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Role of cervicovaginal β-hCG in prediction of preterm delivery: a prospective observational study
Article | IMSEAR | ID: sea-208001
ABSTRACT

Background:

The World Health Organization (WHO) factsheet revealed that 15 million babies are born too early every year and almost 1 million children die each year due to complications of preterm birth. The objective of this study was to determine whether cervicovaginal β-hCG level can be used as predictor of preterm delivery in asymptomatic high-risk pregnant women at 24-34 weeks gestation age.

Methods:

This was prospective observational study. Total 134 asymptomatic pregnant women were taken for study who had at least one risk factor for preterm delivery at 24-34 weeks gestation age. Cervicovaginal secretion was collected and β-hCG level was measured by chemiluminescent immunoassay.

Results:

Out of 134 cases, 42.5% had preterm delivery and 57.5% had term delivery. Mean cervicovaginal β-hCG level (mIU/ml) in preterm delivery group was 39.38±19.66 and term delivery group was 21.86±11.18. Cervicovaginal β-hCG level was significantly higher in preterm group compare to term group demonstrating significant association of raised β-hCG with preterm group (p value <0.001). ROC curve analysis was done to find out best cut off value of cervicovaginal β-hCG for prediction of preterm delivery and optimal cut off value was 36.45 mIU/ml. The optimal cut off value for cervicovaginal β-hCG (36.45 mIU/ml) gave sensitivity 71.9%, specificity 81.8%, positive predictive value 74.5%, negative predictive value 79.7% and diagnostic accuracy of 77.6% for prediction of preterm delivery.

Conclusions:

Cervicovaginal β-hCG can be used as sensitive and specific biomarker of prediction of preterm delivery in asymptomatic high-risk women.

Full text: Available Index: IMSEAR (South-East Asia) Type of study: Observational study / Prognostic study / Risk factors Year: 2020 Type: Article

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Full text: Available Index: IMSEAR (South-East Asia) Type of study: Observational study / Prognostic study / Risk factors Year: 2020 Type: Article