Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective study

Abstract Objective To explore the risk factors and develop a predictive model for postpartum hemorrhage in twin pregnancies. Methods All patients who gave birth at Ningbo Women and Children’s Hospital from January 2018 to August 2022 were recruited. Patients were randomly allocated to a training coh...

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Main Authors: Sangsang Qi, Xianhu Fu
Format: Article
Language:English
Published: BMC 2023-09-01
Series:BMC Pregnancy and Childbirth
Subjects:
Online Access:https://doi.org/10.1186/s12884-023-05933-7
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author Sangsang Qi
Xianhu Fu
author_facet Sangsang Qi
Xianhu Fu
author_sort Sangsang Qi
collection DOAJ
description Abstract Objective To explore the risk factors and develop a predictive model for postpartum hemorrhage in twin pregnancies. Methods All patients who gave birth at Ningbo Women and Children’s Hospital from January 2018 to August 2022 were recruited. Patients were randomly allocated to a training cohort (n $$=$$ = 1395) validation cohort (n $$=$$ = 650) at a 7:3 ratio. In the training cohort, LASSO regression for screening variables and multifactorial logistic regression analysis were performed to identify independent risk factors for postpartum hemorrhage in twin pregnancies. A nomogram was established based on the results of multiple logistic regression analysis. Nomogram performance was quantified using the receiver operating characteristic curve, Hosmer- Lemeshow test and decision curve analysis. Results A total of 2045 patients were included in this study. Multifactorial Logistic regression analysis showed maternal age, assisted reproduction, platelet count, fibrinogen level, albumin level, hypertensive disorders of pregnancy, placenta praevia, number of previous cesarean deliveries, number of previous intrauterine manipulation, and neonatal weight were independent risk factors for postpartum hemorrhage in twin births. The area under curve (AUC) for the training cohort was 0.810 [95 $$\%$$ % CI (0.781, 0.839)], with a sensitivity of 76.5 $$\%$$ % , specificity of 71.0 $$\%$$ % , and positive and negative predictive values of 0.358 and 0.935, respectively, while the AUC for the validation cohort was 0.821 [95 $$\%$$ % CI (0.781, 0.860)], with a sensitivity of 80.9 $$\%$$ % , specificity of 69.49 $$\%$$ % , and positive predictive value and negative predictive value of 0.426 and 0.929, respectively. Conclusion The predictive model can effectively and quantitatively assess the risk of postpartum hemorrhage in twin pregnancies and help clinicians to take personalized preventive measures.
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spelling doaj.art-112f2a87b1a54982b350c1183582e5732023-11-26T14:31:11ZengBMCBMC Pregnancy and Childbirth1471-23932023-09-012311810.1186/s12884-023-05933-7Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective studySangsang Qi0Xianhu Fu1Department of Obstetrics and Gynecology, Ningbo Women and Children’s HospitalDepartment of Obstetrics and Gynecology, Ningbo Women and Children’s HospitalAbstract Objective To explore the risk factors and develop a predictive model for postpartum hemorrhage in twin pregnancies. Methods All patients who gave birth at Ningbo Women and Children’s Hospital from January 2018 to August 2022 were recruited. Patients were randomly allocated to a training cohort (n $$=$$ = 1395) validation cohort (n $$=$$ = 650) at a 7:3 ratio. In the training cohort, LASSO regression for screening variables and multifactorial logistic regression analysis were performed to identify independent risk factors for postpartum hemorrhage in twin pregnancies. A nomogram was established based on the results of multiple logistic regression analysis. Nomogram performance was quantified using the receiver operating characteristic curve, Hosmer- Lemeshow test and decision curve analysis. Results A total of 2045 patients were included in this study. Multifactorial Logistic regression analysis showed maternal age, assisted reproduction, platelet count, fibrinogen level, albumin level, hypertensive disorders of pregnancy, placenta praevia, number of previous cesarean deliveries, number of previous intrauterine manipulation, and neonatal weight were independent risk factors for postpartum hemorrhage in twin births. The area under curve (AUC) for the training cohort was 0.810 [95 $$\%$$ % CI (0.781, 0.839)], with a sensitivity of 76.5 $$\%$$ % , specificity of 71.0 $$\%$$ % , and positive and negative predictive values of 0.358 and 0.935, respectively, while the AUC for the validation cohort was 0.821 [95 $$\%$$ % CI (0.781, 0.860)], with a sensitivity of 80.9 $$\%$$ % , specificity of 69.49 $$\%$$ % , and positive predictive value and negative predictive value of 0.426 and 0.929, respectively. Conclusion The predictive model can effectively and quantitatively assess the risk of postpartum hemorrhage in twin pregnancies and help clinicians to take personalized preventive measures.https://doi.org/10.1186/s12884-023-05933-7TwinsPostpartum hemorrhagePrediction model
spellingShingle Sangsang Qi
Xianhu Fu
Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective study
BMC Pregnancy and Childbirth
Twins
Postpartum hemorrhage
Prediction model
title Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective study
title_full Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective study
title_fullStr Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective study
title_full_unstemmed Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective study
title_short Establishment of a predictive model for postpartum hemorrhage in twins: a retrospective study
title_sort establishment of a predictive model for postpartum hemorrhage in twins a retrospective study
topic Twins
Postpartum hemorrhage
Prediction model
url https://doi.org/10.1186/s12884-023-05933-7
work_keys_str_mv AT sangsangqi establishmentofapredictivemodelforpostpartumhemorrhageintwinsaretrospectivestudy
AT xianhufu establishmentofapredictivemodelforpostpartumhemorrhageintwinsaretrospectivestudy