John Allotey, Lucinda Archer, Kym I E Snell, Dyuti Coomar, Jacques Massé, Line Sletner, Hans Wolf, George Daskalakis, Shigeru Saito, Wessel Ganzevoort, Akihide Ohkuchi, Hema Mistry, Diane Farrar, Fionnuala Mone, Jun Zhang, Paul T Seed, Helena Teede, Fabricio Da Silva Costa, Athena P Souka, Melanie Smuk, Sergio Ferrazzani, Silvia Salvi, Federico Prefumo, Rinat Gabbay-Benziv, Chie Nagata, Satoru Takeda, Evan Sequeira, Olav Lapaire, Jose Guilherme Cecatti, Rachel Katherine Morris, Ahmet A Baschat, Kjell Salvesen, Luc Smits, Dewi Anggraini, Alice Rumbold, Marleen van Gelder, Arri Coomarasamy, John Kingdom, Seppo Heinonen, Asma Khalil, François Goffinet, Sadia Haqnawaz, Javier Zamora, Richard D Riley, Shakila Thangaratinam, 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
{"title":"开发和验证预测出生体重的预后模型：个体参与者数据荟萃分析。","authors":"John Allotey, Lucinda Archer, Kym I E Snell, Dyuti Coomar, Jacques Massé, Line Sletner, Hans Wolf, George Daskalakis, Shigeru Saito, Wessel Ganzevoort, Akihide Ohkuchi, Hema Mistry, Diane Farrar, Fionnuala Mone, Jun Zhang, Paul T Seed, Helena Teede, Fabricio Da Silva Costa, Athena P Souka, Melanie Smuk, Sergio Ferrazzani, Silvia Salvi, Federico Prefumo, Rinat Gabbay-Benziv, Chie Nagata, Satoru Takeda, Evan Sequeira, Olav Lapaire, Jose Guilherme Cecatti, Rachel Katherine Morris, Ahmet A Baschat, Kjell Salvesen, Luc Smits, Dewi Anggraini, Alice Rumbold, Marleen van Gelder, Arri Coomarasamy, John Kingdom, Seppo Heinonen, Asma Khalil, François Goffinet, Sadia Haqnawaz, Javier Zamora, Richard D Riley, Shakila Thangaratinam, 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","doi":"10.1136/bmjmed-2023-000784","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To predict birth weight at various potential gestational ages of delivery based on data routinely available at the first antenatal visit.</p><p><strong>Design: </strong>Individual participant data meta-analysis.</p><p><strong>Data sources: </strong>Individual participant data of four cohorts (237 228 pregnancies) from the International Prediction of Pregnancy Complications (IPPIC) network dataset.</p><p><strong>Eligibility criteria for selecting studies: </strong>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.</p><p><strong>Results: </strong>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<sup>2</sup>) 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 <i>v</i> 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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Trial registration: </strong>PROSPERO CRD42019135045.</p>","PeriodicalId":72433,"journal":{"name":"BMJ medicine","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344865/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a prognostic model to predict birth weight: individual participant data meta-analysis.\",\"authors\":\"John Allotey, Lucinda Archer, Kym I E Snell, Dyuti Coomar, Jacques Massé, Line Sletner, Hans Wolf, George Daskalakis, Shigeru Saito, Wessel Ganzevoort, Akihide Ohkuchi, Hema Mistry, Diane Farrar, Fionnuala Mone, Jun Zhang, Paul T Seed, Helena Teede, Fabricio Da Silva Costa, Athena P Souka, Melanie Smuk, Sergio Ferrazzani, Silvia Salvi, Federico Prefumo, Rinat Gabbay-Benziv, Chie Nagata, Satoru Takeda, Evan Sequeira, Olav Lapaire, Jose Guilherme Cecatti, Rachel Katherine Morris, Ahmet A Baschat, Kjell Salvesen, Luc Smits, Dewi Anggraini, Alice Rumbold, Marleen van Gelder, Arri Coomarasamy, John Kingdom, Seppo Heinonen, Asma Khalil, François Goffinet, Sadia Haqnawaz, Javier Zamora, Richard D Riley, Shakila Thangaratinam, 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\",\"doi\":\"10.1136/bmjmed-2023-000784\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objective: </strong>To predict birth weight at various potential gestational ages of delivery based on data routinely available at the first antenatal visit.</p><p><strong>Design: </strong>Individual participant data meta-analysis.</p><p><strong>Data sources: </strong>Individual participant data of four cohorts (237 228 pregnancies) from the International Prediction of Pregnancy Complications (IPPIC) network dataset.</p><p><strong>Eligibility criteria for selecting studies: </strong>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.</p><p><strong>Results: </strong>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<sup>2</sup>) 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 <i>v</i> 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.</p><p><strong>Conclusions: </strong>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.</p><p><strong>Trial registration: </strong>PROSPERO CRD42019135045.</p>\",\"PeriodicalId\":72433,\"journal\":{\"name\":\"BMJ medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344865/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMJ medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1136/bmjmed-2023-000784\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ medicine","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/bmjmed-2023-000784","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
Development and validation of a prognostic model to predict birth weight: individual participant data meta-analysis.
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 (R2) 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.
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