ArticleBMC pregnancy and childbirth2026
Nomogram model to predict the probability of spontaneous abortion in human assisted reproductive technology.
Article in BMC pregnancy and childbirth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundSpontaneous abortion is a prevalent complication in pregnancies achieved through assisted reproductive technology (ART), posing significant risks to both maternal and fetal health. Despite the importance of preventing this outcome, no accurate predictive model for spontaneous abortion currently exists.
methodsThis retrospective cohort study analyzed clinical data from 15,723 patients who achieved pregnancy via in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) between December 2015 and February 2021. Patients were stratified into abortion and non-abortion groups based on pregnancy outcomes. Univariate and multivariate logistic regression analyses were performed to identify independent predictors, which were subsequently incorporated into a nomogram for predicting spontaneous abortion risk in ART patients.
resultsAmong 15,723 patients who conceived through ART, 2,336 (14.86%) experienced spontaneous abortion, while 13,387 (85.14%) did not. Multivariate logistic regression identified female age (OR 1.050; 95% CI 1.029-1.061; P < 0.001), male age (OR 1.102; 95% CI 1.086-1.115; P < 0.001), follicle-stimulating hormone level (OR 1.044; 95% CI 1.015-1.069; P < 0.001), and anti-Müllerian hormone level (OR 0.891; 95% CI 0.862-0.925; P < 0.001) as independent risk factors for spontaneous abortion. The nomogram demonstrated good predictive performance, with an area under the curve (AUC) of 0.746 (95% CI 0.707-0.784) in the derivation cohort and 0.718 (95% CI 0.699-0.737) in the validation cohort.
conclusionFemale age, male age, follicle-stimulating hormone level, and anti-Müllerian hormone level are independent predictors of spontaneous abortion in ART-conceived pregnancies. The nomogram model provides an effective and intuitive clinical tool for individualized risk assessment.
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