Abstract TP264: Analysis of Emergency Department Code Stroke Activations at a Large County Hospital: Real World Experience

Introduction: Code strokes are activated to rapidly mobilize hospital resources directed at stroke care. Activations for non-strokes and stroke mimics can divert attention away from patients with acute stroke or delay care of other medical emergencies. There is a drive to reduce door to treatment times in acute ischemic stroke (AIS) without a universal triage method to accurately recognize stroke. We looked at the most common reasons for activation of a code stroke in a quality improvement effort to increase the yield of correctly identifying AIS treatment. Methods: Retrospective review of prospectively collected emergency department (ED) code stroke activations between January - December 2018 at our institution. Reasons for code stroke activations, patient demographics, exam and MRI findings, and discharge and neurologist diagnoses were reviewed. Results: A total of 523 patients were activated as an ED code stroke. One hundred forty (26.8%) were discharged with a cerebrovascular-related diagnosis (20.1% AIS, 1.2% TIA, and 5.6% ICH). The average age of all acute cerebrovascular pathology is 61.5 years and the gender were found to be 57.1% male. The non-stroke group had an average age of 51.8 years and were 44.1% male. The average NIHSS of the entire cerebrovascular cohort was 9.1 (7.8 in AIS, 15.1 in ICH) compared with 4.6 in the non-stroke group. The most frequent non-stroke diagnosis was related to non-neurologic medical emergencies (25.3%), headaches and migraine (14.6%), psychiatric diagnoses (14.1%), recrudescence of prior symptoms (9.1%), and other neurologic emergencies (8.4%). Conclusion: In an era of increased treatment expectations, accurate detection of AIS is key. Current activation criteria and screening tools have a low sensitivity and specificity for AIS. They are often complex for the non-neurologist and inconsistently applied during a code stroke. In conclusion, an overwhelming majority of code strokes are not related to AIS and represent other non-neurologic medical emergencies. A simpler more accurate triage method is needed. </jats:p

Influence of sodium chlorate, ferulic acid, and essential oils on Escherichia coli and porcine fecal microbiota

Abstract The influence of sodium chlorate (SC), ferulic acid (FA), and essential oils (EO) was examined on the survivability of two porcine diarrhetic enterotoxigenic Escherichia coli (ETEC) strains (F18 and K88) and populations of porcine fecal bacteria. Fecal bacterial populations were examined by denaturing gradient gel electrophoresis (DGGE) and identification by 16S gene sequencing. The treatments were control (no additives), 10 mM SC, 2.5 mg FA /mL, a 1.5% vol/vol solution of an EO mixture as well as mixtures of EO + SC, EO + FA, and FA + SC at each of the aforementioned concentrations. EO were a commercial blend of oregano oil and cinnamon oil with water and citric acid. Freshly collected porcine feces in half-strength Mueller Hinton broth was inoculated with E. coli F18 (Trial 1) or E. coli K88 (Trial 2). The fecal-E. coli suspensions were transferred to crimp top tubes preloaded with the treatment compounds. Quantitative enumeration was at 0, 6, and 24 h. All treatments reduced (P &amp;lt; 0.05) the counts of E. coli F18 at 6 and 24 h. With the exception of similarity coefficient (%SC), all the other treatments reduced (P &amp;lt; 0.05) the K88 counts at 24 h. The most effective treatments to reduce the F18 and K88 CFU numbers were those containing EO. Results of DGGE revealed that Dice percentage similarity coefficients (%SC) of bacterial profiles among treatment groups varied from 81.3% to 100%SC. The results of gene sequencing showed that, except for SC at 24 h, all the other treatments reduced the counts of the family Enterobacteriaceae, while Lactobacillaceae and Ruminococcaceae increased and Clostridiaceae decreased in all treatments. In conclusion, all treatments were effective in reducing the ETEC, but EO mixture was the most effective. The porcine microbial communities may be influenced by the studied treatments.</jats:p