Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study
Villa, Pia M.; Marttinen, Pekka; Gillberg, Jussi; Lokki, A. Inkeri; Majander, Kerttu; Ordén, Maija-Riitta; Taipale, Pekka; Pesonen, Anukatriina; Räikkönen, Katri; Hämäläinen, Esa; Kajantie, Eero; Laivuori, Hannele (2017-03-28)
Villa PM, Marttinen P, Gillberg J, Lokki AI, Majander K, Ordén M-R, et al. (2017) Cluster analysis to estimate the risk of preeclampsia in the high-risk Prediction and Prevention of Preeclampsia and Intrauterine Growth Restriction (PREDO) study. PLoS ONE 12(3): e0174399. https://doi.org/10.1371/journal.pone.0174399
© 2017 Villa et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Objectives: Preeclampsia is divided into early-onset (delivery before 34 weeks of gestation) and late-onset (delivery at or after 34 weeks) subtypes, which may rise from different etiopathogenic backgrounds. Early-onset disease is associated with placental dysfunction. Late-onset disease develops predominantly due to metabolic disturbances, obesity, diabetes, lipid dysfunction, and inflammation, which affect endothelial function. Our aim was to use cluster analysis to investigate clinical factors predicting the onset and severity of preeclampsia in a cohort of women with known clinical risk factors.
Methods: We recruited 903 pregnant women with risk factors for preeclampsia at gestational weeks 12+0–13+6. Each individual outcome diagnosis was independently verified from medical records. We applied a Bayesian clustering algorithm to classify the study participants to clusters based on their particular risk factor combination. For each cluster, we computed the risk ratio of each disease outcome, relative to the risk in the general population.
Results: The risk of preeclampsia increased exponentially with respect to the number of risk factors. Our analysis revealed 25 number of clusters. Preeclampsia in a previous pregnancy (n = 138) increased the risk of preeclampsia 8.1 fold (95% confidence interval (CI) 5.7–11.2) compared to a general population of pregnant women. Having a small for gestational age infant (n = 57) in a previous pregnancy increased the risk of early-onset preeclampsia 17.5 fold (95%CI 2.1–60.5). Cluster of those two risk factors together (n = 21) increased the risk of severe preeclampsia to 23.8-fold (95%CI 5.1–60.6), intermediate onset (delivery between 34+0–36+6 weeks of gestation) to 25.1-fold (95%CI 3.1–79.9) and preterm preeclampsia (delivery before 37+0 weeks of gestation) to 16.4-fold (95%CI 2.0–52.4). Body mass index over 30 kg/m² (n = 228) as a sole risk factor increased the risk of preeclampsia to 2.1-fold (95%CI 1.1–3.6). Together with preeclampsia in an earlier pregnancy the risk increased to 11.4 (95%CI 4.5–20.9). Chronic hypertension (n = 60) increased the risk of preeclampsia 5.3-fold (95%CI 2.4–9.8), of severe preeclampsia 22.2-fold (95%CI 9.9–41.0), and risk of early-onset preeclampsia 16.7-fold (95%CI 2.0–57.6). If a woman had chronic hypertension combined with obesity, gestational diabetes and earlier preeclampsia, the risk of term preeclampsia increased 4.8-fold (95%CI 0.1–21.7). Women with type 1 diabetes mellitus had a high risk of all subgroups of preeclampsia.
Conclusion: The risk of preeclampsia increases exponentially with respect to the number of risk factors. Early-onset preeclampsia and severe preeclampsia have different risk profile from term preeclampsia.
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