External validation and clinical usefulness of first‐trimester prediction models for small‐ and large‐for‐gestational‐age infants: a prospective cohort study

Abstract

Objective To assess the external validity of all published first‐trimester prediction models based on routinely collected maternal predictors for the risk of small‐ and large‐for‐gestational‐age ( SGA and LGA ) infants. Furthermore, the clinical potential of the best‐performing models was evaluated. Design Multicentre prospective cohort. Setting Thirty‐six midwifery practices and six hospitals (in the Netherlands). Population Pregnant women were recruited at <16 weeks of gestation between 1 July 2013 and 31 December 2015. Methods Prediction models were systematically selected from the literature. Information on predictors was obtained by a web‐based questionnaire. Birthweight centiles were corrected for gestational age, parity, fetal sex, and ethnicity. Main outcome measures Predictive performance was assessed by means of discrimination (C‐statistic) and calibration. Results The validation cohort consisted of 2582 pregnant women. The outcomes of SGA <10th percentile and LGA >90th percentile occurred in 203 and 224 women, respectively. The C‐statistics of the included models ranged from 0.52 to 0.64 for SGA ( n = 6), and from 0.60 to 0.69 for LGA ( n = 6). All models yielded higher C‐statistics for more severe cases of SGA (<5th percentile) and LGA (>95th percentile). Initial calibration showed poor‐to‐moderate agreement between the predicted probabilities and the observed outcomes, but this improved substantially after recalibration. Conclusion The clinical relevance of the models is limited because of their moderate predictive performance, and because the definitions of SGA and LGA do not exclude constitutionally small or large infants. As most clinically relevant fetal growth deviations are related to ‘vascular’ or ‘metabolic’ factors, models predicting hypertensive disorders and gestational diabetes are likely to be more specific. Tweetable abstract The clinical relevance of prediction models for the risk of small‐ and large‐for‐gestational‐age is limited.

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

“…Systematic reviews have revealed that the vast majority of studies aimed at validating prediction models externally were not conducted well and did not report important elements 23,24 . In general, few studies have examined the external validity of SGA models, showing poor reproducibility for first-trimester algorithms 25 . The new FMF approach for SGA has undergone extensive internal validation [12][13][14][15][16][17][18] , but to date it has not been validated in populations other than the ones used for its development.…”

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

“…Few studies have examined the external validity of prediction models for SGA. Meertens et al 27 . demonstrated poor reproducibility for first‐trimester logistic regression algorithms.…”

“…Few studies have examined the external validity of prediction models for SGA. Meertens et al 27 demonstrated poor reproducibility for first-trimester logistic regression algorithms. Comparison of logistic regression models with the competing-risks approach for the prediction of SGA in the context of an internal validation process showed that the latter has superior performance and is more reproducible for predicting preterm SGA 14,15,28 .…”

Serial plotting of symphysis–fundal height and estimated fetal weight to improve the antenatal detection of infants small for gestational age: A cluster randomised trial

“…In light of these results, further research should elucidate the reasons for the failed antenatal detection of infants that are SGA, in order to identify preventable factors, such as through audits. Other approaches are to base screening strategies on a broader set of risk markers, 58 using more information about maternal characteristics (age, parity, height and weight, conception method, smoking, medical and obstetrical history, for instance) and integrating biomarkers and biophysical tests (such as glucose, Rhesus group, EFW, vascular flow indices and placental factors) in prediction models 59–61 . However, existing predictive models have modest performance and a net benefit for predicting SGA births compared with current strategies, leading to calls to change the focus from predicting SGA to predicting adverse outcomes associated with suboptimal fetal growth 59 …”