Journal of Gynecology and Reproductive Health

  • ISSN: 2574-2728

Obstetric Near-Miss Cases-Data from The First Nine Years of a New Portuguese Hospital

Abstract

Inês Ferreira Jorge, Carolina Dinis Nunes Mendonça Rodrigues, Leonor Andrade Faria Aboim, Elsa Maria De Jesus Ferreira Dias Villaverde Gonçalves, Carlos Manuel Alves Mendonça Veríssimo Batista

Aim: The concept of monitoring "near-miss" events or severe maternal morbidity has been implemented to gather essential insights into the quality of obstetric care. Our aim is to determine and analyze the maternal near-miss cases among women admitted to the intensive/intermediate care unit at our institution and determine the maternal near-miss to mortality ratio. Methods: This is a retrospective observational non-interventional study. An audit was made of pregnant women or women within 42 days after the termination of pregnancy that were admitted to the intensive/intermediate care unit at our institution, between January 2012 and December 2020. A near-miss case was defined according to organ dysfunctionbased criteria, which include the clinical, laboratory, and management-based criteria laid down by WHO 2009. A descriptive analysis of the results was conducted. Maternal near-miss cases were classified based on their primary underlying cause. Maternal mortality during the same period was also analyzed. Results: During the study period, there were a total of 128 women admitted to the intensive/intermediate care unit. There were a total of 68 near-misses and two maternal deaths. The near-miss to mortality ratio was 34:1. Among the underlying causes of near-miss events, obstetric hemorrhage (mostly post-partum hemorrhage) and hypertensive disorders were the leading causes. They were followed by medical/surgical/mental disease or complication, other obstetric disease or complication, coincidental conditions and pregnancy-related infection. Conclusion: Hemorrhage and hypertensive disorders were the leading causes of near-miss events. Identifying near-miss cases would improve data quality and enable comparisons across institutions and countries.

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