Abstract 118: External Validation of the TeleStroke Mimic (TM) Score for Predicting Stroke Mimics Evaluated During Telestroke

    loading  Checking for direct PDF access through Ovid

Abstract

Introduction: Up to 30% of acute stroke evaluations are deemed stroke mimics, and these are common in telestroke as well. We recently published a risk prediction score, derived from the Partners TeleStroke Network, for use during telestroke encounters to differentiate stroke mimics (SM) from ischemic cerebrovascular disease (iCVD). Using data from 3 distinct US and European telestroke networks, we sought to externally validate the TM score in a broader population.

Methods: We evaluated the TM score in 1,985 telestroke consults from the University of Utah Telestroke Program (n=190), Georgia Regents University Telestroke Network (n=719) and the Bavarian TeleMedical Project for integrative Stroke Care (TEMPiS) in Germany (n=1076). We report the AUC in ROC curve analysis with 95% CI. The TM score = 0.2*(Age in years) + 6*(Hx of atrial fib) + 3*(Hx of HTN) + 9*(facial weakness) + 5*(NIHSS > 14) - 6*(Hx of seizure). Lower TM scores correspond with a higher likelihood of being a stroke mimic.

Results: Based on final diagnosis at the end of the telestroke consultation, there were 691/1985 (34.8%) SM in the external validation cohort. We tested the association between the TM score and the diagnosis of stroke mimic (Table). The TM score performed well at the external centers on ROC curve analysis with an AUC of 0.70 (0.67 - 0.73; p<0.001), similar to what we observed during the development of the score at our center.

Conclusion: As telestroke consultation expands, increasing numbers of SM patients are being evaluated. The TM score correctly predicted the presence of a SM in these diverse cohorts just as well as in our original cohort. Decision-support tools based on predictive models, like the TM score, may help highlight key clinical differences during complex, time-critical telestroke evaluations.

Related Topics

    loading  Loading Related Articles