Introduction: Stroke is a major public health burden in the United States. In May 2012 at our institution, the Vanderbilt Emergency Department (ED) Stroke Algorithm was implemented. The goal of our study was to: A) Provide a detailed description of our institutional-wide stroke algorithm, and B) Record post-algorithm implementation quality improvement (QI) data.
Methods: A detailed description of the Vanderbilt ED Stroke Algorithm, dictating care from initial evaluation to intervention, is seen in Figure 1. The process begins with an initial Stroke Alert page that notifies the neurology team. Immediate non-contrast head CT, routine labs, and possible CTA/CTP are obtained. After initial evaluation, a second Assessment page with NIH Stroke Scale (NIHSS) is sent. If NIHSS >6, the neurointerventionalist is immediately notified. After the case is reviewed between the neurology attending and neurointerventionalist, a third Treatment page is sent with treatment decision (none, IV tPA, IA therapy, IV tPA plus IA therapy). The following time points were assessed pre- and post-algorithm implementation: code stroke to neurology evaluation, arrival to CT, arrival to IV tPA, and neurology evaluation to NIHSS assessment page.
Results: From Jan-March 2012 (pre-algorithm) to Jan-March 2013 (post-algorithm), several stroke QI data points improved. The total number of stroke alerts increased from 99 to 147 and the number of times IV tPA was used increased from 12 to 20. Moreover, the following QI parameters improved after algorithm implementation: average time from code stroke to neurology evaluation decreased from 8.6 minutes to 6.3 minutes, mean arrival to CT decreased from 28.5 minutes to 21.0 minutes, mean arrival to IV tPA decreased from 76.5 minutes to 57.2 minutes, and time from neurology evaluation to NIHSS assessment page decreased from 36.5 minutes to 29.0 minutes, respectively.
Conclusions: A stroke protocol was successfully implemented at our institution with promising post-implementation results. Several QI parameters significantly increased. We describe our algorithm for the benefit of other developing stroke centers in their mission to improve acute stroke patient care.
Patient Care: Our algorithm streamlines patient care and improves overall times to evaluation and treatment decisions.
Learning Objectives: Our primary goal is to provide a detailed description of our institutional-wide acute stroke algorithm, with corresponding improvements in stroke QI metrics, for the benefit of other developing stroke centers.