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Observational Study
. 2019 Jan:15:42-50.
doi: 10.1016/j.preghy.2018.01.004. Epub 2018 Jan 5.

Temporal and external validation of the fullPIERS model for the prediction of adverse maternal outcomes in women with pre-eclampsia

Affiliations
Free article
Observational Study

Temporal and external validation of the fullPIERS model for the prediction of adverse maternal outcomes in women with pre-eclampsia

U Vivian Ukah et al. Pregnancy Hypertens. 2019 Jan.
Free article

Abstract

The fullPIERS model is a risk prediction model developed to predict adverse maternal outcomes within 48 h for women admitted with pre-eclampsia. External validation of the model is required before implementation for clinical use. We assessed the temporal and external validity of the fullPIERS model in high income settings using five cohorts collected between 2003 and 2016, from tertiary hospitals in Canada, the United States of America, Finland and the United Kingdom. The cohorts were grouped into three datasets for assessing the primary external, and temporal validity, and broader transportability of the model. The predicted risks of developing an adverse maternal outcome were calculated using the model equation and model performance was evaluated based on discrimination, calibration, and stratification. Our study included a total of 2429 women, with an adverse maternal outcome rate of 6.7%, 6.6%, and 7.0% in the primary external, temporal, and combined (broader) validation cohorts, respectively. The model had good discrimination in all datasets: 0.81 (95%CI 0.75-0.86), 0.82 (95%CI 0.76-0.87), and 0.75 (95%CI 0.71-0.80) for the primary external, temporal, and broader validation datasets, respectively. Calibration was best for the temporal cohort but poor in the broader validation dataset. The likelihood ratios estimated to rule in adverse maternal outcomes were high at a cut-off of ≥30% in all datasets. The fullPIERS model is temporally and externally valid and will be useful in the management of women with pre-eclampsia in high income settings although model recalibration is required to improve performance, specifically in the broader healthcare settings.

Keywords: Maternal outcomes; Model validation; Pre-eclampsia; Prediction; Pregnancy hypertension; Prognosis.

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