Model In Screening For Pre-eclampsia Research Articles

Revised competing-risks model in screening for pre-eclampsia in twin pregnancy by maternal characteristics and medical history.

We have proposed previously that the competing-risks model for prediction of pre-eclampsia (PE) based on maternal characteristics and medical history (prior model), developed in singleton pregnancies, can be extended to risk assessment for twins; in dichorionic (DC) and monochorionic (MC) twin pregnancies with the same characteristics as in singleton pregnancies, the distribution of gestational age at delivery with PE was shifted to the left by 8 and 10 weeks, respectively. However, in a subsequent validation study, we found that, in both the training and validation datasets, the observed incidence of PE was lower than the predicted one and such overestimation of risk was particularly marked for early PE. First, to develop a new extension of the competing-risks prior model in screening for PE by maternal demographic characteristics and medical history in twin pregnancies in a training dataset. Second, to examine the predictive performance of this model in screening for PE with delivery < 34 weeks (early PE), < 37 weeks (preterm PE) and at any gestational age (all PE) in twins in a validation dataset. Third, to demonstrate the application of screening in a mixed population of singleton and twin pregnancies. The data for this study were obtained from two prospective non-intervention multicenter screening studies for PE in twin pregnancies at 11 + 0 to 13 + 6 weeks' gestation. The training and validation datasets consisted of 2219 and 2999 women, respectively. We used the training dataset to fit a model in which the effect of twins on shifting the distribution of gestational age at delivery with PE in singletons to the left should not be the same for all gestational ages but the shift should depend on the singleton prior mean; the effect increases with increasing prior mean. We examined the predictive performance of the model in the training and validation datasets using the area under the receiver-operating characteristics curve (AUC) and calibration plots. Data on 16 747 singleton pregnancies obtained from the Screening ProgRamme for prE-Eclampsia (SPREE) study were included to examine the performance of screening in a mixed population of singleton and twin pregnancies. Calibration plots and calibration intercept and slope demonstrate superior predictive performance of the new model in the validation dataset. Although the AUC for twin pregnancies is lower than in singleton pregnancies, performance of screening in a mixed population of singleton and twin pregnancies is superior to that in singletons (AUC of 0.790 in a mixed population comprising 2% twins and 98% singletons compared to 0.775 in singletons). For the risk cut-offs likely to be used in practice, all twin pregnancies screen positive using maternal characteristics and medical history. A new competing-risks model in screening for PE by maternal risk factors in twin pregnancy has been developed and, using this model, the predicted risks for early PE, preterm PE and all PE are in relatively good agreement with the observed incidence of the disease. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Validation of competing-risks model in screening for pre-eclampsia in twin pregnancy by maternal factors.

To examine the predictive performance of the competing-risks model in screening for pre-eclampsia (PE) by maternal demographic characteristics and medical history in twin pregnancy, in a training dataset used for development of the model and a validation dataset. The data for this study were derived from two prospective non-intervention multicenter screening studies for PE in twin pregnancies at 11 + 0 to 13 + 6 weeks' gestation. The first study of 2219 women, which was reported previously, was used to develop the competing-risks model for prediction of PE and is therefore considered to be the training set. The validation study comprised 2999 women. Patient-specific risks of delivery with PE at < 34 (early), < 37 (preterm) and < 41 + 3 (all) weeks' gestation were calculated using the competing-risks model and the performance of screening for PE in the training and validation datasets was assessed. We examined the predictive performance of the model by, first, its ability to discriminate between the PE and no-PE groups using the area under the receiver-operating characteristics curve (AUC) and, second, calibration, which assesses agreement between the predicted risk and observed incidence of PE. The incidence of early PE, preterm PE and all PE in the training and validation datasets was similar (1.8% vs 1.4%, 5.6% vs 5.6% and 7.7% vs 7.2%, respectively) and this was substantially higher than in our previous studies in singleton pregnancies. The training and validation datasets had similar AUCs for early PE (0.670 (95% CI, 0.593-0.747) vs 0.677 (95% CI, 0.594-0.760)), preterm PE (0.666 (95% CI, (0.617-0.715) vs 0.652 (95% CI, 0.609-0.694)) and all PE (0.656 (95% CI, 0.615-0.697) vs 0.644 (95% CI, 0.606-0.682)). Calibration plots of the predictive performance of the competing-risks model demonstrated that, in both the training and validation datasets, the observed incidence of PE was lower than the predicted one and such overestimation of risk was particularly marked for early PE. Discrimination and calibration of the competing-risks model for PE in a validation dataset are consistent with those in the training dataset. However, the model needs to be adjusted to correct the observed overestimation of risk for early PE. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Predictive performance of the competing risk model in screening for preeclampsia

The established method of screening for preeclampsia is to identify risk factors from maternal demographic characteristics and medical history; in the presence of such factors the patient is classified as high risk and in their absence as low risk. However, the performance of such an approach is poor. We developed a competing risks model, which allows combination of maternal factors (age, weight, height, race, parity, personal and family history of preeclampsia, chronic hypertension, diabetes mellitus, systemic lupus erythematosus or antiphospholipid syndrome, method of conception and interpregnancy interval), with biomarkers to estimate the individual patient-specific risks of preeclampsia requiring delivery before any specified gestation. The performance of this approach is by far superior to that of the risk scoring systems. The objective of the study was to examine the predictive performance of the competing risks model in screening for preeclampsia by a combination of maternal factors, mean arterial pressure, uterine artery pulsatility index, and serum placental growth factor, referred to as the triple test, in a training data set for the development of the model and 2 validation studies. The data for this study were derived from 3 previously reported prospective, nonintervention, multicenter screening studies for preeclampsia in singleton pregnancies at 11+0 to 13+6 weeks' gestation. In all 3 studies, there was recording of maternal factors and biomarkers and ascertainment of outcome by appropriately trained personnel. The first study of 35,948 women, which was carried out between February 2010 and July 2014, was used to develop the competing risks model for prediction of preeclampsia and is therefore considered to be the training set. The 2 validation studies were comprised of 8775 and 16,451 women, respectively, and they were carried out between February and September 2015 and between April and December 2016, respectively. Patient-specific risks of delivery with preeclampsia at <34, <37, and <41+3 weeks' gestation were calculated using the competing risks model and the performance of screening for preeclampsia by maternal factors alone and the triple test in each of the 3 data sets was assessed. We examined the predictive performance of the model by first, the ability of the model to discriminate between the preeclampsia and no-preeclampsia groups using the area under the receiver operating characteristic curve and the detection rate at fixed screen-positive rate of 10%, and second, calibration by measurements of calibration slope and calibration in the large. The detection rate at the screen-positive rate of 10% of early-preeclampsia, preterm-preeclampsia, and all-preeclampsia was about 90%, 75%, and 50%, respectively, and the results were consistent between the training and 2 validation data sets. The area under the receiver operating characteristic curve was >0.95, >0.90, and >0.80, respectively, demonstrating a very high discrimination between affected and unaffected pregnancies. Similarly, the calibration slopes were very close to 1.0, demonstrating a good agreement between the predicted risks and observed incidence of preeclampsia. In the prediction of early-preeclampsia and preterm-preeclampsia, the observed incidence in the training set and 1 of the validation data sets was consistent with the predicted one. In the other validation data set, which was specifically designed for evaluation of the model, the incidence was higher than predicted, presumably because of better ascertainment of outcome. The incidence of all-preeclampsia was lower than predicted in all 3 data sets because at term many pregnancies deliver for reasons other than preeclampsia, and therefore, pregnancies considered to be at high risk for preeclampsia that deliver for other reasons before they develop preeclampsia can be wrongly considered to be false positives. The competing risks model provides an effective and reproducible method for first-trimester prediction of early preeclampsia and preterm preeclampsia as long as the various components of screening are carried out by appropriately trained and audited practitioners. Early prediction of preterm preeclampsia is beneficial because treatment of the high-risk group with aspirin is highly effective in the prevention of the disease.

Competing-risks model in screening for pre-eclampsia by maternal factors and biomarkers at 35-37 weeks' gestation

To develop a model for prediction of term pre-eclampsia (PE) based on a combination of maternal factors and late third-trimester biomarkers. Data were derived from prospective screening for adverse obstetric outcomes in women attending their routine hospital visit at 35-37 weeks' gestation in two maternity hospitals in the UK. Uterine artery pulsatility index (UtA-PI) was measured in 5362 pregnancies, mean arterial pressure (MAP) in 5386 and serum placental growth factor (PlGF) and serum soluble fms-like tyrosine kinase-1 (sFlt-1) in 3920. Bayes' theorem was used to combine the a-priori risk of PE from maternal factors with various combinations of biomarkers, expressed as multiples of the median (MoM). Five-fold cross-validation was used to estimate the performance of screening for PE, requiring delivery at some stage after assessment. The empirical performance of screening was compared to model predictions. In pregnancies that developed PE, the values of MAP, UtA-PI and sFlt-1 were increased and PlGF was decreased compared to unaffected pregnancies. For all biomarkers evaluated, the deviation from normal was inversely related to the gestational age at which delivery became necessary for maternal or fetal indications. Screening by maternal factors and by a combination of maternal factors with all biomarkers predicted 35% and 84% of PE, respectively, at a 10% false-positive rate. A combination of maternal factors and biomarkers at 35-37 weeks' gestation can provide effective screening for term PE. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd.

Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 30–34 weeks’ gestation

Preeclampsia (PE) affects 2-3% of all pregnancies and is a major cause of maternal and perinatal morbidity and mortality. We have proposed a 2-stage strategy for the identification of pregnancies at high risk of developing PE. The objective of the first stage, at 11-13 weeks' gestation, is a reduction in the prevalence of the disease through pharmacological intervention in the high-risk group. The objective of the second stage, during the second and/or third trimesters, is to improve perinatal outcome through close monitoring of the high-risk group for earlier diagnosis of the clinical signs of the disease and selection of the appropriate, time, place, and method of delivery. The objective of the study was to examine the performance of screening for PE by a combination of maternal factors with early third-trimester biomarkers. This was a cohort study and data were derived from consecutive women with singleton pregnancies attending for their routine hospital visit at 30-34 weeks' gestation in 3 maternity hospitals in England between March 2011 and December 2014. In the first phase of the study, only uterine artery pulsatility index (UTPI) was measured and then measurement of mean arterial pressure (MAP) was added, and in the final phase, the serum concentration of placental growth factor (PLGF) was measured and then soluble fms-like tyrosine kinase-1 (SFLT) was added. We had data on UTPI, MAP, PLGF, and SFLT from 30,935, 29,042, 10,123, and 8,264 pregnancies, respectively. The Bayes theorem was used to combine the a priori risk from maternal factors with various combinations of biomarker multiple of the median values. Ten-fold cross-validation was used to estimate the performance of screening for PE requiring delivery at < 37 weeks' gestation (preterm-PE) and those delivering at ≥ 37 weeks (term-PE). The empirical performance was compared with model predictions. In pregnancies that developed PE, the values of MAP, UTPI, and SFLT were increased and PLGF was decreased. For all biomarkers the deviation from normal was greater for preterm-PE than term-PE, and therefore, the performance of screening was inversely related to the gestational age at which delivery become necessary for maternal and/or fetal indications. Combined screening by maternal factors, MAP, UTPI, PLGF, and SFLT predicted 98% (95% confidence interval, 88-100%) of preterm-PE and 49% (95% confidence interval, 42-57%) of term-PE, at a false-positive rate of 5%. These empirical detection rates are compatible with the respective model-based rates of 98% and 54%, but the latter were optimistically biased. Combination of maternal factors and biomarkers in the early third trimester could predict nearly all cases of preterm-PE and half of those with term-PE, at 5% false-positive rate.

Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 19–24 weeks’ gestation

Preeclampsia (PE) affects 2-3% of all pregnancies and is a major cause of maternal and perinatal morbidity and mortality. The traditional approach to screening for PE is to use a risk-scoring system based on maternal demographic characteristics and medical history (maternal factors), but the performance of such an approach is very poor. To develop a model for PE based on a combination of maternal factors with second-trimester biomarkers. The data for this study were derived from prospective screening for adverse obstetric outcomes in women attending their routine hospital visit at 19-24 weeks' gestation in 3 maternity hospitals in England between January 2006 and July 2014. We had data from maternal factors, uterine artery pulsatility index (UTPI), mean arterial pressure (MAP), serum placental growth factor (PLGF), and serum soluble fms-like tyrosine kinase-1 (SFLT) from 123,406, 67,605, 31,120, 10,828, and 8079 pregnancies, respectively. Bayes' theorem was used to combine the a priori risk from maternal factors with various combinations of biomarker multiple of the median (MoM) values. The modeled performance of screening for PE requiring delivery at <32, <37, and ≥37 weeks' gestation was estimated. The modeled performance was compared to the empirical one, which was derived from 5-fold cross validation. We also examined the performance of screening based on risk factors from the medical history, as recommended by the American Congress of Obstetricians and Gynecologists (ACOG). In pregnancies that developed PE, the values of MAP, UTPI, and SFLT were increased and PLGF was decreased. For all biomarkers the deviation from normal was greater for early than for late PE, and therefore the performance of screening was inversely related to the gestational age at which delivery became necessary for maternal and/or fetal indications. Screening by maternal factors predicted 52%, 47%, and 37% of PE at <32, <37, and ≥37 weeks' gestation, respectively, at a false-positive rate of 10%. The respective values for combined screening with maternal factors and MAP, UTPI, and PLGF were 99%, 85%, and 46%; the performance was not improved by the addition of SFLT. In our population of 123,406 pregnancies, the DR of PE at <32, <37, and ≥37 weeks with the ACOG recommendations was 91%, 90%, and 91%, respectively, but at a screen positive rate of 67%. The performance of screening for PE by maternal factors and biomarkers in the middle trimester is superior to taking a medical history.

Competing risks model in screening for preeclampsia by maternal factors and biomarkers at 11-13 weeks gestation

Preeclampsia affects approximately 3% of all pregnancies and is a major cause of maternal and perinatal morbidity and death. In the last decade, extensive research has been devoted to early screening for preeclampsia with the aim of reducing the prevalence of the disease through pharmacologic intervention in the high-risk group starting from the first trimester of pregnancy. The purpose of this study was to develop a model for preeclampsia based on maternal demographic characteristics and medical history (maternal factors) and biomarkers. The data for this study were derived from prospective screening for adverse obstetric outcomes in women who attended for their routine first hospital visit at 11-13 weeks gestation in 2 maternity hospitals in England. We screened 35,948 singleton pregnancies that included 1058 pregnancies (2.9%) that experienced preeclampsia. Bayes theorem was used to combine the a priori risk from maternal factors with various combinations of uterine artery pulsatility index, mean arterial pressure, serum pregnancy-associated plasma protein-A, and placental growth factor multiple of the median values. Five-fold cross validation was used to assess the performance of screening for preeclampsia that delivered at <37 weeks gestation (preterm-preeclampsia) and ≥37 weeks gestation (term-preeclampsia) by models that combined maternal factors with individual biomarkers and their combination with screening by maternal factors alone. In pregnancies that experienced preeclampsia, the values of uterine artery pulsatility index and mean arterial pressure were increased, and the values of serum pregnancy-associated plasma protein-A and placental growth factor were decreased. For all biomarkers, the deviation from normal was greater for early than late preeclampsia; therefore, the performance of screening was related inversely to the gestational age at which delivery became necessary for maternal and/or fetal indications. Combined screening by maternal factors, uterine artery pulsatility index, mean arterial pressure, and placental growth factor predicted 75% (95%confidence interval, 70-80%) of preterm-preeclampsia and 47% (95% confidence interval, 44-51%) of term-preeclampsia, at a false-positive rate of 10%; inclusion of pregnancy-associated plasma protein-A did not improve the performance of screening. Such detection rates are superior to the respective values of 49% (95% confidence interval, 43-55%) and 38% (34-41%) that were achieved by screening with maternal factors alone. Combination of maternal factors and biomarkers provides effective first-trimester screening for preterm-preeclampsia.

Competing risks model in screening for preeclampsia by maternal characteristics and medical history

The purpose of this study was to develop a model for preeclampsia based on maternal demographic characteristics and medical history. This was a screening study of 120,492 singleton pregnancies at 11-13 weeks' gestation, including 2704 pregnancies (2.2%) that experienced preeclampsia. A survival-time model for the gestational age at delivery with preeclampsia was developed from variables of maternal characteristics and history. This approach assumes that, if the pregnancy was to continue indefinitely, all women would experience preeclampsia and that whether they do so or not before a specified gestational age depends on competition between delivery before or after development of preeclampsia. A 5-fold cross validation study was conducted to compare the performance of the new model with the National Institute for Health and Clinical Excellence (NICE) guidelines. In the new model, increased risk for preeclampsia, with a consequent shift in the Gaussian distribution of the gestational age at delivery with preeclampsia to the left, is provided by advancing maternal age, increasing weight, Afro-Caribbean and South Asian racial origin, medical history of chronic hypertension, diabetes mellitus and systemic lupus erythematosus or antiphospholipid syndrome, family history and personal history of preeclampsia, and conception by invitro fertilization. The risk for preeclampsia decreases with increasing maternal height and in parous women with no previous preeclampsia; in the latter, the protective effect, which is related inversely to the interpregnancy interval, persists beyond 15 years. At a screen-positive rate of 11%, as defined by NICE, the new model predicted 40%, 48%, and 54% of cases of total preeclampsia and preeclampsia requiring delivery at <37 and <34 weeks' gestation, respectively, which were significantly higher than the respective values of 35%, 40%, and 44% achieved by application of NICE guidelines. A new model that is based on maternal characteristics and medical history has been developed for the estimation of patient-specific risks for preeclampsia. Such estimation of the a priori risk for preeclampsia is an essential first step in the use of Bayes theorem to combine maternal factors with biomarkers for the continuing development of more effective methods of screening for the disease.

Competing Risk Model in Screening for Preeclampsia by Mean Arterial Pressure and Uterine Artery Pulsatility Index at 30-33 Weeks' Gestation

Objective: To assess risk for preeclampsia (PE) based on maternal characteristics, mean arterial pressure (MAP) and uterine artery pulsatility index (Ut-PI) at 30-33 weeks' gestation. Methods: Screening study in singleton pregnancies including 2,140 that subsequently developed PE and 83,615 that were unaffected by PE, gestational hypertension or delivery of small-for-gestational-age neonates (normal group). We developed a survival time model for the time of delivery for PE by combination of maternal characteristics and history with MAP and Ut-PI multiple of the median (MoM) values (biophysical test). Data on third-trimester MAP and Ut-PI were available in 350 cases of PE and 13,878 of the normal group. The detection rate of PE requiring delivery within 4, 6 and 8 weeks of the visit was estimated. Results: In pregnancies with PE the log₁₀ MoM values of MAP and Ut-PI were inversely related to gestational age at delivery. Biophysical testing detected 90, 65 and 53% of PE with delivery within 4, 6 and 8 weeks of the visit, at a fixed false-positive rate of 5%. Interpretation: Testing by maternal characteristics, Ut-PI and MAP at 30-33 weeks could identify 90% of pregnancies developing PE and requiring delivery within the subsequent 4 weeks.

Competing Risks Model in Screening for Preeclampsia by Biophysical and Biochemical Markers at 30-33 Weeks' Gestation

Objective: To assess the risk for preeclampsia (PE) by maternal characteristics, uterine artery pulsatility index (Ut-PI), mean arterial pressure (MAP), serum placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) at 30-33 weeks' gestation. Methods: This was a screening study in singleton pregnancies including 2,140 that developed PE and 83,615 that were unaffected by PE. We developed a survival time model for the time of delivery for PE by combining maternal characteristics and history with Ut-PI, MAP, PlGF and sFlt-1 multiple of the median (MoM) values (combined test). Data on third-trimester MAP and Ut-PI were available in 350 cases of PE, and 13,878 unaffected pregnancies and data on PlGF and sFlt-1 were available in 118 cases of PE and 3,734 unaffected pregnancies. Modelled detection rate of all PE and PE requiring delivery within 4 and 6 weeks of the visit was estimated. Results: Screening by the combined test would detect 66, 98 and 86% of all PE and PE requiring delivery within 4 and 6 weeks of the visit, respectively, at a false positive rate of 5%. Interpretation: Screening by biophysical and biochemical testing at 30-33 weeks could identify most pregnancies developing PE and requiring delivery within the subsequent 4 weeks.

Competing Risks Model in Screening for Preeclampsia by Serum Placental Growth Factor and Soluble fms-Like Tyrosine Kinase-1 at 30-33 Weeks' Gestation

Objective: To assess the risk for preeclampsia (PE) by maternal characteristics, serum placental growth factor (PlGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) at 30-33 weeks' gestation. Methods: This was a screening study in singleton pregnancies including 2,140 that subsequently developed PE and 83,615 that were unaffected by PE, gestational hypertension or delivery of small-for-gestational-age neonates (normal group). We developed a survival time model for the time of delivery for PE by combination of maternal characteristics and history with PlGF and sFlt-1 multiple of the median (MoM) values (biochemical test). Data on third-trimester PlGF and sFlt-1 were available in 118 cases of PE and 3,734 of normal group. The detection rate (DR) of PE requiring delivery within 4, 6 and 8 weeks of the visit was estimated. Results: In pregnancies with PE, the log₁₀ MoM values of PlGF and sFlt-1 were linearly related to gestational age at delivery. Screening by the biochemical test detected 100, 76, and 62% of PE with delivery within 4, 6 and 8 weeks of the visit, at a fixed false-positive rate of 5%. Interpretation: Testing by PlGF and sFlt-1 at 30-33 weeks could identify all pregnancies developing PE and requiring delivery within the subsequent 4 weeks.