[Risk assessment of residual dizziness after repositioning in patients with benign paroxysmal positional vertigo according on multivariate analysis and nomogram].

Objective

To investigate the clinical characteristics of residual dizziness（RD） after repositioning in patients with benign paroxysmal positional vertigo（BPPV）, identify its potential risk factors, and develop a predictive risk model.

Methods

A total of 137 patients diagnosed with BPPV at the First Affiliated Hospital of Xi'an Jiaotong University between January 2023 and June 2023 were enrolled. Based on the presence or absence of subjective discomfort within 3 months after successful repositioning, patients were divided into the non-RD group（NRD, n=93） and the RD group（n=44）. Differences in demographic characteristics, comorbidities, and disease-related features were compared between groups. Multivariate logistic regression analysis was used to identify independent risk factors for RD, and a nomogram was constructed based on these factors. The predictive performance of the model was assessed using the area under the curve（AUC）.

Results

The RD group showed significantly higher values in body mass index, prevalence of diabetes and motion sickness history, dizziness duration before repositioning, history of repositioning at external hospitals, number of treatments, and recurrence（all P<0.001）. Multivariate logistic regression revealed that diabetes（adjusted OR=8.73, P=0.039）, motion sickness history（adjusted OR=23.08, P<0.001）, dizziness duration ≥30 days before repositioning（adjusted OR=15.16, P<0.001）, and recurrence（adjusted OR=15.72, P=0.001） were independent risk factors for RD. The nomogram model based on these variables demonstrated good predictive ability, with an AUC of 0.804（95%CI 0.684-0.924）.

Conclusion

Diabetes, motion sickness history, dizziness duration ≥30 days, and recurrence are independent risk factors for RD after repositioning in patients with BPPV. The nomogram model based on these variables shows good predictive performance, with recurrence having the highest predictive value. This model can aid in early identification of high-risk patients and guide individualized intervention strategies.