Prediction of pre-eclampsia in nulliparous women using routinely collected maternal characteristics: A model development and validation study
Background Guidelines recommend identifying in early pregnancy women at elevated risk of pre-eclampsia. The aim of this study was to develop and validate a pre-eclampsia risk prediction model for nulliparous women attending routine antenatal care “the Western Sydney (WS) model”; and to compare its performance with the National Institute of Health and Care Excellence (NICE) risk factor-list approach for classifying women as high-risk.
Methods This retrospective cohort study included all nulliparous women who gave birth in three public hospitals in the Western-Sydney-Local-Health-District, Australia 2011-2014. Using births from 2011-2012, multivariable logistic regression incorporated established maternal risk factors to develop and internally validate the WS model. The WS model was then externally validated using births from 2013-2014, assessing its discrimination and calibration. We fitted the final WS model for all births from 2011-2014, and compared its accuracy in predicting pre-eclampsia with the NICE approach.
Results Among 12,395 births to nulliparous women in 2011-2014, there were 293 (2.4%) pre-eclampsia events. The WS model included: maternal age, body mass index, ethnicity, multiple pregnancy, family history of pre-eclampsia, autoimmune disease, chronic hypertension and chronic renal disease. In the validation sample (6201 births), the model c-statistic was 0.70 (95% confidence interval 0.65–0.75). The observed:expected ratio for pre-eclampsia was 0.91, with a Hosmer-Lemeshow goodness-of-fit test p-value of 0.20. In the entire study sample of 12,395 births, 374 (3.0%) women had a WS model-estimated pre-eclampsia risk ≥8%, the pre-specified risk-threshold for considering aspirin prophylaxis. Of these, 54 (14.4%) developed pre-eclampsia (sensitivity 18% (14–23), specificity 97% (97–98)). Using the NICE approach, 1173 (9.5%) women were classified as high-risk, of which 107 (9.1%) developed pre-eclampsia (sensitivity 37% (31-42), specificity 91% (91–92)). The final model showed similar accuracy to the NICE approach when using lower risk-threshold of ≥4% to classify women as high-risk for pre-eclampsia.
Conclusion The WS risk model that combines readily-available maternal characteristics achieved modest performance for prediction of pre-eclampsia in nulliparous women. The model did not outperform the NICE approach, but has the advantage of providing individualised absolute risk estimates, to assist with counselling, inform decisions for further testing, and consideration of aspirin prophylaxis.
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Posted 31 Dec, 2019
On 06 Jan, 2020
On 27 Dec, 2019
On 26 Dec, 2019
On 25 Dec, 2019
On 25 Dec, 2019
On 23 Dec, 2019
On 16 Dec, 2019
On 15 Dec, 2019
On 15 Dec, 2019
On 03 Dec, 2019
Received 16 Nov, 2019
Received 16 Nov, 2019
On 14 Nov, 2019
On 10 Nov, 2019
Received 08 Nov, 2019
Invitations sent on 04 Nov, 2019
On 04 Nov, 2019
On 30 Oct, 2019
On 29 Oct, 2019
On 28 Oct, 2019
On 26 Oct, 2019
Prediction of pre-eclampsia in nulliparous women using routinely collected maternal characteristics: A model development and validation study
Posted 31 Dec, 2019
On 06 Jan, 2020
On 27 Dec, 2019
On 26 Dec, 2019
On 25 Dec, 2019
On 25 Dec, 2019
On 23 Dec, 2019
On 16 Dec, 2019
On 15 Dec, 2019
On 15 Dec, 2019
On 03 Dec, 2019
Received 16 Nov, 2019
Received 16 Nov, 2019
On 14 Nov, 2019
On 10 Nov, 2019
Received 08 Nov, 2019
Invitations sent on 04 Nov, 2019
On 04 Nov, 2019
On 30 Oct, 2019
On 29 Oct, 2019
On 28 Oct, 2019
On 26 Oct, 2019
Background Guidelines recommend identifying in early pregnancy women at elevated risk of pre-eclampsia. The aim of this study was to develop and validate a pre-eclampsia risk prediction model for nulliparous women attending routine antenatal care “the Western Sydney (WS) model”; and to compare its performance with the National Institute of Health and Care Excellence (NICE) risk factor-list approach for classifying women as high-risk.
Methods This retrospective cohort study included all nulliparous women who gave birth in three public hospitals in the Western-Sydney-Local-Health-District, Australia 2011-2014. Using births from 2011-2012, multivariable logistic regression incorporated established maternal risk factors to develop and internally validate the WS model. The WS model was then externally validated using births from 2013-2014, assessing its discrimination and calibration. We fitted the final WS model for all births from 2011-2014, and compared its accuracy in predicting pre-eclampsia with the NICE approach.
Results Among 12,395 births to nulliparous women in 2011-2014, there were 293 (2.4%) pre-eclampsia events. The WS model included: maternal age, body mass index, ethnicity, multiple pregnancy, family history of pre-eclampsia, autoimmune disease, chronic hypertension and chronic renal disease. In the validation sample (6201 births), the model c-statistic was 0.70 (95% confidence interval 0.65–0.75). The observed:expected ratio for pre-eclampsia was 0.91, with a Hosmer-Lemeshow goodness-of-fit test p-value of 0.20. In the entire study sample of 12,395 births, 374 (3.0%) women had a WS model-estimated pre-eclampsia risk ≥8%, the pre-specified risk-threshold for considering aspirin prophylaxis. Of these, 54 (14.4%) developed pre-eclampsia (sensitivity 18% (14–23), specificity 97% (97–98)). Using the NICE approach, 1173 (9.5%) women were classified as high-risk, of which 107 (9.1%) developed pre-eclampsia (sensitivity 37% (31-42), specificity 91% (91–92)). The final model showed similar accuracy to the NICE approach when using lower risk-threshold of ≥4% to classify women as high-risk for pre-eclampsia.
Conclusion The WS risk model that combines readily-available maternal characteristics achieved modest performance for prediction of pre-eclampsia in nulliparous women. The model did not outperform the NICE approach, but has the advantage of providing individualised absolute risk estimates, to assist with counselling, inform decisions for further testing, and consideration of aspirin prophylaxis.
Figure 1
Figure 2
Figure 3
Figure 4