Competing‐risks model in screening for pre‐eclampsia in twin pregnancy by maternal characteristics and medical history

Francisco, Carla; Wright, David; Benkő, Z.; Syngelaki, Argyro; Nicolaides, Kypros H.

doi:10.1002/uog.17529

Cited by 31 publications

(36 citation statements)

References 25 publications

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“…The training dataset was derived from pregnancies examined at King's College Hospital, London and Medway Maritime Hospital, Gillingham, UK, between January 2006 and December 2015.…”

Section: Methodsmentioning

confidence: 99%

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

Benkő

Chaveeva

Matallana

et al. 2019

Ultrasound in Obstet & Gyne

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Objective 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. Methods 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. Results 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. Conclusions 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.

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“…The training dataset was derived from pregnancies examined at King's College Hospital, London and Medway Maritime Hospital, Gillingham, UK, between January 2006 and December 2015.…”

Section: Methodsmentioning

confidence: 99%

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

Benkő

Chaveeva

Matallana

et al. 2019

Ultrasound in Obstet & Gyne

Self Cite

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“…Calibration plots for screening using competing‐risks model for prediction of early (a,b) and preterm (c,d) pre‐eclampsia (PE) in validation dataset, according to previous (a,c) and new (b,d) models, after adjustment for effect of censoring due to births from causes other than PE. Diagonal line is line of perfect agreement.…”

Section: Resultsmentioning

confidence: 99%

“…Three datasets were used for this study. First, 2219 twin pregnancies (training dataset) that were examined at King's College Hospital and Medway Maritime Hospital, UK, between January 2006 and December 2015. Second, 2999 twin pregnancies (validation dataset) that were examined at five hospitals in England (King's College Hospital and Medway Maritime Hospital, between December 2015 and April 2018; Homerton University Hospital, between January 2014 and April 2018; North Middlesex University Hospital, between May 2015 and April 2018; and Southend University Hospital, between June 2015 and April 2018), one hospital in Bulgaria (Dr. Shterev Hospital in Sofia, between January 2013 and April 2018) and one hospital in Spain (Hospital Clínico Universitario Virgen de la Arrixaca in Murcia, between March 2009 and April 2018).…”

Section: Methodsmentioning

confidence: 99%

“…Using data on 120 492 singleton pregnancies, we developed a parametric survival model in which the distribution of gestational age at delivery with PE has a Gaussian distribution with a mean determined from maternal characteristics and a constant standard deviation. We extended this model using data on the 2219 pregnancies in the training dataset by including effects for DC and MC twins. Using this model, the prior distribution of gestational age at delivery with PE is the same as that in singleton pregnancy with the same maternal characteristics but with the mean reduced by 8 weeks in DC twins and 10 weeks in MC twins.…”

Section: Methodsmentioning

confidence: 99%

“…The objectives of this study were, first, to develop a new extension of the competing‐risks prior model in screening for PE in twin pregnancies in the original 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, and, third, to demonstrate the application of screening in a mixed population of singleton and twin pregnancies.…”

Section: Introductionmentioning

confidence: 99%

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

Benkő

Chaveeva

Matallana

et al. 2019

Ultrasound in Obstet & Gyne

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Background 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. Objectives 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. Methods 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. Results 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. Conclusions 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 agr...

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Mechanisms of Sex Disparities in Cardiovascular Function and Remodeling

Chaudhari

Cushen

Osikoya

et al. 2018

Comprehensive Physiology

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Epidemiological studies demonstrate disparities between men and women in cardiovascular disease prevalence, clinical symptoms, treatments, and outcomes. Enrollment of women in clinical trials is lower than men, and experimental studies investigating molecular mechanisms and efficacy of certain therapeutics in cardiovascular disease have been primarily conducted in male animals. These practices bias data interpretation and limit the implication of research findings in female clinical populations. This review will focus on the biological origins of sex differences in cardiovascular physiology, health, and disease, with an emphasis on the sex hormones, estrogen and testosterone. First, we will briefly discuss epidemiological evidence of sex disparities in cardiovascular disease prevalence and clinical manifestation. Second, we will describe studies suggesting sexual dimorphism in normal cardiovascular function from fetal life to older age. Third, we will summarize and critically discuss the current literature regarding the molecular mechanisms underlying the effects of estrogens and androgens on cardiac and vascular physiology and the contribution of these hormones to sex differences in cardiovascular disease. Fourth, we will present cardiovascular disease risk factors that are positively associated with the female sex, and thus, contributing to increased cardiovascular risk in women. We conclude that inclusion of both men and women in the investigation of the role of estrogens and androgens in cardiovascular physiology will advance our understanding of the mechanisms underlying sex differences in cardiovascular disease. In addition, investigating the role of sex‐specific factors in the development of cardiovascular disease will reduce sex and gender disparities in the treatment and diagnosis of cardiovascular disease. © 2019 American Physiological Society. Compr Physiol 9:375‐411, 2019.

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Competing‐risks model in screening for pre‐eclampsia in twin pregnancy by maternal characteristics and medical history

Cited by 31 publications

References 25 publications

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

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

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

Mechanisms of Sex Disparities in Cardiovascular Function and Remodeling

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