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Demonstration of High Diagnostic Accuracy for Cerebral Ischemia in a Large Academic Hub-and -Spoke Telestroke Network
- Source :
- Mayo Clinic Proceedings: Digital Health, Vol 1, Iss 4, Pp 490-497 (2023)
- Publication Year :
- 2023
- Publisher :
- Elsevier, 2023.
-
Abstract
- Objective: To determine the diagnostic accuracy of the initial telestroke consultation in a large academic hub-and-spoke telemedicine network. Patients and Methods: This retrospective study includes all patients evaluated for cerebral ischemia through video telestroke consultation in a large academic hub-and-spoke telemedicine network from January 1, 2019 to December 31, 2020. A detailed chart review was conducted to identify the initial suspected diagnosis and final diagnosis. Cerebral ischemia was defined as acute ischemic stroke and transient ischemic attack. All other diagnoses were defined as stroke mimics. Data were organized into continuous and categorical variables. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the curve (AUC), and likelihood ratio (LR+) for the telestroke-consultation diagnosis were calculated while using the final diagnosis as the gold standard diagnosis. Results: A total of 1043 patients met the inclusion criteria. The final diagnosis of cerebral ischemia was made in 63.5% of all patients (539 of the 1043 with acute ischemic stroke,123 of the 1043 with transient ischemic attack). Stroke mimic was diagnosed in 36.5% patients (381 of the 1043). The sensitivity and specificity of telestroke evaluation for diagnosis of cerebral ischemia were 97.1% and 81.4%, respectively. Positive predictive value was 90.1%, and negative predictive value was 94.2%. Overall diagnostic accuracy was 91.4%, with an LR+ of 5.21 and AUC of 0.89. Conclusion: This study highlights the high diagnostic accuracy of telestroke providers in diagnosing cerebral ischemia. Further research exploring the application of teleneurology in the nonstroke setting and other medical subspecialties is warranted.
- Subjects :
- Computer applications to medicine. Medical informatics
R858-859.7
Subjects
Details
- Language :
- English
- ISSN :
- 29497612
- Volume :
- 1
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- Mayo Clinic Proceedings: Digital Health
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.3c8af656364d9ab6491d0f28daf7e4
- Document Type :
- article
- Full Text :
- https://doi.org/10.1016/j.mcpdig.2023.08.004