Article ; Online: Implementation of a maternal early warning system during early postpartum. A prospective observational study.
PLoS ONE, Vol 16, Iss 6, p e
2021 Volume 0252446
Abstract: Objective To evaluate the implementation of a maternal early warning system (MEWS) for monitoring patients during the first two hours after delivery in a tertiary level hospital. Methods Implementation of the criteria between 15 March and 15 September ... ...
Abstract | Objective To evaluate the implementation of a maternal early warning system (MEWS) for monitoring patients during the first two hours after delivery in a tertiary level hospital. Methods Implementation of the criteria between 15 March and 15 September 2018 was evaluated in 1166 patients. The parameters collected were systolic and diastolic blood pressure, heart rate, oxygen saturation, urine output, uterine involution, and bleeding. Out-of-range values of any of these parameters triggered a warning, and an obstetrician was called to examine the patient. The obstetrician then decided whether to call the anesthesiologist. We carried out a sensitivity-specificity study of triggers and a multivariate analysis of the factors involved in developing potentially fatal disorders (PFD), reintervention, critical care admission, and stay. Results The MEWS was triggered in 75 patients (6.43%). Leading trigger was altered systolic blood pressure in 32 patients (42.7%), and 11 patients had a PFD. Twenty-eight triggers were false-negatives. Sensitivity and specificity of the system was 0.28 (0.15, 0.45) and 0.94 (0.93, 0.96), respectively. The multivariate analysis showed a correlation between trigger activation and PFD. Conclusion Our MEWS presented low sensitivity and high specificity, with a significant number of false-negatives. |
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Keywords | Medicine ; R ; Science ; Q |
Subject code | 610 |
Language | English |
Publishing date | 2021-01-01T00:00:00Z |
Publisher | Public Library of Science (PLoS) |
Document type | Article ; Online |
Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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