Abstract

The objective was to evaluate the sequentially updated predictive capacity for preeclampsia during pregnancy, using multivariable longitudinal models including data from antenatal care. This population-based cohort study in the Stockholm-Gotland Counties, Sweden, included 58,899 pregnancies of nulliparous women 2008–2013. Prospectively collected data from each antenatal care visit was used, including maternal characteristics, reproductive and medical history, and repeated measurements of blood pressure, weight, symphysis-fundal height, proteinuria, hemoglobin and blood glucose levels. We used a shared-effects joint longitudinal model including all available information up until a given gestational length (week 24, 28, 32, 34 and 36), to update preeclampsia prediction sequentially. Outcome measures were prediction of preeclampsia, preeclampsia with delivery < 37, and preeclampsia with delivery ≥ 37 weeks’ gestation. The area under the curve (AUC) increased with gestational length. AUC for preeclampsia with delivery < 37 weeks’ gestation was 0.73 (95% CI 0.68–0.79) at week 24, and increased to 0.87 (95% CI 0.84–0.90) in week 34. For preeclampsia with delivery ≥ 37 weeks’ gestation, the AUC in week 24 was 0.65 (95% CI 0.63–0.68), but increased to 0.79 (95% CI 0.78–0.80) in week 36. The addition of routinely collected clinical measurements throughout pregnancy improve preeclampsia prediction and may be useful to individualize antenatal care.

Highlights

  • The objective was to evaluate the sequentially updated predictive capacity for preeclampsia during pregnancy, using multivariable longitudinal models including data from antenatal care

  • Previous knowledge implies that patterns of blood pressure, hemoglobin, weight gain and symphysis-fundal height throughout pregnancy, as well as hyperglycemia and isolated proteinuria may represent useful markers for the risk of preeclampsia, possibly improving with gestational age and if combined in multivariable ­models[13,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33]

  • The pregnancies in the database were individually linked to the National Patient R­ egister[39], including diagnoses on inpatient admissions and hospital outpatient visits according to the Swedish version of International Classification of Diseases (ICD) 10th revision

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Summary

Introduction

The objective was to evaluate the sequentially updated predictive capacity for preeclampsia during pregnancy, using multivariable longitudinal models including data from antenatal care. This population-based cohort study in the Stockholm-Gotland Counties, Sweden, included 58,899 pregnancies of nulliparous women 2008–2013. Our hypothesis was that routinely collected early pregnancy and antenatal care data throughout pregnancy could improve prediction of preeclampsia when evaluated in a multivariable fashion, with updated prediction at each visit In this population-based cohort study of 58 899 nulliparous women we included 20 early pregnancy variables and seven longitudinal repeatedly collected variables relevant for the prediction of preeclampsia. Using millions of data points, we created a shared-effect joint longitudinal model using all available information up until a given visit, irrespective of varying number and timing of visits, with the objective to iteratively update preeclampsia prediction over time

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