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Blood Test Accurately Predicts Preterm Birth Risk

Update Date: May 14, 2014 07:16 PM EDT

Blood tests can predict the risk of women delivering preterm. 

New research shows that blood-based diagnostic tests accurately calculated 70 percent of female participants with high risk of preterm labor who would or would not give birth prematurely.

"A lot of TPTL women are unnecessarily hospitalized," researcher Professor Stephen Lye from the Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada, said in a news release. "We want to develop a test that can differentiate between true and false labor so that women in true labor can receive the appropriate medical care while women in false labor will receive supportive care and be discharged."

The latest findings are significant as pre-term birth is still one of the main causes of infant mortality worldwide. Research reveals that one in 20 women hospitalized with indications of premature labor will give birth prematurely within ten days.

Researchers noted that because the current preterm labor diagnostic evaluation called fetal fibronectin (fFN) test is easily affected by variables that can trigger false positives, many women are unable to undergo fFN testing.

In an effort to develop of a diagnostic test that can be used by all women, researchers used microarrays to study different whole blood gene expression associated with spontaneous premature birth within 48 hours in women admitted to hospital with signs of premature labor, which is an important time window to stall and prevent premature birth. 

The latest findings revealed that knowledge of a set of nine genes as well as clinical blood data significantly predicted whether 70% of participants would or would not experience a spontaneous preterm birth within 48 hours of hospital admission.

What's more, the latest study revealed that data from the nine genes coupled with clinical blood data was significantly more accurate than the current standardized fFN test.

The findings were published May 14, 2014 in the open access journal PLOS ONE.

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