Nathani, Karim

Study Design: Retrospective cohort analysis. Objective: To determine the effect of long-segment posterior cervical decompression and fusion (PCDF) on perioperative changes in vertebral Hounsfield Units (HUs) and the impact of these changes on radiographic outcomes and fusion status. Summary of Background Data: Low preoperative CT HUs have been associated with adverse outcomes after spinal fusion, including hardware complications and reoperation. No existing studies have investigated the preoperative to postoperative change in HUs after cervical spinal fusion. Stress shielding may decrease bone quality within the fusion construct. Whereas, altered junctional loading and increased segmental mobility may precipitate bony hypertrophy in the terminal and adjacent levels. Methods: All adult patients who underwent C2-T2 PCDF for myelopathy/ myeloradiculopathy at an academic center between 2013 and 2020 were retrospectively identified. Preoperative and postoperative HUs were assessed on axial CT images in the cranial, middle, and caudal C2, C6, T2, and T3 vertebral bodies, outside of the region of instrumentation and artifact by 2 independent reviewers. Paired t test compared preoperative to postoperative changes in HUs. Preoperative and long-term postoperative cervical alignment, fusion, and revision rates were assessed. Univariate and regression analysis compared patient factors and outcomes between groups with perioperative increase versus decrease in vertebral HUs. Results: One hundred patients were included. The average preoperative to postoperative change in HUs in the C2, C6, T2, and T3 vertebral bodies were −19.80 (P=0.008), −52.63 (P<0.001), 15.15 (P=0.014), and 9.41 (P=0.346), respectively. Perioperative increase in C2 HUs was predictive of increased postoperative distal junctional angle (β=3.41, P=0.048) and increased T1–T4 kyphosis (β=6.50, P=0.003). Perioperative increase in C6 HU predicted pseudoarthrosis (fusion odds ratio: 0.19, P=0.007). Conclusions: Significant perioperative decreases in C6 HUs within a long-fused construct may demonstrate stress shielding. The significant association between greater distal kyphosis and an increase in C2 HUs may be attributed to a stress riser effect. Level of Evidence: Level III.

OBJECTIVE High-grade gliomas (HGGs) are among the rarest yet most aggressive tumor types in neurosurgical practice. In the current literature, few studies have assessed the drivers of early outcomes following resection of these tumors and investigated their association with quality of care. The authors aimed to identify the clinical predictors for 30-day readmission and reoperation following HGG surgery using the American College of Surgeons (ACS) National Surgical Quality Improvement Project (NSQIP) database and sought to create web-based applications predicting each outcome. METHODS Using the ACS NSQIP database, the authors conducted a retrospective, multicenter cohort analysis of patients who underwent resection of supratentorial HGGs between January 1, 2016, and December 31, 2020. Demographics and comorbidities were extracted. The primary outcomes were 30-day unplanned readmission and reoperation. A stratified 80:20 split of the available data was carried out. Supervised machine learning algorithms were trained to predict 30-day outcomes. RESULTS A total of 9418 patients were included in our cohort. The observed rate of unplanned readmission within 30 days of surgery was 13.0% (n = 1221). In terms of predictors, weight, chronic steroid use, preoperative blood urea nitrogen level, and white blood cell count were associated with a higher risk of readmission. The observed rate of unplanned reoperation within 30 days of surgery was 5.2% (n = 489). In terms of predictors, increased weight, longer operative time, and more days between hospital admission and operation were associated with an increased risk of early reoperation. The random forest algorithm showed the highest predictive performance for early readmission (area under the curve [AUC] = 0.967), while the XGBoost algorithm showed the highest predictive performance for early reoperation (AUC = 0.985). Web-based tools for both outcomes were deployed ( https://glioma-readmission.herokuapp.com/ and https://glioma-reoperation.herokuapp.com/ ). CONCLUSIONS In this study, the authors provide the first nationwide analysis for short-term outcomes in patients undergoing resection of supratentorial HGGs. Multiple patient, hospital, and admission factors were associated with readmission and reoperation, confirmed by machine learning predicting patients’ prognosis, leading to better planning preoperatively and subsequently improved personalized patient care.