摘要： 目的 利用logistic回归模型和决策树模型分析辅助生殖患者发生卵巢高反应（HOR）的影响因素，探讨2种模型的临床应用价值。方法 回顾性收集行辅助生殖治疗的4 472个控制性超促排卵周期的临床资料，分别建立logistic回归模型和决策树模型，分析发生HOR的影响因素，比较2个模型的预测效果和优缺点。结果 logistic回归结果显示，年龄（OR=0.908）、拮抗剂方案（OR=0.664）和卵泡刺激素（OR=0.844）是HOR的保护因素，黄体生成素（OR=1.028）、抗米勒管激素（OR=1.174）和窦卵泡数（OR=1.104）是HOR的危险因素。决策树结果显示，患者抗米勒管激素水平是影响HOR的最主要因素，其次为窦卵泡数，再次级影响因素为用药方案、卵泡刺激素、黄体生成素。结论 logistic回归模型和决策树模型在HOR的预测方面均具有良好的应用价值，应结合2种模型的优点，为临床决策和早期干预提供新方法。

基于logistic回归模型和决策树模型分析辅助生殖患者卵巢高反应的影响因素

杜超, 侯开波, 关小川, 高艳, 孙凯旋, 于月新

北部战区总医院生殖医学科, 沈阳 110016

收稿日期:2021-11-16发布日期:2022-11-09
通讯作者:于月新E-mail:yuyuexinpingan@163.com
作者简介:杜超（1994-），男，医师，硕士.
基金资助:辽宁省科学技术计划（2020JH2/10300118）；军队后勤科研重点项目（BLB19J012）


关键词: 辅助生殖, 决策树, 卵巢高反应, 抗米勒管激素
Abstract: Objective To use a logistic regression model and decision tree model to analyze influencing factors of high ovarian response in assisted reproduction patients and explore the clinical application value of these two methods.Methods The clinical data from 4 472 females with controlled ovarian hyperstimulation cycles，treated with assisted reproductive technology，were collected retrospectively. A logistic regression model and decision tree model were established to analyze influencing factors of ovarian hyperresponsiveness， and the advantages and disadvantages of these two models were compared.Results Logistic regression found that age（OR= 0.908）， gonadotropin-releasing hormone antagonist protocol（OR= 0.664），and follicle-stimulating hormone（FSH） （OR= 0.844）were protective factors for high ovarian response，while luteinizing hormone（LH） （OR= 1.028），anti-Müllerian hormone（AMH） （OR= 1.174），and antral follicle counting（AFC） （OR= 1.104）were risk factors for high ovarian response. The decision tree showed that AMH was the most important factor affecting high ovarian response，followed by AFC； the secondary influencing factors were medication regimen，in addition to FSH and LH levels. Conclusion The logistic regression model and decision tree model have good application value in the prediction of high ovarian response. Making full use of both predictions is helpful for clinical decision-making and early intervention.
Key words: assisted reproduction, decision tree, high ovarian response, anti-Müllerian hormone
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