Predicting seizures in pregnant women with epilepsy: Development and external validation of a prognostic model

John Allotey, Borja M. Fernandez-Felix, Javier Zamora, Ngawai Moss, Manny Bagary, Andrew Kelso, Rehan Khan, Joris A.M. van der Post, Ben W. Mol, Alexander M. Pirie, Dougall McCorry, Khalid S. Khan, Shakila Thangaratinam

Research output: Contribution to journalArticleResearchpeer-review

Abstract

BACKGROUND: Seizures are the main cause of maternal death in women with epilepsy, but there are no tools for predicting seizures in pregnancy. We set out to develop and validate a prognostic model, using information collected during the antenatal booking visit, to predict seizure risk at any time in pregnancy and until 6 weeks postpartum in women with epilepsy on antiepileptic drugs. METHODS AND FINDINGS: We used datasets of a prospective cohort study (EMPiRE) of 527 pregnant women with epilepsy on medication recruited from 50 hospitals in the UK (4 November 2011-17 August 2014). The model development cohort comprised 399 women whose antiepileptic drug doses were adjusted based on clinical features only; the validation cohort comprised 128 women whose drug dose adjustments were informed by serum drug levels. The outcome was epileptic (non-eclamptic) seizure captured using diary records. We fitted the model using LASSO (least absolute shrinkage and selection operator) regression, and reported the performance using C-statistic (scale 0-1, values > 0.5 show discrimination) and calibration slope (scale 0-1, values near 1 show accuracy) with 95% confidence intervals (CIs). We determined the net benefit (a weighted sum of true positive and false positive classifications) of using the model, with various probability thresholds, to aid clinicians in making individualised decisions regarding, for example, referral to tertiary care, frequency and intensity of monitoring, and changes in antiepileptic medication. Seizures occurred in 183 women (46%, 183/399) in the model development cohort and in 57 women (45%, 57/128) in the validation cohort. The model included age at first seizure, baseline seizure classification, history of mental health disorder or learning difficulty, occurrence of tonic-clonic and non-tonic-clonic seizures in the 3 months before pregnancy, previous admission to hospital for seizures during pregnancy, and baseline dose of lamotrigine and levetiracetam. The C-statistic was 0.79 (95% CI 0.75, 0.84). On external validation, the model showed good performance (C-statistic 0.76, 95% CI 0.66, 0.85; calibration slope 0.93, 95% CI 0.44, 1.41) but with imprecise estimates. The EMPiRE model showed the highest net proportional benefit for predicted probability thresholds between 12% and 99%. Limitations of this study include the varied gestational ages of women at recruitment, retrospective patient recall of seizure history, potential variations in seizure classification, the small number of events in the validation cohort, and the clinical utility restricted to decision-making thresholds above 12%. The model findings may not be generalisable to low- and middle-income countries, or when information on all predictors is not available. CONCLUSIONS: The EMPiRE model showed good performance in predicting the risk of seizures in pregnant women with epilepsy who are prescribed antiepileptic drugs. Integration of the tool within the antenatal booking visit, deployed as a simple nomogram, can help to optimise care in women with epilepsy.

Original languageEnglish
Article numbere1002802
Number of pages18
JournalPLoS Medicine
Volume16
Issue number5
DOIs
Publication statusPublished - 13 May 2019

Cite this

Allotey, J., Fernandez-Felix, B. M., Zamora, J., Moss, N., Bagary, M., Kelso, A., ... Thangaratinam, S. (2019). Predicting seizures in pregnant women with epilepsy: Development and external validation of a prognostic model. PLoS Medicine, 16(5), [e1002802]. https://doi.org/10.1371/journal.pmed.1002802
Allotey, John ; Fernandez-Felix, Borja M. ; Zamora, Javier ; Moss, Ngawai ; Bagary, Manny ; Kelso, Andrew ; Khan, Rehan ; van der Post, Joris A.M. ; Mol, Ben W. ; Pirie, Alexander M. ; McCorry, Dougall ; Khan, Khalid S. ; Thangaratinam, Shakila. / Predicting seizures in pregnant women with epilepsy : Development and external validation of a prognostic model. In: PLoS Medicine. 2019 ; Vol. 16, No. 5.
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title = "Predicting seizures in pregnant women with epilepsy: Development and external validation of a prognostic model",
abstract = "BACKGROUND: Seizures are the main cause of maternal death in women with epilepsy, but there are no tools for predicting seizures in pregnancy. We set out to develop and validate a prognostic model, using information collected during the antenatal booking visit, to predict seizure risk at any time in pregnancy and until 6 weeks postpartum in women with epilepsy on antiepileptic drugs. METHODS AND FINDINGS: We used datasets of a prospective cohort study (EMPiRE) of 527 pregnant women with epilepsy on medication recruited from 50 hospitals in the UK (4 November 2011-17 August 2014). The model development cohort comprised 399 women whose antiepileptic drug doses were adjusted based on clinical features only; the validation cohort comprised 128 women whose drug dose adjustments were informed by serum drug levels. The outcome was epileptic (non-eclamptic) seizure captured using diary records. We fitted the model using LASSO (least absolute shrinkage and selection operator) regression, and reported the performance using C-statistic (scale 0-1, values > 0.5 show discrimination) and calibration slope (scale 0-1, values near 1 show accuracy) with 95{\%} confidence intervals (CIs). We determined the net benefit (a weighted sum of true positive and false positive classifications) of using the model, with various probability thresholds, to aid clinicians in making individualised decisions regarding, for example, referral to tertiary care, frequency and intensity of monitoring, and changes in antiepileptic medication. Seizures occurred in 183 women (46{\%}, 183/399) in the model development cohort and in 57 women (45{\%}, 57/128) in the validation cohort. The model included age at first seizure, baseline seizure classification, history of mental health disorder or learning difficulty, occurrence of tonic-clonic and non-tonic-clonic seizures in the 3 months before pregnancy, previous admission to hospital for seizures during pregnancy, and baseline dose of lamotrigine and levetiracetam. The C-statistic was 0.79 (95{\%} CI 0.75, 0.84). On external validation, the model showed good performance (C-statistic 0.76, 95{\%} CI 0.66, 0.85; calibration slope 0.93, 95{\%} CI 0.44, 1.41) but with imprecise estimates. The EMPiRE model showed the highest net proportional benefit for predicted probability thresholds between 12{\%} and 99{\%}. Limitations of this study include the varied gestational ages of women at recruitment, retrospective patient recall of seizure history, potential variations in seizure classification, the small number of events in the validation cohort, and the clinical utility restricted to decision-making thresholds above 12{\%}. The model findings may not be generalisable to low- and middle-income countries, or when information on all predictors is not available. CONCLUSIONS: The EMPiRE model showed good performance in predicting the risk of seizures in pregnant women with epilepsy who are prescribed antiepileptic drugs. Integration of the tool within the antenatal booking visit, deployed as a simple nomogram, can help to optimise care in women with epilepsy.",
author = "John Allotey and Fernandez-Felix, {Borja M.} and Javier Zamora and Ngawai Moss and Manny Bagary and Andrew Kelso and Rehan Khan and {van der Post}, {Joris A.M.} and Mol, {Ben W.} and Pirie, {Alexander M.} and Dougall McCorry and Khan, {Khalid S.} and Shakila Thangaratinam",
year = "2019",
month = "5",
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language = "English",
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Allotey, J, Fernandez-Felix, BM, Zamora, J, Moss, N, Bagary, M, Kelso, A, Khan, R, van der Post, JAM, Mol, BW, Pirie, AM, McCorry, D, Khan, KS & Thangaratinam, S 2019, 'Predicting seizures in pregnant women with epilepsy: Development and external validation of a prognostic model' PLoS Medicine, vol. 16, no. 5, e1002802. https://doi.org/10.1371/journal.pmed.1002802

Predicting seizures in pregnant women with epilepsy : Development and external validation of a prognostic model. / Allotey, John; Fernandez-Felix, Borja M.; Zamora, Javier; Moss, Ngawai; Bagary, Manny; Kelso, Andrew; Khan, Rehan; van der Post, Joris A.M.; Mol, Ben W.; Pirie, Alexander M.; McCorry, Dougall; Khan, Khalid S.; Thangaratinam, Shakila.

In: PLoS Medicine, Vol. 16, No. 5, e1002802, 13.05.2019.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Predicting seizures in pregnant women with epilepsy

T2 - Development and external validation of a prognostic model

AU - Allotey, John

AU - Fernandez-Felix, Borja M.

AU - Zamora, Javier

AU - Moss, Ngawai

AU - Bagary, Manny

AU - Kelso, Andrew

AU - Khan, Rehan

AU - van der Post, Joris A.M.

AU - Mol, Ben W.

AU - Pirie, Alexander M.

AU - McCorry, Dougall

AU - Khan, Khalid S.

AU - Thangaratinam, Shakila

PY - 2019/5/13

Y1 - 2019/5/13

N2 - BACKGROUND: Seizures are the main cause of maternal death in women with epilepsy, but there are no tools for predicting seizures in pregnancy. We set out to develop and validate a prognostic model, using information collected during the antenatal booking visit, to predict seizure risk at any time in pregnancy and until 6 weeks postpartum in women with epilepsy on antiepileptic drugs. METHODS AND FINDINGS: We used datasets of a prospective cohort study (EMPiRE) of 527 pregnant women with epilepsy on medication recruited from 50 hospitals in the UK (4 November 2011-17 August 2014). The model development cohort comprised 399 women whose antiepileptic drug doses were adjusted based on clinical features only; the validation cohort comprised 128 women whose drug dose adjustments were informed by serum drug levels. The outcome was epileptic (non-eclamptic) seizure captured using diary records. We fitted the model using LASSO (least absolute shrinkage and selection operator) regression, and reported the performance using C-statistic (scale 0-1, values > 0.5 show discrimination) and calibration slope (scale 0-1, values near 1 show accuracy) with 95% confidence intervals (CIs). We determined the net benefit (a weighted sum of true positive and false positive classifications) of using the model, with various probability thresholds, to aid clinicians in making individualised decisions regarding, for example, referral to tertiary care, frequency and intensity of monitoring, and changes in antiepileptic medication. Seizures occurred in 183 women (46%, 183/399) in the model development cohort and in 57 women (45%, 57/128) in the validation cohort. The model included age at first seizure, baseline seizure classification, history of mental health disorder or learning difficulty, occurrence of tonic-clonic and non-tonic-clonic seizures in the 3 months before pregnancy, previous admission to hospital for seizures during pregnancy, and baseline dose of lamotrigine and levetiracetam. The C-statistic was 0.79 (95% CI 0.75, 0.84). On external validation, the model showed good performance (C-statistic 0.76, 95% CI 0.66, 0.85; calibration slope 0.93, 95% CI 0.44, 1.41) but with imprecise estimates. The EMPiRE model showed the highest net proportional benefit for predicted probability thresholds between 12% and 99%. Limitations of this study include the varied gestational ages of women at recruitment, retrospective patient recall of seizure history, potential variations in seizure classification, the small number of events in the validation cohort, and the clinical utility restricted to decision-making thresholds above 12%. The model findings may not be generalisable to low- and middle-income countries, or when information on all predictors is not available. CONCLUSIONS: The EMPiRE model showed good performance in predicting the risk of seizures in pregnant women with epilepsy who are prescribed antiepileptic drugs. Integration of the tool within the antenatal booking visit, deployed as a simple nomogram, can help to optimise care in women with epilepsy.

AB - BACKGROUND: Seizures are the main cause of maternal death in women with epilepsy, but there are no tools for predicting seizures in pregnancy. We set out to develop and validate a prognostic model, using information collected during the antenatal booking visit, to predict seizure risk at any time in pregnancy and until 6 weeks postpartum in women with epilepsy on antiepileptic drugs. METHODS AND FINDINGS: We used datasets of a prospective cohort study (EMPiRE) of 527 pregnant women with epilepsy on medication recruited from 50 hospitals in the UK (4 November 2011-17 August 2014). The model development cohort comprised 399 women whose antiepileptic drug doses were adjusted based on clinical features only; the validation cohort comprised 128 women whose drug dose adjustments were informed by serum drug levels. The outcome was epileptic (non-eclamptic) seizure captured using diary records. We fitted the model using LASSO (least absolute shrinkage and selection operator) regression, and reported the performance using C-statistic (scale 0-1, values > 0.5 show discrimination) and calibration slope (scale 0-1, values near 1 show accuracy) with 95% confidence intervals (CIs). We determined the net benefit (a weighted sum of true positive and false positive classifications) of using the model, with various probability thresholds, to aid clinicians in making individualised decisions regarding, for example, referral to tertiary care, frequency and intensity of monitoring, and changes in antiepileptic medication. Seizures occurred in 183 women (46%, 183/399) in the model development cohort and in 57 women (45%, 57/128) in the validation cohort. The model included age at first seizure, baseline seizure classification, history of mental health disorder or learning difficulty, occurrence of tonic-clonic and non-tonic-clonic seizures in the 3 months before pregnancy, previous admission to hospital for seizures during pregnancy, and baseline dose of lamotrigine and levetiracetam. The C-statistic was 0.79 (95% CI 0.75, 0.84). On external validation, the model showed good performance (C-statistic 0.76, 95% CI 0.66, 0.85; calibration slope 0.93, 95% CI 0.44, 1.41) but with imprecise estimates. The EMPiRE model showed the highest net proportional benefit for predicted probability thresholds between 12% and 99%. Limitations of this study include the varied gestational ages of women at recruitment, retrospective patient recall of seizure history, potential variations in seizure classification, the small number of events in the validation cohort, and the clinical utility restricted to decision-making thresholds above 12%. The model findings may not be generalisable to low- and middle-income countries, or when information on all predictors is not available. CONCLUSIONS: The EMPiRE model showed good performance in predicting the risk of seizures in pregnant women with epilepsy who are prescribed antiepileptic drugs. Integration of the tool within the antenatal booking visit, deployed as a simple nomogram, can help to optimise care in women with epilepsy.

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