ArticleBelitung nursing journal2024
Lower accuracy of estimated delivery date using last menstrual period recall and related factors of adverse events during childbirth: A sub-analysis of a birth cohort study in Makassar, Indonesia.
Article in Belitung nursing journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Early pregnancy ultrasound provides a more accurate estimate of the estimated delivery date (EDD) than the last menstrual period (LMP). However, LMP-based EDD calculation remains common in resource-limited settings. Inaccurate EDDs can contribute to adverse birth outcomes. However, research examining the accuracy of LMP-based EDDs and their association with delivery-related adverse events is limited. Objective: This study aimed to evaluate the accuracy of self-reported LMP in predicting EDD and its association with adverse events during delivery. Methods: Data were analyzed from 171 pregnant women participating in the Indonesian Birth Cohort Study based in Makassar, Indonesia. This study utilized a prospective cohort design, employing descriptive statistics and Poisson regression analysis in Stata. Adverse events were defined as any unexpected negative occurrence during delivery, including issues related to healthcare access, birth attendance, and the medical condition of the mother and newborn. Results: Mode of delivery and categorized differences between actual delivery date and estimated delivery date (ADD-EDD) were significantly associated with adverse events during delivery. Cesarean delivery was associated with a higher risk of adverse events compared to vaginal birth (Adjusted Risk Ratio [ARR] 1.11, 95% CI: 1.02-1.22, Conclusion: Lower accuracy of LMP-based EDDs and delivery mode is associated with increased adverse events during delivery. Transitioning from LMP-based EDDs to more accurate methods is necessary to improve maternal and neonatal care outcomes. Integrating routine early ultrasound for EDD calculation can better equip healthcare providers and nurses to plan deliveries, reduce risks, and improve patient outcomes in resource-limited settings.
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