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

Introduction: 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...

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Published in:Archives of Academic Emergency Medicine Vol. 13; no. 1; pp. 1 - 14
Main Authors: Soma, Shunsuke, Ito, Katsunori, Kamitani, Tsukasa
Format: research tables/charts Journal Article
Published: Shahid Beheshti University of Medical Sciences 2025
Online Access:View this record in EBSCOhost
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      dt: 2025
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        atl: Accuracy and Clinical Utility of Clinical Predictive Models for Identifying Dizziness with Central Causes; A Retrospective Diagnostic Accuracy Study.
      aug:
        au:
          Soma, Shunsuke
          Ito, Katsunori
          Kamitani, Tsukasa
        affil: Department of General Medicine, Aomori Prefectural Central Hospital, Aomori City, Aomori, Japan.
      sug:
        subj:
          Dizziness Etiology
          Dizziness Diagnosis
          Central Nervous System Diseases Risk Factors
          Prediction Models Evaluation
          Sensitivity and Specificity
          Risk Assessment
          Human
          Middle Age
          Aged, 80 and Over
          Male
          Female
          Japan
          Funding Source
          Retrospective Design
          Record Review
          Emergency Service
          ROC Curve
          Tomography, X-Ray Computed
          Magnetic Resonance Imaging
          Neurologists
          Calibration
          Patient Safety
          Descriptive Statistics
          Prevalence
          Predictive Value of Tests
          Decision Making, Clinical
          Vertigo
          Neurologic Examination
          Logistic Regression
          Confidence Intervals
          Data Analysis Software
          Middle Aged: 45-64 years
          Aged, 80 & over
          Male
          Female
      ab: Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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