Evidence map›Paper›PMID 39184566›Full record

ArticleBMJ medicine2024

Development and validation of a prognostic model to predict birth weight: individual participant data meta-analysis.

John Allotey, Lucinda Archer, Kym I E Snell, Dyuti Coomar, Jacques Massé, Line Sletner, Hans Wolf, George Daskalakis, Shigeru Saito, Wessel Ganzevoort and 128 more

Abstract readComment
In one paragraph

Article in BMJ medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

138 authors.

Lucinda Archer
Dyuti Coomar
Jacques Massé
Line Sletner
Hans Wolf
George Daskalakis
Shigeru Saito
Wessel Ganzevoort
Akihide Ohkuchi
Diane Farrar
Fionnuala Mone
Jun Zhang
Paul T Seed
Helena Teede
Fabricio Da Silva Costa
Athena P Souka
Melanie Smuk
Sergio Ferrazzani
Silvia Salvi
Rinat Gabbay-Benziv
Chie Nagata
Satoru Takeda
Evan Sequeira
Olav Lapaire
Jose Guilherme Cecatti
Rachel Katherine MorrisORCID 0000-0003-1247-429X
Ahmet A Baschat
Kjell Salvesen
Luc Smits
Dewi Anggraini
Alice Rumbold
Marleen van GelderORCID 0000-0003-4853-4434
Arri Coomarasamy
John Kingdom
Seppo Heinonen
François Goffinet
Sadia Haqnawaz
Javier Zamora
Shakila Thangaratinam
International Prediction of Pregnancy Complications collaborative network
Alex Kwong
Ary I Savitri
Sohinee Bhattacharya
Cuno Spm Uiterwaal
Annetine C Staff
Louise Bjoerkholt Andersen
Elisa Llurba Olive
Christopher Redman
Maureen Macleod
Baskaran Thilaganathan
Javier Arenas Ramírez
Francois Audibert
Per Minor Magnus
Anne Karen Jenum
Fionnuala M McAuliffe
Jane West
Lisa M Askie
Peter A Zimmerman
Catherine Riddell
Joris van de Post
Sebastián E Illanes
Claudia Holzman
Sander M J van Kuijk
Lionel Carbillon
Pia M Villa
Anne Eskild
Lucy Chappell
Luxmi Velauthar
Miriam van Oostwaard
Stefan Verlohren
Lucilla Poston
Enrico Ferrazzi
Christina A Vinter
Mark Brown
Karlijn C Vollebregt
Josje Langenveld
Mariana Widmer
Camilla Haavaldsen
Guillermo Carroli
Jørn Olsen
Nelly Zavaleta
Inge Eisensee
Patrizia Vergani
Pisake Lumbiganon
Maria Makrides
Fabio Facchinetti
Marleen Temmerman
Robert Gibson
Tiziana Frusca
Jane E Norman
Ernesto A Figueiró-Filho
Hannele Laivuori
Jacob A Lykke
Agustin Conde-Agudelo
Alberto Galindo
Alfred Mbah
Ana Pilar Betran
Ignacio Herraiz
Lill Trogstad
Gordon G S Smith
Eric A P Steegers
Read Salim
Tianhua Huang
Annemarijne Adank
Wendy S Meschino
Joyce L Browne
Rebecca E Allen
Kerstin Klipstein-Grobusch
Caroline A Crowther
Jan Stener Jørgensen
Jean-Claude Forest
Ben W Mol
Yves Giguère
Louise C Kenny
Anthony O Odibo
Jenny Myers
SeonAe Yeo
Lesley McCowan
Eva Pajkrt
Bassam G Haddad
Gustaaf Dekker
Emily C Kleinrouweler
Édouard LeCarpentier
Claire T Roberts
Henk Groen
Ragnhild Bergene Skråstad
Kajantie Eero
Athanasios Pilalis
Renato T Souza
Lee Ann Hawkins
Francesc Figueras
Francesca Crovetto

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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 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.

Indexed as

ObstetricsPregnancy complications

Identifiers

PMID39184566
PMCPMC11344865

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.