Elmi-Terander, Adrian

Abstract Background Although typically benign, 5% of spinal meningiomas (SMs) present with higher-grade features (World Health Organization grades 2 and 3). High-grade SMs are poorly studied and the role of adjuvant radiotherapy in their management remains controversial. We hence aimed to study the demographic characteristics of this rare tumor and investigate the outcomes associated with the use of surgery with adjuvant therapy in contrast to surgery alone. Methods The National Cancer Database was queried for patients with SMs from 2004 to 2017. Basic statistics were used to identify differences between low- and high-grade tumors in terms of baseline characteristics. Surgery with and without adjuvant radiotherapy were compared after (1:1) propensity-score matching. Kaplan–Meier survival analysis was conducted to study overall survival. All analyses were performed on R. Results A total of 13 184 patients diagnosed with SMs were included, of whom only 5% (n = 669) had high-grade SMs. Patients with high-grade SMs presented at a younger median age (57 years [IQR: 44–68] versus 65 years [54–75]; P < .001) and were more commonly males (33% vs 20%; P < .001). After propensity-score matching, survival analysis revealed similar overall survival outcomes in patients with high-grade SM undergoing both surgery and radiotherapy as compared to those only receiving surgery (P = .19). Conclusions This study reveals major demographic differences between high- and low-grade SMs. There were no benefits associated with the use of adjuvant radiotherapy. However, due to confounding, overall survival outcomes between patients receiving surgery alone and those receiving surgery with adjuvant radiotherapy are not causally interpretable.

BACKGROUND AND OBJECTIVES: Epilepsy is considered one of the most prevalent and severe chronic neurological disorders worldwide. Our study aims to analyze the national trends in different treatment modalities for individuals with drug-resistant epilepsy and investigate the outcomes associated with these procedural trends in the United States. METHODS: Using the National Inpatient Sample database from 2010 to 2020, patients with drug-resistant focal epilepsy who underwent laser interstitial thermal therapy (LITT), open surgical resection, vagus nerve stimulation (VNS), or responsive neurostimulation (RNS) were identified. Trend analysis was performed using piecewise joinpoint regression. Propensity score matching was used to compare outcomes between 10 years prepandemic before 2020 and the first peak of the COVID-19 pandemic. RESULTS: This study analyzed a total of 33 969 patients with a diagnosis of drug-resistant epilepsy, with 3343 patients receiving surgical resection (78%), VNS (8.21%), RNS (8%), and LITT (6%). Between 2010 and 2020, there was an increase in the use of invasive electroencephalography monitoring for seizure zone localization ( P = .003). There was an increase in the use of LITT and RNS ( P < .001), while the use of surgical resection and VNS decreased over time ( P < .001). Most of these patients (89%) were treated during the pre-COVID pandemic era (2010-2019), while a minority (11%) underwent treatment during the COVID pandemic (2020). After propensity score matching, the rate of pulmonary complications, postprocedural hematoma formation, and mortality were slightly higher during the pandemic compared with the prepandemic period ( P = .045, P = .033, and P = .026, respectively). CONCLUSION: This study indicates a relative decrease in the use of surgical resections, as a treatment for drug-resistant focal epilepsy. By contrast, newer, minimally invasive surgical approaches including LITT and RNS showed gradual increases in usage.

Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models’ effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model’s area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.