Construction and validation of a nomogram for predicting remission of migraine patients with patent foramen ovale after closure

Abstract

ObjectiveThis study aimed to construct and validate a nomogram model that predicts the remission of migraine attacks by screening factors that affect the prognosis of migraine patients with patent foramen ovale (PFO) after closure. MethodRetrospective analysis was conducted in migraine patients with PFO who underwent PFO closure in the Department of Cardiology of Jiangsu Province Hospital from September 2020 to April 2023. Based on the Migraine Disability Assessment (MIDAS) scores from the 1-year follow-up after PFO closure, all patients who met the inclusion criteria were categorized into a remission group and a non-remission group. The primary efficacy endpoint was remission of migraine headache. After collecting clinical data, transcranial doppler sonography (TCD) results and MIDAS scores, LASSO (least absolute shrinkage and selection operator) regression and multivariable logistic regression analysis were used to filter variables predictive to migraine remission and construct the nomogram model. The Nomogram's accuracy and consistency were respectively assessed through Receiver Operating Characteristic (ROC) curves and calibration curves. Additionally, an analysis of decision curves (DCA) was conducted to evaluate the clinical utility of this newly developed model. ResultA total of 241 consecutive patients were included in the study. The remission group included 21 males and 93 females, with a median age of 39 (30.25,50) years. The non-remission group included 26 males and 101 females, with a median age of 35 (25.5,47.5) years. All Patients were randomly divided into a training cohort and a validation cohort. Multivariable logistic regression analysis showed that 5 independent predictors, including MIDAS before closure (p = 0.0002), mitigating factors (p = 0.0057), number of attacks/month (p = 0.0058), TCD (p = 0.0093) and Platelet Crit (PCT) (p = 0.0351), played a significant role in the prediction of remission of migraine patients with PFO after closure. Based on these independent predictors, the predictive nomogram model of migraine remission in PFO patients was constructed. The application of the nomogram in the training cohort exhibited good discrimination (area under the ROC curve was 0.7763[95% CI 0.7108–0.8418]), which was confirmed in the validation cohort (AUC was 0.704[95% CI 0.5533–0.8547]). The calibration curve showed that the nomogram model demonstrated good calibration performance. Additionally, the decision curve analysis indicated the clinical utility of the nomogram model. ConclusionThe construction of the nomogram model had a considerable predictive accuracy for migraine remission in patients after PFO closure, which may provide constructive guidance for clinical decision making.