Suboptimal Dosing of Benzodiazepines and Levetiracetam in a Cohort of Status Epilepticus Patients and Outcomes Associated with Inadequate Dosing.

Background: Status epilepticus (SE) is a neurologic emergency that can result in serious morbidity and mortality. Recent studies have suggested underdosing of both benzodiazepines (BZDs) and antiseizure medications (ASM) which may result in poorer outcomes. Objectives: This study aims to determine the dose of BZDs and levetiracetam given in our emergency department for episodes of SE and determine the outcomes associated with this dosing. Methods: We conducted a retrospective cohort study of all adult patients with SE admitted to our hospital from 2017 to 2020. We collected demographic data, type of SE, dose of BZD and levetiracetam, and outcomes which included mortality and a calculated Glasgow outcome scale (GOS). We compared outcomes of patients with SE who received adequate dosing (according to practice guidelines) to those who did not. Results: 111 adult patients were included of whom 91% were seen initially in our emergency department. 75% had convulsive SE on presentation. Approximately 55% and 68% of patients did not receive an appropriate dose of BZD or levetiracetam, respectively. Inadequate dosing of BZD was associated with worse clinical outcomes based on GOS (43.6% favorable outcome vs 62.5% with adequate dosing P = .046 (95% CI, 1.01-4.60)) and inadequate dosing of both drugs was also associated with a worse GOS outcome (HR, 2.91 (95% CI, 1.05-9.67, P = .02). No difference was found in length of stay or mortality alone. Conclusion: Our study found inadequate dosing of drugs to treat SE in adults was common in our institution and was associated with worse outcomes.

MRI Radiogenomics of Pediatric Medulloblastoma: A Multicenter Study.

Background Radiogenomics of pediatric medulloblastoma (MB) offers an opportunity for MB risk stratification, which may aid therapeutic decision making, family counseling, and selection of patient groups suitable for targeted genetic analysis. Purpose To develop machine learning strategies that identify the four clinically significant MB molecular subgroups. Materials and Methods In this retrospective study, consecutive pediatric patients with newly diagnosed MB at MRI at 12 international pediatric sites between July 1997 and May 2020 were identified. There were 1800 features extracted from T2- and contrast-enhanced T1-weighted preoperative MRI scans. A two-stage sequential classifier was designed-one that first identifies non-wingless (WNT) and non-sonic hedgehog (SHH) MB and then differentiates therapeutically relevant WNT from SHH. Further, a classifier that distinguishes high-risk group 3 from group 4 MB was developed. An independent, binary subgroup analysis was conducted to uncover radiomics features unique to infantile versus childhood SHH subgroups. The best-performing models from six candidate classifiers were selected, and performance was measured on holdout test sets. CIs were obtained by bootstrapping the test sets for 2000 random samples. Model accuracy score was compared with the no-information rate using the Wald test. Results The study cohort comprised 263 patients (mean age ± SD at diagnosis, 87 months ± 60; 166 boys). A two-stage classifier outperformed a single-stage multiclass classifier. The combined, sequential classifier achieved a microaveraged F1 score of 88% and a binary F1 score of 95% specifically for WNT. A group 3 versus group 4 classifier achieved an area under the receiver operating characteristic curve of 98%. Of the Image Biomarker Standardization Initiative features, texture and first-order intensity features were most contributory across the molecular subgroups. Conclusion An MRI-based machine learning decision path allowed identification of the four clinically relevant molecular pediatric medulloblastoma subgroups. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Chaudhary and Bapuraj in this issue.

Machine Assist for Pediatric Posterior Fossa Tumor Diagnosis: A Multinational Study.

BACKGROUND: Clinicians and machine classifiers reliably diagnose pilocytic astrocytoma (PA) on magnetic resonance imaging (MRI) but less accurately distinguish medulloblastoma (MB) from ependymoma (EP). One strategy is to first rule out the most identifiable diagnosis. OBJECTIVE: To hypothesize a sequential machine-learning classifier could improve diagnostic performance by mimicking a clinician's strategy of excluding PA before distinguishing MB from EP. METHODS: We extracted 1800 total Image Biomarker Standardization Initiative (IBSI)-based features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a 2-step sequential classifier - first ruling out PA, and next distinguishing MB from EP. For each step, we selected the best performing model from 6-candidate classifier using a reduced feature set, and measured performance on a holdout test set with the microaveraged F1 score. RESULTS: Optimal diagnostic performance was achieved using 2 decision steps, each with its own distinct imaging features and classifier method. A 3-way logistic regression classifier first distinguished PA from non-PA, with T2 uniformity and T1 contrast as the most relevant IBSI features (F1 score 0.8809). A 2-way neural net classifier next distinguished MB from EP, with T2 sphericity and T1 flatness as most relevant (F1 score 0.9189). The combined, sequential classifier was with F1 score 0.9179. CONCLUSION: An MRI-based sequential machine-learning classifiers offer high-performance prediction of pediatric posterior fossa tumors across a large, multinational cohort. Optimization of this model with demographic, clinical, imaging, and molecular predictors could provide significant advantages for family counseling and surgical planning.