The 2CAN score: Development of a novel inpatient stroke detection tool

Background and Purpose-A quarter of acute strokes occur in patients hospitalized for another reason. A stroke recognition instrument may be useful for non-neurologists to discern strokes from mimics such as seizures or delirium. We aimed to derive and validate a clinical score to distinguish stroke from mimics among inhospital suspected strokes. Methods-We reviewed consecutive inpatient stroke alerts in a single academic center from January 9, 2014, to December 7, 2016. Data points, including demographics, stroke risk factors, stroke alert reason, postoperative status, neurological examination, vital signs and laboratory values, and final diagnosis, were collected. Using multivariate logistic regression, we derived a weighted scoring system in the first half of patients (derivation cohort) and validated it in the remaining half of patients (validation cohort) using receiver operating characteristics testing. Results-Among 330 subjects, 116 (35.2%) had confirmed stroke, 43 (13.0%) had a neurological mimic (eg, seizure), and 171 (51.8%) had a non-neurological mimic (eg, encephalopathy). Four risk factors independently predicted stroke: clinical deficit score (clinical deficit score 1: 1 point; clinical deficit score ≥2: 3 points), recent cardiac procedure (1 point), history of atrial fibrillation (1 point), and being a new patient (<24 hours from admission: 1 point). The score showed excellent discrimination in the first 165 patients (derivation cohort, area under the curve=0.93) and remaining 165 patients (validation cohort, area under the curve=0.88). A score of ≥2 had 92.2% sensitivity, 69.6% specificity, 62.2% positive predictive value, and 94.3% negative predictive value for identifying stroke. Conclusions-The 2CAN score for recognizing inpatient stroke performs well in a single-center study. A future prospective multicenter study would help validate this score.