Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study

Shakila Thangaratinam*, John Allotey, Nadine Marlin, Ben W. Mol, Peter von Dadelszen, Wessel Ganzevoort, Joost Akkermans, Asif Ahmed, Jane Daniels, Jon Deeks, Khaled Ismail, Ann Marie Barnard, Julie Dodds, Sally Kerry, Carl Moons, Richard D. Riley, Khalid S. Khan

*Corresponding author for this work

Research output: Contribution to journalReview article

Abstract

Background: The prognosis of early-onset pre-eclampsia (before 34 weeks’ gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women. Objective: To develop and validate prediction models for outcomes in early-onset pre-eclampsia. Design: Prospective cohort for model development, with validation in two external data sets. Setting: Model development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-eclampsia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Eclampsia TRial Amsterdam) studies. Participants: Pregnant women with early-onset pre-eclampsia. Sample size: Nine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets. Predictors: The predictors were identified from systematic reviews of tests to predict complications in pre-eclampsia and were prioritised by Delphi survey. Main outcome measures: The primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications. Analysis: We developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain individual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). Results: The PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with an optimism-adjusted c-statistic of 0.82 [95% confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95% CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with a c-statistic of 0.81 (95% CI 0.77 to 0.85) in PIERS and 0.75 (95% CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95% CI 0.72 to 1.10) and 0.90 (95% CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had a c-statistic of 0.71 (95% CI 0.67 to 0.75) and a calibration slope of 0.67 (95% CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications. Conclusions: The PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high-or low-risk individuals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation.

Original languageEnglish
Article number130
JournalHealth Technology Assessment
Volume21
Issue number18
DOIs
Publication statusPublished - 30 Apr 2017

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Pre-Eclampsia
Cohort Studies
Prospective Studies
Confidence Intervals
Mothers
Magnesium Sulfate
Creatinine
Antihypertensive Agents
Calibration
Gestational Age
Urea
Logistic Models
Serum
Urine
Blood Pressure
Stretch Reflex
Uterine Artery
Fetal Weight
Aptitude
Triage

Bibliographical note

© Queen’s Printer and Controller of HMSO 2017. This work was produced by Thangaratinam et al. under the terms of a
commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of
private research and study and extracts (or indeed, the full report) may be included in professional journals provided that
suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for
commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials
and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.

Cite this

Thangaratinam, S., Allotey, J., Marlin, N., Mol, B. W., von Dadelszen, P., Ganzevoort, W., ... Khan, K. S. (2017). Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study. Health Technology Assessment, 21(18), [130]. https://doi.org/10.3310/hta21180
Thangaratinam, Shakila ; Allotey, John ; Marlin, Nadine ; Mol, Ben W. ; von Dadelszen, Peter ; Ganzevoort, Wessel ; Akkermans, Joost ; Ahmed, Asif ; Daniels, Jane ; Deeks, Jon ; Ismail, Khaled ; Barnard, Ann Marie ; Dodds, Julie ; Kerry, Sally ; Moons, Carl ; Riley, Richard D. ; Khan, Khalid S. / Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP) : a prospective cohort study. In: Health Technology Assessment. 2017 ; Vol. 21, No. 18.
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abstract = "Background: The prognosis of early-onset pre-eclampsia (before 34 weeks’ gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women. Objective: To develop and validate prediction models for outcomes in early-onset pre-eclampsia. Design: Prospective cohort for model development, with validation in two external data sets. Setting: Model development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-eclampsia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Eclampsia TRial Amsterdam) studies. Participants: Pregnant women with early-onset pre-eclampsia. Sample size: Nine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets. Predictors: The predictors were identified from systematic reviews of tests to predict complications in pre-eclampsia and were prioritised by Delphi survey. Main outcome measures: The primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications. Analysis: We developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain individual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). Results: The PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with an optimism-adjusted c-statistic of 0.82 [95{\%} confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95{\%} CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with a c-statistic of 0.81 (95{\%} CI 0.77 to 0.85) in PIERS and 0.75 (95{\%} CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95{\%} CI 0.72 to 1.10) and 0.90 (95{\%} CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had a c-statistic of 0.71 (95{\%} CI 0.67 to 0.75) and a calibration slope of 0.67 (95{\%} CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications. Conclusions: The PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high-or low-risk individuals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation.",
author = "Shakila Thangaratinam and John Allotey and Nadine Marlin and Mol, {Ben W.} and {von Dadelszen}, Peter and Wessel Ganzevoort and Joost Akkermans and Asif Ahmed and Jane Daniels and Jon Deeks and Khaled Ismail and Barnard, {Ann Marie} and Julie Dodds and Sally Kerry and Carl Moons and Riley, {Richard D.} and Khan, {Khalid S.}",
note = "{\circledC} Queen’s Printer and Controller of HMSO 2017. This work was produced by Thangaratinam et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.",
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Thangaratinam, S, Allotey, J, Marlin, N, Mol, BW, von Dadelszen, P, Ganzevoort, W, Akkermans, J, Ahmed, A, Daniels, J, Deeks, J, Ismail, K, Barnard, AM, Dodds, J, Kerry, S, Moons, C, Riley, RD & Khan, KS 2017, 'Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP): a prospective cohort study', Health Technology Assessment, vol. 21, no. 18, 130. https://doi.org/10.3310/hta21180

Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP) : a prospective cohort study. / Thangaratinam, Shakila; Allotey, John; Marlin, Nadine; Mol, Ben W.; von Dadelszen, Peter; Ganzevoort, Wessel; Akkermans, Joost; Ahmed, Asif; Daniels, Jane; Deeks, Jon; Ismail, Khaled; Barnard, Ann Marie; Dodds, Julie; Kerry, Sally; Moons, Carl; Riley, Richard D.; Khan, Khalid S.

In: Health Technology Assessment, Vol. 21, No. 18, 130, 30.04.2017.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Development and validation of Prediction models for Risks of complications in Early-onset Pre-eclampsia (PREP)

T2 - a prospective cohort study

AU - Thangaratinam, Shakila

AU - Allotey, John

AU - Marlin, Nadine

AU - Mol, Ben W.

AU - von Dadelszen, Peter

AU - Ganzevoort, Wessel

AU - Akkermans, Joost

AU - Ahmed, Asif

AU - Daniels, Jane

AU - Deeks, Jon

AU - Ismail, Khaled

AU - Barnard, Ann Marie

AU - Dodds, Julie

AU - Kerry, Sally

AU - Moons, Carl

AU - Riley, Richard D.

AU - Khan, Khalid S.

N1 - © Queen’s Printer and Controller of HMSO 2017. This work was produced by Thangaratinam et al. under the terms of a commissioning contract issued by the Secretary of State for Health. This issue may be freely reproduced for the purposes of private research and study and extracts (or indeed, the full report) may be included in professional journals provided that suitable acknowledgement is made and the reproduction is not associated with any form of advertising. Applications for commercial reproduction should be addressed to: NIHR Journals Library, National Institute for Health Research, Evaluation, Trials and Studies Coordinating Centre, Alpha House, University of Southampton Science Park, Southampton SO16 7NS, UK.

PY - 2017/4/30

Y1 - 2017/4/30

N2 - Background: The prognosis of early-onset pre-eclampsia (before 34 weeks’ gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women. Objective: To develop and validate prediction models for outcomes in early-onset pre-eclampsia. Design: Prospective cohort for model development, with validation in two external data sets. Setting: Model development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-eclampsia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Eclampsia TRial Amsterdam) studies. Participants: Pregnant women with early-onset pre-eclampsia. Sample size: Nine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets. Predictors: The predictors were identified from systematic reviews of tests to predict complications in pre-eclampsia and were prioritised by Delphi survey. Main outcome measures: The primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications. Analysis: We developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain individual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). Results: The PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with an optimism-adjusted c-statistic of 0.82 [95% confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95% CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with a c-statistic of 0.81 (95% CI 0.77 to 0.85) in PIERS and 0.75 (95% CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95% CI 0.72 to 1.10) and 0.90 (95% CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had a c-statistic of 0.71 (95% CI 0.67 to 0.75) and a calibration slope of 0.67 (95% CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications. Conclusions: The PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high-or low-risk individuals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation.

AB - Background: The prognosis of early-onset pre-eclampsia (before 34 weeks’ gestation) is variable. Accurate prediction of complications is required to plan appropriate management in high-risk women. Objective: To develop and validate prediction models for outcomes in early-onset pre-eclampsia. Design: Prospective cohort for model development, with validation in two external data sets. Setting: Model development: 53 obstetric units in the UK. Model transportability: PIERS (Pre-eclampsia Integrated Estimate of RiSk for mothers) and PETRA (Pre-Eclampsia TRial Amsterdam) studies. Participants: Pregnant women with early-onset pre-eclampsia. Sample size: Nine hundred and forty-six women in the model development data set and 850 women (634 in PIERS, 216 in PETRA) in the transportability (external validation) data sets. Predictors: The predictors were identified from systematic reviews of tests to predict complications in pre-eclampsia and were prioritised by Delphi survey. Main outcome measures: The primary outcome was the composite of adverse maternal outcomes established using Delphi surveys. The secondary outcome was the composite of fetal and neonatal complications. Analysis: We developed two prediction models: a logistic regression model (PREP-L) to assess the overall risk of any maternal outcome until postnatal discharge and a survival analysis model (PREP-S) to obtain individual risk estimates at daily intervals from diagnosis until 34 weeks. Shrinkage was used to adjust for overoptimism of predictor effects. For internal validation (of the full models in the development data) and external validation (of the reduced models in the transportability data), we computed the ability of the models to discriminate between those with and without poor outcomes (c-statistic), and the agreement between predicted and observed risk (calibration slope). Results: The PREP-L model included maternal age, gestational age at diagnosis, medical history, systolic blood pressure, urine protein-to-creatinine ratio, platelet count, serum urea concentration, oxygen saturation, baseline treatment with antihypertensive drugs and administration of magnesium sulphate. The PREP-S model additionally included exaggerated tendon reflexes and serum alanine aminotransaminase and creatinine concentration. Both models showed good discrimination for maternal complications, with an optimism-adjusted c-statistic of 0.82 [95% confidence interval (CI) 0.80 to 0.84] for PREP-L and 0.75 (95% CI 0.73 to 0.78) for the PREP-S model in the internal validation. External validation of the reduced PREP-L model showed good performance with a c-statistic of 0.81 (95% CI 0.77 to 0.85) in PIERS and 0.75 (95% CI 0.64 to 0.86) in PETRA cohorts for maternal complications, and calibrated well with slopes of 0.93 (95% CI 0.72 to 1.10) and 0.90 (95% CI 0.48 to 1.32), respectively. In the PIERS data set, the reduced PREP-S model had a c-statistic of 0.71 (95% CI 0.67 to 0.75) and a calibration slope of 0.67 (95% CI 0.56 to 0.79). Low gestational age at diagnosis, high urine protein-to-creatinine ratio, increased serum urea concentration, treatment with antihypertensive drugs, magnesium sulphate, abnormal uterine artery Doppler scan findings and estimated fetal weight below the 10th centile were associated with fetal complications. Conclusions: The PREP-L model provided individualised risk estimates in early-onset pre-eclampsia to plan management of high-or low-risk individuals. The PREP-S model has the potential to be used as a triage tool for risk assessment. The impacts of the model use on outcomes need further evaluation.

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