Search

Your search keyword '"Kingdom, J"' showing total 683 results
Start Over Author "Kingdom, J"
683 results on '"Kingdom, J"'

1. Safety and feasibility pilot study of continuous low‐dose maternal supplemental oxygen in fetal single ventricle heart disease.

Author

Lee, F.‐T., Sun, L., Szabo, A., Milligan, N., Saini, A., Chetan, D., Hunt, J.‐L., Macgowan, C. K., Freud, L., Jaeggi, E., Van Mieghem, T., Kingdom, J., Miller, S. P., and Seed, M.

Subjects
CARDIAC magnetic resonance imaging, PREGNANT women, HEART ventricles, FETAL development, PERINATAL growth
Abstract

Objective: Fetuses with single ventricle physiology (SVP) exhibit reductions in fetal cerebral oxygenation, with associated delays in fetal brain growth and neurodevelopmental outcomes. Maternal supplemental oxygen (MSO) has been proposed to improve fetal brain growth, but current evidence on dosing, candidacy and outcomes is limited. In this pilot study, we evaluated the safety and feasibility of continuous low‐dose MSO in the setting of SVP. Methods: This single‐center, open‐label, pilot phase‐1 safety and feasibility clinical trial included 25 pregnant individuals with a diagnosis of fetal SVP. Participants self‐administered continuous MSO using medical‐grade oxygen concentrators for up to 24 h per day from the second half of gestation until delivery. The primary aim was the evaluation of the safety profile and feasibility of MSO. A secondary preliminary analysis was performed to assess the impact of MSO on the fetal circulation using echocardiography and late‐gestation cardiovascular magnetic resonance imaging. Early outcomes were assessed, including perinatal growth and preoperative brain injury, and neurodevelopmental outcomes were assessed at 18 months using the Bayley Scales of Infant and Toddler Development 3rd edition, and compared with those of a contemporary fetal SVP cohort (n = 217) that received the normal standard of care (SOC). Results: Among the 25 participants, the median maternal age at conception was 35 years, and fetal SVP diagnoses included 16 with right ventricle dominant, eight with left ventricle dominant and one with indeterminate ventricular morphology. Participants started the trial at approximately 29 + 2 weeks' gestation and self‐administered MSO for a median of 16.1 h per day for 63 days, accumulating a median of 1029 h of oxygen intake from enrolment until delivery. The only treatment‐associated adverse events were nasal complications that were resolved typically by attaching a humidifier unit to the oxygen concentrator. No premature closure of the ductus arteriosus or unexpected fetal demise was observed. In the secondary analysis, MSO was not associated with any changes in fetal growth, middle cerebral artery pulsatility index, cerebroplacental ratio or head‐circumference‐to‐abdominal‐circumference ratio Z‐scores over gestation compared with SOC. Although MSO was associated with changes in umbilical artery pulsatility index Z‐score over the study period compared with SOC (P = 0.02), this was probably due to initial baseline differences in placental resistance. At late‐gestation cardiovascular magnetic resonance imaging, MSO was not associated with an increase in fetal cerebral oxygen delivery. Similarly, no differences were observed in neonatal outcomes, including preoperative brain weight Z‐score and brain injury, mortality by 18 months of age and neurodevelopmental outcomes at 18 months of age. Conclusions: This pilot phase‐1 clinical trial indicates that low‐dose MSO therapy is safe and well tolerated in pregnancies diagnosed with fetal SVP. However, our protocol was not associated with an increase in fetal cerebral oxygen delivery or improvements in early neurological or neurodevelopmental outcomes. © 2024 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

2. Peripheral cord insertion is associated with adverse pregnancy outcome only when accompanied by clinically significant placental pathology

Author

Stapleton, D., primary, Darmonkow, A., additional, Ravi Chandran, A., additional, Milligan, N., additional, Saghian, R., additional, Shinar, S., additional, Whitehead, C. L., additional, Hobson, S. R., additional, Serghides, L., additional, Macgowan, C. K., additional, Sled, J. G., additional, Kingdom, J. C., additional, Baschat, A. A., additional, Parks, W. T., additional, and Cahill, L. S., additional

Published
2023
Full Text
View/download PDF

3. 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

4. Effect of High-dose Folic Acid Supplementation in Pregnancy on Preeclampsia (FACT): Double-blind, Phase III, Randomized Controlled, International, Multicenter Trial

Author

Wen, S.W., White, R.R., Rybak, N., Gaudet, L.M., Robson, S., Hague, W., Simms-Stewart, D., Carroli, G., Smith, G., Fraser, W.D., Wells, G., Davidge, S.T., Kingdom, J., Coyle, D., Fergusson, D., Corsi, D.J., Champagne, J., Sabri, E., Ramsay, T., Mol, B.W.J., Oudijk, M.A., and Walker, M.C.

Published
2019
Full Text
View/download PDF

8. 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
Full Text
View/download PDF

14. Clinical Opinion: The diagnosis and management of suspected fetal growth restriction: an evidence-based approach

Author

Lees C, Romero R, Stampalija T, Dall'asta A, DeVore G, Prefumo F, Frusca T, Visser GHA, Hobbins J, Baschat A, Bilardo CM, Galan H, Campbell S, Maulik D, Figueras-Retuerta F, Lee W, Unterscheider J, Valensise H, Da Silva Costa F, Salomon L, Poon L, Ferrazzi E, Mari GC, Rizzo G, Kingdom J, Kiserud T, Hecher K, Lees, Christoph C, Romero, Roberto, Stampalija, Tamara, Dall'Asta, Andrea, Devore, Greggory A, Prefumo, Federico, Frusca, Tiziana, Visser, Gerard H A, Hobbins, John C, Baschat, Ahmet A, Bilardo, Caterina M, Galan, Henry L, Campbell, Stuart, Maulik, Dev, Figueras, Francesc, Lee, Wesley, Unterscheider, Julia, Valensise, Herbert, Da Silva Costa, Fabricio, Salomon, Laurent J, Poon, Liona C, Ferrazzi, Enrico, Mari, Giancarlo, Rizzo, Giuseppe, Kingdom, John C, Kiserud, Torvid, and Hecher, Kurt

Subjects
Trial of Umbilical and Fetal Flow in Europe, Prospective Observational Trial to Optimize Pediatric Health, Placenta, randomized controlled trial, fetal biometry, Disproportionate Intrauterine Growth Intervention Trial at Term, abdominal circumference, Umbilical Arteries, small for gestational age, systematic review, Pregnancy, Prenatal, Ultrasonography, Randomized Controlled Trials as Topic, short-term variation, fetal death, Fetal Growth Retardation, Doppler, neurodevelopmental outcome, fetal distress, umbilical artery pH, Fetal Weight, Settore MED/40, embryonic structures, uterine artery, Female, cardiotocography, Human, cesarean delivery, Doppler velocimetry, ductus venosus, fetal growth, longitudinal, middle cerebral artery, umbilical artery Doppler, ductus venosu, Gestational Age, Article, Ultrasonography, Prenatal, Humans, Obstetrics & Reproductive Medicine, Infant, Ultrasonography, Doppler, fetal distre, Umbilical Arterie, 1114 Paediatrics and Reproductive Medicine
Abstract

This study reviewed the literature about the diagnosis, antepartum surveillance, and time of delivery of fetuses suspected to be small for gestational age or growth restricted. Several guidelines have been issued by major professional organizations, including the International Society of Ultrasound in Obstetrics and Gynecology and the Society for Maternal-Fetal Medicine. The differences in recommendations, in particular about Doppler velocimetry of the ductus venosus and middle cerebral artery, have created confusion among clinicians, and this review has intended to clarify and highlight the available evidence that is pertinent to clinical management. A fetus who is small for gestational age is frequently defined as one with an estimated fetal weight of

Published
2022

15. Is middle cerebral artery Doppler related to neonatal and 2-year infant outcome in early fetal growth restriction?

Author

Brezinka, C., Derks, J.B., Diemert, A., Duvekot, J.J., Ferrazzi, E., Frusca, T., Ganzevoort, W., Hecher, K., Kingdom, J., Marlow, N., Marsal, K., Martinelli, P., Ostermayer, E., Papageorghiou, A.T., Schlembach, D., Schneider, K.T.M., Thilaganathan, B., Thornton, J., Todros, T., Valcamonico, A., Valensise, H., van Wassenaer-Leemhuis, A, Visser, G.H.A., Aktas, A., Borgione, S., Chaoui, R., Cornette, J.M.J., Diehl, T., van Eyck, J, Fratelli, N., van Haastert, I.C., Lobmaier, S., Lopriore, E., Missfelder-Lobos, H., Mansi, G., Martelli, P., Maso, G., Maurer-Fellbaum, U., Mensing van Charante, N., Mulder-de Tollenaer, S., Napolitano, R., Oberto, M, Oepkes, D., Ogge, G., van der Post, J.A.M., Prefumo, F., Preston, L., Raimondi, F., Reiss, I.K.M., Scheepers, L.S., Skabar, A., Spaanderman, M., Weisglas-Kuperus, N., Zimmermann, A., Stampalija, Tamara, Arabin, Birgit, Wolf, Hans, Bilardo, Caterina M., and Lees, Christoph

Published
2017
Full Text
View/download PDF

16. 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

17. MRI characterization of hemodynamic patterns of human fetuses with cyanotic congenital heart disease

Author

Sun, L., primary, van Amerom, J. F. P., additional, Marini, D., additional, Portnoy, S., additional, Lee, F.‐T., additional, Saini, B. S., additional, Lim, J. M., additional, Aguet, J., additional, Jaeggi, E., additional, Kingdom, J. C., additional, Macgowan, C. K., additional, Miller, S. P., additional, Huang, G., additional, and Seed, M., additional

Published
2021
Full Text
View/download PDF

21. ISUOG Practice Guidelines (updated): use of Doppler velocimetry in obstetrics

Author

Bhide, A., primary, Acharya, G., additional, Baschat, A., additional, Bilardo, C. M., additional, Brezinka, C., additional, Cafici, D., additional, Ebbing, C., additional, Hernandez‐Andrade, E., additional, Kalache, K., additional, Kingdom, J., additional, Kiserud, T., additional, Kumar, S., additional, Lee, W., additional, Lees, C., additional, Leung, K. Y., additional, Malinger, G., additional, Mari, G., additional, Prefumo, F., additional, Sepulveda, W., additional, and Trudinger, B., additional

Published
2021
Full Text
View/download PDF

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

Author

Kingdom J., Poston L., Thilaganathan B., Staff A.C., Smith G.C.S., Ganzevoort W., Laivuori H., Odibo A.O., Arenas Ramirez 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., 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., Kingdom J., Poston L., Thilaganathan B., Staff A.C., Smith G.C.S., Ganzevoort W., Laivuori H., Odibo A.O., Arenas Ramirez 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., 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., and Myers J.

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. METHOD(S): 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. RESULT(S): 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 decis

Published
2021

23. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: An individual participant data meta-analysis.

Author

Allotey J., Smuk M., Hooper R., Chan C.L., Ahmed A., Chappell L.C., von Dadelszen P., Dodds J., Green M., Kenny L., Khalil A., Khan K.S., Mol B.W., Myers J., Poston L., Thilaganathan B., Eskild A., Bhattacharya S., McAuliffe F.M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S., Teede H.J., Browne J.L., Moons K.G.M., Riley R.D., Thangaratinam S., Snell K.I.E., Staff A.C., Smith G.C.S., Ganzevoort W., Laivuori H., Odibo A.O., Ramirez J.A., 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.M., Eero K., Villa P.M., Jenum A.K., Andersen L.B., Norman J.E., Ohkuchi A., Allotey J., Smuk M., Hooper R., Chan C.L., Ahmed A., Chappell L.C., von Dadelszen P., Dodds J., Green M., Kenny L., Khalil A., Khan K.S., Mol B.W., Myers J., Poston L., Thilaganathan B., Eskild A., Bhattacharya S., McAuliffe F.M., Galindo A., Herraiz I., Carbillon L., Klipstein-Grobusch K., Yeo S., Teede H.J., Browne J.L., Moons K.G.M., Riley R.D., Thangaratinam S., Snell K.I.E., Staff A.C., Smith G.C.S., Ganzevoort W., Laivuori H., Odibo A.O., Ramirez J.A., 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.M., Eero K., Villa P.M., Jenum A.K., Andersen L.B., Norman J.E., and Ohkuchi A.

Abstract

Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. Objective(s): To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. Design(s): This was an individual participant data meta-analysis of cohort studies. Setting(s): Source data from secondary and tertiary care. Predictors: We identified predictors from systematic reviews, and prioritised for importance in an international survey. Primary outcomes: Early-onset (delivery at < 34 weeks' gestation), late-onset (delivery at >= 34 weeks' gestation) and any-onset pre-eclampsia. Analysis: We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration.We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of >= 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I2 and 2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. Result(s): The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models c

Published
2021

24. FIGO (international Federation of Gynecology and obstetrics) initiative on fetal growth: best practice advice for screening, diagnosis, and management of fetal growth restriction.

Author

Melamed, N, Baschat, A, Yinon, Y, Athanasiadis, A, Mecacci, F, Figueras, F, Berghella, V, Nazareth, A, Tahlak, M, McIntyre, HD, Da Silva Costa, F, Kihara, AB, Hadar, E, McAuliffe, F, Hanson, M, Ma, RC, Gooden, R, Sheiner, E, Kapur, A, Divakar, H, Ayres-de-Campos, D, Hiersch, L, Poon, LC, Kingdom, J, Romero, R, Hod, M, Melamed, N, Baschat, A, Yinon, Y, Athanasiadis, A, Mecacci, F, Figueras, F, Berghella, V, Nazareth, A, Tahlak, M, McIntyre, HD, Da Silva Costa, F, Kihara, AB, Hadar, E, McAuliffe, F, Hanson, M, Ma, RC, Gooden, R, Sheiner, E, Kapur, A, Divakar, H, Ayres-de-Campos, D, Hiersch, L, Poon, LC, Kingdom, J, Romero, R, and Hod, M

Abstract

Fetal growth restriction (FGR) is defined as the failure of the fetus to meet its growth potential due to a pathological factor, most commonly placental dysfunction. Worldwide, FGR is a leading cause of stillbirth, neonatal mortality, and short- and long-term morbidity. Ongoing advances in clinical care, especially in definitions, diagnosis, and management of FGR, require efforts to effectively translate these changes to the wide range of obstetric care providers. This article highlights agreements based on current research in the diagnosis and management of FGR, and the areas that need more research to provide further clarification of recommendations. The purpose of this article is to provide a comprehensive summary of available evidence along with practical recommendations concerning the care of pregnancies at risk of or complicated by FGR, with the overall goal to decrease the risk of stillbirth and neonatal mortality and morbidity associated with this condition. To achieve these goals, FIGO (the International Federation of Gynecology and Obstetrics) brought together international experts to review and summarize current knowledge of FGR. This summary is directed at multiple stakeholders, including healthcare providers, healthcare delivery organizations and providers, FIGO member societies, and professional organizations. Recognizing the variation in the resources and expertise available for the management of FGR in different countries or regions, this article attempts to take into consideration the unique aspects of antenatal care in low-resource settings (labelled “LRS” in the recommendations). This was achieved by collaboration with authors and FIGO member societies from low-resource settings such as India, Sub-Saharan Africa, the Middle East, and Latin America.

Published
2021

25. Expression of breast cancer resistance protein (BCRP/ABCG2) in human placenta throughout gestation and at term before and after labor

Author

Yeboah, D., Sun, M., Kingdom, J., Baczyk, D., Lye, S.J., Matthews, S.G., and Gibb, W.

Subjects
Gene expression -- Observations -- Chemical properties -- Usage, G proteins -- Chemical properties -- Structure -- Usage, Polymerase chain reaction -- Usage -- Chemical properties, Biological sciences, Structure, Observations, Usage, Chemical properties
Abstract

Abstract: Breast cancer resistance protein, BCRP, is a multidrug resistance protein that is highly expressed in the human placenta. In cancer tissues, this protein actively extrudes a wide variety of [...]

Published
2006

41. Validation and development of models using clinical, biochemical and ultrasound markers for predicting pre-eclampsia: An individual participant data meta-analysis

Author

Allotey, J. Snell, K.I.E. Smuk, M. Hooper, R. Chan, C.L. Ahmed, A. Chappell, L.C. von Dadelszen, P. Dodds, J. 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. Ramírez, J.A. Kingdom, J. Daskalakis, G. Farrar, D. Baschat, A.A. Seed, P.T. Prefumo, F. da Silva Costa, F. Groen, H. Audibert, F. Massé, J. Skråstad, 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.M. 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. Teede, H.J. Browne, J.L. Moons, K.G.M. Riley, R.D. Thangaratinam, S. The IPPIC Collaborative Network

Abstract

Background: Pre-eclampsia is a leading cause of maternal and perinatal mortality and morbidity. Early identification of women at risk is needed to plan management. Objectives: To assess the performance of existing pre-eclampsia prediction models and to develop and validate models for pre-eclampsia using individual participant data meta-analysis. We also estimated the prognostic value of individual markers. Design: This was an individual participant data meta-analysis of cohort studies. Setting: Source data from secondary and tertiary care. Predictors: We identified predictors from systematic reviews, and prioritised for importance in an international survey. Primary outcomes: Early-onset (delivery at < 34 weeks’ gestation), late-onset (delivery at ≥ 34 weeks’ gestation) and any-onset pre-eclampsia. Analysis: We externally validated existing prediction models in UK cohorts and reported their performance in terms of discrimination and calibration.We developed and validated 12 new models based on clinical characteristics, clinical characteristics and biochemical markers, and clinical characteristics and ultrasound markers in the first and second trimesters. We summarised the data set-specific performance of each model using a random-effects meta-analysis. Discrimination was considered promising for C-statistics of ≥ 0.7, and calibration was considered good if the slope was near 1 and calibration-in-the-large was near 0. Heterogeneity was quantified using I2 and 2. A decision curve analysis was undertaken to determine the clinical utility (net benefit) of the models. We reported the unadjusted prognostic value of individual predictors for pre-eclampsia as odds ratios with 95% confidence and prediction intervals. Results: The International Prediction of Pregnancy Complications network comprised 78 studies (3,570,993 singleton pregnancies) identified from systematic reviews of tests to predict pre-eclampsia. Twenty-four of the 131 published prediction models could be validated in 11 UK cohorts. Summary C-statistics were between 0.6 and 0.7 for most models, and calibration was generally poor owing to large between-study heterogeneity, suggesting model overfitting. The clinical utility of the models varied between showing net harm to showing minimal or no net benefit. The average discrimination for IPPIC models ranged between 0.68 and 0.83. This was highest for the second-trimester clinical characteristics and biochemical markers model to predict early-onset pre-eclampsia, and lowest for the first-trimester clinical characteristics models to predict any pre-eclampsia. Calibration performance was heterogeneous across studies. Net benefit was observed for International Prediction of Pregnancy Complications first and second-trimester clinical characteristics and clinical characteristics and biochemical markers models predicting any pre-eclampsia, when validated in singleton nulliparous women managed in the UK NHS. History of hypertension, parity, smoking, mode of conception, placental growth factor and uterine artery pulsatility index had the strongest unadjusted associations with pre-eclampsia. Limitations: Variations in study population characteristics, type of predictors reported, too few events in some validation cohorts and the type of measurements contributed to heterogeneity in performance of the International Prediction of Pregnancy Complications models. Some published models were not validated because model predictors were unavailable in the individual participant data. Conclusion: For models that could be validated, predictive performance was generally poor across data sets. Although the International Prediction of Pregnancy Complications models show good predictive performance on average, and in the singleton nulliparous population, heterogeneity in calibration performance is likely across settings. © 2020, NIHR Journals Library. All rights reserved.

Published
2020

47. Core outcome set for studies on pregnant women with vasa previa (COVasP): A study protocol

Author

D'Souza, R, Villani, L, Hall, C, Seyoum, M, Kingdom, J, Krznaric, M, Donnolley, N, Javid, N, D'Souza, R, Villani, L, Hall, C, Seyoum, M, Kingdom, J, Krznaric, M, Donnolley, N, and Javid, N

Abstract

Introduction Vasa previa is a condition where fetal blood vessels run unprotected in the membranes, outside the umbilical cord, and cross the internal opening of the cervix. During rupture of membranes, these vessels can rupture and put the baby at serious risk of severe blood loss and death. Numerous studies are being conducted to improve diagnostic modalities and establish clear management plans to improve pregnancy outcomes. However, the lack of a standardised set of outcomes for studies on vasa previa makes it difficult to compare study findings and draw meaningful conclusions. Through this project, we will be developing a core outcome set for studies on pregnant women with vasa previa (COVasP). Methods and analysis The development of COVasP will involve five steps. The first will be a systematic review, in which we will generate a long list of outcomes based on published studies in pregnancies complicated with vasa previa. The second will involve in-depth interviews with current and former patients, their family members and healthcare providers who care for these patients. This will be followed by a two-round Delphi survey, which will aim to narrow down the long list of outcomes into those considered important by four groups of € stakeholders': (1) patients, family members and patient advocates/representatives, (2) healthcare providers, (3) researchers, epidemiologists and methodologists and (4) other stakeholders directly or indirectly involved in the management of these pregnancies such as administrators, guideline developers and policymakers. The fourth step will involve a face-to-face consensus meeting using a nominal group approach to establish a finalised core outcome set. The final step will involve measuring and defining the identified outcomes using a combination of systematic reviews and Delphi surveys. Ethics and dissemination This study as well as consent forms for stakeholder participation have received approval from the Mount Sinai Hospital Resear

Published
2020

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

Author

Snell, KIE, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, LC, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, KS, Mol, BW, Myers, J, Poston, L, Thilaganathan, B, Staff, AC, Smith, GCS, Ganzevoort, W, Laivuori, H, Odibo, AO, Arenas Ramírez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, AA, Seed, PT, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skråstad, RB, Salvesen, KÅ, Haavaldsen, C, Nagata, C, Rumbold, AR, Heinonen, S, Askie, LM, Smits, LJM, Vinter, CA, Magnus, P, Eero, K, Villa, PM, Jenum, AK, Andersen, LB, Norman, JE, Ohkuchi, A, Eskild, A, Bhattacharya, S, McAuliffe, FM, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, SA, Browne, JL, Moons, KGM, Riley, RD, Thangaratinam, S, IPPIC Collaborative Network, Snell, KIE, Allotey, J, Smuk, M, Hooper, R, Chan, C, Ahmed, A, Chappell, LC, Von Dadelszen, P, Green, M, Kenny, L, Khalil, A, Khan, KS, Mol, BW, Myers, J, Poston, L, Thilaganathan, B, Staff, AC, Smith, GCS, Ganzevoort, W, Laivuori, H, Odibo, AO, Arenas Ramírez, J, Kingdom, J, Daskalakis, G, Farrar, D, Baschat, AA, Seed, PT, Prefumo, F, da Silva Costa, F, Groen, H, Audibert, F, Masse, J, Skråstad, RB, Salvesen, KÅ, Haavaldsen, C, Nagata, C, Rumbold, AR, Heinonen, S, Askie, LM, Smits, LJM, Vinter, CA, Magnus, P, Eero, K, Villa, PM, Jenum, AK, Andersen, LB, Norman, JE, Ohkuchi, A, Eskild, A, Bhattacharya, S, McAuliffe, FM, Galindo, A, Herraiz, I, Carbillon, L, Klipstein-Grobusch, K, Yeo, SA, Browne, JL, Moons, KGM, Riley, RD, Thangaratinam, S, and IPPIC Collaborative Network

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

Catalog

Books, media, physical & digital resources
See catalog results