Clinical risk assessment in early pregnancy for preeclampsia in nulliparous women: A population based cohort study

Abstract

ObjectiveTo evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women.Design and settingA population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008–13 in the Stockholm-Gotland Counties in Sweden.MethodsMaternal social, reproductive and medical history and medical examinations (including mean arterial pressure, proteinuria, hemoglobin and capillary glucose levels) routinely collected at the first visit in antenatal care, constitute the predictive variables. Predictive models for preeclampsia were created by three methods; logistic regression models using 1) pre-specified variables (similar to the Fetal Medicine Foundation model including maternal factors and mean arterial pressure), 2) backward selection starting from the full suite of variables, and 3) a Random forest model using the same candidate variables. The performance of the British National Institute for Health and Care Excellence (NICE) binary risk classification guidelines for preeclampsia was also evaluated. The outcome measures were diagnosis of preeclampsia with delivery <34, <37, and ≥37 weeks’ gestation.ResultsA total of 2 773 (4.4%) nulliparous women subsequently developed preeclampsia. The pre-specified variables model was superior the other two models, regarding prediction of preeclampsia with delivery <34 and <37 weeks, both with areas under the curve of 0.68, and sensitivity of 30.6% (95% CI 24.5–37.2) and 29.2% (95% CI 25.2–33.4) at a 10% false positive rate, respectively. The performance of these customizable multivariable models at the chosen false positive rate, was significantly better than the binary NICE-guidelines for preeclampsia with delivery <37 and ≥37 weeks’ gestation.ConclusionMultivariable models in early pregnancy had a modest performance, although providing advantages over the NICE-guidelines, in predicting preeclampsia in nulliparous women. Use of a machine learning algorithm (Random forest) did not result in superior prediction.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

“…The characteristics of study populations showed that pregnant women and fetuses or newborns were the populations of most studies developed using LR and non-LR models, respectively. Among pregnant women, the LR algorithm was mostly applied to develop predictions for outcome categories of obstetric labor (13/77, 17%) [36,46,47,54,57,62,64,70,83,86,91,97,103], pregnancy-induced hypertension (12/77, 16%) [30,31,43,48,55,65,66,68,76,81,93,105], and gestational diabetes (7/77, 9%) [33,45,49,84,94,100,104]. Among fetus or newborn populations, non-LR algorithms were mostly applied to develop predictions for outcome categories of premature birth (12/50, 24%) [111,112,115,116,118,119,121,122,125,130,141,143] and fetal distress (9/50, 18%) [113,…”

“…The same algorithm was applied to a prediction model from a non-LR low ROB study in pre-eclampsia [147]. For random effects modeling, this model also significantly outperformed those from 4 LR studies (1.2, 95% CI 0.72-1.67) [31,48,65,76].…”

Pre-Pregnancy Obesity vs. Other Risk Factors in Probability Models of Preeclampsia and Gestational Hypertension

“…In this analysis, maternal age and primiparity were selected for the primary predictive model and therefore were not assessed separately. Of course, the choice of these two features for the basic predictive model was dictated by the results found in the literature, as both are known risk factors for hypertension in pregnancy [9,20,51,52].…”

“…However, despite significant advances in understanding many of the molecular processes involved in the pathogenesis of preeclampsia, a reliable method of predicting hypertension in pregnancy remains yet to be developed. Maternal risk factors are still an important element in risk identification [ 13 , 15 , 20 ]. In many studies, a higher risk of preeclampsia (PE) and/or GH has been associated with pre-pregnancy obesity (body mass index, BMI ≥ 30 kg/m 2 ), comorbidities (such as chronic hypertension), older maternal age, primiparity, diagnosis of PE in a previous pregnancy, as well as with PE in the mother or a sister, and short or long gaps between pregnancies.…”

“…In many studies, a higher risk of preeclampsia (PE) and/or GH has been associated with pre-pregnancy obesity (body mass index, BMI ≥ 30 kg/m 2 ), comorbidities (such as chronic hypertension), older maternal age, primiparity, diagnosis of PE in a previous pregnancy, as well as with PE in the mother or a sister, and short or long gaps between pregnancies. Other factors include assisted reproductive techniques, race, and ethnicity (higher risk can be observed in Afro-Caribbean and South Asian women), as well as excess weight gain in pregnancy (GWG) [ 13 , 15 , 20 ].…”