards. Distant metastasis predicted 3 times higher mortality hazard (p < 0.001). Hispanic ethnicity was linked to lower resection odds (OR = 0.342, p = 0.043), possibly due to language barriers affecting patient understanding and care decisions. CONCLUSIONS: In conclusion, our examination of NCDB offers a thorough exploration of demographics, treatment patterns, and results, highlighting the importance of personalized approaches to enhance patient outcomes.

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OBJECTIVE: Meningiomas are the most common primary central nervous tumor and are often treated with radiation therapy. This study examines the long-term volumetric changes of intracranial meningiomas in response to radiation therapy. The objective is to analyze and model the volumetric changes following treatment. METHODS: Data from a retrospective single-institution database (2005-2015) were used, with inclusion criteria being patients with a diagnosis of meningiomas, along with additional inclusion criteria consisting of treatment with radiation, having at least three magnetic resonance imaging (MRI) scans with one or more before and after radiation treatment, and the patients following up for at least eighteen months. Exclusion criteria consisted of patients less than 18 years old, patients receiving surgery and/or adjuvant chemotherapy following radiation, and patients without any available details regarding radiation treatment parameters. Tumor volumes were measured via T1-weighted post-contrast MRI and calculated using the ABC/2 ellipsoidal approximation, a method allowing for the measurement of non-linear growth volume reduction. RESULTS: Of 48 meningioma patients considered, 10 % experienced post-radiation growth, while 75 % witnessed a ≥50 % decrease in volume over a follow-up period of 0.3-14.9 years. Median decay rate was 0.81, and within 1.17 years, 90 % achieved the predicted volume reduction. Predicted vs. actual volumes showed a mean difference of 0.009 ± 0.347 cc. Initial tumor volumes strongly correlated (Pearson's R=0.98, R-squared=0.96) with final asymptotic volumes, which had a median of 1.50 cc, with interquartile range (IQR) = [0.39, 3.67]. CONCLUSION: 90 % of patients achieved tumor-volume reduction at 1.17 years post-treatment, reaching a non-zero asymptote strongly correlated with initial tumor volume, and 75 % experienced at least a 50 % volume decrease. Individual volume changes for responsive meningiomas can be modeled and predicted using exponential decay curves.

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STUDY DESIGN: A retrospective machine learning (ML) classification study for prognostic modeling after anterior cervical corpectomy (ACC). PURPOSE: To evaluate the effectiveness of ML in predicting ACC outcomes and develop an accessible, user-friendly tool for this purpose. OVERVIEW OF LITERATURE: Based on our literature review, no study has examined the capability of ML algorithms to predict major shortterm ACC outcomes, such as prolonged length of hospital stay (LOS), non-home discharge, and major complications. METHODS: The American College of Surgeons' National Surgical Quality Improvement Program database was used to identify patients who underwent ACC. Prolonged LOS, non-home discharges, and major complications were assessed as the outcomes of interest. ML models were developed with the TabPFN algorithm and integrated into an open-access website to predict these outcomes. RESULTS: The models for predicting prolonged LOS, non-home discharges, and major complications demonstrated mean areas under the receiver operating characteristic curve (AUROC) of 0.802, 0.816, and 0.702, respectively. These findings highlight the discriminatory capacities of the models: fair (AUROC >0.7) for differentiating patients with major complications from those without, and good (AUROC >0.8) for distinguishing between those with and without prolonged LOS and non-home discharges. According to the SHapley Additive Explanations analysis, single- versus multiple-level surgery, age, body mass index, preoperative hematocrit, and American Society of Anesthesiologists physical status repetitively emerged as the most important variables for each outcome. CONCLUSIONS: This study has considerably enhanced the prediction of postoperative results after ACC surgery by implementing advanced ML techniques. A major contribution is the creation of an accessible web application, highlighting the practical value of the developed models. Our findings imply that ML can serve as an invaluable supplementary tool to stratify patient risk for this procedure and can predict diverse postoperative adverse outcomes.

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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. 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 5 cm 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 5-year overall survival 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. CONCLUSIONS: 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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