Abstract

Over the last two decades, there has been a steady rise in severe maternal morbidity and pregnancy-related deaths in Canada and the USA. The Modified Early Obstetric Warning System (MEOWS) is a risk stratification tool designed to predict severe maternal morbidity and mortality and has been validated for use in obstetrical wards. The objective of this study was to determine if MEOWS could accurately identify patients at risk of severe maternal morbidity and mortality in the ED setting. This was a chart review of patients presenting to an academic tertiary care centre (annual ED census 65,000) with a postpartum complaint within 6 weeks of delivery between May 2020 and March 2022. The exposure was the presence of a trigger, defined as one severely abnormal (red) or two mildly abnormal (yellow) physiological measurements during the ED visit. The diagnostic accuracy of the tool to identify patients at risk of severe maternal morbidity severe maternal morbidity or mortality was estimated by calculating the sensitivity, specificity, positive predictive value and negative predictive value. Two hundred and sixty-seven patients were included, of which 21 (7.9%) met the criteria for severe maternal morbidity. There were no maternal deaths. Overall, the sensitivity of the MEOWS tool was 85.7% (95% CI 63.7-97.0%), specificity was 67.9% (95% CI 61.7-73.7%), positive predictive value was 18.6% (95% CI 15.1-22.7%), and negative predictive value was 98.2% (95% CI 95.1-99.4%). Severe preeclampsia and eclampsia were the most common outcome category of severe maternal morbidity, representing 16 (76.2%) cases. Most patients (86.5%) were discharged directly from the ED, and 90.0% did not return within 30days. This study is the first to explore the utility of MEOWS in postpartum ED patients. The presence of a MEOWS trigger at ED triage or the first ED nursing assessment was associated with severe maternal morbidity. Thus, MEOWS may be a valuable tool to identify postpartum patients at risk of severe outcomes early in an ED visit.

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