Funding outcome

An emergency caesarean section (eCS), a higher risk surgery, is done when the mother or baby health is at significant risk. Unfortunately, women who have had an eCS are more likely to have an infection or bleeding. Babies born by eCS are more likely to be admitted to the Neonatal Intensive Care Unit. Already developed tools to predict a woman's chance of needing an eCS use information from when the woman is in active labour. As a result, these tools cannot be used to predict and potentially prevent an eCS. We recently developed eCS prediction tool that does not use active labour information. The tool is able to identify women at risk for an eCS with similar accuracy to the prior tools that needed active labour information. We need to test the tool in different groups of pregnant women before we can use the tool in clinical practice. To that end, we propose to test the tool among women delivering in Edmonton, Canada and Rio Branco, Brazil. In both locations, we will evaluate the tool among women delivering at university-affiliated and community-based hospitals. Implementation of the tool into practice will provide healthcare providers a chance to identify potential interventions to prevent an eCS in addition to assisting in operation room planning. Our team is well positioned to complete this research. Dr. Mandhane developed the eCS prediction tool. Dr. Chari is an obstetrician and Professor at the UofA. Dr. de Lima, an associate professor, is the coordinator of the obstetric nursing specialization at Federal University of Acre (UFAC). Dr. Mamede, a post-doctoral fellow with Dr. Mandhane, is a nurse midwife with over 20 years of clinical experience in Brazil.