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Prediction of Adverse Maternal Outcomes in Preeclampsia Using a Risk Prediction Model

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Abstract

Background

This study was conducted to evaluate how the preeclampsia integrated estimate of risk (fullPIERS) model performs in the prediction of adverse maternal outcomes when the predictor variables are all obtained within 24-h of admission for preeclampsia.

Methods

A prospective cohort study on 323 women who fulfilled definite inclusion and exclusion criteria was conducted. Subjects were monitored for clinical symptoms of preeclampsia, biochemical parameters, and adverse maternal and neonatal outcomes. A risk prediction score was calculated using the fullPIERS calculator. Statistical analysis of rates and ratios was carried out by assessing χ 2 test and odds ratio.

Results

18.3 % (n = 60) had adverse maternal outcome and 42.8 % (n = 138) had adverse fetal outcome, and 43 (13.35 %) had combined adverse maternal and perinatal outcome. Dyspnea, visual disturbances, epigastric pain, and \( {\text{Sp}}_{{{\text{O}}_{2} }} \) appeared to be highly significant risk factors. In the biochemical variables studied, serum creatinine and serum uric acid were found to have a significant association. The association between adverse perinatal outcome and vaginal delivery was highly significant (OR 0.35, 95 % CI 0.19, 0.63), and the P value was 0.0005. The likelihood ratio associated with the highest risk group (predicted probability of the outcome ≥30 %) showed excellent performance (i.e., 17.5) of fullPIERS model as a rule in test.

Conclusion

The fullPIERS model performed well in the prediction of adverse maternal outcomes in women with preeclampsia. It is easy to use. The model is based on the use of few important clinical and biochemical parameters and does not require extensive laboratory testing. Although it might be of limited use in a well-equipped tertiary care facility, this model can be utilized in the setting of district or sub-district level hospitals to identify patients who are at risk of complications due to preeclampsia. Timely referral to a higher center will help in reducing the morbidity and mortality associated with this condition.

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Correspondence to Nandita Maitra.

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Conflict of interest

Dr. Shruti Agrawal has no conflict of interest, and Dr. Nandita Maitra has no conflict of interest.

Informed Consent Disclosure

All procedures followed were in accordance with the ethical standards of the Responsible Committee on Human Experimentation (Institutional and National) and with the Helsinki Declaration of 1975, as revised in 2008 (5).

Additional information

Shruti Agrawal is a Third-Year Resident; Nandita Maitra is a Professor and Head.

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Agrawal, S., Maitra, N. Prediction of Adverse Maternal Outcomes in Preeclampsia Using a Risk Prediction Model. J Obstet Gynecol India 66 (Suppl 1), 104–111 (2016). https://doi.org/10.1007/s13224-015-0779-5

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  • DOI: https://doi.org/10.1007/s13224-015-0779-5

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