HIGHLIGHTS
- What: The authors report the development of predictive models that can discriminate LNSP from multiple adverse outcomes (CAO group) at first presentation using combinations of hormone levels, metabolites and clinicodemographic features. The study design was pragmatic to demonstrate the utility of the method in real world situations. This study has generated a composite biomarker of early LNSP when compared with adverse pregnancy outcomes (miscarriage, PUL and tEP) using a novel machine_learning pipeline.
- Who: Christopher J. Hill from the DepartmentUniversity of have published the Article: Diagnostic utility of clinicodemographic, biochemical and metabolite variables to identify viable . . .
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