Development and validation of a prognostic model to predict birth weight: individual participant data meta-analysis
Authors: Allotey John , Archer Lucinda , Snell Kym I E , Coomar Dyuti , Massé Jacques , Sletner Line , Wolf Hans , Daskalakis George , Saito Shigeru , Ganzevoort Wessel , Ohkuchi Akihide , Mistry Hema , Farrar Diane , Mone Fionnuala , Zhang Jun , Seed Paul T , Teede Helena , Da Silva Costa Fabricio , Souka Athena P , Smuk Melanie , Ferrazzani Sergio , Salvi Silvia , Prefumo Federico , Gabbay-Benziv Rinat , Nagata Chie , Takeda Satoru , Sequeira Evan , Lapaire Olav , Cecatti Jose Guilherme , Morris Rachel Katherine , Baschat Ahmet A , Salvesen Kjell , Smits Luc , Anggraini Dewi , Rumbold Alice , van Gelder Marleen , Coomarasamy Arri , Kingdom John , Heinonen Seppo , Khalil Asma , Goffinet François , Haqnawaz Sadia , Zamora Javier , Riley Richard D , Thangaratinam Shakila 
Published: August 2024

Objective

To predict birth weight at various potential gestational ages of delivery based on data routinely available at the first antenatal visit.

Design

Individual participant data meta-analysis.

Data sources

Individual participant data of four cohorts (237 228 pregnancies) from the International Prediction of Pregnancy Complications (IPPIC) network dataset.

Eligibility criteria for selecting studies

Studies in the IPPIC network were identified by searching major databases for studies reporting risk factors for adverse pregnancy outcomes, such as pre-eclampsia, fetal growth restriction, and stillbirth, from database inception to August 2019. Data of four IPPIC cohorts (237 228 pregnancies) from the US (National Institute of Child Health and Human Development, 2018; 233 483 pregnancies), UK (Allen et al, 2017; 1045 pregnancies), Norway (STORK Groruddalen research programme, 2010; 823 pregnancies), and Australia (Rumbold et al, 2006; 1877 pregnancies) were included in the development of the model.

Results

The IPPIC birth weight model was developed with random intercept regression models with backward elimination for variable selection. Internal-external cross validation was performed to assess the study specific and pooled performance of the model, reported as calibration slope, calibration-in-the-large, and observed versus expected average birth weight ratio. Meta-analysis showed that the apparent performance of the model had good calibration (calibration slope 0.99, 95% confidence interval (CI) 0.88 to 1.10; calibration-in-the-large 44.5 g, −18.4 to 107.3) with an observed versus expected average birth weight ratio of 1.02 (95% CI 0.97 to 1.07). The proportion of variation in birth weight explained by the model (R2) was 46.9% (range 32.7-56.1% in each cohort). On internal-external cross validation, the model showed good calibration and predictive performance when validated in three cohorts with a calibration slope of 0.90 (Allen cohort), 1.04 (STORK Groruddalen cohort), and 1.07 (Rumbold cohort), calibration-in-the-large of −22.3 g (Allen cohort), −33.42 (Rumbold cohort), and 86.4 g (STORK Groruddalen cohort), and observed versus expected ratio of 0.99 (Rumbold cohort), 1.00 (Allen cohort), and 1.03 (STORK Groruddalen cohort); respective pooled estimates were 1.00 (95% CI 0.78 to 1.23; calibration slope), 9.7 g (−154.3 to 173.8; calibration-in-the-large), and 1.00 (0.94 to 1.07; observedvexpected ratio). The model predictions were more accurate (smaller mean square error) in the lower end of predicted birth weight, which is important in informing clinical decision making.

Conclusions

The IPPIC birth weight model allowed birth weight predictions for a range of possible gestational ages. The model explained about 50% of individual variation in birth weights, was well calibrated (especially in babies at high risk of fetal growth restriction and its complications), and showed promising performance in four different populations included in the individual participant data meta-analysis. Further research to examine the generalisability of performance in other countries, settings, and subgroups is required.

Trial registration

PROSPERO CRD42019135045