Ana Rubio-Álvarez1, Milagros Molina-Alarcón2, Ángel Arias-Arias3, Antonio Hernández-Martínez4. 1. "Mancha Centro" Hospital, Av. Constitución, 3, 13600, Alcázar de San Juan, Ciudad Real, Spain. Electronic address: a.rubio.alvarez@hotmail.es. 2. University of Castilla-La Mancha, Facultad de Enfermería, Edificio Benjamín Palencia, Campus Universitario, s/n. 02071, Albacete, Spain. Electronic address: milagros.molina@uclm.es. 3. "Mancha Centro" Hospital, Av. Constitución, 3, 13600. Alcázar de San Juan, Ciudad Real, Spain. Electronic address: aariasa@sescam.jccm.es. 4. "Mancha-Centro" Midwifery Teaching Unit, Av. Constitución, 3, 13600. Alcázar de San Juan, Ciudad Real, Spain. Electronic address: antomatron@gmail.com.
Abstract
BACKGROUND: postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. Despite the use of uterotonics agents as preventive measure, it remains a challenge to identify those women who are at increased risk of postpartum bleeding. OBJECTIVE: to develop and to validate a predictive model to assess the risk of excessive bleeding in women with vaginal birth. DESIGN: retrospective cohorts study. SETTING: "Mancha-Centro Hospital" (Spain). PARTICIPANTS: the elaboration of the predictive model was based on a derivation cohort consisting of 2336 women between 2009 and 2011. For validation purposes, a prospective cohort of 953 women between 2013 and 2014 were employed. Women with antenatal fetal demise, multiple pregnancies and gestations under 35 weeks were excluded METHODS: we used a multivariate analysis with binary logistic regression, Ridge Regression and areas under the Receiver Operating Characteristic curves to determine the predictive ability of the proposed model. RESULTS: there was 197 (8.43%) women with excessive bleeding in the derivation cohort and 63 (6.61%) women in the validation cohort. Predictive factors in the final model were: maternal age, primiparity, duration of the first and second stages of labour, neonatal birth weight and antepartum haemoglobin levels. Accordingly, the predictive ability of this model in the derivation cohort was 0.90 (95% CI: 0.85-0.93), while it remained 0.83 (95% CI: 0.74-0.92) in the validation cohort. CONCLUSIONS: this predictive model is proved to have an excellent predictive ability in the derivation cohort, and its validation in a latter population equally shows a good ability for prediction. This model can be employed to identify women with a higher risk of postpartum haemorrhage.
BACKGROUND: postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. Despite the use of uterotonics agents as preventive measure, it remains a challenge to identify those women who are at increased risk of postpartum bleeding. OBJECTIVE: to develop and to validate a predictive model to assess the risk of excessive bleeding in women with vaginal birth. DESIGN: retrospective cohorts study. SETTING: "Mancha-Centro Hospital" (Spain). PARTICIPANTS: the elaboration of the predictive model was based on a derivation cohort consisting of 2336 women between 2009 and 2011. For validation purposes, a prospective cohort of 953 women between 2013 and 2014 were employed. Women with antenatal fetal demise, multiple pregnancies and gestations under 35 weeks were excluded METHODS: we used a multivariate analysis with binary logistic regression, Ridge Regression and areas under the Receiver Operating Characteristic curves to determine the predictive ability of the proposed model. RESULTS: there was 197 (8.43%) women with excessive bleeding in the derivation cohort and 63 (6.61%) women in the validation cohort. Predictive factors in the final model were: maternal age, primiparity, duration of the first and second stages of labour, neonatal birth weight and antepartum haemoglobin levels. Accordingly, the predictive ability of this model in the derivation cohort was 0.90 (95% CI: 0.85-0.93), while it remained 0.83 (95% CI: 0.74-0.92) in the validation cohort. CONCLUSIONS: this predictive model is proved to have an excellent predictive ability in the derivation cohort, and its validation in a latter population equally shows a good ability for prediction. This model can be employed to identify women with a higher risk of postpartum haemorrhage.
Authors: Jill M Westcott; Francine Hughes; Wenke Liu; Mark Grivainis; Iffath Hoskins; David Fenyo Journal: J Med Internet Res Date: 2022-07-18 Impact factor: 7.076