Cesarean Section Avoidance based on Obstetric Hemorrhagic Risk: A Decision Support System Juliano S. Gaspar, Marcelo R. S. Junior, Regina A. L. P. Lopes and Zilma S. N. Reis Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte, Brazil Keywords: Obstetric Hemorrhage, Robson Classification, Cesarean Section, Decision Support System. Abstract: Introduction: The junction of postpartum hemorrhage (PPH) and cesarean section (C-section) is a potential burden to take into account as a strategy to avoid unnecessary, and dangerous interventions. Despite most of the maternal death could have been prevented, rates are unacceptably high. According to the WHO, the rates of C-section are above recommended. The hypertension and PPH are the leading causes of maternal death worldwide. Aim: This study propose to analyze the association between C-section and PPH in a electronic health record (EHR) database and subsequently implementing an algorithm to assist health professionals in the avoidance of unnecessary C-section based on the estimation of obstetric hemorrhagic risk. Methods: Statistical analysis was performed using SISMater® database within 9,412 records about admissions to childbirth. The C-section rates associated with the occurrence of obstetric hemorrhage reported in the EHR was used to analysis. To implement the algorithm, the WHO and American College of Obstetricians and Gynecologists (ACOG) recommendations were used. The decision rules were developed to estimate the hemorrhagic risk score within the 10 groups proposed by the Robson classification. Discussion: It's expected that the system will help to reduce unnecessary C-section rates and prevent PPH, providing better conditions of prognosis for mother and her newborn. 1 INTRODUCTION Rising cesarean deliveries is a worrisome reality in the world. Many women worldwide are delivering by cesarean section (C-section) without a clear medical indication (WHO, 2009). Compared with vaginal birth, delivery a child by C-section is independently associated with additional risk of maternal morbidity and mortality, even by elective surgery (Villar, 2006). Last delivery by C-section increases risk of severe maternal morbidity regardless the mode of birth in the current pregnancy, among them postpartum hemorrhage (Villar, 2006). In accordance with the United Nations' Sustainable Development Goals (SDG) agreed in 2015, the reduction of unnecessary C-sections is supported by 3rd goal, good healthy and well-being. The goal 3 is to ensure healthy lives and promoting the well-being for all at all ages is essential to sustainable development (UN, 2018). Among the actions, the recommendation of the use of quality standards in obstetric care has been proposed as it may improve maternal and child health. The monitoring of proportion of women undergoing C- section in the health facility according to Robson classification groups is part of the best practices in obstetrician (WHO, 2016). This classification groups pregnant women based on their obstetric characteristics, thus provide the systematic analysis of C-section rates and comparing similar profile institutions (WHO, 2015). The data collection process and C-section rates analysis by clusters helps institutions to evaluate the medical indicated reasons for C-sections and propose actions to avoid unnecessary surgeries (WHO, 2015). The model proposed by Robson classify all women admitted for delivery in ten homogeneous groups, based on distinct characteristics of each individual woman and her gestation instead of focusing on the indication of the operative birth, and takes into account: single or multiple gestation; parity and presence of previous C-Section; presentation; form of onset or C-Section before labor and gestational age at birth (Robson, 2001). In its turn, hemorrhagic complications in pregnancy are associated with severe maternal morbidity, as well as being one of the frequent Gaspar, J., S. Junior, M., Lopes, R. and Reis, Z. Cesarean Section Avoidance based on Obstetric Hemorrhagic Risk: A Decision Support System. DOI: 10.5220/0007373802810285 In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2019), pages 281-285 ISBN: 978-989-758-353-7 Copyright c  2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 281