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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. Quantifying the advantages of conducting a prospective meta-analysis (PMA): a case study of early childhood obesity prevention

Author

Seidler, A. L., Hunter, K. E., Espinoza, D., Mihrshahi, S., Askie, L. M., Martin, A., Daniels, L. A., Taylor, R., Wen, L. M., Campbell, Karen, Hesketh, Kylie, Rissel, C., Taylor, B., Magarey, A., Baur, L. A., Seidler, A. L., Hunter, K. E., Espinoza, D., Mihrshahi, S., Askie, L. M., Martin, A., Daniels, L. A., Taylor, R., Wen, L. M., Campbell, Karen, Hesketh, Kylie, Rissel, C., Taylor, B., Magarey, A., and Baur, L. A.

Abstract

Background: For prospective meta-analyses (PMAs), eligible studies are identified, and the PMA hypotheses, selection criteria, and analysis methods are pre-specified before the results of any of the studies are known. This reduces publication bias and selective outcome reporting and provides a unique opportunity for outcome standardisation/harmonisation. We conducted a world-first PMA of four trials investigating interventions to prevent early childhood obesity. The aims of this study were to quantitatively analyse the effects of prospective planning on variations across trials, outcome harmonisation, and the power to detect intervention effects, and to derive recommendations for future PMA. Methods: We examined intervention design, participant characteristics, and outcomes collected across the four trials included in the EPOCH PMA using their registration records, protocol publications, and variable lists. The outcomes that trials planned to collect prior to inclusion in the PMA were compared to the outcomes that trials collected after PMA inclusion. We analysed the proportion of matching outcome definitions across trials, the number of outcomes per trial, and how collaboration increased the statistical power to detect intervention effects. Results: The included trials varied in intervention design and participants, this improved external validity and the ability to perform subgroup analyses for the meta-analysis. While individual trials had limited power to detect the main intervention effect (BMI z-score), synthesising data substantially increased statistical power. Prospective planning led to an increase in the number of collected outcome categories (e.g. weight, child’s diet, sleep), and greater outcome harmonisation. Prior to PMA inclusion, only 18% of outcome categories were included in all trials. After PMA inclusion, this increased to 91% of outcome categories. However, while trials mostly collected the same out

Published
2021

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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