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A new interesting article has been published in Obstet Gynecol. 2019 Dec; 134(6):1333-1338. doi: 10.1097/AOG.0000000000003511. and titled:

Effects of Selective Exclusion of Patients on Preterm Birth Test Performance.

Authors of this article are:

Boniface JJ, Burchard J, Saade GR.

A summary of the article is shown below:

The need to reduce the rate of preterm delivery and the recent emergence of technologies that measure hundreds of biological analytes (eg, genomics, transcriptomics, metabolomics, proteomics; collectively referred to as “omics approaches”) have led to proliferation of potential diagnostic biomarkers. On review of the literature, a concern must be raised regarding experimental design and data analysis reporting. Specifically, inaccurate performance has often been reported after selective exclusion of patients around the definition boundary of preterm birth. For example, authors may report the performance of a preterm delivery predictor by using patients who delivered early preterm compared with deliveries at 37 weeks of gestation or greater. A key principle that must be maintained during the development of any predictive test is to communicate performance for all patients for whom the test will be applicable clinically (ie, the intended-use population), which for prediction of preterm birth includes patients delivering throughout the spectrum of gestational ages, as this is what is to be predicted, and not known at the time of testing. Using biomarker data collected from the U.S.-based Proteomic Assessment of Preterm Risk clinical trial, we provide examples where the area under the receiver operating characteristic curve for the same test artifactually improves from 0.68 (for preterm delivery at less than 37 weeks of gestation) or 0.76 (for preterm delivery at less than 32 weeks of gestation) to 0.91 when patients who deliver late preterm are excluded. We review this phenomenon in this commentary and offer recommendations for clinicians and investigators going forward. FUNDING SOURCE:: Sera Prognostics.

Check out the article’s website on Pubmed for more information:

[link-preview url=https://www.ncbi.nlm.nih.gov/pubmed/31764747 forceshot=true]

This article is a good source of information and a good way to become familiar with topics such as: .

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