Carlin, Rachel

Introduction: Telestroke consultations enable hospital providers to administer IV alteplase to patients who would otherwise not receive it due to lack of an in-house neurologist. However, up to 30% of acute stroke evaluations are deemed stroke mimics. These present a challenge with the limitations of a virtual neurological exam. The use of IV alteplase in these patients is not risk-free. With the rise in cost of IV alteplase, there are ethical and practical incentives to avoid administering alteplase to a stroke-mimic. Recently a retrospective analysis validated a TeleStroke Mimic Score (TM-Score) to help detect stroke mimics. We retrospectively applied this tool to our Stroke Telemedicine for Arizona Rural Residents (STARR) telestroke database to provide external validation in an independent study population. Methods: We analyzed 339 patients in our STARR database for validation of the TM-Score, which was applied in retrospect to determine if it predicts stroke-mimic given data available during each patient’s telestroke consult. We assessed its performance with a receiver-operating characteristic (ROC) curve. Patient characteristics are displayed in table form. A scatter plot of the data shows the relationship between the TM-Score and the likelihood of having a stroke mimic, compared to the nomogram in the original study. Results: When the TM-Score was applied to our validation cohort, the area under the ROC curve was 0.78, larger than that of the derivation cohort in the original study (0.75). Further analysis suggests that a TM-Score greater than 25 or less than 10 would provide a greater degree of confidence that the patient is presenting with stroke or stroke mimic, respectively. In our cohort, additional variables were significantly different between stroke and stroke mimic populations, including history of sleep apnea or diabetes. Conclusion: We found that the TM-Score is validated when applied to Mayo Clinic’s STARR telestroke population. Validation of this tool helps establish the utility and broader generalizability of this equation. Effective use of this tool can help reduce risk to the patient and has additional cost-saving implications. This should help ensure that the patient receives the appropriate targeted workup and treatment.