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  

Migraineur patent foramen ovale risk prediction model for female migraine patient streaming and clinical decision-making

作　　者：张晓春 范家宁 朱丽 张峰 林大卫 王婉凌 潘文志 周达新 葛均波 ZHANG Xiao-chun;FAN Jia-ning;ZHU Li;ZHANG Feng;LIN Da-wei;WANG Wan-ling;PAN Wen-zhi;ZHOU Da-xin;GE Jun-bo(Department of Cardiology,Zhongshan Hospital,Fudan University,Shanghai 200032,China;Department of Cardiology,Jinshan Hospital,Fudan University,Shanghai 201508,China;Department of Analytics,Northeastern University,Boston 02115,MA,USA)

机构地区：[1]复旦大学附属中山医院心内科,上海200032 [2]复旦大学附属金山医院心内科,上海201508 [3]美国东北大学数据分析系,波士顿02115

出　　处：《复旦学报（医学版）》2024年第4期505-514,共10页Fudan University Journal of Medical Sciences

基　　金：上海市介入医学临床研究中心基金(19MC1910300)。

摘　　要：目的探究合并卵圆孔未闭(patent foramen ovale,PFO)女性偏头痛患者的临床特征并设计女性偏头痛患者PFO风险预测模型(migraineur PFO risk prediction model,MPRPM)。方法选取2019年6月1日至2022年12月31日期间于复旦大学附属中山医院就诊的女性偏头痛患者。收集患者术前信息以及停药后的随访结果,并根据食管超声结果,将患者分为PFO阳性组和PFO阴性组。构建多因素Logistic回归模型和随机森林模型并对其多维度验证。根据准确率下降系数(mean decrease accuracy,MDA)筛选关键特征并构建MPRRM。结果共纳入305名女性患者,其中PFO阳性组204人,PFO阴性组101人,多因素Logistic回归分析显示,偏头痛发病年龄、发作频率、发作时严重影响生活、运动相关性头痛、月经引起的头痛、先兆性偏头痛和隐源性脑卒中病史是PFO阳性患者的预测因素。随机森林模型可以预测女性偏头痛患者PFO发病率,其AUC为0.895(95%CI:0.847~0.943)。MPRPM的灵敏度为71.6%,特异度为91.1%(AUC:0.862,95%CI:0.818~0.906,P<0.001)。最佳临界值为2.5分。模型分类正确的患者症状改善率高于分类不正确的患者(94.3%vs.82.0%,P=0.023)。结论确定了女性偏头痛患者合并PFO的预测因素。MPRPM可为女性偏头痛患者的诊断过程和治疗决策提供指导,辅助偏头痛患者就诊分流,减轻医疗负担。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.

关 键 词：卵圆孔未闭(PFO) 偏头痛 机器学习 预测因子 预测模型 

分 类 号：R747.2[医药卫生—神经病学与精神病学]