Frontiers in neurology · 2025
Construction and clinical validation of benign paroxysmal positional vertigo intelligent auxiliary diagnosis model based on big data analysis.
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Han Jingyang, Wang Tao, Du Xiaoyan, Wang Yali, Guo Ziyang, Li Dandan, Yu Xinjun
Аннотация
BACKGROUND: Benign paroxysmal positional vertigo (BPPV) is the most common type of vertigo in clinical practice. Previous studies have suggested that inflammatory responses and metabolic disorders may be involved in the pathogenesis of BPPV, but systematic analyses based on large samples are lacking. The aim of this study is to construct an intelligent auxiliary diagnostic model for BPPV based on the big data of SRM-IV vertigo diagnostic and treatment system, and to carry out clinical validation.
METHODS: The clinical data of 522 vertigo patients were retrospectively analyzed, including 303 BPPV patients and 219 non-BPPV patients. LASSO regression and random forest algorithm were used to screen feature variables, and based on the screened feature variables, multifactor logistic regression analysis was performed to establish a prediction model for BPPV auxiliary diagnosis. Finally, the model was applied to BPPV patients diagnosed by SRM-IV diagnosis and treatment system for external validation.
RESULTS: Multifactorial logistic regression analysis showed that disease duration, neutrophils, lymphocytes, C-reactive protein (CRP), ferritin, and vitamin D deficiency were independent risk factors for the diagnosis of BPPV (OR>1, p < 0.05), monocyte count was an independent protective factor for the diagnosis of BPPV (OR<1, p < 0.05), and the area under curve (AUC) was 0.927.
CONCLUSION: The intelligent assisted diagnostic model of BPPV constructed based on the big data of SRM-IV vertigo diagnostic and treatment system has high diagnostic accuracy and clinical application value, and it is expected to assist the clinicians to improve the diagnostic efficiency.
METHODS: The clinical data of 522 vertigo patients were retrospectively analyzed, including 303 BPPV patients and 219 non-BPPV patients. LASSO regression and random forest algorithm were used to screen feature variables, and based on the screened feature variables, multifactor logistic regression analysis was performed to establish a prediction model for BPPV auxiliary diagnosis. Finally, the model was applied to BPPV patients diagnosed by SRM-IV diagnosis and treatment system for external validation.
RESULTS: Multifactorial logistic regression analysis showed that disease duration, neutrophils, lymphocytes, C-reactive protein (CRP), ferritin, and vitamin D deficiency were independent risk factors for the diagnosis of BPPV (OR>1, p < 0.05), monocyte count was an independent protective factor for the diagnosis of BPPV (OR<1, p < 0.05), and the area under curve (AUC) was 0.927.
CONCLUSION: The intelligent assisted diagnostic model of BPPV constructed based on the big data of SRM-IV vertigo diagnostic and treatment system has high diagnostic accuracy and clinical application value, and it is expected to assist the clinicians to improve the diagnostic efficiency.