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.
10.13703/j.0255-2930.20230706-k0001
- Author:
Chen-Chen SU
1
;
Xue-Zhong ZHOU
2
;
Huan-Fang XU
3
;
Li YANG
3
;
Jia-Shan LI
3
;
Qi-Wei XIAO
3
;
Wei-Xin LI
3
;
Yi-Gong FANG
4
,
5
Author Information
1. Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700, China. 983715433@qq.com.
2. School of Computer and Information Technology, Beijing Jiaotong University.
3. Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700, China.
4. Institute of Acupuncture and Moxibustion, China Academy of Chinese Medical Sciences, Beijing 100700, China. fangyigong@
5. com.
- Publication Type:Journal Article
- Keywords:
acupuncture;
clinical prediction model;
poor ovarian response (POR);
pregnancy outcome
- MeSH:
Pregnancy;
Female;
Humans;
Pregnancy Outcome;
Models, Statistical;
Prognosis;
Acupuncture Therapy;
Registries
- From:
Chinese Acupuncture & Moxibustion
2023;43(12):1390-1398
- CountryChina
- Language:English
-
Abstract:
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.
