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":"胎儿生长受限和出生体重预测模型的开发与验证:个体参与者数据荟萃分析。","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. 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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. 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引用次数: 0
摘要
背景:胎儿生长受限与围产期发病率和死亡率有关。早期识别高危胎儿的妇女可减少围产期不良结局:评估现有胎儿生长受限和出生体重预测模型的预测性能,并在必要时利用个体参与者数据开发和验证新的多变量模型:设计:对国际妊娠并发症预测网络中的队列进行个体参与者数据荟萃分析、决策曲线分析和健康经济学分析:预约时的孕妇。现有模型的外部验证(9 个队列,441 415 例妊娠);国际妊娠并发症预测模型的开发和验证(4 个队列,237 228 例妊娠):主要结果:胎儿生长受限,定义为出生体重 分析:首先,我们利用个体参与者数据荟萃分析对现有模型进行外部验证。如有必要,我们使用随机截距回归模型和反向排除法选择变量,开发并验证了新的妊娠并发症国际预测模型,并进行了内部-外部交叉验证。我们估算了每个模型的研究特异性表现(c 统计量、校准斜率、大校准),并使用随机效应荟萃分析进行了汇总。异质性采用 τ2 和 95% 预测区间进行量化。我们使用决策曲线分析评估了胎儿生长受限模型的临床实用性,并根据美国国家健康与护理卓越研究所(National Institute for Health and Care Excellence 2008)的模型进行了健康经济学分析:在已发表的 119 个模型中,有一个出生体重模型(Poon)可以通过验证。根据我们的定义,没有一个模型报告了胎儿生长受限。在所有队列中,Poon 模型的校准斜率为 0.93(95% 置信区间为 0.90 至 0.96),具有良好的汇总校准斜率,但略有过拟合,出生体重平均低估了 90.4 克(95% 置信区间为 37.9 克至 142.9 克)。新开发的国际妊娠并发症预测-胎儿生长受限模型包括产妇年龄、身高、奇偶数、吸烟状况、种族、高血压病史、子痫前期、死胎或小于胎龄儿以及分娩时的胎龄。这样就可以根据一系列假定的分娩时胎龄进行预测。汇总的表观 c 统计量和校准值分别为 0.96(95% 置信区间为 0.51 至 1.0)和 0.95(95% 置信区间为 0.67 至 1.23)。预测概率阈值在 1%至 90%之间时,模型显示出正净效益。除了妊娠并发症国际预测-胎儿生长受限模型中的预测因素外,妊娠并发症国际预测-出生体重模型还包括产妇体重、糖尿病史和受孕方式。在内部-外部交叉验证中,各组群的平均校准斜率为 1.00(95% 置信区间为 0.78 至 1.23),没有过度拟合的迹象。出生体重平均被低估了 9.7 克(95% 置信区间为 -154.3 克至 173.8 克):局限性:由于结果定义的不同,我们无法对大部分已发表的模型进行外部验证。国际妊娠并发症预测-胎儿生长受限模型的内部-外部交叉验证受到了所纳入队列中事件较少的限制。使用国家健康与护理卓越研究所公布的 2008 年模型进行的经济评估可能无法反映当前的实践,而且由于数据匮乏,无法进行全面的经济评估:国际妊娠并发症预测模型的性能需要在日常实践中进行评估,其对决策和临床结果的影响也需要评估:结论:妊娠并发症国际预测-胎儿生长受限模型和妊娠并发症国际预测-出生体重模型能准确预测不同假定孕龄分娩时的胎儿生长受限和出生体重。这些模型可用于对预约时的风险状况进行分层、计划监测和管理:本研究注册为 PROSPERO CRD42019135045:该奖项由美国国家健康与护理研究所(NIHR)健康技术评估项目资助(NIHR奖项编号:17/148/07),全文发表于《健康技术评估》第28卷第14期。如需了解更多奖项信息,请访问 NIHR Funding and Awards 网站。
Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis.
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.