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Cited by 40Pregnancy and preeclampsia studiesPrenatal Screening and DiagnosticsGestational Diabetes Research and Management

Competing‐risks model for prediction of small‐for‐gestational‐age neonate from maternal characteristics, serum pregnancy‐associated plasma protein‐A and placental growth factor at 11–13 weeks' gestation

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Papastefanou, I., Wright, D., Lolos, M., Anampousi, K., Mamalis, M., Nicolaides, K. H.·2021·Ultrasound in Obstetrics & Gynecology
DOI 10.1002/uog.23118
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Abstract

Objectives: To expand a new competing‐risks model for prediction of a small‐for‐gestational‐age (SGA) neonate, by the addition of pregnancy‐associated plasma protein‐A (PAPP‐A) and placental growth factor (PlGF), and to evaluate and compare PAPP‐A and PlGF in predicting SGA. Methods: This was a prospective observational study of 60 875 women with singleton pregnancy undergoing routine ultrasound examination at 11 + 0 to 13 + 6 weeks' gestation. We fitted a folded‐plane regression model for the PAPP‐A and PlGF likelihoods. A previously developed maternal history model and the likelihood models were combined, according to Bayes' theorem, to obtain individualized distributions for gestational age (GA) at delivery and birth‐weight Z‐score. We assessed the discrimination and calibration of the model. McNemar's test was used to compare the detection rates for SGA with, without or independently of pre‐eclampsia (PE) occurrence, of different combinations of maternal history, PAPP‐A and PlGF, for a fixed false‐positive rate. Results: The distributions of PAPP‐A and PlGF depend on both GA at delivery and birth‐weight Z‐score, in the same continuous likelihood, according to a folded‐plane regression model. The new approach offers the capability for risk computation for any desired birth‐weight Z‐score and GA at delivery cut‐off. PlGF was consistently and significantly better than PAPP‐A in predicting SGA delivered before 37 weeks, especially in cases with co‐existence of PE. PAPP‐A had similar performance to PlGF for the prediction of SGA without PE. At a fixed false‐positive rate of 10%, the combination of maternal history, PlGF and PAPP‐A predicted 33.8%, 43.8% and 48.4% of all cases of a SGA neonate with birth weight < 10 th percentile delivered at ≥ 37, < 37 and < 32 weeks' gestation, respectively. The respective values for birth weight < 3 rd percentile were 38.6%, 48.7% and 51.0%. The new model performed well in terms of risk calibration. Conclusions: The combination of PAPP‐A and PlGF values with maternal characteristics, according to Bayes' theorem, improves prediction of SGA. PlGF is a better predictor of SGA than PAPP‐A, especially when PE is present. The new competing‐risks model for SGA can be tailored to each pregnancy and to the relevant clinical requirements. © 2020 International Society of Ultrasound in Obstetrics and Gynecology

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  • Traditional approaches relying on maternal characteristics and serum markers such as PAPP-A and PlGF offer only modest predictive power.

    “Traditional approaches rely on maternal characteristics and serum markers such as pregnancy-associated plasma protein A (PAPP-A) and placental growth factor (PlGF), which offer only modest predictive power.9,12–14”

    From Early detection of severe fetal growth restriction using multimodal deep learning based on ultrasound and prenatal biomarkers (2026) · cites this paper as [12]

    Table 3 (indexed under Conclusions; page unavailable) supports modest detection for one outcome: maternal factors plus both markers detected 33.8% of term SGA cases at a 10% false-positive rate. But Model evaluation says prediction improves for earlier and more severe SGA, and Conclusions calls the markers effective; the source does not support describing their predictive power as uniformly modest. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Low levels of first-trimester PAPP-A and abnormal β-hCG are known to reflect impaired placental function.

    “Low levels of first-trimester PAPP-A and abnormal β-hCG are known to reflect impaired placental function, and their predictive value was confirmed in ablation experiments.12”

    From Early detection of severe fetal growth restriction using multimodal deep learning based on ultrasound and prenatal biomarkers (2026) · cites this paper as [12]

    The Main findings section calls first-trimester PAPP-A an established marker of impaired placentation and reports that it predicts small-for-gestational-age birth. The study examines PAPP-A and PlGF, not β-hCG, and does not report ablation experiments; those parts of the citing sentence are unsupported by this paper. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. ACOG Practice Bulletin No. 202: Gestational Hypertension and Preeclampsia. Obstet Gynecol 2019; 133: e1-25.

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Authors
Papastefanou, I., Wright, D., Lolos, M., Anampousi, K., Mamalis, M., & Nicolaides, K. H.
Affiliations
  • King's College Hospital (GB)
  • University of Exeter (GB)
Corresponding author
Nicolaides, K. H.
ORCID
Venue
Ultrasound in Obstetrics & Gynecology
Publisher
Wiley
Published
2021-02-15
Volume
57
Issue
3
Pages
392-400
ISSN
  • 0960-7692 (ISSN-L)
  • 1469-0705
DOI
10.1002/uog.23118
OpenAlex
W3084836403
Cited by
40
References
35
Citations by year
2026: 6 · 2025: 5 · 2024: 7 · 2023: 8 · 2022: 6 · 2021: 7 · 2020: 1
FWCI
3.36
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations
  • Ultrasound in Obstetrics and Gynecology (publishedVersion)
  • PubMed (publishedVersion)
Funders
Fetal Medicine Foundation
Topics
  • Health Sciences › Medicine › Obstetrics and Gynecology › Pregnancy and preeclampsia studies
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Prenatal Screening and Diagnostics
  • Health Sciences › Medicine › Obstetrics and Gynecology › Gestational Diabetes Research and Management
Keywords
small for gestational age, PAPP-A, placental growth factor, competing risk model, first-trimester ultrasound, preeclampsia, birth weight prediction, gestational age at delivery, maternal risk factors, risk prediction model, singleton pregnancy, fetal growth restriction, Bayes' theorem
MeSH terms
Placenta Growth Factor, Adult, Bayes Theorem, Birth Weight, Female, Fetal Growth Retardation, Gestational Age, Humans, Infant, Newborn, Infant, Small for Gestational Age, Predictive Value of Tests, Pregnancy, Pregnancy Trimester, First, Pregnancy-Associated Plasma Protein-A, Prenatal Diagnosis, Prospective Studies, Regression Analysis, Likelihood Functions, Risk Assessment

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