Study for Improving Maternal, Pregnancy and Child Outcomes

Summary

Participation Requirements

Description

Preeclampsia is a pregnancy-specific syndrome that affects 3-5% of all pregnancies and traditionally defined as new onset hypertension (blood pressure ? 140/90) and proteinuria after gestational week 20. The syndrome is one of the leading causes of maternal and perinatal acute morbidity and long-ter...

Preeclampsia is a pregnancy-specific syndrome that affects 3-5% of all pregnancies and traditionally defined as new onset hypertension (blood pressure ? 140/90) and proteinuria after gestational week 20. The syndrome is one of the leading causes of maternal and perinatal acute morbidity and long-term disability and accounts for about 50 000 maternal deaths annually worldwide. Morbidity risks for the mother include seizures, intracranial hemorrhage, kidney failure, heat failure and pulmonary edema. Risks for the fetus include fetal growth restriction preterm birth and hypoxia. Generally preterm preeclampsia (<37 weeks) is more severe than term preeclampsia. Risk assessment for preeclampsia enables both prevention and early prediction of the disease. Swedish risk assessment for preeclampsia in early pregnancy is still obtained by maternal history and characteristics, without medical examinations, which only detects about 30% of women that will develop preeclampsia. Risk factors are evaluated individually without being incorporated into a combined model that would allow multivariable analysis. This approach has been proven to be poor due to low specificity and sensitivity. Lately a more complex prediction model has been developed by the Fetal Medicine Foundation, using multivariable analysis and including serum biomarkers and physiological measurements reflecting maternal adaption to pregnancy. Intervention with aspirin given to identified high-risk pregnancies according this model has been shown to decrease the incidence of preterm (< 37 gestational weeks) preeclampsia (OR: 0.38; 95% CI 0.20-0.74), compared to placebo. Detection rates and cut-off values have been shown to vary between populations, depending on differences in population characteristics and incidence of disease, overfitting of the original model and differences in healthcare systems. Therefore, the model needs to be validated in Sweden. Further, the Fetal Medicine Foundation prediction model includes expensive covariates such as several biochemical markers and uterine artery Doppler. There is a need to create, validate and implement a cost-effective prediction model for first trimester screening for preeclampsia in a Swedish population, with the purpose to select who might benefit from aspirin prophylaxis to prevent preterm preeclampsia. Early detection of preeclampsia remains one of the major focuses of maternal health care and is emphasized by the WHO, since it has proven to be beneficial for both the mother and unborn child. Small-for-gestational-age fetuses not identified before delivery have an increased risk of adverse perinatal outcomes, compared to those identified during pregnancy. Identification of high-risk pregnancies is therefore important in early pregnancy not only to plan for prophylactic interventions, but also to optimize surveillance and to plan deliveries. Today most Swedish women attend the same maternal health care program with increasing number of visits in the end of pregnancy. By risk identification in early pregnancy we can individualize maternal health care and target women at high risk early in pregnancy. High-risk pregnancies can be referred to specialized health care and normal pregnancies followed at the basic maternal health care. The Swedish registry data is unique and combining it with a biobank containing blood samples from the first trimester could improve maternal healthcare and in the long run reduce adverse outcomes for Swedish women. A national first trimester pregnancy biobank would facilitate future research on prevention and prediction of pregnancy complications. 10000 inrolled indiviuals will be needed for creating the model and another 10000 for validation.

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