Model for Early Prediction of Preeclampsia: A Nested Case Controlled Study in Indian Women.

Autor: Yadav, Sonali, Khandpur, Sukhanshi, Yadav, Yogendra Singh, Goel, Madhu Mati, Singh, Urmila, Natu, Shankar Madhav, Negi, Mahendra Pal S., Sharma, Lokendra Kumar, Tiwari, Swasti
Zdroj: Journal of Obstetrics & Gynecology of India; Aug2022, Vol. 72 Issue 4, p299-306, 8p
Abstrakt: Purpose: Preeclampsia (PE) affects 5–7% of the pregnancies worldwide, and is one of the most dreaded disorders of pregnancy contributing to maternal and neonatal mortality. PE is mostly presented in the third trimester of pregnancy. Here, we used serum placental growth factor (PIGF) and soluble fms-like tyrosine kinase-1 (sFlt-1) to develop a model for predicting PE in Indian women in early second trimester. Methods: In this case–control study, a total 1452 healthy pregnant women were recruited. Blood samples were collected at the following gestational weeks (GWs), 12–20 (GW1), 21–28 (GW2) and 29-term (GW3), and post-delivery. Body mass index (BMI) was calculated by anthropometric measurements. Serum sFlt-1, PIGF and VEGF were analyzed by ELISA. A predictive model for PE was developed using multivariable logistic regression analysis. Results: In PE cases, serum PlGF and VEGF levels were significantly lower at each GW, while serum sFlt-1 was lower only at GW1, relative to age-matched controls, (n = 132/group). Age-matched comparison between PE cases and controls indicated that sFlt-1 was associated with decreased PE outcome (Odds ratio. OR = 0.988, CI = 0.982–0.993), whereas sFlt-1/PlGF ratio (OR = 1.577, CI = 1.344–1.920) and BMI (OR = 1.334, CI = 1.187–1.520) were associated with increased PE outcome. Logistic regression was used to develop a predictive model for PE at GW1. Using testing dataset, model was externally validated which resulted in 88% accuracy in predicting PE cases at 0.5 probability cutoff. Conclusion: Prediction model using sFlt-1, sFlt-1/PlGF ratio and BMI may be useful to predict PE as early as 12–20 weeks in women with optimal sensitivity and specificity. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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