Two-Stage Clinical Model for Screening the Suspected Cases of Acute Ischemic Stroke in Need of Imaging in Emergency Department; a Cross-sectional Study

Abstract

Introduction: Just as failure to diagnose an acute ischemic stroke (AIS) in a timely manner affects the patient's outcome; an inaccurate and misplaced impression of the AIS diagnosis is not without its drawbacks. Here, we introduce a two-stage clinical tool to aid in the screening of AIS cases in need of imaging in the emergency department (ED). Methods: This was a multicenter cross-sectional study, in which suspected AIS patients who underwent a brain magnetic resonance imaging (MRI) were included. The 18 variables from nine existing AIS screening tools were extracted and a two-stage screening tool was developed based on expert opinion (stage-one or rule in stage) and multivariate logistic regression analysis (stage-two or rule out stage). Then, the screening performance characteristics of the two-stage mode was evaluated. Results: Data from 803 patients with suspected AIS were analyzed. Among them, 57.4 % were male, and their overall mean age was 66.9 ± 13.9 years. There were 561 (69.9%) cases with a final confirmed diagnosis of AIS. The total sensitivity and specificity of the two-stage screening model were 99.11% (95% CI: 98.33 to 99.89) and 35.95% (95% CI: 29.90 to 42.0), respectively. Also, the positive and negative predictive values of two-stage screening model were 78.20% (95% CI: 75.17 to 81.24) and 94.57% (95% CI: 89.93 to 81.24), respectively. The area under the receiver operating characteristic (ROC) curve of the two-stage screening model for AIS was 67.53% (95% CI: 64.48 to 70.58). Overall, using the two-stage screening model presented in this study, more than 11% of suspected AIS patients were not referred for MRI, and the error of this model is about 5%. Conclusion: Here, we proposed a 2-step model for approaching suspected AIS patients in ED for an attempt to safely exclude patients with the least probability of having an AIS as a diagnosis. However, further surveys are required to assess its accuracy and it may even need some modifications.

Keywords

• Decision support techniques
• Emergency service, Hospital
• Stroke
• Ruling out