External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta‐analysis. (1st February 2022)
- Record Type:
- Journal Article
- Title:
- External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta‐analysis. (1st February 2022)
- Main Title:
- External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta‐analysis
- Authors:
- Allotey, J.
Whittle, R.
Snell, K. I. E.
Smuk, M.
Townsend, R.
von Dadelszen, P.
Heazell, A. E. P.
Magee, L.
Smith, G. C. S.
Sandall, J.
Thilaganathan, B.
Zamora, J.
Riley, R. D.
Khalil, A.
Thangaratinam, S. - Other Names:
- Coomarasamy Arri investigator.
Kwong Alex investigator.
Savitri Ary I. investigator.
Salvesen Kjell åsmund investigator.
Bhattacharya Sohinee investigator.
Uiterwaal Cuno S. P. M. investigator.
Staff Annetine C. investigator.
Andersen Louise Bjoerkholt investigator.
Olive Elisa Llurba investigator.
Redman Christopher investigator.
Sletner Line investigator.
Daskalakis George investigator.
Macleod Maureen investigator.
Thilaganathan Baskaran investigator.
Abdollahain Mali investigator.
Ramírez Javier Arenas investigator.
Massé Jacques investigator.
Khalil Asma investigator.
Audibert Francois investigator.
Magnus Per Minor investigator.
Jenum Anne Karen investigator.
Baschat Ahmet investigator.
Ohkuchi Akihide investigator.
McAuliffe Fionnuala M. investigator.
West Jane investigator.
Askie Lisa M. investigator.
Mone Fionnuala investigator.
Farrar Diane investigator.
Zimmerman Peter A. investigator.
Smits Luc J. M. investigator.
Riddell Catherine investigator.
Kingdom John C. investigator.
van de Post Joris investigator.
Illanes Sebastián E. investigator.
Holzman Claudia investigator.
van Kuijk Sander M. J. investigator.
Carbillon Lionel investigator.
Villa Pia M. investigator.
Eskild Anne investigator.
Chappell Lucy investigator.
Prefumo Federico investigator.
Velauthar Luxmi investigator.
Seed Paul investigator.
van Oostwaard Miriam investigator.
Verlohren Stefan investigator.
Poston Lucilla investigator.
Ferrazzi Enrico investigator.
Vinter Christina A. investigator.
Nagata Chie investigator.
Brown Mark investigator.
Vollebregt Karlijn C. investigator.
Takeda Satoru investigator.
Langenveld Josje investigator.
Widmer Mariana investigator.
Saito Shigeru investigator.
Haavaldsen Camilla investigator.
Carroli Guillermo investigator.
Olsen Jørn investigator.
Wolf Hans investigator.
Zavaleta Nelly investigator.
Eisensee Inge investigator.
Vergani Patrizia investigator.
Lumbiganon Pisake investigator.
Makrides Maria investigator.
Facchinetti Fabio investigator.
Sequeira Evan investigator.
Gibson Robert investigator.
Ferrazzani Sergio investigator.
Frusca Tiziana investigator.
Norman Jane E. investigator.
Figueiró Ernesto A. investigator.
Lapaire Olav investigator.
Laivuori Hannele investigator.
Lykke Jacob A. investigator.
Conde‐Agudelo Agustin investigator.
Galindo Alberto investigator.
Mbah Alfred investigator.
Betran Ana Pilar investigator.
Herraiz Ignacio investigator.
Trogstad Lill investigator.
Smith Gordon G. S. investigator.
Steegers Eric A. P. investigator.
Salim Read investigator.
Huang Tianhua investigator.
Adank Annemarijne investigator.
Zhang Jun investigator.
Meschino Wendy S. investigator.
Browne Joyce L. investigator.
Allen Rebecca E. investigator.
Costa Fabricio Da Silva investigator.
Klipstein‐Grobusch Browne Kerstin investigator.
Crowther Caroline A. investigator.
Jørgensen Jan Stener investigator.
Forest Jean‐Claude investigator.
Rumbold Alice R. investigator.
Mol Ben W. investigator.
Giguère Yves investigator.
Kenny Louise C. investigator.
Ganzevoort Wessel investigator.
Odibo Anthony O. investigator.
Myers Jenny investigator.
Yeo Seon Ae investigator.
Goffinet Francois investigator.
McCowan Lesley investigator.
Pajkrt Eva investigator.
Teede Helena J. investigator.
Haddad Bassam G. investigator.
Dekker Gustaaf investigator.
Kleinrouweler Emily C. investigator.
LeCarpentier Édouard investigator.
Roberts Claire T. investigator.
Groen Henk investigator.
Skråstad Ragnhild Bergene investigator.
Heinonen Seppo investigator.
Eero Kajantie investigator.
Anggraini Dewi investigator.
Souka Athena investigator.
Cecatti Jose Guilherme investigator.
Monterio Ilza investigator.
Pillalis Athanasios investigator.
Souza Renato investigator.
Hawkins Lee Ann investigator.
Gabbay‐Benziv Rinat investigator.
Crovetto Francesca investigator.
Figuera Francesc investigator.
Jorgensen Laura investigator.
Dodds Julie investigator.
Patel Mehali investigator.
Aviram Amir investigator.
Papageorghiou Aris investigator.
Khan Khalid investigator.
… (more) - Abstract:
- ABSTRACT: Objective: Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta‐analysis to assess their predictive performance. Methods: MEDLINE, EMBASE, DH‐DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C ‐statistic, and calibration was assessed using calibration plots, calibration slope and calibration‐in‐the‐large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random‐effects meta‐analysis. Clinical utility wasABSTRACT: Objective: Stillbirth is a potentially preventable complication of pregnancy. Identifying women at high risk of stillbirth can guide decisions on the need for closer surveillance and timing of delivery in order to prevent fetal death. Prognostic models have been developed to predict the risk of stillbirth, but none has yet been validated externally. In this study, we externally validated published prediction models for stillbirth using individual participant data (IPD) meta‐analysis to assess their predictive performance. Methods: MEDLINE, EMBASE, DH‐DATA and AMED databases were searched from inception to December 2020 to identify studies reporting stillbirth prediction models. Studies that developed or updated prediction models for stillbirth for use at any time during pregnancy were included. IPD from cohorts within the International Prediction of Pregnancy Complications (IPPIC) Network were used to validate externally the identified prediction models whose individual variables were available in the IPD. The risk of bias of the models and cohorts was assessed using the Prediction study Risk Of Bias ASsessment Tool (PROBAST). The discriminative performance of the models was evaluated using the C ‐statistic, and calibration was assessed using calibration plots, calibration slope and calibration‐in‐the‐large. Performance measures were estimated separately in each cohort, as well as summarized across cohorts using random‐effects meta‐analysis. Clinical utility was assessed using net benefit. Results: Seventeen studies reporting the development of 40 prognostic models for stillbirth were identified. None of the models had been previously validated externally, and the full model equation was reported for only one‐fifth (20%, 8/40) of the models. External validation was possible for three of these models, using IPD from 19 cohorts (491 201 pregnant women) within the IPPIC Network database. Based on evaluation of the model development studies, all three models had an overall high risk of bias, according to PROBAST. In the IPD meta‐analysis, the models had summary C ‐statistics ranging from 0.53 to 0.65 and summary calibration slopes ranging from 0.40 to 0.88, with risk predictions that were generally too extreme compared with the observed risks. The models had little to no clinical utility, as assessed by net benefit. However, there remained uncertainty in the performance of some models due to small available sample sizes. Conclusions: The three validated stillbirth prediction models showed generally poor and uncertain predictive performance in new data, with limited evidence to support their clinical application. The findings suggest methodological shortcomings in their development, including overfitting. Further research is needed to further validate these and other models, identify stronger prognostic factors and develop more robust prediction models. © 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology. … (more)
- Is Part Of:
- Ultrasound in obstetrics & gynecology. Volume 59:Number 2(2022)
- Journal:
- Ultrasound in obstetrics & gynecology
- Issue:
- Volume 59:Number 2(2022)
- Issue Display:
- Volume 59, Issue 2 (2022)
- Year:
- 2022
- Volume:
- 59
- Issue:
- 2
- Issue Sort Value:
- 2022-0059-0002-0000
- Page Start:
- 209
- Page End:
- 219
- Publication Date:
- 2022-02-01
- Subjects:
- external validation -- individual participant data -- intrauterine death -- prediction model -- stillbirth
Ultrasonics in obstetrics -- Periodicals
Generative organs, Female -- Diseases -- Diagnosis -- Periodicals
Diagnosis, Ultrasonic -- Periodicals
Genital Diseases, Female -- ultrasonography -- Periodicals
Ultrasonography, Prenatal -- Periodicals
618.047543 - Journal URLs:
- http://obgyn.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)1469-0705/ ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/uog.23757 ↗
- Languages:
- English
- ISSNs:
- 0960-7692
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9082.815300
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 21094.xml