TY - JOUR
T1 - Development and validation of a prognostic model to predict birth weight:
T2 - individual participant data meta-analysis
AU - Allotey, John
AU - Archer, Lucinda
AU - Snell, Kym I E
AU - Coomar, Dyuti
AU - Massé, Jacques
AU - Sletner, Line
AU - Wolf, Hans
AU - Daskalakis, George
AU - Saito, Shigeru
AU - Ganzevoort, Wessel
AU - Ohkuchi, Akihide
AU - Mistry, Hema
AU - Farrar, Diane
AU - Mone, Fionnuala
AU - Zhang, Jun
AU - Seed, Paul T
AU - Teede, Helena
AU - Costa, Fabricio Da Silva
AU - Souka, Athena P
AU - Smuk, Melanie
AU - Ferrazzani, Sergio
AU - Salvi, Silvia
AU - Prefumo, Federico
AU - Gabbay-Benziv, Rinat
AU - Nagata, Chie
AU - Takeda, Satoru
AU - Sequeira, Evan
AU - Lapaire, Olav
AU - Cecatti, Jose Guilherme
AU - Morris, Rachel Katherine
AU - Baschat, Ahmet A
AU - Salvesen, Kjell
AU - Smits, Luc
AU - Anggraini, Dewi
AU - Rumbold, Alice
AU - Gelder, Marleen van
AU - Coomarasamy, Arri
AU - Kingdom, John
AU - Heinonen, Seppo
AU - Khalil, Asma
AU - Goffinet, François
AU - Haqnawaz, Sadia
AU - Zamora, Javier
AU - Riley, Richard D
AU - Thangaratinam, Shakila
AU - International Prediction of Pregnancy Complications Collaborative network
AU - Kwong, Alex
AU - Savitri, Ary I.
AU - Bhattacharya, Sohinee
AU - Uiterwaal, Cuno S.P.M.
AU - Staff, Annetine C.
AU - Andersen, Louise Bjoerkholt
AU - Olive, Elisa Llurba
AU - Redman, Christopher
AU - Macleod, Maureen
AU - Thilaganathan, Baskaran
AU - Ramírez, Javier Arenas
AU - Audibert, Francois
AU - Magnus, Per Minor
AU - Jenum, Anne Karen
AU - McAuliffe, Fionnuala M
AU - West, Jane
AU - Askie, Lisa M
AU - Zimmerman, Peter A.
AU - Riddell, Catherine
AU - van de Post, Joris
AU - Illanes, Sebastian E.
AU - Holzman, Claudia
AU - van Kuijk, Sander M.J.
AU - Carbillon, Lionel
AU - Villa, Pia M.
AU - Eskild, Anne
AU - Chappell, Lucy
AU - Velauthar, Luxmi
AU - van Oostwaard, Miriam
AU - Verlohren, Stefan
AU - Poston, Lucilla
AU - Ferrazzi, Enrico
AU - Vinter, Christina A.
AU - Brown, Mark
AU - Vollebregt, Karlijn C.
AU - Langenveld, Josje
AU - Widmer, Mariana
AU - Haavaldsen, Camilla
AU - Carroli, Guillermo
AU - Olsen, Jørn
AU - Zavaleta, Nelly
AU - Eisensee, Inge
AU - Vergani, Patrizia
AU - Lumbiganon, Pisake
AU - Makrides, Maria
AU - Facchinetti, Fabio
AU - Temmerman, Marleen
AU - Gibson, Robert
AU - Frusca, Tiziana
AU - Norman, Jane E.
AU - Figueiró-Filho, Ernesto A.
AU - Laivuori, Hannele
AU - Lykke, Jacob A.
AU - Conde-Agudelo, Agustin
AU - Galindo, Alberto
AU - Mbah, Alfred
AU - Betran, Ana P.
AU - Herraiz, Ignacio
AU - Trogstad, Lill
AU - Smith, Gordon G.S.
AU - Steegers, Eric A.P.
AU - Salim, Read
AU - Huang, Tianhua
AU - Adank, Annemarijne
AU - Meschino, Wendy S.
AU - Browne, Joyce L.
AU - Allen, Rebecca E.
AU - Klipstein-Grobusch, Kerstin
AU - Crowther, Caroline A.
AU - Jørgensen, Jan Stener
AU - Forest, Jean-Claude
AU - Mol, Ben W
AU - Giguère, Yves
AU - Kenny, Louise C.
AU - Odibo, Anthony O.
AU - Myers, Jenny
AU - Yeo, Seon Ae
AU - McCowan, Lesley
AU - Pajkrt, Eva
AU - Haddad, Bassam G.
AU - Dekker, Gustaaf
AU - Kleinrouweler, Emily C.
AU - LeCarpentier, Édouard
AU - Roberts, Claire T
AU - Groen, Henk
AU - Skråstad, Ragnhild Bergene
AU - Eero, Kajantie
AU - Pilalis, Athanasios
AU - Souza, Renato T.
AU - Ann Hawkins, Lee
AU - Figueras, Francesc
AU - Crovetto, Francesca
PY - 2024/2
Y1 - 2024/2
N2 - 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 (R 2) 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; observed v expected 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
AB - 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 (R 2) 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; observed v expected 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
KW - birth weight
KW - birth weight prediction
KW - prognostic model
KW - gestational age
KW - meta-analysis
U2 - 10.1136/bmjmed-2023-000784
DO - 10.1136/bmjmed-2023-000784
M3 - Article
VL - 3
JO - BMJ Medicine
JF - BMJ Medicine
IS - 1
M1 - e000784
ER -