Development and validation of a diagnostic model for migraine without aura in inpatients

This study aimed to develop and validate a robust predictive model for accurately identifying migraine without aura (MWoA) individuals from migraine patients.

We recruited 637 migraine patients, randomizing them into training and validation cohorts. Participant's medical data were collected such as demographic data (age, gender, self-reported headache characteristics) and clinical details including symptoms, triggers, and comorbidities. The model stability, which was developed using multivariable logistic regression, was tested by the internal validation cohort. Model efficacy was evaluated using the area under the receiver operating characteristic curve (AUC), alongside with nomogram, calibration curve, and decision curve analysis (DCA).

The study included 477 females (average age 46.62 ± 15.64) and 160 males (average age 39.78 ± 19.53). A total of 397 individuals met the criteria for MWoA. Key predictors in the regression model included patent foramen ovale (PFO) (OR = 2.30, p = 0.01), blurred vision (OR = 0.40, p = 0.001), dizziness (OR = 0.16, p < 0.01), and anxiety/depression (OR = 0.41, p = 0.02). Common symptoms like nausea (OR = 0.79, p = 0.43) and vomiting (OR = 0.64, p = 0.17) were not statistically significant predictors for MWoA. The AUC values were 79.1% and 82.8% in the training and validation cohorts, respectively, with good calibration in both.

The predictive model developed and validated in this study demonstrates significant efficacy in identifying MWoA. Our findings highlight PFO as a potential key risk factor, underscoring its importance for early prevention, screening, and diagnosis of MWoA.