First‐trimester serum PAPP‐A and fβ‐hCG concentrations and other maternal characteristics to establish logistic regression‐based predictive rules for adverse pregnancy outcome

Ravenswaaij, R. van; Goot, M. Tesselaar-van der; Wolf, S. de; Leeuwen-Spruijt, M. van; Visser, Gerhard H.; Schielen, Peter C. J. I.

doi:10.1002/pd.2610

Cited by 40 publications

(32 citation statements)

References 29 publications

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“…Predictive performance of a model might be improved by taking into account biomarkers or ultrasound imaging (for example, cervical length). A few models based on cervical length measurements and biomarkers such as pregnancy‐associated plasma protein A (PAPP‐A) or alpha‐fetoprotein (AFP) have been published 29, 34, 35. The reported discriminative performance of these models was only slightly better than the performance of models using maternal characteristics alone.…”

Section: Discussionmentioning

confidence: 99%

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

Meertens

Montfort

Scheepers

et al. 2018

Acta Obstet Gynecol Scand

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IntroductionPrediction models may contribute to personalized risk‐based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models.Material and methodsPrediction models based on routinely collected maternal parameters obtainable during first 16 weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web‐based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation. Clinical value was evaluated by means of decision curve analysis and calculating classification accuracy for different risk thresholds.ResultsFour studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation, respectively. A subanalysis showed that the models discriminated poorly (AUC 0.51–0.56) for nulliparous women. Although we recalibrated the models, two models retained evidence of overfitting. The decision curve analysis showed low clinical benefit for the best performing models.ConclusionsThis review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth.

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Section: Discussionmentioning

confidence: 99%

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

Meertens

Montfort

Scheepers

et al. 2018

Acta Obstet Gynecol Scand

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“…It is anticipated that, in this group of women, pregnancy outcomes would be less favorable compared with women with PAPP‐A levels closer to 1 MoM but would be considerably better compared with women with PAPP‐A levels below 0.3 MoM. It is expected that this would include most outcomes of interest, including aneuploidy, spontaneous abortion, preterm delivery, pre‐eclampsia, and SGA neonates …”

Section: Discussionmentioning

confidence: 99%

Low maternal pregnancy‐associated plasma protein A during the first trimester of pregnancy and pregnancy outcomes

Kaijomaa

Rahkonen

Ulander

et al. 2016

Intl J Gynecology & Obste

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“…However, all models demonstrated areas under the curve of greater than 0.90. Prior literature has linked low levels of PAPP-A with adverse pregnancy outcomes such as growth restriction, preeclampsia [2, 5, 10, 11, 20], and miscarriage [3]. …”

Section: Discussionmentioning

confidence: 99%

First trimester serum analytes, maternal characteristics and ultrasound markers to?predict pregnancies at risk for preterm birth

et al. 2013

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Introduction Markers of placental dysfunction are used for risk prediction of adverse obstetric outcomes including preeclampsia and growth restriction. Although medically indicated preterm birth is often distinguished from spontaneous preterm birth, we hypothesize that similar placental dysfunction may underlay all preterm birth. We aimed to investigate whether first trimester placental protein 13 (PP-13), pregnancy associated plasma protein A (PAPP-A) and uterine artery pulsatility index, with maternal characteristics could be used to predict all preterm birth. Methods Prospective cohort study of singleton gestations between 11-14 weeks who underwent serum measurement of PP-13, PAPP-A, and measurement of uterine artery Doppler pulsatility index. Primary outcomes were preterm birth (PTB) at less than 37 and 33 weeks. Analysis performed both including and excluding preeclampsia to assess the utility of the predictors for all types of preterm birth. Predictive models assembled using logistic regression with each predictor alone and in combination, along with maternal characteristics. Predictive utility of models was assessed using receiver operating curve (ROC) analysis and sensitivities for fixed-false positive values. Results Of 471 women, PTB occurred in 12.5% and early PTB (<33 weeks) occurred in 4.7%. PP13 was decreased in PTB <37 weeks. PAPP-A was decreased in a dose-response pattern for PTB at <37 weeks and <33 weeks. Uterine artery pulsatility index was increased in early PTB. All patterns of predictors remained the same whether patients with preeclampsia were excluded or included suggesting predictive utility for all causes of PTB. Predictive models all demonstrated good predictive ability with ROC ≥ 0.90. Conclusions PP-13, PAPP-A, and uterine artery Doppler pulsatility index obtained in the first trimester are good predictors of all types of preterm birth, both indicated and spontaneous. Models including first trimester markers combined with maternal characteristics demonstrated good predictive ability and could be investigated for application of targeted prophylactic strategies.

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First‐trimester serum PAPP‐A and fβ‐hCG concentrations and other maternal characteristics to establish logistic regression‐based predictive rules for adverse pregnancy outcome

Cited by 40 publications

References 29 publications

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

Prediction models for the risk of spontaneous preterm birth based on maternal characteristics: a systematic review and independent external validation

Low maternal pregnancy‐associated plasma protein A during the first trimester of pregnancy and pregnancy outcomes

First trimester serum analytes, maternal characteristics and ultrasound markers to?predict pregnancies at risk for preterm birth

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