Interrater agreement for consensus definitions of delayed ischemic events after aneurysmal subarachnoid hemorrhage

Background: Thirty percent of patients with subarachnoid hemorrhage experience delayed cerebral ischemia or delayed ischemic neurologic decline (DIND). Variability in the definitions of delayed ischemia makes outcome studies difficult to compare. A recent consensus statement advocates standardized definitions for delayed ischemia in clinical trials of subarachnoid hemorrhage. We sought to evaluate the interrater agreement of these definitions. Methods: Based on consensus definitions, we assessed for: (1) delayed cerebral infarction, defined as radiographic cerebral infarction; (2) DIND type 1 (DIND1), defined as focal neurologic decline; and (3) DIND2, defined as a global decline in arousal. Five neurologists retrospectively reviewed electronic records of 58 patients with subarachnoid hemorrhage. Three reviewers had access to and reviewed neuroradiology imaging. We assessed interrater agreement using the Gwet kappa statistic. Results: Interrater agreement statistics were excellent (95.83%) for overall agreement on the presence or absence of any delayed ischemic event (DIND1, DIND2, or delayed cerebral infarction). Agreement was "moderate" for specifically identifying DIND1 (56.58%) and DIND2 (48.66%) events. We observed greater agreement for DIND1 when there was a significant focal motor decline of at least 1 point in the motor score. There was fair agreement (39.20%) for identifying delayed cerebral infarction; CT imaging was the predominant modality. Conclusions: Consensus definitions for delayed cerebral ischemia yielded near-perfect overall agreement and can thus be applied in future large-scale studies. However, a strict process of adjudication, explicit thresholds for determining focal neurologic decline, and MRI techniques that better discriminate edema from infarction seem critical for reproducibility of determination of specific outcome phenotypes, and will be important for successful clinical trials.SCOPUS: re.jinfo:eu-repo/semantics/publishe

Assessment of the Validity of the 2HELPS2B Score for Inpatient Seizure Risk Prediction

Importance: Seizure risk stratification is needed to boost inpatient seizure detection and to improve continuous electroencephalogram (cEEG) cost-effectiveness. 2HELPS2B can address this need but requires validation. Objective: To use an independent cohort to validate the 2HELPS2B score and develop a practical guide for its use. Design, Setting, and Participants: This multicenter retrospective medical record review analyzed clinical and EEG data from patients 18 years or older with a clinical indication for cEEG and an EEG duration of 12 hours or longer who were receiving consecutive cEEG at 6 centers from January 2012 to January 2019. 2HELPS2B was evaluated with the validation cohort using the mean calibration error (CAL), a measure of the difference between prediction and actual results. A Kaplan-Meier survival analysis was used to determine the duration of EEG monitoring to achieve a seizure risk of less than 5% based on the 2HELPS2B score calculated on first- hour (screening) EEG. Participants undergoing elective epilepsy monitoring and those who had experienced cardiac arrest were excluded. No participants who met the inclusion criteria were excluded. Main Outcomes and Measures: The main outcome was a CAL error of less than 5% in the validation cohort. Results: The study included 2111 participants (median age, 51 years; 1113 men [52.7%]; median EEG duration, 48 hours) and the primary outcome was met with a validation cohort CAL error of 4.0% compared with a CAL of 2.7% in the foundational cohort (P =.13). For the 2HELPS2B score calculated on only the first hour of EEG in those without seizures during that hour, the CAL error remained at less than 5.0% at 4.2% and allowed for stratifying patients into low- (2HELPS2B = 0; 25%) groups. Each of the categories had an associated minimum recommended duration of EEG monitoring to achieve at least a less than 5% risk of seizures, a 2HELPS2B score of 0 at 1-hour screening EEG, a 2HELPS2B score of 1 at 12 hours, and a 2HELPS2B score of 2 or greater at 24 hours. Conclusions and Relevance: In this study, 2HELPS2B was validated as a clinical tool to aid in seizure detection, clinical communication, and cEEG use in hospitalized patients. In patients without prior clinical seizures, a screening 1-hour EEG that showed no epileptiform findings was an adequate screen. In patients with any highly epileptiform EEG patterns during the first hour of EEG (ie, a 2HELPS2B score of ≥2), at least 24 hours of recording is recommended.SCOPUS: cp.jinfo:eu-repo/semantics/publishe

Deep active learning for Interictal Ictal Injury Continuum EEG patterns

Objectives: Seizures and seizure-like electroencephalography (EEG) patterns, collectively referred to as “ictal interictal injury continuum” (IIIC) patterns, are commonly encountered in critically ill patients. Automated detection is important for patient care and to enable research. However, training accurate detectors requires a large labeled dataset. Active Learning (AL) may help select informative examples to label, but the optimal AL approach remains unclear. Methods: We assembled >200,000 h of EEG from 1,454 hospitalized patients. From these, we collected 9,808 labeled and 120,000 unlabeled 10-second EEG segments. Labels included 6 IIIC patterns. In each AL iteration, a Dense-Net Convolutional Neural Network (CNN) learned vector representations for EEG segments using available labels, which were used to create a 2D embedding map. Nearest-neighbor label spreading within the embedding map was used to create additional pseudo-labeled data. A second Dense-Net was trained using real- and pseudo-labels. We evaluated several strategies for selecting candidate points for experts to label next. Finally, we compared two methods for class balancing within queries: standard balanced-based querying (SBBQ), and high confidence spread-based balanced querying (HCSBBQ). Results: Our results show: 1) Label spreading increased convergence speed for AL. 2) All query criteria produced similar results to random sampling. 3) HCSBBQ query balancing performed best. Using label spreading and HCSBBQ query balancing, we were able to train models approaching expert-level performance across all pattern categories after obtaining ∼7000 expert labels. Conclusion: Our results provide guidance regarding the use of AL to efficiently label large EEG datasets in critically ill patients.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

American Clinical Neurophysiology Society's Standardized Critical Care EEG Terminology: 2021 Version

SCOPUS: ar.jinfo:eu-repo/semantics/publishe