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Competing‐risks model for prediction of small‐for‐gestational‐age neonate from maternal characteristics and serum pregnancy‐associated plasma protein‐A at 11–13 weeks' gestation
Author(s) -
Papastefanou I.,
Wright D.,
Syngelaki A.,
Lolos M.,
Anampousi K.,
Nicolaides K. H.
Publication year - 2020
Publication title -
ultrasound in obstetrics and gynecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.202
H-Index - 141
eISSN - 1469-0705
pISSN - 0960-7692
DOI - 10.1002/uog.22175
Subject(s) - medicine , logistic regression , small for gestational age , gestational age , context (archaeology) , birth weight , percentile , nomogram , gestation , pregnancy associated plasma protein a , obstetrics , bayes' theorem , pregnancy , statistics , bayesian probability , mathematics , first trimester , paleontology , biology , genetics
Objectives To develop a continuous likelihood model for pregnancy‐associated plasma protein‐A (PAPP‐A), in the context of a new competing‐risks model for prediction of a small‐for‐gestational‐age (SGA) neonate, and to compare the predictive performance of the new model for SGA to that of previous methods. 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. The dataset was divided randomly into a training dataset and a test dataset. The training dataset was used for PAPP‐A likelihood model development. We used Bayes' theorem to combine the previously developed prior model for the joint Gaussian distribution of gestational age (GA) at delivery and birth‐weight Z ‐score with the PAPP‐A likelihood to obtain a posterior distribution. This patient‐specific posterior joint Gaussian distribution of GA at delivery and birth‐weight Z ‐score allows risk calculation for SGA defined in terms of different birth‐weight percentiles and GA. The new model was validated internally in the test dataset and we compared its predictive performance to that of the risk‐scoring system of the UK National Institute for Health and Care Excellence (NICE) and that of logistic regression models for different SGA definitions. Results PAPP‐A has a continuous association with both birth‐weight Z ‐score and GA at delivery according to a folded‐plane regression. The new model, with the addition of PAPP‐A, was equal or superior to several logistic regression models. The new model performed well in terms of risk calibration and consistency across different GAs and birth‐weight percentiles. In the test dataset, at a false‐positive rate of about 30% using the criteria defined by NICE, the new model predicted 62.7%, 66.5%, 68.1% and 75.3% of cases of a SGA neonate with birth weight < 10 th percentile delivered at < 42, < 37, < 34 and < 30 weeks' gestation, respectively, which were significantly higher than the respective values of 46.7%, 55.0%, 55.9% and 52.8% achieved by application of the NICE guidelines. Conclusions Using Bayes' theorem to combine PAPP‐A measurement data with maternal characteristics improves the prediction of SGA and performs better than logistic regression or NICE guidelines, in the context of a new competing‐risks model for the joint distribution of birth‐weight Z ‐score and GA at delivery. © 2020 International Society of Ultrasound in Obstetrics and Gynecology