Tenecteplase versus alteplase before mechanical thrombectomy: experience from a US healthcare system undergoing a system-wide transition of primary thrombolytic.

BACKGROUND: Tenecteplase (TNK) is a genetically modified variant of alteplase (TPA) and has been established as a non-inferior alternative to TPA in acute ischemic stroke (AIS). Whether TNK exerts distinct benefits in large vessel occlusion (LVO) AIS is still being investigated. OBJECTIVE: To describe our first-year experience after a healthcare system-wide transition from TPA to TNK as the primary thrombolytic. METHODS: Patients with AIS who received intravenous thrombolytics between January 2020 and August 2022 were retrospectively reviewed. All patients with LVO considered for mechanical thrombectomy (MT) were included in this analysis. Spontaneous recanalization (SR) after TNK/TPA was a composite variable of reperfusion >50% of the target vessel territory on cerebral angiography or rapid, significant neurological recovery averting MT. Propensity score matching (PSM) was performed to compare SR rates between TNK and TPA. RESULTS: A total of 148 patients were identified; 51/148 (34.5%) received TNK and 97/148 (65.5%) TPA. The middle cerebral arteries M1 (60.8%) and M2 (29.7%) were the most frequent occlusion sites. Baseline demographics were comparable between TNK and TPA groups. Spontaneous recanalization was significantly more frequently observed in the TNK than in the TPA groups (unmatched: 23.5% vs 10.3%, P=0.032). PSM substantiated the observed SR rates (20% vs 10%). Symptomatic intracranial hemorrhage, 90-day mortality, and functional outcomes were similar. CONCLUSIONS: The preliminary experience from a real-world setting demonstrates the effectiveness and safety of TNK before MT. The higher spontaneous recanalization rates with TNK are striking. Additional studies are required to investigate whether TNK is superior to TPA in LVO AIS.

Machine Learning-Enabled 30-Day Readmission Model for Stroke Patients.

Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting-XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64-0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

Accuracy of pre-hospital trauma notification calls.

STUDY OBJECTIVE: The aim of this study is to determine the accuracy of pre-hospital trauma notifications and the effects of inaccurate information on trauma triage. METHODS: This study was conducted at a level-1 trauma center over a two-year period. Data was collected from pre-notification forms on trauma activations that arrived to the emergency department via ambulance. Trauma activations with pre-notification were compared to those without notification and pre-notification forms were assessed for accuracy and completeness. RESULTS: A total of 2186 trauma activations were included in the study, 1572 (71.9%) had pre-notifications, 614 (28.1%) did not and were initially under-triaged. Pre-notification forms were completed for 1505 (95.7%) patients, of which EMS provided incomplete/inaccurate information for 1204 (80%) patients and complete/accurate information for 301 (20%) patients. Missing GCS/AVPU score (1099, 91.3%), wrong age (357, 29.6%), and missing vitals (303, 25.2%) were the main problems. Missing/wrong information resulted in trauma tier over-activation in 25 (2.1%) patients and under-activation in 20 (1.7%) patients. Under-triaged patients were predominantly male (18, 90%), sustained a fall (9, 45%), transported by BLS EMS teams (12, 60%), and arrived on a weekday (13, 65%) during the time period of 11 pm-7 am (9, 45%). A total of 13 (65%) required emergent intubation, 2 (10%) required massive transfusion activation, 7 (35%) were admitted to ICU, 3 (15%) were admitted directly to the OR, and 1 (15%) died. CONCLUSION: EMS crews frequently provide inaccurate pre-hospital information or do not provide any pre-hospital notification at all, which results in over/under triage of trauma patients.

Select topics in neurocritical care.

Neurocritical care aims to improve outcomes in patients with life-threatening neurologic illness. The scope of neurocritical care extends beyond the more commonly encountered and important field of cerebrovascular disease, as described previously. This article focuses on neuromuscular, neuroinfectious, and neuroimmunologic conditions that are significant causes of morbidity and mortality in the acutely neurologically ill patient. As understanding continues to increase regarding the physiology of these conditions and the best treatment, rapid identification, triage, and treatment of these patients in the emergency department is paramount.