Nomogram for the cumulative live birth in women undergoing the first IVF cycle: Base on 26, 689 patients in China

Author:

Qu Pengfei,Chen Lijuan,Zhao Doudou,Shi Wenhao,Shi Juanzi

Abstract

ObjectivePredictive models of the cumulative live birth (CLB) in women undergoing in vitro fertilization (IVF) treatment are limited. The aim of this study was to develop and validate a nomogram for the CLB in women undergoing the first IVF cycle.MethodsBased on a cross-sectional study in assisted reproduction center of Northwest Women’s and Children’s Hospital, 26,689 Chinese patients who underwent IVF treatment was used to develop and validate a prediction model for the CLB. Among those participants, 70% were randomly assigned to the training set (18,601 patients), while the remaining 30% were assigned to the validation set (8,088 patients). A nomogram was constructed based on the results of the multivariate logistic regression analysis. The model performance was evaluated using the C statistic and the calibration performance was assessed by Hosmer-Lemeshow (HL) χ2 statistics and calibration plots.ResultsMultivariate logistic regression analyses revealed that female age, female body mass index (BMI), tubal factor infertility, male infertility, uterine factor infertility, unexplained infertility, antral follicle count (AFC) and basal serum follicle stimulating hormone (FSH) were significant factors for CLB in women undergoing the first IVF cycle. An area under the receiver operating characteristic curve (AUC) in the prediction model was 0.676 (95% CI 0.668 to 0.684) in the training group. The validation set showed possibly helpful discrimination with an AUC of 0.672 (95% CI 0.660 to 0.684). Additionally, the prediction model had a good calibration (HL χ2 = 8.240, P=0.410).ConclusionsWe developed and validated a nomogram to predict CLB in women undergoing the first IVF cycle using a single center database in China. The validated nomogram to predict CLB could be a potential tool for IVF counselling.

Funder

National Natural Science Foundation of China

Key Research and Development Projects of Shaanxi Province

Publisher

Frontiers Media SA

Subject

Endocrinology, Diabetes and Metabolism

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