Prediction of preterm birth in symptomatic women using decision tree modeling for biomarkers.

Prediction of preterm birth in symptomatic women using decision tree modeling for biomarkers. Research Abstract Details 

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Prediction of preterm birth in symptomatic women using decision tree modeling for biomarkers. Abstract Text:

OBJECTIVE: The objective of the study was to use recursive partitioning (RP) to identify gestational age-specific and threshold values for infectious and endocrine biomarkers of imminent delivery. STUDY DESIGN: RP was developed using a previously collected data set and then applied to a prospectively collected cohort of women in threatened preterm labor. Predictors of preterm birth were considered, including white blood cell count (WBC), corticotrophin-releasing hormone (CRH), cortisol, and maternal age. RESULTS: At 22-27 weeks' gestation, WBC of greater than 12,000/mL was the most accurate predictor of delivery within 48 hours; at 28-31 weeks' gestation, CRH of greater than 684 pg/mL was the most accurate predictor; and at 32-26 weeks' gestation, CRH and maternal age were the most important variables. CONCLUSIONS: These results indicate that maternal WBC greater than 12,000/mL prior to 28 weeks' gestation and CRH beyond 28 weeks are the most accurate biomarkers in predicting preterm birth within 48 hours. RP assists in establishing clinically relevant and gestational age-specific threshold levels for these variables.

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Prediction of preterm birth in symptomatic women using decision tree modeling for biomarkers. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: American journal of obstetrics and gynecology

VOLUME: 198

Page Numbers: 468.e1-9

Journal Abbreviation: Am. J. Obstet. Gynecol.

ISSN: 1097-6868

DAY: 8

MONTH: Apr

YEAR: 2008

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LANGUAGE: eng

NlmUniqueID: 370476

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Grant and Affiliation Information for Prediction of preterm birth in symptomatic women using decision tree modeling for biomarkers.

AFFILIATION: Department of Epidemiology and Biostatistics, the University of Western Ontario, London, ON, Canada.

Country: United States

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MEDLINETA: Am J Obstet Gynecol

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