External validation of Fetal Medicine Foundation competing‐risks model for midgestation prediction of small‐for‐gestational‐age neonates in Spanish population

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ObjectivesTo examine the external validity of the new Fetal Medicine Foundation (FMF) competing risk model for the prediction of small for gestational age (SGA) at 11‐14 weeks of gestation in Asian population.MethodsThis is a secondary analysis of a multicenter prospective cohort study in 10,120 women with singleton pregnancies undergoing routine assessment at 11‐14 weeks of gestation. We applied the FMF competing risk model for the first‐trimester prediction of SGA combining maternal characteristics and medical history with measurements of mean arterial pressure (MAP), uterine artery pulsatility index (UtA‐PI), and serum placental growth factor (PlGF). We obtained risks for different cut‐offs of birth weight percentile and gestational age at delivery. We examined the predictive performance in terms of discrimination and calibration.ResultsThe predictive performance of the competing risk model for SGA was similar to that reported in the FMF study. Specifically, the combination of maternal factors with MAP, UtA‐PI, and PlGF yielded the best performance for the prediction of preterm SGA <10th percentile (SGA<10th) and preterm SGA <5th percentile (SGA<5th), with the areas under the curves (AUCs) of 0.765 (95% confidence interval [CI], 0.720‐0.809) and 0.789 (95%CI, 0.736‐0.841), respectively. Combining maternal factors, MAP, and PlGF yielded the best model for predicting preterm SGA <3rd percentile (SGA<3rd), with an AUC of 0.797 (95%CI, 0.744‐0.850). After excluding preeclampsia (PE) cases, the combination of maternal factors with MAP, UtA‐PI, and PlGF yielded the best performance for the prediction of preterm SGA<10th and SGA<5th, with AUCs of 0.743 (95%CI, 0.691‐0.795) and 0.762 (95%CI, 0.700‐0.824), respectively. However, the best model for predicting preterm SGA<3rd without PE was the combination of maternal factors and PlGF, with an AUC of 0.786 (95%CI, 0.723‐0.849). The FMF competing risk model including maternal factors, MAP, UtA‐PI, PlGF achieved DRs of 42.2%, 47.3%, and 48.1%, at the fixed FPR of 10%, for the prediction of preterm SGA with birth weight <10th, 5th and 3rd percentiles, respectively. The calibration of the new model was satisfactory.ConclusionThe screening performance of the new FMF first trimester competing risk model for SGA in an independent large cohort of Asian women is comparable to that reported in the original FMF study on a mixed European population.This article is protected by copyright. All rights reserved.

Section: Discussionsupporting

confidence: 79%

Evaluation of screening performance of first‐trimester competing‐risks prediction model for small‐for‐gestational age in Asian population

Nguyen-Hoang

Papastefanou

Sahota

et al. 2024

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“…Only a few small, heterogeneous studies have evaluated the use of the sFlt‐1/PlGF ratio in predicting prognosis and for management of pregnancies with a small‐for‐gestational‐age (SGA) fetus 4–6 . An alternative approach to identify SGA is a competing‐risks model, which is effective, demonstrates superior performance compared with the traditional methods and has been internally and externally validated at all pregnancy stages 7–14 . The Bayesian structure of this model, which has birth weight and gestational age at delivery as two continuous dimensions, allows the inclusion of any desired biomarker at any stage of pregnancy 7–10 .…”

Section: Introductionmentioning

confidence: 99%

Evaluation of angiogenic factors in prediction of growth‐related neonatal morbidity at term and comparison with competing‐risks model

Papastefanou,

Nobile Recalde,

Silva Souza

et al. 2024

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ObjectivesFirst, to describe the distribution of biomarkers of impaired placentation in small for gestational age (SGA) pregnancies with neonatal morbidity, second, to examine the predictive performance for growth related neonatal morbidity of high soluble fms‐like tyrosine kinase‐1 (sFLT‐1) / placental growth factor (PlGF) ratio or low PlGF, and third, to compare the performance of the high sFLT‐1/PlGF ratio or low PlGF with that of the competing risks model for SGA.MethodsThis was a prospective observational study in women attending for a routine hospital visit at 36 weeks’ gestation in two maternity hospitals in England. The visit included recording of maternal demographic characteristics and medical history, carrying out an ultrasound scan and measuring serum PlGF and sFLT‐1. The primary outcome was delivery within 4 weeks from assessment and <42 weeks’ gestation of SGA neonate with birth weight <10th or <3rd percentile for gestational age, combined with neonatal unit (NNU) admission for ≥48 hours or a composite of major neonatal morbidity. The detection rates in screening by either PlGF <10th percentile, sFLT‐1/PlGF ratio >90th percentile, sFLT‐1/PlGF ratio >38 and the competing risks model for SGA, using combinations of maternal risk factors and z scores of EFW with MoM values of uterine artery pulsatility index (UtA‐PI), PlGF and sFLT‐1, were estimated. The detection rates by the different methods of screening were compared using McNemar's test.ResultsIn the study population of 29,035 women prediction of growth related neonatal morbidity at term provided by the competing risks model was superior to that of screening by low PlGF concentration or high sFlt‐1/PlGF concentration ratio. For example, at SPR of 13.1%, as defined by the sFLT‐1/ PlGF ratio >38, the competing risks model using maternal risk factors and EFW predicted 77.5% (95% CI 71.7‐83.3) of SGA <10th percentile and 89.2% (83.7‐94.8) of SGA <3rd percentile with NNU admission for ≥48 hours delivered within 4 weeks after assessment; these were significantly higher than the respective values of 41.0% (34.2‐47.8) and 48.8% (39.9‐57.7), achieved by the application of the sFLT‐1/ PlGF ratio >38 (p<0.0001, p<0.0001). The respective values for SGA with major neonatal morbidity were 71.4% (56.5‐86.4), 90.0% (76.9‐100), 37.1% (21.1‐53.2) and 55.0% (33.2‐76.8) (p=0.003, p=0.035).At a SPR of 10.0%, as defined by the PlGF ratio <10th percentile, the competing risks model using maternal factors and EFW predicted 71.5% (65.2‐77.8) of SGA <10th percentile and 84.3% (77.8‐90.8) of SGA <3rd percentile with NNU admission for ≥48 hours delivered within 4 weeks after assessment; these were significantly higher than the respective values of 36.5% (29.8‐43.2) and 46.3% (37.4‐55.2), achieved by the application of PlGF <10th percentile (p<0.0001, p<0.0001). The respective values for SGA with major neonatal morbidity were 68.5% (53.1‐83.9), 85.0% (69.4‐100), 37.1% (21.1‐53.2) and 55.0% (33.2‐76.80) (p=0.003, p=0.021).ConclusionAt 36 weeks’ gestation, the prediction of growth related neonatal morbidity by the competing risks model for SGA, using maternal risk factors and EFW, is superior to that of high sFlt‐1/PlGF ratio or low PlGF.This article is protected by copyright. All rights reserved.

“…Emerging data support the external validity of the FMF competing‐risks algorithm for SGA in diverse populations 34 . It appears that the 36‐week FMF model for SGA can be effectively used at 30 + 0 to 34 + 0 weeks as well.…”

Section: Discussionmentioning

confidence: 89%

Validation of Fetal Medicine Foundation competing‐risks model for small‐for‐gestational‐age neonate in early third trimester

Dagklis,

Papastefanou,

Tsakiridis

et al. 2024

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ObjectivesTo evaluate the new 36 weeks’ Fetal Medicine Foundation (FMF) competing risks model for the prediction of small for gestational age (SGA) at an earlier gestation of 30+0 to 34+0 weeks.MethodsThis is a retrospective multi‐center cohort study of prospectively collected data on 3,012 women with singleton pregnancies undergoing ultrasound examination at 30+0 ‐ 34+0 weeks’ gestation, as part of a universal screening program. We used the default 36 weeks’ FMF competing risks model for prediction of SGA, combining maternal factors (age, obstetric and medical history, weight, height, smoking, race, conception), estimated fetal weight (EFW) and uterine artery pulsatility index (UtA‐PI), to calculate risks for different cut‐offs of birth weight percentile and gestational age at delivery. We examined the model's accuracy by means of discrimination and calibration.ResultsThe prediction of SGA<3rd percentile improves with the addition of UtA‐PI and with shorter examination‐to‐delivery interval. For 10% false positive rate, maternal factors, EFW and UtA‐PI predicted 88.0%, 74.4% and 72.8% of SGA <3rd percentile delivered <37, <40 and <42 weeks, respectively. The corresponding figures for SGA 10th percentile were 83.9%, 69.3% and 66.2%. In terms of population stratification, if the biomarkers used are EFW and UtA‐PI and the aim is to detect 90% of SGA <10thpercentile, then 10.8% of the population should be scanned within two weeks after the initial assessment, an additional 7.2% (total screen positive rate, SPR 18%) should be scanned within two and four weeks after the initial assessment and an additional 11.7% (total SPR 29.7%) should be examined within four and six weeks after the initial assessment. The new model was well calibrated.ConclusionsThe 36 weeks’ FMF competing risks model for SGA is also applicable and accurate at 30+0 ‐ 34+0 weeks’ gestation, providing effective risk stratification, especially for cases leading to birth <37 weeks.This article is protected by copyright. All rights reserved.