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Prediction of preterm birth with and without preeclampsia using mid-pregnancy immune and growth-related molecular factors and maternal characteristics.


ABSTRACT: OBJECTIVE:To evaluate if mid-pregnancy immune and growth-related molecular factors predict preterm birth (PTB) with and without (±) preeclampsia. STUDY DESIGN:Included were 400 women with singleton deliveries in California in 2009-2010 (200 PTB and 200 term) divided into training and testing samples at a 2:1 ratio. Sixty-three markers were tested in 15-20 serum samples using multiplex technology. Linear discriminate analysis was used to create a discriminate function. Model performance was assessed using area under the receiver operating characteristic curve (AUC). RESULTS:Twenty-five serum biomarkers along with maternal age <34 years and poverty status identified >80% of women with PTB?±?preeclampsia with best performance in women with preterm preeclampsia (AUC?=?0.889, 95% confidence interval (0.822-0.959) training; 0.883 (0.804-0.963) testing). CONCLUSION:Together with maternal age and poverty status, mid-pregnancy immune and growth factors reliably identified most women who went on to have a PTB?±?preeclampsia.

SUBMITTER: Jelliffe-Pawlowski LL 

PROVIDER: S-EPMC6089890 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Prediction of preterm birth with and without preeclampsia using mid-pregnancy immune and growth-related molecular factors and maternal characteristics.

Jelliffe-Pawlowski Laura L LL   Rand Larry L   Bedell Bruce B   Baer Rebecca J RJ   Oltman Scott P SP   Norton Mary E ME   Shaw Gary M GM   Stevenson David K DK   Murray Jeffrey C JC   Ryckman Kelli K KK  

Journal of perinatology : official journal of the California Perinatal Association 20180524 8


<h4>Objective</h4>To evaluate if mid-pregnancy immune and growth-related molecular factors predict preterm birth (PTB) with and without (±) preeclampsia.<h4>Study design</h4>Included were 400 women with singleton deliveries in California in 2009-2010 (200 PTB and 200 term) divided into training and testing samples at a 2:1 ratio. Sixty-three markers were tested in 15-20 serum samples using multiplex technology. Linear discriminate analysis was used to create a discriminate function. Model perfor  ...[more]

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