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Enregistrement W4402428458 · doi:10.3310/dabw4814

Development and validation of prediction models for fetal growth restriction and birthweight: an individual participant data meta-analysis

2024· review· en· W4402428458 sur OpenAlex
John Allotey, Lucinda Archer, Dyuti Coomar, Kym I E Snell, Melanie Smuk, Lucy Oakey, Sadia Haqnawaz, Ana Pilar Betrán, Lucy C. Chappell, Wessel Ganzevoort, Sanne J. Gordijn, Asma Khalil, Ben W. Mol, R. Katie Morris, Jenny Myers, Aris T. Papageorghiou, B. Thilaganathan, Fabrício da Silva Costa, Fabio Facchinetti, Arri Coomarasamy, Akihide Ohkuchi, Anne Eskild, J. Arenas Ramírez, Alberto Galindo, Ignacio Herraı̀z, Federico Prefumo, Shigeru Saito, Line Sletner, José Guilherme Cecatti, Rinat Gabbay‐Benziv, François Goffinet, Ahmet Baschat, Renato T. Souza, Fionnuala Mone, Diane Farrar, Seppo Heinonen, Kjell Å. Salvesen, Luc Smits, Sohinee Bhattacharya, Chie Nagata, Satoru Takeda, Marleen M. H. J. van Gelder, Dewi Anggraini, SeonAe Yeo, Jane West, Javier Zamora, Hema Mistry, Richard D Riley, Shakila Thangaratinam

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Notice bibliographique

RevueHealth Technology Assessment · 2024
Typereview
Langueen
DomaineMedicine
ThématiquePregnancy and preeclampsia studies
Établissements canadiensnon disponible
Organismes subventionnairesNIHR Oxford Biomedical Research CentreHealth Technology Assessment ProgrammeSyddansk UniversitetRadboud Universitair Medisch CentrumUniversità Cattolica del Sacro CuoreNational Health and Medical Research CouncilUniversidade de São PauloUniversitetet i OsloRigshospitaletMinisterio de Asuntos Económicos y Transformación Digital, Gobierno de EspañaUniversitair Medisch Centrum GroningenUniversity of North Carolina at Chapel HillUniversità degli Studi di ParmaKhon Kaen UniversityNorwegian Institute of Public HealthPortland State UniversityAcademisch Medisch CentrumNational Institute of Child Health and Human DevelopmentSouth Australian Health and Medical Research InstituteUniversidad Complutense de MadridNational and Kapodistrian University of AthensZonMwUniversity of CambridgeQueen Mary University of LondonNational Institute for Health and Care ResearchInstituto de Salud Carlos IIIUniversitat de BarcelonaRadboud UniversiteitInternational Society of Ultrasound in Obstetrics and GynecologyUniversità degli Studi di Milano-BicoccaDepartment of Health and Social CareUniversitätsspital BaselMcGill UniversityNational Institute for Health and Care ExcellenceTommy'sWorld Health OrganizationNational Institute of Nursing ResearchAarhus UniversitetUniversity of South FloridaAkershus UniversitetssykehusCentre Hospitalier Universitaire de QuébecMonash UniversityUniversité LavalHelsingin Yliopisto
Mots-clésMeta-analysisMedicineFetal growthStatisticsObstetricsFetusPregnancyInternal medicineMathematics

Résumé

récupéré en direct d'OpenAlex

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 τ 2 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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,598
Score d'incertitude au seuil0,990

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0030,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,495
Tête enseignante GPT0,488
Écart entre enseignants0,007 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle