Accuracy and Clinical Utility of Clinical Predictive Models for Identifying Dizziness with Central Causes; A...

Although several clinical prediction models (CPMs) have been developed for identifying acute dizziness with central causes, their application in clinical practice remains unclear. This study aimed to evaluate the accuracy and clinical utility of four CPMs in identifying dizziness with central lesions.

This single-center, retrospective, diagnostic accuracy study was conducted at the ED of Aomori Hospital, Japan, from April to March 2023. The area under the receiver operating characteristic curve (AUROC) of four risk stratification models (ABCD2, TriAGe+, PCI, and Sudbury) in predicting dizziness with central causes were evaluated considering the brain imaging (computed tomography (CT) scan and magnetic resonance imaging (MRI)) findings, interpreted by a neurologist or neurosurgeon, as the gold standard. Calibration was evaluated visually using calibration plots. Additionally, analyses of efficacy, safety, and clinical utility using a decision curve were conducted.

Of the 3,606 patients identified, 2,958 with the mean age of 65.3 ± 16.4 (range: 15-97.) years were included in the final analysis (64.7% female). 155 (5.2 %) were diagnosed with central lesions. The AUROCs were 0.67 (95% confidence interval (CI): 0.62-0.71) for ABCD2, 0.80 (95% CI: 0.76-0.84) for TriAGe+, 0.82 (0.78-0.86) for PCI, and 0.85 (95% CI: 0.82-0.88) for Sudbury. TriAGe+, PCI, and Sudbury demonstrated good calibration. Among these, the Sudbury model demonstrated the highest diagnostic efficiency, was the only model to meet safety criteria, and provided the highest net benefit in decision curve analysis, particularly at lower predicted prevalence thresholds.

The TriAGe+, PCI, and Sudbury models demonstrated strong discriminatory performance and reliable calibration when applied during ED admission at a community hospital. Particularly, the Sudbury model may reduce false-negative outcomes for central lesions, thereby potentially minimizing the need for unnecessary neuroimaging in patients identified as low-risk.