Construction of a clinical prediction model for the impact of acupuncture on pregnancy outcomes in poor ovarian response (POR) patients based on a patient registry research platform

SU Chen-chen; ZHOU Xue-zhong; XU Huan-fang; YANG Li; LI Jia-shan; XIAO Qi-wei; LI Wei-xin; FANG Yi-gong

doi:10.13703/j.0255-2930.20230706-k0001

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Construction of a clinical prediction model for the impact of acupuncture on pregnancy outcomes in poor ovarian response (POR) patients based on a patient registry research platform

Clinical Research | 更新时间：2024-06-03

- Construction of a clinical prediction model for the impact of acupuncture on pregnancy outcomes in poor ovarian response (POR) patients based on a patient registry research platform
- Chinese Acupuncture & Moxibustion Vol. 43, Issue 12, Pages: 1390-1398(2023)
- 作者机构：
  
  1.中国中医科学院针灸研究所，北京100700
  2.北京交通大学计算机与信息技术学院
- 作者简介：
- 基金信息：
- DOI：10.13703/j.0255-2930.20230706-k0001
  CLC： R 246.3
- Received：06 July 2023，
  
  Online First：11 October 2023，
  
  Published：12 December 2023
- 稿件说明：
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苏晨晨,周雪忠,许焕芳等.基于病例注册登记研究平台构建针灸对卵巢低反应患者妊娠结局的临床预测模型[J].中国针灸,2023,43(12):1390-1398.

SU Chen-chen,ZHOU Xue-zhong,XU Huan-fang,et al.Construction of a clinical prediction model for the impact of acupuncture on pregnancy outcomes in poor ovarian response (POR) patients based on a patient registry research platform[J].Chinese Acupuncture & Moxibustion,2023,43(12):1390-1398.
苏晨晨,周雪忠,许焕芳等.基于病例注册登记研究平台构建针灸对卵巢低反应患者妊娠结局的临床预测模型[J].中国针灸,2023,43(12):1390-1398. DOI： 10.13703/j.0255-2930.20230706-k0001.

SU Chen-chen,ZHOU Xue-zhong,XU Huan-fang,et al.Construction of a clinical prediction model for the impact of acupuncture on pregnancy outcomes in poor ovarian response (POR) patients based on a patient registry research platform[J].Chinese Acupuncture & Moxibustion,2023,43(12):1390-1398. DOI： 10.13703/j.0255-2930.20230706-k0001.

摘要

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（

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。

Abstract

Objective

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 (

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

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 mod

el was close to the ideal model's prediction curve

indicating good calibration of the prediction model.

Conclusion

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.

关键词

Keywords

references

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