Emergence delirium (ED) is a post-surgical phenomenon associated with an increase in morbidity, mortality and resource utilization. A systematic review of all current treatment modalities for this condition is imperative given its prevalence during postoperative recovery. This review scrutinizes a number of studies in order to better characterize ED, with particular focus paid to the condition's etiology, associated risk factors, prevention and management. Extensive studies have been conducted on the pediatric population with little attention directed towards the adult and geriatric age groups. Despite this, evidence-based protocols for the management of this condition are still lacking for all age groups. In order to ensure cost effective management and improved safety outcomes for ED, future research should establish and further improve upon screening protocols to identify at risk patients, in addition to formulating a standardized treatment regimen for perioperative patients at risk. Keywords: Emergence delirium, anesthesia recovery period, anesthetic issues and peri-operative care, perioperative complications, preoperative evaluation and anesthesia risk, complications in the post anesthesia care unit

We present an approach to airway management in a patient with machete injuries culminating in near-complete cricotracheal transection, in addition to a gunshot wound to the neck. Initial airway was established by direct intubation through the cricotracheal wound. Once the airway was secured, a bronchoscopy-guided orotracheal intubation was performed with simultaneous retraction of the cricotracheal airway to optimize the surgical field. This case offers insight into a rarely performed approach to airway management. Furthermore, our case report demonstrates that, in select airway injuries, performing through-the-wound intubation engenders a multitude of benefits.

Background Interpretations of the electrocardiogram (ECG) are often prepared using software outside the electronic health record (EHR) and imported via an interface as a narrative note. Thus, natural language processing is required to create a computable representation of the findings. Challenges include misspellings, nonstandard abbreviations, jargon, and equivocation in diagnostic interpretations. Objectives Our objective was to develop an algorithm to reliably and efficiently extract such information and map it to the standardized ECG ontology developed jointly by the American Heart Association, the American College of Cardiology Foundation, and the Heart Rhythm Society. The algorithm was to be designed to be easily modifiable for use with EHRs and ECG reporting systems other than the ones studied. Methods An algorithm using natural language processing techniques was developed in structured query language to extract and map quantitative and diagnostic information from ECG narrative reports to the cardiology societies' standardized ECG ontology. The algorithm was developed using a training dataset of 43,861 ECG reports and applied to a test dataset of 46,873 reports. Results Accuracy, precision, recall, and the F1-measure were all 100% in the test dataset for the extraction of quantitative data (e.g., PR and QTc interval, atrial and ventricular heart rate). Performances for matches in each diagnostic category in the standardized ECG ontology were all above 99% in the test dataset. The processing speed was approximately 20,000 reports per minute. We externally validated the algorithm from another institution that used a different ECG reporting system and found similar performance. Conclusion The developed algorithm had high performance for creating a computable representation of ECG interpretations. Software and lookup tables are provided that can easily be modified for local customization and for use with other EHR and ECG reporting systems. This algorithm has utility for research and in clinical decision-support where incorporation of ECG findings is desired.

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