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Better Estimation of Spontaneous Preterm Birth Prediction Performance through Improved Gestational Age Dating
Journal article   Peer reviewed

Better Estimation of Spontaneous Preterm Birth Prediction Performance through Improved Gestational Age Dating

Julja Burchard, George R. Saade, Kim A. Boggess, Glenn R. Markenson, Jay D. Iams, Dean V. Coonrod, Leonardo M. Pereira, Matthew K. Hoffman, Ashoka D. Polpitiya, Ryan Treacy, …
Journal of clinical medicine, Vol.11(10), p.2885
05/19/2022
PMID: 35629011

Abstract

General & Internal Medicine Life Sciences & Biomedicine Medicine, General & Internal Science & Technology
The clinical management of pregnancy and spontaneous preterm birth (sPTB) relies on estimates of gestational age (GA). Our objective was to evaluate the effect of GA dating uncertainty on the observed performance of a validated proteomic biomarker risk predictor, and then to test the generalizability of that effect in a broader range of GA at blood draw. In a secondary analysis of a prospective clinical trial (PAPR; NCT01371019), we compared two GA dating categories: both ultrasound and dating by last menstrual period (LMP) (all subjects) and excluding dating by LMP (excluding LMP). The risk predictor's performance was observed at the validated risk predictor threshold both in weeks 19(1/7)-20(6/7) and extended to weeks 18(0/7)-20(6/7). Strict blinding and independent statistical analyses were employed. The validated biomarker risk predictor showed greater observed sensitivity of 88% at 75% specificity (increases of 17% and 1%) in more reliably dated (excluding-LMP) subjects, relative to all subjects. Excluding dating by LMP significantly improved the sensitivity in weeks 19(1/7)-20(6/7). In the broader blood draw window, the previously validated risk predictor threshold significantly stratified higher and lower risk of sPTB, and the risk predictor again showed significantly greater observed sensitivity in excluding-LMP subjects. These findings have implications for testing the performance of models aimed at predicting PTB.
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https://doi.org/10.3390/jcm11102885View
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