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

 Then roll this out to all stroke hospitals and get it implemented in each one.

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

Ying Bi and Fei Cao^*

• Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

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

Method: A 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.

Results: In 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% CI: 0.855–0.923), compared with the ABCD2 score, nystagmus scheme, and finger to nose test [0.712 (95% CI, 0.652–0.771), 0.602 (95% CI, 0.556–0.648), and 61.7 (95% CI, 0.568–0.666) respectively].

Conclusion: Our 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.

Introduction

It has been established that stroke accounts for 2–13.4% of patients presenting to the emergency department (ED) with dizziness (1–5). Stroke is the second leading cause of death globally and has a limited treatment time window (6–8). Misdiagnosis can affect treatment decision-making, thus seriously impacting disease outcomes and patient quality of life, emphasizing the importance of timely stroke diagnosis in patients with dizziness (9–11). In this respect, it has been shown that over one-third of stroke cases are missed at the first visit by emergency physicians (EPs) (12). The misdiagnosis rate can be as high as 24–60%, especially when the symptoms are mild, non-specific, and transient (13).

Many efforts have been made to differentiate stroke from other causes of dizziness. The age, blood pressure, clinical features, duration, and diabetes (ABCD2) score with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.79, and head impulse, nystagmus pattern, test of skew (HINTS) test with high sensitivity/specificity (100%/96%), are the two most widely acknowledged tools for stroke identification in patients with acute dizziness (14–17). The features of posterior circulation stroke, which had a triple misdiagnosis rate than that of anterior circulation stroke, are not included in ABCD2 score, which could probably decrease its diagnostic accuracy (18–21). Recently, a combination of ABCD2 ≥ 4 and a central pattern of nystagmus has been shown to yield higher sensitivity than the ABCD2 score alone for identifying stroke (22). Notwithstanding that the HINTS is reported to yield good diagnostic performance, studies report their low usage during clinical practice in EDs, as only 30% of EPs agree with the use of HINTS in patients with dizziness, not to mention the usage of head impulse, nystagmus pattern, test of skew, acute hearing loss (HINTS-plus) (23). Truncal ataxia, an easy-to-evaluate test, has been shown to yield high sensitivity to differentiate stroke from acute vestibular syndrome when combined with the nystagmus test (24). Posterior circulation ischemia (PCI) score, TriAGe+ score, STANDING algorithm, and DEFENSIVE scale were recently studied to estimate the risk of stroke in patients with dizziness (25–28). NLR, S100B, and NSE have been reported as new blood markers for the prediction of stroke in patients with dizziness (29–31). However, most of these prediction approaches were developed with small sample sizes, retrospectively collection, no validation, and have not been applied in clinical practice. Early risk stratification of dizzy patients is crucial, and misdiagnosis of stroke can lead to serious complications and poor outcomes. Thus, it is still challenging to discriminate stroke from patients with dizziness in the ED.

The present study sought to develop and validate a clinical prediction model based on easy-to-get predictors to identify patients with dizziness at risk of stroke in the ED.

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