Competing‐risks model for prediction of small‐for‐gestational‐age neonate from estimated fetal weight at 19–24 weeks' gestation

Papastefanou, Ioannis; Nowacka, Urszula; Syngelaki, Argyro; Drăgoi, Vlad; Karamanis, George; Wright, David; Nicolaides, Kypros H.

doi:10.1002/uog.23593

Cited by 18 publications

(57 citation statements)

References 33 publications

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“…The recently developed competing risks approach for the prediction of SGA is based on the personalized joint distribution of Z BW and GA Delivery [7][8][9][10][11][12][13][14]. We combined the prior joint distribution of Z BW and GA Delivery with the likelihoods of the biochemical markers, according to Bayes' theorem, to obtain a pregnancy-specific joint posterior distribution that allows the calculation of risk for any chosen cut-off for Z BW and GA Delivery .…”

Section: Statistical Analysesmentioning

confidence: 99%

“…We converted PlGF and sFlt-1 to multiples of the median (MoM) values, as previously described [8][9][10][11][12][13][14]. We calculated the ratio sFlt-1 MoM to PlGF MoM, and we log 10 transformed it to approximate a Gaussian distribution.…”

Section: Statistical Analysesmentioning

confidence: 99%

“…Small for gestational age (SGA) fetuses/neonates are characterized by increased risk for stillbirth, morbidities and adverse outcomes that can be substantially reduced if the condition is identified antenatally [1][2][3][4][5][6]. We have recently presented a new approach in SGA prediction that considers SGA a spectrum condition that is reflected in two dimensions: gestational age at delivery (GA Delivery ) and Z score in birth weight for gestational age (Z BW ) [7][8][9][10][11][12][13][14]. This new method is a model for the joint distribution of GA Delivery and Z BW that uses the same traditional maternal factors and the already known biomarkers of impaired placentation but in a radically different new way.…”

Section: Introductionmentioning

confidence: 99%

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Competing Risks Model for Prediction of Small for Gestational Age Neonates and the Role of Second Trimester Soluble Fms-like Tyrosine Kinase-1

Nowacka

Papastefanou

Bouariu

et al. 2021

JCM

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Small for gestational age (SGA) fetuses/neonates are characterized by the increased risk for adverse outcomes that can be reduced if the condition is identified antenatally. We have recently developed a new approach in SGA prediction that considers SGA a spectrum condition that is reflected in two dimensions: gestational age at delivery and Z score in birth weight for gestational age. The new method has a better predictive ability than the traditionally used risk-scoring systems and logistic regression models. In this prospective study in 40241 singleton pregnancies, at 19–24 weeks’ gestation, we examined the potential value of the antiangiogenic soluble fms-like tyrosine kinase-1 (sFlt-1) and the ratio of sFlt-1 to the angiogenic placental growth factor (PlGF) in the prediction of SGA. We found that first, sFlt-1 did not improve the performance of screening by maternal risk factors, and second, the ratio of sFlt-1/PlGF had a worse performance than PlGF alone in the prediction of SGA. Consequently, second trimester sFlt-1 and sFlt-1/PlGF are not useful in screening for SGA.

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Section: Statistical Analysesmentioning

confidence: 99%

Section: Statistical Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Competing Risks Model for Prediction of Small for Gestational Age Neonates and the Role of Second Trimester Soluble Fms-like Tyrosine Kinase-1

Nowacka

Papastefanou

Bouariu

et al. 2021

JCM

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“…For example, in the UK, according to guidelines by the Royal College of Obstetricians and Gynaecologists (RCOG), a scoring system is applied to identify a high risk group for SGA in need of serial ultrasound scans from 26 weeks onwards 3 . An alternative method is provided by our novel two dimensional continuous competing risks model in which SGA is considered as a spectrum disorder whose severity is continuously reflected in both the gestational age at delivery (GA) and Z score in birth weight for gestational age (Z) [4][5][6][7][8] . The building block of this model is a patient-specific joint distribution of Z and GA, that is obtained by combining a history model with multivariate likelihood of biomarkers according to Bayes theorem [4][5][6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

“…An alternative method is provided by our novel two dimensional continuous competing risks model in which SGA is considered as a spectrum disorder whose severity is continuously reflected in both the gestational age at delivery (GA) and Z score in birth weight for gestational age (Z) [4][5][6][7][8] . The building block of this model is a patient-specific joint distribution of Z and GA, that is obtained by combining a history model with multivariate likelihood of biomarkers according to Bayes theorem [4][5][6][7][8] . Risk computation is feasible for any chosen cut-off in GA and Z, at any stage of pregnancy by adding any desired biomarker in the same model.…”

Section: Introductionmentioning

confidence: 99%

Predictive performance for placental dysfunction related stillbirth of the competing risks model for small for gestational age fetuses

Nicolaides

Papastefanou²,

Syngelaki

et al. 2021

Preprint

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Objectives: First, to examine the predictive performance for placental dysfunction related stillbirths of the competing risks model for small for gestational age (SGA) fetuses based on a combination of maternal risk factors, estimated fetal weight (EFW) and uterine artery pulsatility index (UtA-PI); and second, to compare the performance of this model to that of stillbirth-specific model utilizing the same biomarkers and to the Royal College of Obstetricians and Gynecologists (RCOG) guideline for the investigation and management of the SGA fetus. Design: Prospective observational study. Setting: Two UK maternity hospitals. Population: 131,514 women with singleton pregnancies attending for routine ultrasound examination at 19-24 weeks’ gestation. Methods: The predictive performance for stillbirth achieved by three models was compared. Main outcome measures: Placental dysfunction related stillbirth. Results: At 10% false positive rate, the competing risks model predicted 59%, 66% and 71% of placental dysfunction related stillbirths, at any gestation, at <37 weeks and at <32 weeks, respectively, which were similar to the respective figures of 62%, 70% and 73% for the stillbirth-specific model. At a screen positive rate of 21.8 %, as defined by the RCOG guideline, the new model predicted 71%, 76% and 79% of placental dysfunction related stillbirths at any gestation, at <37 weeks and at <32 weeks, respectively, and the respective figures for the RCOG guideline were 42%, 44% and 40%. Conclusion: The predictive performance for placental dysfunction related stillbirths by the competing risks model for SGA was similar to the stillbirth-specific model and superior to the RCOG guideline.

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Second‐trimester contingent screening for small‐for‐gestational‐age neonate

Nowacka

Papastefanou

Bouariu

et al. 2022

Ultrasound in Obstet & Gyne

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Objectives First, to investigate the additive value of second‐trimester placental growth factor (PlGF) for the prediction of a small‐for‐gestational‐age (SGA) neonate. Second, to examine second‐trimester contingent screening strategies. Methods This was a prospective observational study in women with singleton pregnancy undergoing routine ultrasound examination at 19–24 weeks' gestation. We used the competing‐risks model for prediction of SGA. The parameters for the prior model and the likelihoods for estimated fetal weight (EFW) and uterine artery pulsatility index (UtA‐PI) were those presented in previous studies. A folded‐plane regression model was fitted in the dataset of this study to describe the likelihood of PlGF. We compared the prediction of screening by maternal risk factors against the prediction provided by a combination of maternal risk factors, EFW, UtA‐PI and PlGF. We also examined the additive value of PlGF in a policy that uses maternal risk factors, EFW and UtA‐PI. Results The study population included 40 241 singleton pregnancies. Overall, the prediction of SGA improved with increasing degree of prematurity, with increasing severity of smallness and in the presence of coexisting pre‐eclampsia. The combination of maternal risk factors, EFW, UtA‐PI and PlGF improved significantly the prediction provided by maternal risk factors alone for all the examined cut‐offs of birth weight and gestational age at delivery. Screening by a combination of maternal risk factors and serum PlGF improved the prediction of SGA when compared to screening by maternal risk factors alone. However, the incremental improvement in prediction was decreased when PlGF was added to screening by a combination of maternal risk factors, EFW and UtA‐PI. If first‐line screening for a SGA neonate with birth weight < 10th percentile delivered at < 37 weeks' gestation was by maternal risk factors and EFW, the same detection rate of 90%, at an overall false‐positive rate (FPR) of 50%, as that achieved by screening with maternal risk factors, EFW, UtA‐PI and PlGF in the whole population can be achieved by reserving measurements of UtA‐PI and PlGF for only 80% of the population. Similarly, in screening for a SGA neonate with birth weight < 10th percentile delivered at < 30 weeks, the same detection rate of 90%, at an overall FPR of 14%, as that achieved by screening with maternal risk factors, EFW, UtA‐PI and PlGF in the whole population can be achieved by reserving measurements of UtA‐PI and PlGF for only 70% of the population. The additive value of PlGF in reducing the FPR to about 10% with a simultaneous detection rate of 90% for a SGA neonate with birth weight < 3rd percentile born < 30 weeks, is gained by measuring PlGF in only 50% of the population when first‐line screening is by maternal factors, EFW and UtA‐PI. Conclusions The combination of maternal risk factors, EFW, UtA‐PI and PlGF provides effective second‐trimester prediction of SGA. Serum PlGF is useful for predicting a SGA neonate with birth weight < 3rd percenti...

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Competing‐risks model for prediction of small‐for‐gestational‐age neonate from estimated fetal weight at 19–24 weeks' gestation

Cited by 18 publications

References 33 publications

Competing Risks Model for Prediction of Small for Gestational Age Neonates and the Role of Second Trimester Soluble Fms-like Tyrosine Kinase-1

Competing Risks Model for Prediction of Small for Gestational Age Neonates and the Role of Second Trimester Soluble Fms-like Tyrosine Kinase-1

Predictive performance for placental dysfunction related stillbirth of the competing risks model for small for gestational age fetuses

Second‐trimester contingent screening for small‐for‐gestational‐age neonate

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