A Dynamic Nomogram to Predict the Risk of Stroke in Emergency Department Patients With Acute Dizziness.

objectiveTo develop a risk prediction tool for acute ischemic stroke (AIS) for patients presenting to the emergency department (ED) with acute dizziness/vertigo or imbalance.

methodA prospective, multicenter cohort study was designed, and adult patients presenting with dizziness/vertigo or imbalance within 14 days were consecutively enrolled from the EDs of 4 tertiary hospitals between August 10, 2020, and June 10, 2021. Stroke was diagnosed by CT or MRI performed within 14 days of symptom onset. Participants were followed-up for 30 days. The least absolute shrinkage and selection operator (LASSO) logistic regression analysis was conducted to extract predictive factors that best identified patients at high risk of stroke to establish a prediction model. Model discrimination and calibration were assessed and its prediction performance was compared with the age, blood pressure, clinical features, duration, and diabetes (ABCD2) score, nystagmus scheme, and finger to nose test.

resultsIn this study, 790 out of 2,360 patients were enrolled {median age, 60.0 years [interquartile range (IQR), 51-68 years]; 354 (44.8%) men}, with complete follow-up data available. AIS was identified in 80 patients. An online web service tool (https://neuroby.shinyapps.io/dynnomapp/) was developed for stroke risk prediction, including the variables of sex, trigger, isolated symptom, nausea, history of brief dizziness, high blood pressure, finger to nose test, and tandem gait test. The model exhibited excellent discrimination with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.889 (95%

conclusionOur new prediction model exhibited good performance and could be useful for stroke identification in patients presenting with dizziness, vertigo, or imbalance. Further externally validation study is needed to increase the strength of our findings.