预测出生体重的预后模型的开发与验证：个体参与者数据荟萃分析

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

作者信息

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, Kwong Alex, Savitri Ary I, Bhattacharya Sohinee, Uiterwaal Cuno Spm, Staff Annetine C, Andersen Louise Bjoerkholt, Olive Elisa Llurba, Redman Christopher, Macleod Maureen, Thilaganathan Baskaran, Ramírez Javier Arenas, Audibert Francois, Magnus Per Minor, Jenum Anne Karen, McAuliffe Fionnuala M, West Jane, Askie Lisa M, Zimmerman Peter A, Riddell Catherine, van de Post Joris, Illanes Sebastián E, Holzman Claudia, van Kuijk Sander M J, Carbillon Lionel, Villa Pia M, Eskild Anne, Chappell Lucy, Velauthar Luxmi, van Oostwaard Miriam, Verlohren Stefan, Poston Lucilla, Ferrazzi Enrico, Vinter Christina A, Brown Mark, Vollebregt Karlijn C, Langenveld Josje, Widmer Mariana, Haavaldsen Camilla, Carroli Guillermo, Olsen Jørn, Zavaleta Nelly, Eisensee Inge, Vergani Patrizia, Lumbiganon Pisake, Makrides Maria, Facchinetti Fabio, Temmerman Marleen, Gibson Robert, Frusca Tiziana, Norman Jane E, Figueiró-Filho Ernesto A, Laivuori Hannele, Lykke Jacob A, Conde-Agudelo Agustin, Galindo Alberto, Mbah Alfred, Betran Ana Pilar, Herraiz Ignacio, Trogstad Lill, Smith Gordon G S, Steegers Eric A P, Salim Read, Huang Tianhua, Adank Annemarijne, Meschino Wendy S, Browne Joyce L, Allen Rebecca E, Klipstein-Grobusch Kerstin, Crowther Caroline A, Jørgensen Jan Stener, Forest Jean-Claude, Mol Ben W, Giguère Yves, Kenny Louise C, Odibo Anthony O, Myers Jenny, Yeo SeonAe, McCowan Lesley, Pajkrt Eva, Haddad Bassam G, Dekker Gustaaf, Kleinrouweler Emily C, LeCarpentier Édouard, Roberts Claire T, Groen Henk, Skråstad Ragnhild Bergene, Eero Kajantie, Pilalis Athanasios, Souza Renato T, Hawkins Lee Ann, Figueras Francesc, Crovetto Francesca

出版信息

BMJ Med. 2024 Aug 14;3(1):e000784. doi: 10.1136/bmjmed-2023-000784. eCollection 2024.

DOI:10.1136/bmjmed-2023-000784

PMID:39184566

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11344865/

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) 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 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.

摘要

目的

基于首次产前检查时常规可得的数据，预测不同潜在分娩孕周的出生体重。

设计

个体参与者数据荟萃分析。

数据来源

国际妊娠并发症预测（IPPIC）网络数据集的四个队列（237228例妊娠）的个体参与者数据。

选择研究的纳入标准

通过检索主要数据库来识别IPPIC网络中的研究，这些研究报告了自数据库建立至2019年8月期间不良妊娠结局的危险因素，如子痫前期、胎儿生长受限和死产。来自美国（国家儿童健康与人类发展研究所，2018年；233483例妊娠）、英国（艾伦等人，2017年；1045例妊娠）、挪威（STORK Groruddalen研究项目，2010年；823例妊娠）和澳大利亚（朗博尔德等人，2006年；1877例妊娠）的四个IPPIC队列的数据被纳入模型的开发。

结果

IPPIC出生体重模型采用随机截距回归模型并通过向后剔除法进行变量选择而开发。进行了内部 - 外部交叉验证以评估模型的研究特异性和合并性能，结果以校准斜率、总体校准以及观察到的与预期的平均出生体重比来报告。荟萃分析表明，该模型的表观性能具有良好的校准（校准斜率0.99，95%置信区间（CI）0.88至1.10；总体校准44.5克，-18.4至107.3），观察到的与预期的平均出生体重比为1.02（95%CI 0.97至1.07）。该模型解释的出生体重变异比例（R）为46.9%（每个队列范围为32.7 - 56.1%）。在内部 - 外部交叉验证中，当在三个队列中进行验证时，该模型显示出良好的校准和预测性能，其中艾伦队列的校准斜率为0.90，STORK Groruddalen队列的校准斜率为1.04，朗博尔德队列的校准斜率为1.07；艾伦队列的总体校准为 - 22.3克，朗博尔德队列的总体校准为 - 33.42克，STORK Groruddalen队列的总体校准为86.4克；朗博尔德队列的观察到的与预期的比例为0.99，艾伦队列的观察到的与预期的比例为1.00，STORK Groruddalen队列的观察到的与预期的比例为1.03；各自的合并估计值为1.00（95%CI 0.78至1.23；校准斜率），9.7克（-154.3至173.8；总体校准），以及1.00（0.94至1.07；观察到的与预期的比例）。该模型在预测出生体重较低端的预测更准确（均方误差较小），这对临床决策具有重要意义。