Authors' objectives: Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes. To assess the predictive performance of existing models predicting fetal growth restriction and birthweight, and if needed, to develop and validate new multivariable models using individual participant data. Fetal growth restriction (FGR) is associated with perinatal mortality and morbidity. Early and accurate identification and appropriate management of pregnant women with growth-restricted fetuses can reduce perinatal complications. Primary Using individual personal data (IPD) meta-analysis To externally validate the predictive accuracy of existing prediction models for FGR (birthweight 

Authors' results and conclusions: Of the 119 published models, one birthweight model (Poon) could be validated. None reported fetal growth restriction using our definition. Across all cohorts, the Poon model had good summary calibration slope of 0.93 (95% confidence interval 0.90 to 0.96) with slight overfitting, and underpredicted birthweight by 90.4 g on average (95% confidence interval 37.9 g to 142.9 g). The newly developed International Prediction of Pregnancy Complications-fetal growth restriction model included maternal age, height, parity, smoking status, ethnicity, and any history of hypertension, pre-eclampsia, previous stillbirth or small for gestational age baby and gestational age at delivery. This allowed predictions conditional on a range of assumed gestational ages at delivery. The pooled apparent c-statistic and calibration were 0.96 (95% confidence interval 0.51 to 1.0), and 0.95 (95% confidence interval 0.67 to 1.23), respectively. The model showed positive net benefit for predicted probability thresholds between 1% and 90%. In addition to the predictors in the International Prediction of Pregnancy Complications-fetal growth restriction model, the International Prediction of Pregnancy Complications-birthweight model included maternal weight, history of diabetes and mode of conception. Average calibration slope across cohorts in the internal-external cross-validation was 1.00 (95% confidence interval 0.78 to 1.23) with no evidence of overfitting. Birthweight was underestimated by 9.7 g on average (95% confidence interval −154.3 g to 173.8 g). The International Prediction of Pregnancy Complications-fetal growth restriction and International Prediction of Pregnancy Complications-birthweight models accurately predict fetal growth restriction and birthweight for various assumed gestational ages at delivery. These can be used to stratify the risk status at booking, plan monitoring and management. External validation of existing prediction models Overall, 119 published prediction models (55 articles) for FGR and birthweight were identified, with various definitions of FGR or birthweight outcome dichotomised. No study reported our predefined outcome of FGR. Of the eleven models that predicted birthweight on a continuous scale, only one (Poon 2011; 33,602 pregnancies) reported variables available in the IPPIC cohorts and was externally validated in nine IPPIC cohorts involving 441,415 pregnancies. The Poon model included gestational age at delivery, maternal weight, height, age, parity, smoking status, ethnicity, history of chronic hypertension, diabetes and assisted conception. Calibration slopes of the model ranged from 0.91 to 1.05, with a pooled calibration slope across all cohorts of 0.974 [95% confidence interval (CI) 0.938 to 1.011, τ2=0.0018]. On average, the model systematically underpredicted birthweight by 90.4 g (37.9 g to 142.9 g) across the validation cohorts and showed moderate heterogeneity in performance. IPPIC-FGR and IPPIC-birthweight models accurately predict FGR and birthweight. The latter has better calibration than existing model. IPPIC-FGR model use is cost-effective. Both IPPIC models can help plan intensity of fetal monitoring in pregnancy and timing of delivery, to minimise adverse perinatal outcomes.

Authors' recommendations: Incorporation of personalised predicted birthweight estimates (for various potential gestational ages) within existing growth charts, and risk stratification at booking for FGR can help plan intensity of fetal monitoring and timing of delivery. The impact of using IPPIC-FGR and IPPIC-birthweight models on changes in clinical practice and clinical outcomes needs further evaluation. Qualitative data are needed to determine the barriers and facilitators of their routine implementation in clinical practice. Our health economics analysis was based on the 2008 NICE model which is no longer reflective of current management strategies for risk assessing FGR. Therefore, in light of significant changes to current guidelines and care pregnant women at risk of FGRs receive, a detailed full economic evaluation is needed, which evaluates various strategies to risk assess FGR along current care pathways.

Authors' methods: Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis. Pregnant women at booking. External validation of existing models (9 cohorts, 441,415 pregnancies); International Prediction of Pregnancy Complications model development and validation (4 cohorts, 237,228 pregnancies). We could not externally validate most of the published models due to variations in the definitions of outcomes. Internal-external cross-validation of our International Prediction of Pregnancy Complications-fetal growth restriction model was limited by the paucity of events in the included cohorts. The economic evaluation using the published National Institute for Health and Care Excellence 2008 model may not reflect current practice, and full economic evaluation was not possible due to paucity of data. We followed existing recommendations for prediction model development and validation and reported in line with guidelines for prognostic research and IPD meta-analysis. Our meta-analysis utilised IPD within the IPPIC Network database. IPPIC is a living data repository of cleaned and harmonised data of pregnant women from large birth or population-based cohorts, study cohort data, registries or unpublished data from hospital records. The primary outcomes were (1) FGR defined as birthweight 

Project Status: Completed

URL for project: https://www.journalslibrary.nihr.ac.uk/programmes/hta/17/148/07

Year Published: 2024

URL for published report: https://www.journalslibrary.nihr.ac.uk/hta/DABW4814

URL for additional information: English

English language abstract: An English language summary is available

Publication Type: Full HTA

Country: United Kingdom

DOI: 10.3310/DABW4814

Organisation Name: NIHR Health and Social Care Delivery Program

Contact Name: Rhiannon Miller

Contact Email: rhiannon.m@prepress-projects.co.uk