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. 2025 Jun 5;23(1):86.
doi: 10.1186/s12958-025-01419-7.

A prediction model of low birthweight risk in singleton pregnancies reduced from dichorionic twin pregnancies

Minyue Tang #  1   2 , Qingfang Li #  3 , Guiquan Wang #  4 , Jiayu Xu  5   6 , Saijun Sun  5   6 , Yimin Zhu  7   8
Affiliations

A prediction model of low birthweight risk in singleton pregnancies reduced from dichorionic twin pregnancies

Minyue Tang et al. Reprod Biol Endocrinol. .

Abstract

Background: The incidence of twin pregnancies has risen in recent decades, which is mainly attributable to assisted reproduction technology. Fetal reduction (FR) can significantly reduce the risk of low birthweight (LBW) in twins; however, the LBW risk is higher in singletons reduced from twins than that in primary singletons. Factors including maternal factors and FR timing may affect the LBW risk after FR, but there are no relevant prediction models reported to date. Our main study objective was to develop a nomogram to predict LBW risk in singleton pregnancies reduced from dichorionic (DC) twins.

Methods: We retrospectively reviewed and analysed data from women with DC twin pregnancies who underwent FR at Women's Hospital School of Medicine, Zhejiang University between July 2005 and August 2021. Least absolute shrinkage and selection operator (LASSO) regression was used to identify relevant variables associated with LBW. A nomogram was constructed and receiver operating characteristic curve, calibration curves, and decision clinical analysis were used for model performance assessment and visualization. The model was evaluated using cohorts produced by 500 resampling bootstrap analysis to test its stability.

Results: A total of 471 patients were enrolled in the analysis. Finally, seven independent predictive factors for LBW were identified and integrated to construct the nomogram, including maternal height, nulliparous, conception method, reasons for FR, gestational age at FR, gestational diabetes, and pregnancy hypertensive disease. The area under the receiver operating characteristic curve of our prediction model was 0.793, which was validated in internal confirmation (0.762) using bootstrap analysis. The nomogram had well-fitted calibration curves. Decision curve analysis demonstrated that the nomogram was clinically useful.

Conclusion: We first developed a reliable predictive nomogram for the risk of LBW in DC twin pregnancies reduced to singleton pregnancies, providing a useful guide for clinicians and patients in making appropriate decisions regarding FR.

Keywords: Fetal reduction; Low birthweight; Nomogram; Prediction model; Twins.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: The Institutional Review Board (IRB) of Women's Hospital, School of Medicine, Zhejiang University, approved the study (no: 20181077), which was conducted in accordance with the Declaration of Helsinki. All human participants were required for written informed consent for participation in this study in accordance with the national legislation and the institutional requirements. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The patient selection process for the present study
Fig. 2
Fig. 2
Nomogram of the prediction model to estimate the risk of LBW. Abbreviation: LBW, low birth weight
Fig. 3
Fig. 3
Prediction performances of the prediction model. ROC curve for Boostrap validation cohort
Fig. 4
Fig. 4
Calibration curve for the validation cohort
Fig. 5
Fig. 5
Decision curve analysis of the nomogram. Abbreviation: ROC, receiver operating characteristic
See this image and copyright information in PMC

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