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Yagiz Ugur Yolcu, Anshit Goyal, Mohammed Ali Alvi, FM Moinuddin and Mohamad Bydon

OBJECTIVE

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

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Anshit Goyal, Che Ngufor, Panagiotis Kerezoudis, Brandon McCutcheon, Curtis Storlie and Mohamad Bydon

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.