Emergency airway management outside the operating room (OR) is often associated with an increased risk of airway related, as well as cardiopulmonary, complications which can impact morbidity and mortality. These emergent airways may take place in the intensive care unit (ICU), where patients are critically ill with minimal physiological reserve, or other areas of the hospital where advanced equipment and personnel are often unavailable. As such, emergency airway management outside the OR requires expertise at manipulation of not only the anatomically difficult airway but also the physiologically and situationally difficult airway. Adequate preparation and appropriate use of airway management techniques are important to prevent complications. Judicious utilization of pre- and apneic oxygenation is important as is the choice of medications to facilitate intubation in this at-risk population. Recent study in critically ill patients has shown that postintubation hemodynamic and respiratory compromise is common, independently associated with poor outcomes and can be impacted by the choice of drugs and techniques used. In addition to adequately preparing for a physiologically difficult airway, enhancing the ability to predict an anatomically difficult airway is essential in reducing complication rates. The use of artificial intelligence in the identification of difficult airways has shown promising results and could be of significant advantage in uncooperative patients as well as those with a questionable airway examination. Incorporating this technology and understanding the physiological, anatomical, and logistical challenges may help providers better prepare for managing such precarious airways and lead to successful outcomes. This review discusses the various challenges associated with airway management outside the OR, provides guidance on appropriate preparation, airway management skills, medication use, and highlights the role of a coordinated multidisciplinary approach to out-of-OR airway management.

Glioblastoma is a devastating malignancy with a dismal survival rate and median survival time of 14 months. Currently, the biomarkers for glioblastoma are mostly molecular and include EGFRvIII, ATRX, PTEN, IDH1, MGMT, and others. These prognostic tumor biomarkers are obtained through a surgical biopsy and thus are not easily attainable. Clinicians would benefit from a robust, non-invasive, and readily available indicator for early diagnosis and accurate prognostication for glioblastoma patients. In this study, we assessed whether specific patient symptoms could provide an early diagnosis of glioblastoma. Further, we also assessed if any patient symptomatology could provide clinicians with the ability to prognosticate patient survival more accurately. We retrospectively reviewed the clinical data for 218 patients. We determined whether symptoms including headache, weakness, seizure, memory loss/confusion, visual changes, speech changes, and loss of consciousness led to a patient being diagnosed earlier and if any of these symptoms predicted diminished patient survival. Our study determined that weakness and memory loss/confusion were the symptoms that predicted diminished survival, and weakness alone was the symptom that predicted an earlier diagnosis. This study further elucidates the complexities of glioblastoma and provides clinicians with more data for their patients when discussing prognostication after diagnosis of glioblastoma.

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