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Impact of Chorionicity on Maternal, Perinatal, and Neonatal Outcomes in Triplet Pregnancies: A 15-Year Experience From a Tertiary Care Center in Italy

Published online by Cambridge University Press:  20 January 2026

Oumaima Ammar
Affiliation:
Department of Health Sciences, Section of Obstetrics and Gynecology, Careggi Hospital, University of Florence, Florence, Italy
Beatrice Baldassari
Affiliation:
Department of Health Sciences, Section of Obstetrics and Gynecology, Careggi Hospital, University of Florence, Florence, Italy
Mor Huri
Affiliation:
Department of Health Sciences, Section of Obstetrics and Gynecology, Careggi Hospital, University of Florence, Florence, Italy
Serena Lucarelli
Affiliation:
Department of Health Sciences, Section of Obstetrics and Gynecology, Careggi Hospital, University of Florence, Florence, Italy
Viola Seravalli
Affiliation:
Department of Health Sciences, Section of Obstetrics and Gynecology, Careggi Hospital, University of Florence, Florence, Italy
Mariarosaria Di Tommaso*
Affiliation:
Department of Health Sciences, Section of Obstetrics and Gynecology, Careggi Hospital, University of Florence, Florence, Italy
*
Corresponding author: Mariarosaria Di Tommaso; Email: mariarosaria.ditommaso@unifi.it

Abstract

Triplet pregnancies are linked to a higher risk of maternal and fetal complications compared to twins and singletons. Chorionicity has been suggested as a key factor influencing perinatal and maternal outcomes in triplet pregnancies; however, more evidence is needed to clarify its role. This study aimed to assess the impact of chorionicity on adverse maternal, fetal, and neonatal outcomes in triplet pregnancies. This retrospective observational study was conducted on triplet pregnancies delivered between 2010 and 2025 at Florence University Hospital, a tertiary referral maternity center. A total of 77 triplet pregnancies were included, 51 trichorionic and 26 nontrichorionic, resulting in the delivery of 214 newborns. Maternal characteristics and obstetric, fetal, and neonatal complications were compared based on chorionicity. Multivariate logistic regression analyses identified complications most strongly associated with chorionicity, after adjusting for maternal age, mode of conception, and gestational age at delivery. Women with nontrichorionic triplet pregnancies had higher risks of delivering earlier and postpartum hemorrhage. Their newborns had significantly lower birth weights, a greater need for neonatal intensive care admission and respiratory support, and higher rates of hypoglycemia and sepsis. The multivariate logistic regression confirmed nontrichorionicity as an independent factor associated with earlier delivery and postpartum hemorrhage. Chorionicity plays an essential role in determining the prognosis of triplet pregnancies. Nontrichorionic pregnancies are strongly linked to earlier delivery, lower birth weights, and increased neonatal complications. These findings are useful for counseling patients, helping them understand specific risks based on chorionicity, although intensive prenatal care remains critical for all triplet pregnancies.

Information

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2026. Published by Cambridge University Press on behalf of International Society for Twin Studies
Figure 0

Table 1. Maternal demographic characteristics and obstetric and fetal outcomes

Figure 1

Table 2. Delivery and neonatal complications

Figure 2

Table 3A. Binary logistic regression model for the prediction of postpartum hemorrhage

Figure 3

Table 3B. Multivariable linear regression model for the prediction of the gestational age at delivery

Figure 4

Table 4A. Multivariable linear logistic regression model for the prediction of the newborn birth weight

Figure 5

Table 4B. Binary logistic regression model for predicting admission to Intensive neonatal therapy