Establishment of a Prediction Model for Menstruation after the First Course of Hormone Replacement Therapy in Premature Ovarian Insufficiency Patients af-ter Allogeneic Hematopoietic Stem Cell Transplantation
- VernacularTitle:异体骨髓移植后早发性卵巢功能不全患者首疗程激素补充治疗月经预测模型的建立
- Author:
Ning ZHANG
1
,
2
;
Weizeyu LIU
;
Jingjing ZHANG
;
Xiaoyu LI
;
Fangcan SUN
;
Huiyun CHEN
;
Xiao MA
;
Bing HAN
Author Information
1. 苏州大学附属第一医院:妇产科,江苏 苏州 215000
2. 苏州大学附属第二医院输血科,江苏 苏州 215000
- Keywords:
Allogeneic hematopoietic stem cell transplantation;
Premature ovarian insufficiency;
Hormone re-placement therapy;
Random forests;
Prediction model
- From:
Journal of Practical Obstetrics and Gynecology
2024;40(7):577-581
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.
