Impact of coma duration on functional outcomes at discharge and long-term survival after cardiac arrest.

Introduction: Awakening from coma is crucial for survivors of cardiac arrest, though coma duration is variable. We tested the association of coma duration with short-term functional recovery and long-term survival after cardiac arrest.

Methods: In this retrospective cohort study, we identified post-arrest patients who were comatose on presentation but awakened during hospitalization.

Lessons Learned from National Heart, Lung, and Blood Institute Covid-19 Clinical Trials.

AbstractIn response to the Covid-19 pandemic, the National Heart, Lung, and Blood Institute launched five multisite clinical trials testing candidate host tissue-directed medical interventions to hasten recovery, improve function, and reduce morbidity and mortality. Speed, flexibility, and collaboration were essential. This article from the Steering and Executive committees describes the Collaborating Network of Networks for Evaluating Covid-19 and Therapeutic Strategies (CONNECTS) research program that enrolled 6690 participants and evaluated 18 intervention strategies using 10 molecular agents across the care continuum (outpatient, inpatient, and post discharge), and reports lessons learned from this initiative.

External evaluation of Brain Injury Guideline (BIG) low risk criteria for traumatic brain injury.

Background: Fewer than 20 % of traumatic brain injury (TBI) cases with traumatic intracranial hemorrhage (ICH) result in clinical deterioration. The Brain Injury Guideline (BIG) criteria were published in 2014 and categorize patients with TBI into three risk groups (BIG 1, 2, and 3) based on CT scan findings, neurological examination, anti-coagulant/platelet medications, and intoxication. Early data is promising, suggesting no instances of neurosurgical intervention or death in the low-risk BIG1 category within 30 days.

Clinical usability of deep learning-based saliency maps for occlusion myocardial infarction identification from the prehospital 12-Lead electrocardiogram.

Introduction: Deep learning (DL) models offer improved performance in electrocardiogram (ECG)-based classification over rule-based methods. However, for widespread adoption by clinicians, explainability methods, like saliency maps, are essential.

Methods: On a subset of 100 ECGs from patients with chest pain, we generated saliency maps using a previously validated convolutional neural network for occlusion myocardial infarction (OMI) classification.