Hannah E. Gilder, Ross C. Puffer, Mohamad Bydon and Robert J. Spinner
In this study, the authors sought to compare tumors with intradural extension to those remaining in the epidural or paraspinal space with the hypothesis that intradural extension may be a mechanism for seeding of the CSF with malignant cells, thereby resulting in higher rates of CNS metastases and shorter overall survival.
The authors searched the medical record for cases of malignant peripheral nerve sheath tumors (MPNSTs) identified from 1994 to 2017. The charts of the identified patients were then reviewed for tumor location to identify patients with paraspinal malignancy. All patients included in the study had tumor specimens that were reviewed in the surgical pathology department. Paraspinal tumors with intradural extension were identified in the lumbar, sacral, and spinal accessory nerves, and attempts were made to match this cohort to another cohort of patients who had paraspinal tumors of the cranial nerves and lumbar and sacral spinal regions without intradural extension. Further information was collected on all patients with and without intradural extension, including date of diagnosis by pathology specimen review; nerve or nerves of tumor origin; presence, location, and diagnostic date of any CNS metastases; and either the date of death or date of last follow-up.
The authors identified 6 of 179 (3.4%) patients who had intradural tumor extension and compared these patients with 12 patients who harbored paraspinal tumors that did not have intradural extension. All tumors were diagnosed as high-grade MPNSTs according to the surgical pathology findings. Four of 6 (66.7%) patients with intradural extension had documented CNS metastases. The presence of CNS metastases was significantly higher in the intradural group than in the paraspinal group (intradural, 66.7% vs paraspinal, 0%; p < 0.01). Time from diagnosis until death was 11.2 months in the intradural group and approximately 72 months in the paraspinal, extradural cohort.
In patients with intradural extension of paraspinal MPNSTs, significantly higher rates of CNS metastases are seen with a reduced interval of time from diagnosis to metastatic lesion detection. Intradural tumor extension is also a poor prognostic factor for survival, with these patients showing a reduced mean time from diagnosis to death.
Anthony L. Asher, Mohamad Bydon and Robert E. Harbaugh
Yagiz Ugur Yolcu, Anshit Goyal, Mohammed Ali Alvi, FM Moinuddin and Mohamad Bydon
Recent studies have reported on the utility of radiosurgery for local control and symptom relief in spinal meningioma. The authors sought to evaluate national utilization trends in radiotherapy (including radiosurgery), investigate possible factors associated with its use in patients with spinal meningioma, and its impact on survival for atypical tumors.
Using the ICD-O-3 topographical codes C70.1, C72.0, and C72.1 and histological codes 9530–9535 and 9537–9539, the authors queried the National Cancer Database for patients in whom spinal meningioma had been diagnosed between 2004 and 2015. Patients who had undergone radiation in addition to surgery and those who had received radiation as the only treatment were analyzed for factors associated with each treatment.
From among 10,458 patients with spinal meningioma in the database, the authors found a total of 268 patients who had received any type of radiation. The patients were divided into two main groups for the analysis of radiation alone (137 [51.1%]) and radiation plus surgery (131 [48.9%]). An age > 69 years (p < 0.001), male sex (p = 0.03), and tumor size 5 to < 6 cm (p < 0.001) were found to be associated with significantly higher odds of receiving radiation alone, whereas a Charlson-Deyo Comorbidity Index ≥ 2 (p = 0.01) was associated with significantly lower odds of receiving radiation alone. Moreover, a larger tumor size (2 to < 3 cm, p = 0.01; 3 to < 4 cm, p < 0.001; 4 to < 5 cm, p < 0.001; 5 to < 6 cm, p < 0.001; and ≥ 6 cm, p < 0.001; reference = 1 to < 2 cm), as well as borderline (p < 0.001) and malignant (p < 0.001) tumors were found to be associated with increased odds of undergoing radiation in addition to surgery. Receiving adjuvant radiation conferred a significant reduction in overall mortality among patients with borderline or malignant spinal meningiomas (HR 2.12, 95% CI 1.02–4.1, p = 0.02).
The current analysis of cases from a national cancer database revealed a small increase in the use of radiation for the management of spinal meningioma without a significant increase in overall survival. Larger tumor size and borderline or malignant behavior were found to be associated with increased radiation use. Data in the present analysis failed to show an overall survival benefit in utilizing adjuvant radiation for atypical tumors.
Anthony L. Asher, Clinton J. Devin, Robert E. Harbaugh and Mohamad Bydon
Mohamad Bydon, Rafael De la Garza-Ramos and Ziya L. Gokaslan
Can machine learning algorithms accurately predict discharge to nonhome facility and early unplanned readmissions following spinal fusion? Analysis of a national surgical registry
Presented at the 2019 AANS/CNS Section on Disorders of the Spine and Peripheral Nerves
Anshit Goyal, Che Ngufor, Panagiotis Kerezoudis, Brandon McCutcheon, Curtis Storlie and Mohamad Bydon
Nonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion.
The authors queried the 2012–2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets.
A total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85–0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data.
In an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.