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1. External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis

Author

Allotey, J, Whittle, R, Snell, K, Smuk, M, Townsend, R, von Dadelszen, P, Heazell, A, Magee, L, Smith, G, Sandall, J, Thilaganathan, B, Zamora, J, Riley, R, Khalil, A, Thangaratinam, S, Coomarasamy, A, Kwong, A, Savitri, A, Salvesen, K, Bhattacharya, S, Uiterwaal, C, Staff, A, Andersen, L, Olive, E, Redman, C, Sletner, L, Daskalakis, G, Macleod, M, Abdollahain, M, Ramirez, J, Masse, J, Audibert, F, Magnus, P, Jenum, A, Baschat, A, Ohkuchi, A, Mcauliffe, F, West, J, Askie, L, Mone, F, Farrar, D, Zimmerman, P, Smits, L, Riddell, C, Kingdom, J, van de Post, J, Illanes, S, Holzman, C, van Kuijk, S, Carbillon, L, Villa, P, Eskild, A, Chappell, L, Prefumo, F, Velauthar, L, Seed, P, van Oostwaard, M, Verlohren, S, Poston, L, Ferrazzi, E, Vinter, C, Nagata, C, Brown, M, Vollebregt, K, Takeda, S, Langenveld, J, Widmer, M, Saito, S, Haavaldsen, C, Carroli, G, Olsen, J, Wolf, H, Zavaleta, N, Eisensee, I, Vergani, P, Lumbiganon, P, Makrides, M, Facchinetti, F, Sequeira, E, Gibson, R, Ferrazzani, S, Frusca, T, Norman, J, Figueiro, E, Lapaire, O, Laivuori, H, Lykke, J, Conde-Agudelo, A, Galindo, A, Mbah, A, Betran, A, Herraiz, I, Trogstad, L, Steegers, E, Salim, R, Huang, T, Adank, A, Zhang, J, Meschino, W, Browne, J, Allen, R, Costa, F, Klipstein-Grobusch Browne, K, Crowther, C, Jorgensen, J, Forest, J, Rumbold, A, Mol, B, Giguere, Y, Kenny, L, Ganzevoort, W, Odibo, A, Myers, J, Yeo, S, Goffinet, F, Mccowan, L, Pajkrt, E, Teede, H, Haddad, B, Dekker, G, Kleinrouweler, E, Lecarpentier, E, Roberts, C, Groen, H, Skrastad, R, Heinonen, S, Eero, K, Anggraini, D, Souka, A, Cecatti, J, Monterio, I, Pillalis, A, Souza, R, Hawkins, L, Gabbay-Benziv, R, Crovetto, F, Figuera, F, Jorgensen, L, Dodds, J, Patel, M, Aviram, A, Papageorghiou, A, Khan, K, 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., Coomarasamy A., Kwong A., Savitri A. I., Salvesen K. A., Bhattacharya S., Uiterwaal C. S. P. M., Staff A. C., Andersen L. B., Olive E. L., Redman C., Sletner L., Daskalakis G., Macleod M., Abdollahain M., Ramirez J. A., Masse J., Audibert F., Magnus P. M., Jenum A. K., Baschat A., Ohkuchi A., McAuliffe F. M., West J., Askie L. M., Mone F., Farrar D., Zimmerman P. A., Smits L. J. M., Riddell C., Kingdom J. C., van de Post J., Illanes S. E., Holzman C., van Kuijk S. M. J., Carbillon L., Villa P. M., Eskild A., Chappell L., Prefumo F., Velauthar L., Seed P., van Oostwaard M., Verlohren S., Poston L., Ferrazzi E., Vinter C. A., Nagata C., Brown M., Vollebregt K. C., Takeda S., Langenveld J., Widmer M., Saito S., Haavaldsen C., Carroli G., Olsen J., Wolf H., Zavaleta N., Eisensee I., Vergani P., Lumbiganon P., Makrides M., Facchinetti F., Sequeira E., Gibson R., Ferrazzani S., Frusca T., Norman J. E., Figueiro E. A., Lapaire O., Laivuori H., Lykke J. A., Conde-Agudelo A., Galindo A., Mbah A., Betran A. P., Herraiz I., Trogstad L., Smith G. G. S., Steegers E. A. P., Salim R., Huang T., Adank A., Zhang J., Meschino W. S., Browne J. L., Allen R. E., Costa F. D. S., Klipstein-Grobusch Browne K., Crowther C. A., Jorgensen J. S., Forest J. -C., Rumbold A. R., Mol B. W., Giguere Y., Kenny L. C., Ganzevoort W., Odibo A. O., Myers J., Yeo S. A., Goffinet F., McCowan L., Pajkrt E., Teede H. J., Haddad B. G., Dekker G., Kleinrouweler E. C., LeCarpentier E., Roberts C. T., Groen H., Skrastad R. B., Heinonen S., Eero K., Anggraini D., Souka A., Cecatti J. G., Monterio I., Pillalis A., Souza R., Hawkins L. A., Gabbay-Benziv R., Crovetto F., Figuera F., Jorgensen L., Dodds J., Patel M., Aviram A., Papageorghiou A., Khan K., Allotey, J, Whittle, R, Snell, K, Smuk, M, Townsend, R, von Dadelszen, P, Heazell, A, Magee, L, Smith, G, Sandall, J, Thilaganathan, B, Zamora, J, Riley, R, Khalil, A, Thangaratinam, S, Coomarasamy, A, Kwong, A, Savitri, A, Salvesen, K, Bhattacharya, S, Uiterwaal, C, Staff, A, Andersen, L, Olive, E, Redman, C, Sletner, L, Daskalakis, G, Macleod, M, Abdollahain, M, Ramirez, J, Masse, J, Audibert, F, Magnus, P, Jenum, A, Baschat, A, Ohkuchi, A, Mcauliffe, F, West, J, Askie, L, Mone, F, Farrar, D, Zimmerman, P, Smits, L, Riddell, C, Kingdom, J, van de Post, J, Illanes, S, Holzman, C, van Kuijk, S, Carbillon, L, Villa, P, Eskild, A, Chappell, L, Prefumo, F, Velauthar, L, Seed, P, van Oostwaard, M, Verlohren, S, Poston, L, Ferrazzi, E, Vinter, C, Nagata, C, Brown, M, Vollebregt, K, Takeda, S, Langenveld, J, Widmer, M, Saito, S, Haavaldsen, C, Carroli, G, Olsen, J, Wolf, H, Zavaleta, N, Eisensee, I, Vergani, P, Lumbiganon, P, Makrides, M, Facchinetti, F, Sequeira, E, Gibson, R, Ferrazzani, S, Frusca, T, Norman, J, Figueiro, E, Lapaire, O, Laivuori, H, Lykke, J, Conde-Agudelo, A, Galindo, A, Mbah, A, Betran, A, Herraiz, I, Trogstad, L, Steegers, E, Salim, R, Huang, T, Adank, A, Zhang, J, Meschino, W, Browne, J, Allen, R, Costa, F, Klipstein-Grobusch Browne, K, Crowther, C, Jorgensen, J, Forest, J, Rumbold, A, Mol, B, Giguere, Y, Kenny, L, Ganzevoort, W, Odibo, A, Myers, J, Yeo, S, Goffinet, F, Mccowan, L, Pajkrt, E, Teede, H, Haddad, B, Dekker, G, Kleinrouweler, E, Lecarpentier, E, Roberts, C, Groen, H, Skrastad, R, Heinonen, S, Eero, K, Anggraini, D, Souka, A, Cecatti, J, Monterio, I, Pillalis, A, Souza, R, Hawkins, L, Gabbay-Benziv, R, Crovetto, F, Figuera, F, Jorgensen, L, Dodds, J, Patel, M, Aviram, A, Papageorghiou, A, Khan, K, 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., Coomarasamy A., Kwong A., Savitri A. I., Salvesen K. A., Bhattacharya S., Uiterwaal C. S. P. M., Staff A. C., Andersen L. B., Olive E. L., Redman C., Sletner L., Daskalakis G., Macleod M., Abdollahain M., Ramirez J. A., Masse J., Audibert F., Magnus P. M., Jenum A. K., Baschat A., Ohkuchi A., McAuliffe F. M., West J., Askie L. M., Mone F., Farrar D., Zimmerman P. A., Smits L. J. M., Riddell C., Kingdom J. C., van de Post J., Illanes S. E., Holzman C., van Kuijk S. M. J., Carbillon L., Villa P. M., Eskild A., Chappell L., Prefumo F., Velauthar L., Seed P., van Oostwaard M., Verlohren S., Poston L., Ferrazzi E., Vinter C. A., Nagata C., Brown M., Vollebregt K. C., Takeda S., Langenveld J., Widmer M., Saito S., Haavaldsen C., Carroli G., Olsen J., Wolf H., Zavaleta N., Eisensee I., Vergani P., Lumbiganon P., Makrides M., Facchinetti F., Sequeira E., Gibson R., Ferrazzani S., Frusca T., Norman J. E., Figueiro E. A., Lapaire O., Laivuori H., Lykke J. A., Conde-Agudelo A., Galindo A., Mbah A., Betran A. P., Herraiz I., Trogstad L., Smith G. G. S., Steegers E. A. P., Salim R., Huang T., Adank A., Zhang J., Meschino W. S., Browne J. L., Allen R. E., Costa F. D. S., Klipstein-Grobusch Browne K., Crowther C. A., Jorgensen J. S., Forest J. -C., Rumbold A. R., Mol B. W., Giguere Y., Kenny L. C., Ganzevoort W., Odibo A. O., Myers J., Yeo S. A., Goffinet F., McCowan L., Pajkrt E., Teede H. J., Haddad B. G., Dekker G., Kleinrouweler E. C., LeCarpentier E., Roberts C. T., Groen H., Skrastad R. B., Heinonen S., Eero K., Anggraini D., Souka A., Cecatti J. G., Monterio I., Pillalis A., Souza R., Hawkins L. A., Gabbay-Benziv R., Crovetto F., Figuera F., Jorgensen L., Dodds J., Patel M., Aviram A., Papageorghiou A., and Khan K.

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 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 overa

Published
2022

2. External validation of prognostic models to predict stillbirth using International Prediction of Pregnancy Complications (IPPIC) Network database: individual participant data meta-analysis

Author

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., Coomarasamy, A., Kwong, A., Savitri, A. I., Salvesen, K. A., Bhattacharya, S., Uiterwaal, C. S. P. M., Staff, A. C., Andersen, L. B., Olive, E. L., Redman, C., Sletner, L., Daskalakis, G., Macleod, M., Abdollahain, M., Ramirez, J. A., Masse, J., Audibert, F., Magnus, P. M., Jenum, A. K., Baschat, A., Ohkuchi, A., Mcauliffe, F. M., West, J., Askie, L. M., Mone, F., Farrar, D., Zimmerman, P. A., Smits, L. J. M., Riddell, C., Kingdom, J. C., van de Post, J., Illanes, S. E., Holzman, C., van Kuijk, S. M. J., Carbillon, L., Villa, P. M., Eskild, A., Chappell, L., Prefumo, F., Velauthar, L., Seed, P., van Oostwaard, M., Verlohren, S., Poston, L., Ferrazzi, E., Vinter, C. A., Nagata, C., Brown, M., Vollebregt, K. C., Takeda, S., Langenveld, J., Widmer, M., Saito, S., Haavaldsen, C., Carroli, G., Olsen, J., Wolf, H., Zavaleta, N., Eisensee, I., Vergani, P., Lumbiganon, P., Makrides, M., Facchinetti, F., Sequeira, E., Gibson, R., Ferrazzani, S., Frusca, T., Norman, J. E., Figueiro, E. A., Lapaire, O., Laivuori, H., Lykke, J. A., Conde-Agudelo, A., Galindo, A., Mbah, A., Betran, A. P., Herraiz, I., Trogstad, L., Smith, G. G. S., Steegers, E. A. P., Salim, R., Huang, T., Adank, A., Zhang, J., Meschino, W. S., Browne, J. L., Allen, R. E., Costa, F. D. S., Klipstein-Grobusch Browne, K., Crowther, C. A., Jorgensen, J. S., Forest, J. -C., Rumbold, A. R., Mol, B. W., Giguere, Y., Kenny, L. C., Ganzevoort, W., Odibo, A. O., Myers, J., Yeo, S. A., Goffinet, F., Mccowan, L., Pajkrt, E., Teede, H. J., Haddad, B. G., Dekker, G., Kleinrouweler, E. C., Lecarpentier, E., Roberts, C. T., Groen, H., Skrastad, R. B., Heinonen, S., Eero, K., Anggraini, D., Souka, A., Cecatti, J. G., Monterio, I., Pillalis, A., Souza, R., Hawkins, L. A., Gabbay-Benziv, R., Crovetto, F., Figuera, F., Jorgensen, L., Dodds, J., Patel, M., Aviram, A., Papageorghiou, A., Khan, K., Clinicum, HUS Gynecology and Obstetrics, Department of Obstetrics and Gynecology, HUS Children and Adolescents, Lastentautien yksikkö, Children's Hospital, Allotey, J, Whittle, R, Snell, K, Smuk, M, Townsend, R, von Dadelszen, P, Heazell, A, Magee, L, Smith, G, Sandall, J, Thilaganathan, B, Zamora, J, Riley, R, Khalil, A, Thangaratinam, S, Coomarasamy, A, Kwong, A, Savitri, A, Salvesen, K, Bhattacharya, S, Uiterwaal, C, Staff, A, Andersen, L, Olive, E, Redman, C, Sletner, L, Daskalakis, G, Macleod, M, Abdollahain, M, Ramirez, J, Masse, J, Audibert, F, Magnus, P, Jenum, A, Baschat, A, Ohkuchi, A, Mcauliffe, F, West, J, Askie, L, Mone, F, Farrar, D, Zimmerman, P, Smits, L, Riddell, C, Kingdom, J, van de Post, J, Illanes, S, Holzman, C, van Kuijk, S, Carbillon, L, Villa, P, Eskild, A, Chappell, L, Prefumo, F, Velauthar, L, Seed, P, van Oostwaard, M, Verlohren, S, Poston, L, Ferrazzi, E, Vinter, C, Nagata, C, Brown, M, Vollebregt, K, Takeda, S, Langenveld, J, Widmer, M, Saito, S, Haavaldsen, C, Carroli, G, Olsen, J, Wolf, H, Zavaleta, N, Eisensee, I, Vergani, P, Lumbiganon, P, Makrides, M, Facchinetti, F, Sequeira, E, Gibson, R, Ferrazzani, S, Frusca, T, Norman, J, Figueiro, E, Lapaire, O, Laivuori, H, Lykke, J, Conde-Agudelo, A, Galindo, A, Mbah, A, Betran, A, Herraiz, I, Trogstad, L, Steegers, E, Salim, R, Huang, T, Adank, A, Zhang, J, Meschino, W, Browne, J, Allen, R, Costa, F, Klipstein-Grobusch Browne, K, Crowther, C, Jorgensen, J, Forest, J, Rumbold, A, Mol, B, Giguere, Y, Kenny, L, Ganzevoort, W, Odibo, A, Myers, J, Yeo, S, Goffinet, F, Mccowan, L, Pajkrt, E, Teede, H, Haddad, B, Dekker, G, Kleinrouweler, E, Lecarpentier, E, Roberts, C, Groen, H, Skrastad, R, Heinonen, S, Eero, K, Anggraini, D, Souka, A, Cecatti, J, Monterio, I, Pillalis, A, Souza, R, Hawkins, L, Gabbay-Benziv, R, Crovetto, F, Figuera, F, Jorgensen, L, Dodds, J, Patel, M, Aviram, A, Papageorghiou, A, Khan, K, Tampere University, Obstetrics and Gynaecology, APH - Quality of Care, Amsterdam Reproduction & Development (AR&D), APH - Personalized Medicine, APH - Digital Health, and Obstetrics and gynaecology

Subjects
Calibration (statistics), Perinatal Death, Overfitting, Cohort Studies, Fetal Development, 0302 clinical medicine, Discriminative model, 3123 Gynaecology and paediatrics, Models, Pregnancy, GROWTH RESTRICTION, Statistics, Medicine, Prenatal, 030212 general & internal medicine, Ultrasonography, RISK, 030219 obstetrics & reproductive medicine, PRETERM, Radiological and Ultrasound Technology, LOW-DOSE ASPIRIN, DIAGNOSIS TRIPOD, Obstetrics and Gynecology, General Medicine, Statistical, Stillbirth, Prognosis, Pregnancy Complication, external validation, individual participant data, intrauterine death, prediction model, stillbirth, Female, Humans, Infant, Newborn, Models, Statistical, Pregnancy Complications, Regression Analysis, Risk Assessment, Ultrasonography, Prenatal, 3. Good health, PREECLAMPSIA, Meta-analysis, Human, Cohort study, Prognosi, MEDLINE, Regression Analysi, WEEKS GESTATION, 03 medical and health sciences, VELOCIMETRY, Radiology, Nuclear Medicine and imaging, RECURRENCE, business.industry, Infant, Newborn, R1, HYPERTENSIVE DISORDERS, Reproductive Medicine, Sample size determination, Cohort Studie, RG, business, RA, Predictive modelling
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 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.

Published
2022
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3. External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis

Author

Snell, K, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, L, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, K, Mol, B, Myers, J, Poston, L, Thilaganathan, B, Staff, A, Smith, G, Ganzevoort, W, Laivuori, H, Odibo, A, Arenas Ramirez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, A, Seed, P, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skrastad, R, Salvesen, K, Haavaldsen, C, Nagata, C, Rumbold, A, Heinonen, S, Askie, L, Smits, L, Vinter, C, Magnus, P, Eero, K, Villa, P, Jenum, A, Andersen, L, Norman, J, Ohkuchi, A, Eskild, A, Bhattacharya, S, Mcauliffe, F, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, S, Browne, J, Moons, K, Riley, R, Thangaratinam, S, Vergani, P, Snell K. I. E., Allotey J., Smuk M., Hooper R., Chan C., Ahmed A., Chappell L. C., Von Dadelszen P., Green M., Kenny L., Khalil A., Khan K. S., Mol B. W., Myers J., Poston L., Thilaganathan B., Staff A. C., Smith G. C. S., Ganzevoort W., Laivuori H., Odibo A. O., Arenas Ramirez J., Kingdom J., Daskalakis G., Farrar D., Baschat A. A., Seed P. T., Prefumo F., da Silva Costa F., Groen H., Audibert F., Masse J., Skrastad R. B., Salvesen K. A., Haavaldsen C., Nagata C., Rumbold A. R., Heinonen S., Askie L. M., Smits L. J. M., Vinter C. A., Magnus P., Eero K., Villa P. M., Jenum A. K., Andersen L. B., Norman J. E., Ohkuchi A., Eskild A., Bhattacharya S., McAuliffe F. M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S. A., Browne J. L., Moons K. G. M., Riley R. D., Thangaratinam S., Vergani P., Snell, K, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, L, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, K, Mol, B, Myers, J, Poston, L, Thilaganathan, B, Staff, A, Smith, G, Ganzevoort, W, Laivuori, H, Odibo, A, Arenas Ramirez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, A, Seed, P, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skrastad, R, Salvesen, K, Haavaldsen, C, Nagata, C, Rumbold, A, Heinonen, S, Askie, L, Smits, L, Vinter, C, Magnus, P, Eero, K, Villa, P, Jenum, A, Andersen, L, Norman, J, Ohkuchi, A, Eskild, A, Bhattacharya, S, Mcauliffe, F, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, S, Browne, J, Moons, K, Riley, R, Thangaratinam, S, Vergani, P, Snell K. I. E., Allotey J., Smuk M., Hooper R., Chan C., Ahmed A., Chappell L. C., Von Dadelszen P., Green M., Kenny L., Khalil A., Khan K. S., Mol B. W., Myers J., Poston L., Thilaganathan B., Staff A. C., Smith G. C. S., Ganzevoort W., Laivuori H., Odibo A. O., Arenas Ramirez J., Kingdom J., Daskalakis G., Farrar D., Baschat A. A., Seed P. T., Prefumo F., da Silva Costa F., Groen H., Audibert F., Masse J., Skrastad R. B., Salvesen K. A., Haavaldsen C., Nagata C., Rumbold A. R., Heinonen S., Askie L. M., Smits L. J. M., Vinter C. A., Magnus P., Eero K., Villa P. M., Jenum A. K., Andersen L. B., Norman J. E., Ohkuchi A., Eskild A., Bhattacharya S., McAuliffe F. M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S. A., Browne J. L., Moons K. G. M., Riley R. D., Thangaratinam S., and Vergani P.

Abstract

BACKGROUND: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk during pregnancy is required to plan management. Although there are many published prediction models for pre-eclampsia, few have been validated in external data. Our objective was to externally validate published prediction models for pre-eclampsia using individual participant data (IPD) from UK studies, to evaluate whether any of the models can accurately predict the condition when used within the UK healthcare setting. METHODS: IPD from 11 UK cohort studies (217,415 pregnant women) within the International Prediction of Pregnancy Complications (IPPIC) pre-eclampsia network contributed to external validation of published prediction models, identified by systematic review. Cohorts that measured all predictor variables in at least one of the identified models and reported pre-eclampsia as an outcome were included for validation. We reported the model predictive performance as discrimination (C-statistic), calibration (calibration plots, calibration slope, calibration-in-the-large), and net benefit. Performance measures were estimated separately in each available study and then, where possible, combined across studies in a random-effects meta-analysis. RESULTS: Of 131 published models, 67 provided the full model equation and 24 could be validated in 11 UK cohorts. Most of the models showed modest discrimination with summary C-statistics between 0.6 and 0.7. The calibration of the predicted compared to observed risk was generally poor for most models with observed calibration slopes less than 1, indicating that predictions were generally too extreme, although confidence intervals were wide. There was large between-study heterogeneity in each model's calibration-in-the-large, suggesting poor calibration of the predicted overall risk across populations. In a subset of models, the net benefit of using the models to inform clinical decisions

Published
2020

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