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

English version

Autoři: Anna Sandström ^aff001; Jonathan M. Snowden ^aff003; Jonas Höijer ^aff004; Matteo Bottai ^aff004; Anna-Karin Wikström ^aff001
Působiště autorů: Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden ^aff001; Department of Women’s and Children’s Health, Uppsala University, Uppsala, Sweden ^aff002; School of Public Health, Oregon Health and Science University-Portland State University, Portland, Oregon, United States of America ^aff003; Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden ^aff004
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0225716

Souhrn

Objective

To evaluate the capacity of multivariable prediction of preeclampsia during pregnancy, based on detailed routinely collected early pregnancy data in nulliparous women.

Design and setting

A population-based cohort study of 62 562 pregnancies of nulliparous women with deliveries 2008–13 in the Stockholm-Gotland Counties in Sweden.

Methods

Maternal 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.

Results

A 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.

Conclusion

Multivariable 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.

Klíčová slova:

Obstetrics and gynecology – Pregnancy – Blood pressure – Machine learning – Sweden – Preeclampsia – Hypertensive disorders in pregnancy

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