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. 2022 May 19;11(10):2885.
doi: 10.3390/jcm11102885.

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

Julja Burchard  1 , George R Saade  2 , Kim A Boggess  3 , Glenn R Markenson  4 , Jay D Iams  5 , Dean V Coonrod  6 , Leonardo M Pereira  7 , Matthew K Hoffman  8 , Ashoka D Polpitiya  1 , Ryan Treacy  1 , Angela C Fox  1 , Todd L Randolph  1 , Tracey C Fleischer  1 , Max T Dufford  1 , Thomas J Garite  1 , Gregory C Critchfield  1 , J Jay Boniface  1 , Paul E Kearney  1
Affiliations

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

Julja Burchard et al. J Clin Med. .

Abstract

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 191/7-206/7 and extended to weeks 180/7-206/7. 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 191/7-206/7. 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.

Keywords: gestational age; gestational age dating; preterm birth; proteomic biomarker risk predictor; spontaneous preterm birth.

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Conflict of interest statement

The authors of this manuscript have the following competing interests: J.B., R.T., A.C.F., T.C.F., M.T.D., T.J.G., G.C.C., J.J.B., and P.E.K. are employees and stockholders of Sera Prognostics, Inc. A.D.P. and T.L.R. are consultants to Sera Prognostics, Inc. All other authors report no conflict of interest.

Figures

Figure 1
Figure 1
Performance of a hypothetical perfect preterm birth risk predictor using first date of last menstrual period (LMP) or excluding-LMP gestational age dating. Darker curves represent individual simulations, while the shaded area represents the 95% confidence interval of sensitivity at each value of 1 − specificity. AUC, area under the receiver operating curve.
Figure 2
Figure 2
Separation between spontaneous preterm birth (sPTB) cases and term births (controls) across gestational age (GA) at blood draw, in the excluding-LMP (not dated by first day of last menstrual period) population. (A) Using the proteomic predictor score. Dashed line corresponds to the validated risk predictor threshold (−1.37), representing 15% sPTB risk, or twice the average sPTB risk across all U.S. singleton pregnancies. (B) Using the percent sPTB risk. Dashed line indicates 15% sPTB risk.
See this image and copyright information in PMC

References

    1. Liu L., Oza S., Hogan D., Chu Y., Perin J., Zhu J., Lawn J.E., Cousens S., Mathers C., Black R.E. Global, regional, and national causes of under-5 mortality in 2000-15: An updated systematic analysis with implications for the Sustainable Development Goals. Lancet. 2016;388:3027–3035. doi: 10.1016/S0140-6736(16)31593-8. - DOI - PMC - PubMed
    1. Chawanpaiboon S., Vogel J.P., Moller A.B., Lumbiganon P., Petzold M., Hogan D., Landoulsi S., Jampathong N., Kongwattanakul K., Laopaiboon M., et al. Global, regional, and national estimates of levels of preterm birth in 2014: A systematic review and modelling analysis. Lancet Glob. Health. 2019;7:e37–e46. doi: 10.1016/S2214-109X(18)30451-0. - DOI - PMC - PubMed
    1. Osterman M.J.K., Hamilton B.E., Martin J.A., Driscoll A.K., Valenzuela C.P. Births: Final Data for 2020. [(accessed on 9 February 2022)]; Available online: https://www.cdc.gov/nchs/nvss/births.htm.
    1. Waitzman N.J., Jalali A., Grosse S.D. Preterm birth lifetime costs in the United States in 2016: An update. Semin. Perinatol. 2021;45:151390. doi: 10.1016/j.semperi.2021.151390. - DOI - PMC - PubMed
    1. Alexander G.R., Institute of Medicine (US) Committee on Understanding Premature Birth and Assuring Healthy Outcomes. Behrman R.E., Butler A.S. Preterm Birth: Causes, Consequences, and Prevention. National Academies Press (US); Washington, DC, USA: 2007. Prematurity at Birth: Determinants, Consequences, and Geographic Variation (Appendix B) - PubMed

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