Migraineur patent foramen ovale risk prediction model for female migraine patient streaming and clinical decision-making
10.3969/j.issn.1672-8467.2024.04.007
- VernacularTitle:用于女性偏头痛患者分流和临床决策的偏头痛患者卵圆孔未闭风险预测模型
- Author:
Xiao-Chun ZHANG
1
;
Jia-Ning FAN
;
Li ZHU
;
Feng ZHANG
;
Da-Wei LIN
;
Wan-Ling WANG
;
Wen-Zhi PAN
;
Da-Xin ZHOU
;
Jun-Bo GE
Author Information
1. 复旦大学附属中山医院心内科 上海 200032
- Keywords:
patent foramen ovale(PFO);
migraine;
machine learning;
predictor;
prediction model
- From:
Fudan University Journal of Medical Sciences
2024;51(4):505-514
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the clinical characteristics of female migraine patients with patent foramen ovale(PFO)and design a risk prediction model for PFO in female migraine patients(migraineur patients PFO risk prediction model,MPRPM).Methods Female migraine patients who visited Zhongshan Hospital,Fudan University from Jun 1,2019 to Dec 31,2022 were included.Preoperative information and follow-up results after discontinuation of medication were collected.Patients were divided into PFO-positive and PFO-negative groups based on transesophageal echocardiography results.A multivariate Logistic regression model and a random forest model were constructed,and the random forest model was validated multidimensionally.Key features were selected based on the mean decrease accuracy(MDA)to construct MPRPM.Results A total of 305 female patients were included in the study,with 204 patients in the PFO-positive group and 101 patients in the PFO-negative group.Multivariate Logistic regression analysis showed that age at migraine onset,attack frequency,severe impact on life during attacks,exercise-related headaches,menstruation-induced headaches,aura migraines,and a history of cryptogenic stroke were predictive factors for PFO positivity.The random forest model effectively predicted the incidence of PFO in female migraine patients,with an AUC of 0.895(95%CI:0.847-0.943).MPRPM demonstrated a sensitivity of 71.6%and specificity of 91.1%(AUC:0.862,95%CI:0.818-0.906,P<0.001).The optimal cut-off value was 2.5 points.Patients correctly classified by the model showed a higher rate of symptom improvement compared to incorrectly classified patients(94.3%vs.82.0%,P=0.023).Conclusion We identified predictive factors for PFO in migraine patients.MPRPM can provide guidance in the diagnostic process and therapeutic decision-making for female migraine patients,assist in patient triage,and reduce the healthcare burden.
