Authors:
Juliano S. Gaspar
;
Marcelo R. S. Junior
;
Regina A. L. P. Lopes
and
Zilma S. N. Reis
Affiliation:
Faculdade de Medicina da Universidade Federal de Minas Gerais, Belo Horizonte and Brazil
Keyword(s):
Obstetric Hemorrhage, Robson Classification, Cesarean Section, Decision Support System.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Symbolic Systems
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.
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