Risk Indicators for Eclampsia in Gestational Hypertension or Mild Preeclampsia at Term (original) (raw)
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Systematic review of prediction models for gestational hypertension and preeclampsia
PLOS ONE
Introduction Prediction models for gestational hypertension and preeclampsia have been developed with data and assumptions from developed countries. Their suitability and application for low resource settings have not been tested. This review aimed to identify and assess the methodological quality of prediction models for gestational hypertension and pre-eclampsia with reference to their application in low resource settings. Methods Using combinations of keywords for gestational hypertension, preeclampsia and prediction models seven databases were searched to identify prediction models developed with maternal data obtained before 20 weeks of pregnancy and including at least three predictors (Prospero registration CRD 42017078786). Prediction model characteristics and performance measures were extracted using the CHARMS, STROBE and TRIPOD checklists. The National Institute of Health quality assessment tools for observational cohort and crosssectional studies were used for study quality appraisal. Results We retrieved 8,309 articles out of which 40 articles were eligible for review. Seventy-seven percent of all the prediction models combined biomarkers with maternal clinical characteristics. Biomarkers used as predictors in most models were pregnancy associated plasma protein-A (PAPP-A) and placental growth factor (PlGF). Only five studies were conducted in a low-and middle income country.
Validation of a Predictive Model of Pre-eclampsia from a Cohort Study in Pregnant Women
The Open Biomarkers Journal, 2021
Background & Aims: To validate a predictive model of pre-eclampsia for classifying pregnant women into pre-eclamptic and healthy groups. Materials and Methods: A cohort study was carried out in a total of 132 pregnant women, including biochemical and clinical parameters for assessing the classification performed by a predictive model of pre-eclampsia from a 10-fold cross-validation method and the experts’ criteria. Results: A highly predictive value was obtained from the set of biochemical parameters included in the proposed model. Wilks’ Lambda, eigenvalues, canonical correlation, and distances between centroids of the groups point to the high classificatory power of the discriminant function. Risk indexes, computed from the centroids, provided a measure of different risk levels for this condition. The analysis of these indexes in a prospective study allowed assessing the effect of the parameters. The new ten models obtained from a 10-fold cross-validation achieved a 100% of correc...
Journal of perinatal medicine, 2017
To propose a simple model for predicting preeclampsia (PE) in the 1st trimester of pregnancy on the basis of maternal characteristics (MC) and mean arterial pressure (MAP). A prospective cohort was performed to predict PE between 11 and 13+6 weeks of gestation. The MC evaluated were maternal age, skin color, parity, previous PE, smoking, family history of PE, hypertension, diabetes mellitus and body mass index (BMI). Mean arterial blood pressure (MAP) was measured at the time of the 1st trimester ultrasound. The outcome measures were the incidences of total PE, preterm PE (delivery <37 weeks) and term PE (delivery ≥37 weeks). We performed logistic regression analysis to determine which factors made significant contributions for the prediction of the three outcomes. We analyzed 733 pregnant women; 55 developed PE, 21 of those developed preterm PE and 34 term PE. For total PE, the best model was MC+MAP, which had an area under the receiver operating characteristic curve (AUC ROC) o...
American Journal of Obstetrics and Gynecology, 2021
BACKGROUND: The current approach to predict preeclampsia combines maternal risk factors and evidence from biophysical markers (mean arterial pressure, Doppler velocimetry of the uterine arteries) and maternal blood proteins (placental growth factor, soluble vascular endothelial growth factor receptor-1, pregnancy-associated plasma protein A). Such models require the transformation of biomarker data into multiples of the mean values by using population-and site-specific models. Previous studies have focused on a narrow window in gestation and have not included the maternal blood concentration of soluble endoglin, an important antiangiogenic factor up-regulated in preeclampsia. OBJECTIVE: This study aimed (1) to develop models for the calculation of multiples of the mean values for mean arterial pressure and biochemical markers; (2) to build and assess the predictive models for preeclampsia based on maternal risk factors, the biophysical (mean arterial pressure) and biochemical (placental growth factor, soluble vascular endothelial growth factor receptor-1, and soluble endoglin) markers collected throughout pregnancy; and (3) to evaluate how prediction accuracy is affected by the presence of chronic hypertension and gestational age. STUDY DESIGN: This longitudinal case-cohort study included 1150 pregnant women: women without preeclampsia with (n¼49) and without chronic hypertension (n¼871) and those who developed preeclampsia (n¼166) or superimposed preeclampsia (n¼64). Mean arterial pressure and immunoassay-based maternal plasma placental growth factor, soluble vascular endothelial growth factor receptor-1, and soluble endoglin concentrations were available throughout pregnancy (median of 5 observations per patient). A prior-risk model for preeclampsia was established by using Poisson regression based on maternal characteristics and obstetrical history. Next, multiple regression was used to fit biophysical and biochemical marker data as a function of maternal characteristics by using data collected at 8 to 15 þ6 , 16 to 19 þ6 , 20 to 23 þ6 , 24 to 27 þ6 , 28 to 31 þ6 , and 32 to 36 þ6 week intervals, and observed values were converted into multiples of the mean values. Then, multivariable prediction models for preeclampsia were fit based on the biomarker multiples of the mean data and prior-risk estimates. Separate models were derived for overall, preterm, and term preeclampsia, which were evaluated by receiver operating characteristic curves and sensitivity at fixed false-positive rates. RESULTS: (1) The inclusion of soluble endoglin in prediction models for all preeclampsia, together with the prior-risk estimates, mean arterial pressure, placental growth factor, and soluble vascular endothelial growth factor receptor-1, increased the sensitivity (at a fixed false-positive rate of 10%) for early prediction of superimposed preeclampsia, with the largest increase (from 44% to 54%) noted at 20 to 23 þ6 weeks (McNemar test, P<.05); (2) combined evidence from prior-risk estimates and biomarkers predicted preterm preeclampsia with a sensitivity (false-positive rate, 10%) of 55%, 48%, 62%, 72%, and 84% at 8 to 15 þ6 , 16 to 19 þ6 , 20 to 23 þ6 , 24 to 27 þ6 , and 28 to 31 þ6 week intervals, respectively; (3) the sensitivity for term preeclampsia (false-positive rate, 10%) was 36%, 36%, 41%, 43%, 39%, and 51% at 8 to 15 þ6 , 16 to 19 þ6 , 20 to 23 þ6 , 24 to 27 þ6 , 28 to 31 þ6 , and 32 to 36 þ6 week intervals, respectively; (4) the detection rate for superimposed preeclampsia among women with chronic hypertension was similar to that in women without chronic hypertension, especially earlier in pregnancy, reaching at most 54% at 20 to 23 þ6 weeks (false-positive rate, 10%); and (5) prediction models performed comparably to the Fetal Medicine Foundation calculators when the same maternal risk factors and biomarkers (mean arterial pressure, placental growth factor, and soluble vascular endothelial growth factor receptor-1 multiples of the mean values) were used as input. CONCLUSION: We introduced prediction models for preeclampsia throughout pregnancy. These models can be useful to identify women at risk during the first trimester who could benefit from aspirin treatment or later in pregnancy to inform patient management. Relative to prediction performance at 8 to 15 þ6 weeks, there was a substantial improvement in the detection rate for preterm and term preeclampsia by using data collected after 20 and 32 weeks' gestation, respectively. The inclusion of plasma soluble endoglin improves the early prediction of superimposed preeclampsia, which may be valuable when Doppler velocimetry of the uterine arteries is not available.
Prospective evaluation of the risk of pre-eclampsia using logistic regression analysis
Ultrasound in Obstetrics and Gynecology, 2007
Objectives To calculate the risk of developing preeclampsia (PET) in a consecutive series of low-risk women at 18-24 weeks' gestation, using recently published logistic regression models. Methods This was a prospective study, with complete follow-up, in a consecutive series of unselected low-risk singleton pregnancies. Uterine artery pulsatility index as well as a combination of maternal factors were recorded at 18-24 weeks' gestation. The distribution of the estimated risks for the 16 PET patients was compared with that obtained for 136 women who had a normal pregnancy, as assessed by routine testing. A receiver-operating characteristics (ROC) curve was plotted to evaluate the detection rate at fixed false-positive rates (FPRs) of 5%, 10% and 20% and the corresponding odds cutoffs .
Pregnancy hypertension, 2012
Early recognition of preeclampsia (PE) is crucial for better obstetric care. Clinical risk factors are easier to identify than biochemical markers and may be useful in the prediction of PE. To evaluate which risk factors provide the best prediction for PE in a group at high-risk for developing PE. A prospective cohort study of 100 pregnant women was performed. During the first trimester we included pregnant women who had at least one of the following risk factors for PE: previous history of PE, previous history of HELLP, pre-existing hypertension, diabetes mellitus, multiple pregnancy, obesity or autoimmune diseases. These women were monitored during their pregnancy and their medical data were used to set up a database. We focused on baseline characteristics (parity, maternal age, maternal smoking, ethnic origin, blood pressure at booking, risk factors mentioned above and medication use). Differences between groups were analysed using the Student's t test or the Mann-Whitney U t...
Prediction of pre-eclampsia: review of reviews
Ultrasound in Obstetrics & Gynecology
We included reviews that assessed clinical characteristics, biochemical or ultrasound based variables as predictors or predictive tests for pre-eclampsia. We included reviews evaluating predictors in the first, second or third trimester. Case reports, case series, individual observational or randomised studies, narrative reviews, rapid reviews, editorials and poster abstracts were excluded. Two reviewers (RT, AK) independently extracted relevant data. We obtained data on year of publication, number of databases searched, number of studies included, number of pregnancies/women included, screening tests evaluated and the performance of the tests or degree of association reported with the predictors evaluated. Definitions We accepted the authors' definition of pre-eclampsia and hypertensive disorders, and further collected data where it was reported discriminating between early onset pre-eclampsia (requiring delivery prior to 34 weeks' gestation), late onset (delivery after 34 weeks' gestation) or delivery at any time. Clinical characteristics included signs, symptoms, past medical and obstetric history and environmental exposures elicited through maternal history or physical examination by the booking clinician at the first antenatal visit. Biochemical tests included any measurement of molecules in biological fluids (eg serum and urine). Ultrasound tests included any characteristic identified on ultrasound examination of the pregnancy at any gestation.
Comparison of risk factors and outcomes of gestational hypertension and pre-eclampsia
PloS one, 2017
It remains an enigma whether gestational hypertension (GH) and pre-eclampsia (PE) are distinct entities or different spectrum of the same disease. We aimed to compare the risk factors and outcomes between GH and PE. A total of 7,633 pregnant women recruited between 12 and 20 weeks of gestation in the Ottawa and Kingston Birth Cohort from 2002 to 2009 were included in the analysis. Cox proportional hazards model was used to identify and compare the risk factors for GH and PE by treating gestational age at delivery as the survival time. Logistic regression model was used to compare outcome. Subgroup analysis was performed for early- and late-onset PE. GH and PE shared most risk factors including overweight and obesity, nulliparity, PE history, type 1 and 2 diabetes, and twin birth. Effect size of PE history (RR = 14.1 for GH vs. RR = 6.4 for PE) and twin birth (RR = 4.8 for GH vs. RR = 10.3 for PE) showed substantial difference. Risk factors modified gestational age at delivery in pat...