Characteristics of preeclampsia- and eclampsia-related maternal death in rural Haiti.
OBJECTIVE: The maternal mortality ratio in Haiti remains one of the highest in the world at 600/100 000 live births. Preeclampsia- and eclampsia-related complications are one of the leading causes of maternal death. In this resource-limited setting, effective, efficient hospital-based interventions are necessary to reduce this risk. Our objective was to assess the utility of common laboratory and clinical admission data for the determination of preeclampsia- and eclampsia-related maternal death. STUDY DESIGN: We performed an analysis of women presenting to the Hôpital Albert Schweitzer with preeclampsia and eclampsia during a 3-year period. Factors analyzed were: maternal age, parity, gestational age, hematocrit, serum creatinine, urine protein, systolic and diastolic blood pressure, intrauterine fetal death (IUFD), coma on arrival, and address (residence within or outside hospital catchment area). Stepwise logistic regression identified factors predictive of maternal mortality. RESULTS: Preeclampsia/eclampsia affected 423 of 2295 deliveries (18%) and resulted in 19 deaths. Multivariate analysis identified the following predictors of maternal mortality: IUFD (RR 7.57; 95% CI 2.76-12.69), eclampsia (RR 6.91; 95% CI 2.08-12.64), and oliguria (RR 5.39; 95% CI 1.80-10.69). CONCLUSION: In this setting, traditional admission laboratory and clinical tests were not useful in maternal mortality prediction. The analysis highlights clinical characteristics of women at highest risk for maternal death.
Duke Scholars
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Related Subject Headings
- Rural Population
- Risk
- Pregnancy
- Pre-Eclampsia
- Oliguria
- Obstetrics & Reproductive Medicine
- Multivariate Analysis
- Maternal Mortality
- Logistic Models
- Humans
Citation
Published In
DOI
ISSN
Publication Date
Volume
Issue
Start / End Page
Location
Related Subject Headings
- Rural Population
- Risk
- Pregnancy
- Pre-Eclampsia
- Oliguria
- Obstetrics & Reproductive Medicine
- Multivariate Analysis
- Maternal Mortality
- Logistic Models
- Humans