External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis
Por:
Allotey, J, Whittle, R, Snell, KIE, Smuk, M, Townsend, R, von Dadelszen, P, Heazell, AEP, Magee, L, Smith, GCS, Sandall, J, Thilaganathan, B, Zamora, J, Riley, RD, Khalil, A, Thangaratinam, S, Llurba E., IPPIC Collaborative Network
Publicada:
1 feb 2022
Resumen:
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. (c) 2021 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
Filiaciones:
Allotey, J:
Univ Birmingham, Inst Metab & Syst Res, WHO Collaborating Ctr Global Womens Hlth, Birmingham, W Midlands, England
Univ Birmingham, Inst Appl Hlth Res, Birmingham, W Midlands, England
Whittle, R:
Keele Univ, Ctr Prognosis Res, Sch Med, Keele, Staffs, England
Snell, KIE:
Keele Univ, Ctr Prognosis Res, Sch Med, Keele, Staffs, England
Smuk, M:
London Sch Hyg & Trop Med, Med Stat Dept, London, England
Townsend, R:
Univ London, Fetal Med Unit, St Georges Univ Hosp NHS Fdn Trust, London, England
St Georges Univ London, Vasc Biol Res Ctr, Mol & Clin Sci Res Inst, London, England
von Dadelszen, P:
Kings Coll London, Sch Life Course Sci, Dept Women & Childrens Hlth, London, England
Heazell, AEP:
Univ Manchester, Fac Biol Med & Hlth, Maternal & Fetal Hlth Res Ctr, Sch Med Sci, Manchester, Lancs, England
Magee, L:
Kings Coll London, Sch Life Course Sci, Dept Women & Childrens Hlth, London, England
Smith, GCS:
Univ Cambridge, NIHR Biomed Res Ctr, Dept Obstet & Gynaecol, Cambridge, England
Sandall, J:
Kings Coll London, Sch Life Course Sci, Dept Women & Childrens Hlth, London, England
Kings Coll London, Inst Psychiat Psychol & Neurosci, Ctr Implementat Sci, Hlth Serv & Populat Res Dept, London, England
Thilaganathan, B:
Univ London, Fetal Med Unit, St Georges Univ Hosp NHS Fdn Trust, London, England
St Georges Univ London, Vasc Biol Res Ctr, Mol & Clin Sci Res Inst, London, England
Zamora, J:
Univ Birmingham, Inst Metab & Syst Res, WHO Collaborating Ctr Global Womens Hlth, Birmingham, W Midlands, England
Hosp Univ Ramon y Cajal IRYCIS, Clin Biostat Unit, Madrid, Spain
CIBER Epidemiol & Publ Hlth CIBERESP, Madrid, Spain
Riley, RD:
Keele Univ, Ctr Prognosis Res, Sch Med, Keele, Staffs, England
Khalil, A:
Univ London, Fetal Med Unit, St Georges Univ Hosp NHS Fdn Trust, London, England
St Georges Univ London, Vasc Biol Res Ctr, Mol & Clin Sci Res Inst, London, England
Thangaratinam, S:
Univ Birmingham, Inst Metab & Syst Res, WHO Collaborating Ctr Global Womens Hlth, Birmingham, W Midlands, England
Birmingham Womens & Childrens NHS Fdn Trust, Birmingham, W Midlands, England
Llurba E.:
Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU), Sant Quintí 77-79, 08041 Barcelona, Spain
Green Accepted, Green Published, hybrid
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