Abstract Details

Title A Clinical Decision Rule Predicting Outcomes of Emergency Department Patients with Altered Mental Status

Topic General Neurology

Presentation(s) P5 - Poster Session 5 (11:45 AM-12:45 PM)

Poster/Presentation
Number 2-003

Objective Develop a clinical decision rule predicting admission likelihood among emergency department (ED) patients with altered mental status (AMS).

Background AMS represents an acute change in thought content or level of arousal without focal neurological symptoms. AMS is present in 5-10% of adult ED patients with a high prevalence in the elderly and those with comorbid conditions. AMS is diagnostically challenging because of the many causes and associated high mortality. The evaluation of AMS patients is variable because of the broad differential and lack of universally accepted guidelines.

Design/Methods Using retrospective chart review of ED encounters from a university hospital setting over a 2-month period, we identified causes of AMS and recorded numerous clinical variables. Encounters were bifurcated into those admitted and those discharged from the ED. Using the first month’s data, clinical and laboratory variables correlated with hospital admission were identified and refined using univariate and multivariate statistics, including recursive partitioning. These variables were organized into a clinical decision rule and validated on the second month’s data. The decision rule results were compared to 1-year mortality.

Results We identified 351 AMS encounters over a 2-month period. Significant contributors to AMS included intoxication and chronic medical disorder decompensation. A primary neurologic etiology (ex: stroke, seizure) was identified in only 6.3% of encounters. ED data predicting hospital admission included vital signs, select lab studies, and psychiatric/intoxicant history. The developed decision rule sorted patients into low, moderate, or high risk of admission (11.1%, 44.3%, and 89.1%) and was predictive of 1-year mortality (low-risk 1.8%, high-risk 34.3%).

Conclusions

We catalogued causes for AMS among ED patients and our data-driven decision tool triaged these patients for admission risk with good predictive accuracy. These statistical methodologies and the resultant AMS decision tree can be further refined and adapted for different ED environments either by manual or machine learning algorithms.

Authors/Disclosures
Tyrell J. Simkins, DO PRESENTER	Dr. Simkins has nothing to disclose.
David P. Bissig, MD (UC Davis Neurology)	Dr. Bissig has received personal compensation in the range of $100,000-$499,999 for serving as a Physician with University of California - Davis.
Gabriel M. Moreno, MD	Dr. Moreno has nothing to disclose.
	No disclosure on file
Fredric A. Gorin, MD, PhD	The institution of Dr. Gorin has received research support from NIH. The institution of Dr. Gorin has received research support from UC Davis School of Veterinary Medicine .
Alexandra O. Duffy, MD, FAAN (UC Davis Health)	Dr. Duffy has nothing to disclose.

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