Objectives: To construct a clinical prediction model for the impact of acupuncture on pregnancy outcomes in poor ovarian response (POR) patients, providing insights and methods for predicting pregnancy outcomes in POR patients undergoing acupuncture treatment.
Methods: Clinical data of 268 POR patients (2 cases were eliminated) primarily treated with "thirteen needle acupuncture for Tiaojing Cuyun (regulating menstruation and promoting pregnancy)" was collected from the international patient registry platform of acupuncture moxibustion (IPRPAM) from September 19, 2017 to April 30, 2023, involving 24 clinical centers including Acupuncture-Moxibustion Hospital of China Academy of Chinese Medical Sciences. LASSO and univariate Cox regression were used to screen factors influencing pregnancy outcomes, and a multivariate Cox regression model was established based on the screening results. The best model was selected using the Akaike information criterion (AIC), and a nomogram for clinical pregnancy prediction was constructed. The prediction model was evaluated using receiver operating characteristic (ROC) curves and calibration curves, and internal validation was performed using the Bootstrap method.
Results: (1) Age, level of anti-Müllerian hormone (AMH), and total treatment numbers of acupuncture were independent predictors of pregnancy outcomes in POR patients receiving acupuncture (P<0.05). (2) The AIC value of the best subset-Cox multivariate model (560.6) was the smallest, indicating it as the optimal model. (3) The areas under curve (AUCs) of the clinical prediction model after 6, 12, 24, and 36 months treatment were 0.627, 0.719, 0.770, and 0.766, respectively, and in the validation group, they were 0.620, 0.704, 0.759, and 0.765, indicating good discrimination and repeatability of the prediction model. (4) The calibration curve showed that the prediction curve of the clinical prediction model was close to the ideal model's prediction curve, indicating good calibration of the prediction model.
Conclusions: The clinical prediction model for the impact of acupuncture on pregnancy outcomes in POR patients based on the IPRPAM platform has good clinical application value and provides insights into predicting pregnancy outcomes in POR patients undergoing acupuncture treatment.
目的: 构建针灸对卵巢低反应（POR）患者妊娠结局影响的临床预测模型，为针刺改善POR患者的妊娠结局提供预测的思路及方法。方法: 收集2017年9月19日至2023年4月30日中国中医科学院针灸医院等24家中心在国际针灸病例注册登记研究平台（IPRPAM）登记的接受以“调经促孕十三针”针刺治疗为主的268例（2例剔除）POR患者临床资料。采用LASSO、单因素Cox回归对妊娠结局的影响因素进行筛选，并根据筛选结果建立多因素Cox回归模型。利用赤池信息准则（AIC）筛选最优模型，并构建列线图临床妊娠预测模型。采用受试者工作特征曲线、校准曲线对预测模型进行评价，并使用重复抽样法对模型进行内部验证。结果: ①年龄、基础抗缪勒管激素（AMH）水平、针灸总次数（P<0.05）是针刺对POR患者妊娠结局影响的独立预测因素。②最优子集-Cox多因素模型的AIC值（560.6）最小，为最优模型。③POR患者接受针灸治疗后6、12、24、36个月内，临床妊娠预测模型的曲线下面积（AUC）分别为0.627、0.719、0.770、0.766，模型验证组的AUC分别为0.620、0.704、0.759、0.765，表明该预测模型具有较佳的区分度和可重复性。④校准曲线显示临床预测模型的预测曲线较为接近理想模型的预测曲线，表明该预测模型具有较好的校准度。结论: 基于IPRPAM平台构建的针灸对POR患者妊娠结局影响的临床预测模型，具有较好的临床应用价值，可为针灸对POR患者妊娠结局影响提供预测思路。.
Keywords: acupuncture; clinical prediction model; poor ovarian response (POR); pregnancy outcome.