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Early Stroke Detection in Perioperative Patients: A Simple Educational Intervention and Feasibility Study

Background: Perioperative stroke is uncommon. However, it carries high morbidity and mortality. Unfortunately, the diagnosis is usually delayed resulting in reduced possibility for therapeutic intervention.
Objective: To develop a protocol directed at shortening the time to detect neurological deficits in postoperative patients.
Materials and Methods: By using a pre-post intervention design to evaluate the time to stroke recognition in post-open heart surgery patients. The intervention consisted of 1) A new protocol to evaluate new neurological deficits within 14 days after surgery, composed of six items of simple neurological assessment applied by CVT nurses during routine vital sign measurement. 2) An educational program for nurses, patients, and family focusing on postoperative stroke complications.
Results: Between January 2014 and October 2015, the authors retrospectively reviewed 27 consecutive patients with acute neurological deficit within 14 days after surgery as the pre-intervention population. Twenty-seven consecutive patients with postoperative neurological deficits were enrolled during a post-intervention period, which was between November 2015 and September 2016. Comparing pre- and post-intervention periods, the authors found that stroke fast track activation was significantly increased from 4/27 (14.80%) to 15/27 (55.60%) (p=0.002). The median (min-max) duration from time last seen normal to first neurological deficit detection was reduced from 690 (19 to 9,190) to 130 (5 to 5,935) minutes (p=0.003). The number needed to treat for early detection when the protocol was used was 2.94. There was an increasing trend to endovascular treatment. 
Conclusion: Perioperative stroke is rare. However, it has negative impact to postoperative patients’ outcome. A simple protocol for postoperative neurological assessment after cardiac surgery is feasible to detect perioperative stroke.
Keywords: Postoperative, In-hospital, Detection, Recognition, Stroke