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Research Article

The value of the 24-h proteinuria in evaluating the severity of preeclampsia and predicting its adverse maternal outcomes

, , , , , , , , , , , , , & show all
Pages 118-125 | Received 25 Jan 2018, Accepted 04 Jun 2018, Published online: 24 Jul 2018
 

ABSTRACT

Background: To identify the 24-h proteinuria value with quantitative analysis and how it correlates with the severity of preeclampsia and subsequent adverse maternal outcomes in the Chinese population. Study design: Eleven hospitals in 10 provinces across China were chosen, in which 1,738 pregnant women complicated by hypertensive disorders of pregnancy (HDP) with the records of 24 h proteinuria were enrolled. They were allocated into four groups: patients with maximal quantified proteinuria < 0.3 g/24 h (Group 1, n = 328); patients with maximal quantified proteinuria ≥ 0.3 g/24 h and < 2.0 g/24 h (Group 2, n = 638); patients with maximal quantified proteinuria ≥ 2.0 g/24 h and < 5.0 g/24 h (Group 3, n = 353); and patients with maximal quantified proteinuria ≥ 5.0 g/24 h (Group 4, n = 419). Logistic regression analysis were conducted to assess the differences in maternal outcomes between different subgroups of 24-h proteinuria and to identify independent risk factors of adverse maternal outcomes in preeclampsia. The multivariable risk prediction model of adverse maternal outcome for HDP was established with receiver operating characteristic curve (ROC) curve and its predicted value was assessed. Results: Thrombocytopenia and cerebral or visual symptoms were more frequent in Groups 3 and 4 than Groups 1 and 2 but no differences were found between Groups 3 and 4 or Groups 1 and 2. Maternal complications were more frequent in Groups 3 and 4 than in Groups 1 and 2 [Group 3 vs. Group 1, odds ratios (ORs) = 3.359 (1.067–10.571); Group 4 vs. Group 1, OR = 3.628 (1.189–11.086); Group 3 vs. Group 2, OR = 2.845 (1.155–7.003); Group 4 vs. Group 2, OR = 3.082 (1.304–7.288)]. However, no significant difference was found between Groups 4 and 3 or between Groups 2 and 1. The proteinuria ≥ 2 g/24 h had an area under the receiver operating characteristic curve (AUC ROC) of 0.668 (95% confidence interval (CI) 0.632–0.705) for predicting adverse maternal outcome. After adjusting for the effects of other symptoms, signs, and laboratory tests, it was the independent risk factor and predictor factor of the adverse maternal outcome (OR = 3.683, 95% CI 2.439–5.562, P<0.001). The final risk prediction model had an AUC ROC of 0.800 (95% CI 0.769–0.830, P<0.001). Conclusion: The proteinuria ≥ 2 g/24 h is an independent predictive factor of adverse maternal outcomes in preeclampsia, but its individual predictive value is limited. The risk prediction model is effective in assessing the risk of adverse maternal outcomes in patients with HDP.

Acknowledgments

The authors acknowledge the support of their research participants. They also acknowledge the useful suggestions and criticisms of reviewers of earlier drafts of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Ethics approval and consent to participate

The study was reviewed and approved by the Institutional Review Board of the First Hospital, Peking University (Reference Number: 2013[572]). All participants provided written informed consent, and the Ethics Committee approved the consent procedure.

Availability of data and materials

The data sets analyzed during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China [Grant No. 81490745], the Major National Scientific Research Program of China [Grant No. 2015CB943304], the Beijing Science and Technology Project [Grant No. Z151100001615051], and the National Science and Technology Support Program of China [Grant No. 2015BAI13B06].

Notes on contributors

Huixia Yang

Conceived and designed the experiments: BL, LL, HY, and YZ. Performed the experiments: BL, LL, XL, DC, MZ, SC, HD, GD, HM, HW, XS, and HX. Analyzed the data: BL, YZ, and YW. Acquisition and interpretation of data: HY, CW, RS, and WM. Wrote the paper: BL, HY, and YM.

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