John Allotey, Lucinda Archer, Dyuti Coomar, Kym Ie Snell, Melanie Smuk, Lucy Oakey, Sadia Haqnawaz, Ana Pilar Betrán, Lucy C Chappell, Wessel Ganzevoort, Sanne Gordijn, Asma Khalil, Ben W Mol, Rachel K Morris, Jenny Myers, Aris T Papageorghiou, Basky Thilaganathan, Fabricio Da Silva Costa, Fabio Facchinetti, Arri Coomarasamy, Akihide Ohkuchi, Anne Eskild, Javier Arenas Ramírez, Alberto Galindo, Ignacio Herraiz, Federico Prefumo, Shigeru Saito, Line Sletner, Jose Guilherme Cecatti, Rinat Gabbay-Benziv, Francois Goffinet, Ahmet A Baschat, Renato T Souza, Fionnuala Mone, Diane Farrar, Seppo Heinonen, Kjell Å Salvesen, Luc Jm Smits, Sohinee Bhattacharya, Chie Nagata, Satoru Takeda, Marleen Mhj van Gelder, Dewi Anggraini, SeonAe Yeo, Jane West, Javier Zamora, Hema Mistry, Richard D Riley, Shakila Thangaratinam
{"title":"Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis.","authors":"John Allotey, Lucinda Archer, Dyuti Coomar, Kym Ie Snell, Melanie Smuk, Lucy Oakey, Sadia Haqnawaz, Ana Pilar Betrán, Lucy C Chappell, Wessel Ganzevoort, Sanne Gordijn, Asma Khalil, Ben W Mol, Rachel K Morris, Jenny Myers, Aris T Papageorghiou, Basky Thilaganathan, Fabricio Da Silva Costa, Fabio Facchinetti, Arri Coomarasamy, Akihide Ohkuchi, Anne Eskild, Javier Arenas Ramírez, Alberto Galindo, Ignacio Herraiz, Federico Prefumo, Shigeru Saito, Line Sletner, Jose Guilherme Cecatti, Rinat Gabbay-Benziv, Francois Goffinet, Ahmet A Baschat, Renato T Souza, Fionnuala Mone, Diane Farrar, Seppo Heinonen, Kjell Å Salvesen, Luc Jm Smits, Sohinee Bhattacharya, Chie Nagata, Satoru Takeda, Marleen Mhj van Gelder, Dewi Anggraini, SeonAe Yeo, Jane West, Javier Zamora, Hema Mistry, Richard D Riley, Shakila Thangaratinam","doi":"10.3310/DABW4814","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes.</p><p><strong>Objectives: </strong>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.</p><p><strong>Design: </strong>Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis.</p><p><strong>Participants: </strong>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).</p><p><strong>Predictors: </strong>Maternal clinical characteristics, biochemical and ultrasound markers.</p><p><strong>Primary outcomes: </strong>fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight.</p><p><strong>Analysis: </strong>First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (<i>c</i>-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ<sup>2</sup> and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model.</p><p><strong>Results: </strong>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 <i>c</i>-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).</p><p><strong>Limitations: </strong>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.</p><p><strong>Future work: </strong>International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation.</p><p><strong>Conclusion: </strong>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.</p><p><strong>Study registration: </strong>This study is registered as PROSPERO CRD42019135045.</p><p><strong>Funding: </strong>This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in <i>Health Technology Assessment</i>; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.</p>","PeriodicalId":12898,"journal":{"name":"Health technology assessment","volume":"28 47","pages":"1-119"},"PeriodicalIF":3.5000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11404361/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Health technology assessment","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3310/DABW4814","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Fetal growth restriction is associated with perinatal morbidity and mortality. Early identification of women having at-risk fetuses can reduce perinatal adverse outcomes.
Objectives: 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.
Design: Individual participant data meta-analyses of cohorts in International Prediction of Pregnancy Complications network, decision curve analysis and health economics analysis.
Participants: 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).
Predictors: Maternal clinical characteristics, biochemical and ultrasound markers.
Primary outcomes: fetal growth restriction defined as birthweight <10th centile adjusted for gestational age and with stillbirth, neonatal death or delivery before 32 weeks' gestation birthweight.
Analysis: First, we externally validated existing models using individual participant data meta-analysis. If needed, we developed and validated new International Prediction of Pregnancy Complications models using random-intercept regression models with backward elimination for variable selection and undertook internal-external cross-validation. We estimated the study-specific performance (c-statistic, calibration slope, calibration-in-the-large) for each model and pooled using random-effects meta-analysis. Heterogeneity was quantified using τ2 and 95% prediction intervals. We assessed the clinical utility of the fetal growth restriction model using decision curve analysis, and health economics analysis based on National Institute for Health and Care Excellence 2008 model.
Results: 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).
Limitations: 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.
Future work: International Prediction of Pregnancy Complications models' performance needs to be assessed in routine practice, and their impact on decision-making and clinical outcomes needs evaluation.
Conclusion: 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.
Study registration: This study is registered as PROSPERO CRD42019135045.
Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 17/148/07) and is published in full in Health Technology Assessment; Vol. 28, No. 14. See the NIHR Funding and Awards website for further award information.
期刊介绍:
Health Technology Assessment (HTA) publishes research information on the effectiveness, costs and broader impact of health technologies for those who use, manage and provide care in the NHS.