Background: Instrumented spinal fusions can be used in the treatment of vertebral fractures, spinal instability, and scoliosis or kyphosis. Construct-level selection has notable implications on postoperative recovery, alignment, and mobility. This study sought to project future trends in the implementation rates and associated costs of single-level versus multilevel instrumentation procedures in US Medicare patients aged older than 65 years in the United States. Methods: Data were acquired from the Centers for Medicare & Medicaid Services from January 1, 2000, to December 31, 2019. Procedure costs and counts were abstracted using Current Procedural Terminology codes to identify spinal level involvement. The Prophet machine learning algorithm was used, using a Bayesian Inference framework, to generate point forecasts for 2020 to 2050 and 95% forecast intervals (FIs). Sensitivity analyses were done by comparing projections from linear, log-linear, Poisson and negative-binomial, and autoregressive integrated moving average models. Costs were adjusted for inflation using the 2019 US Bureau of Labor Statistics' Consumer Price Index. Results: Between 2000 and 2019, the annual spinal instrumentation volume increased by 776% (from 7,342 to 64,350 cases) for single level, by 329% (from 20,319 to 87,253 cases) for two-four levels, by 1049% (from 1,218 to 14,000 cases) for five-seven levels, and by 739% (from 193 to 1,620 cases) for eight-twelve levels (P < 0.0001). The inflation-adjusted reimbursement for single-level instrumentation procedures decreased 45.6% from $1,148.15 to $788.62 between 2000 and 2019, which is markedly lower than for other prevalent orthopaedic procedures: total shoulder arthroplasty (−23.1%), total hip arthroplasty (−39.2%), and total knee arthroplasty (−42.4%). By 2050, the number of single-level spinal instrumentation procedures performed yearly is projected to be 124,061 (95% FI, 87,027 to 142,907), with associated costs of $93,900,672 (95% FI, $80,281,788 to $108,220,932). Conclusions: The number of single-level instrumentation procedures is projected to double by 2050, while the number of two-four level procedures will double by 2040. These projections offer a measurable basis for resource allocation and procedural distribution.

In this study, we provide the largest analysis to date of a US-based cancer cohort to characterize death from COVID-19. A total of 4,020,669 patients across 15 subtypes living with cancer in 2020 and included in the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database were abstracted. We investigated prognostic factors for death due to COVID-19 using a cox proportional hazards model and calculated hazard ratios (HRs). Standardized mortality ratios (SMRs) were calculated using observed mortality counts from SEER and expected mortality based on U.S. mortality rates. 291,323 patients died, with 14,821 (5.1%) deaths attributed to COVID-19 infection. The COVID-19 disease-specific mortality rate was 11.81/10,000-persons years, and SMR of COVID-19 was 2.30 (95% CI: 2.26-2.34, p < .0001). COVID-19 ranked as the second leading cause of death following ischemic heart disease (5.2%) among 26 non-cancer causes of death. Patients who are older (80+ vs < =49 years old: HR 21.47, 95% CI: 19.34-23.83), male (vs female: HR 1.46, 95% CI: 1.40-1.51), unmarried (vs married: HR 1.47, 95% CI: 1.42-1.53), and Hispanic or Non-Hispanic African American (vs Non-Hispanic White: HR 2.04, 95% CI: 1.94-2.14 and HR 2.03, 95% CI: 1.94-2.14, respectively) were at greatest risk of COVID-19 mortality. We observed that people living with cancer are at two times greater risk of dying from COVID-19 compared to the general US population. This work may be used by physicians and public health officials in the creation of survivorship programs that mitigate the risk of COVID-19 mortality.

Chordomas are rare sarcomas arising from notochordal tissue and occur most commonly in the spine. The standard of care for chordomas without evidence of metastatic disease generally consists of en bloc resection followed by adjuvant radiotherapy. However, long-term (20-year) survival rates are approximately 30%. Chordomas are generally considered as chemo resistant. Therefore, systemic therapies have rarely been employed. Novel immunotherapies, including antibody therapy and tumor vaccines, have shown promise in early trials, leading to extended progression-free survival and symptom relief. However, the outcomes of larger trials using these vectors are heterogeneous. The aim of this review is to summarize novel chordoma treatments in immune-targeted therapies. The current merits, trial outcomes, and toxicities of these novel immune and targeted therapies, including those targeting vascular endothelial growth factor receptor (VEGFR) targets and the epidermal growth factor receptor (EGFR), will be discussed.

Background: The effect of socioeconomic status (SES) on the outcomes of patients with metastatic cancer to bone has not been adequately studied. We analyzed the association between the Yost Index, a composite geocoded SES score, and overall survival among patients who underwent nonprimary surgical resection for bone metastases. Methods: This population-based study used data from the National Cancer Institute's Surveillance, Epidemiology, and End Results database (2010 to 2018). We categorized bone and joint sites using International Classification of Disease-O-3 recodes. The Yost Index was geocoded using a factor analysis and categorized into quintiles using census tract–level American Community Service 5-year estimates and seven measures: median household income, median house value, median rent, percent below 150% of the poverty line, education index, percent working class, and percent unemployed. Multivariate Cox regression models were used to calculate adjusted hazard ratios of overall survival and 95% confidence intervals. Results: A total of 138,158 patients were included. Patients with the lowest SES had 34% higher risk of mortality compared with those with the highest SES (adjusted hazard ratio of 1.34, 95% confidence interval: 1.32 to 1.37, P < 0.001). Among patients who underwent nonprimary surgery of the distant bone tumor (n = 11,984), the age-adjusted mortality rate was 31.3% higher in the lowest SES patients compared with the highest SES patients (9.9 versus 6.8 per 100,000, P < 0.001). Patients in the lowest SES group showed more racial heterogeneity (63.0% White, 33.5% Black, 3.1% AAPI) compared with the highest SES group (83.9% White, 4.0% Black, 11.8% AAPI, P < 0.001). Higher SES patients are more likely to be married (77.5% versus 59.0%, P < 0.0001) and to live in metropolitan areas (99.6% versus 73.6%, P < 0.0001) compared with lower SES patients. Discussion: Our results may have implications for developing interventions to improve access and quality of care for patients from lower SES backgrounds, ultimately reducing disparities in orthopaedic surgery.

Introduction: Standard spine surgery machine learning (ML) models often rely on structured clinical data, overlooking nuanced free text, such as preoperative surgical notes. The aims of this work were to develop a multimodal ML model combining structured electronic health record (EHR) data with natural language–processed unstructured clinical narratives. Methods: After testing against Convolutional Neural Network, Support Vector Machine, LightGBM, and Random Forest algorithms, the XGBoost algorithm was selected for model development. Three models were developed: (1) a structured EHR-based ML model; (2) an NLP-based model using preoperative notes; (3) a combined multimodal model. Perioperative outcomes included extended length of stay (≥8.0 days) and nonhome discharge. Preprocessing included tokenization, stemming, and bag-of-words vectorization. Hyperparameters were tuned through grid search and 10-fold cross-validation. Key performance metrics included area-under-the-receiver-operating characteristic curve, Brier score, calibration slope and intercept, precision, recall, and F1 score. Results: A total of 486 patients (58.8% female, n = 281) were included, with a median age of 61.0 years (interquartile range: 52.0 to 68.0 years) and median body mass index of 29.4 kg/m ² (interquartile range: 25.1 to 34.5 kg/m ² ). For extended length of stay, the multimodal model excelled (ROC-AUC: 0.908, Brier: 0.114, F1: 0.896), followed by the NLP-only model (ROC-AUC: 0.868, Brier: 0.132, F1: 0.877), and the XGBoost-only model (ROC-AUC: 0.736, Brier: 0.201, F1: 0.815). For nonhome discharge, the multimodal model led (ROC-AUC: 0.920, Brier: 0.105, F1: 0.907), compared with the NLP-only model (ROC-AUC: 0.892, Brier: 0.102, F1: 0.916) and XGBoost-only model (ROC-AUC: 0.771, Brier: 0.144, F1: 0.893). Explainable AI revealed that body mass index, age, Medicare insurance, Charlson comorbidity index, Medicaid status, Hispanic ethnicity, fusion history, and thoracolumbar and cervical levels of surgery were the most important model features. Conclusion: Incorporating unstructured surgeon notes into ML models markedly enhanced the prediction of perioperative outcomes in spinal surgery, suggesting that free-text notes may provide greater predictive utility than standard EHR variables. Level of Evidence: III