Nomogram prediction model for the clinical efficacy of flunarizine hydrochloride in patients with vertigo based on 5-HT and oxidative stress indicators✰

• First nomogram integrates 5-HT and oxidative stress markers for vertigo treatment. • Model shows high accuracy (C-index 0.86‒0.87) and identifies key predictive factors. • Facilitates personalized therapy by early detection of flunarizine poor responders. This study aimed to develop and validate a nomogram model integrating 5-Hydroxytryptamine (5-HT) and oxidative stress markers to predict the efficacy of flunarizine hydrochloride in treating vertigo. A retrospective study included 244 vertigo patients treated with flunarizine hydrochloride from September 2022 to December 2024. Patients were divided into training (70%) and validation (30%) sets. Clinical data, 5-HT levels, and oxidative stress indicators (Superoxide Dismutase SOD, Malondialdehyde MDA, Glutathione Peroxidase GSH-Px) were collected and analyzed. The influencing factors of treatment efficacy were screened using univariate and multivariate logistic regression. Least Absolute Shrinkage and Selection Operator (LASSO) regression was implemented using the R 'glmnet' package with 10-fold cross-validation. A nomogram model was constructed by integrating the independent influencing factors of treatment efficacy. Model performance was assessed via calibration curves, Consistency index (C-index), Decision Curve Analysis (DCA), and Receiver Operating Characteristic (ROC) curve. There were 176 cases in the effective group and 68 cases in the ineffective group. Age, 5-HT, SOD, MDA, and GSH-Px were identified as independent predictors of treatment efficacy ( p 70-points should be considered for alternative therapies due to a high predicted risk of treatment non-response.