Evidence map›Paper›PMID 33268420›Full record

ArticleBMJ open2020

Maternal metabolic profiling to assess fetal gestational age and predict preterm delivery: a two-centre retrospective cohort study in the US.

Karl G Sylvester, Shiying Hao, Jin You, Le Zheng, Lu Tian, Xiaoming Yao, Lihong Mo, Subhashini Ladella, Ronald J Wong, Gary M Shaw and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMJ open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Karl G SylvesterDepartment of Surgery, Stanford University School of Medicine, Stanford, California, USA bxling@stanford.edu karls@stanford.edu.
Shiying HaoDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California, USA.
Jin YouDepartment of Surgery, Stanford University School of Medicine, Stanford, California, USA.
Le ZhengDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California, USA.
Lu TianDepartment of Health Research and Policy, Stanford University, Stanford, California, USA.
Xiaoming YaoTranslational Medicine Laboratory, West China Hospital, Chengdu, China.
Lihong MoDepartment of Obstetrics and Gynecology, University of California San Francisco-Fresno, Fresno, California, USA.
Subhashini LadellaDepartment of Obstetrics and Gynecology, University of California San Francisco-Fresno, Fresno, California, USA.
Ronald J WongDepartment of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
Gary M ShawDepartment of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
David K StevensonDepartment of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
Harvey J CohenDepartment of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
John C WhitinDepartment of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.
Doff B McElhinneyDepartment of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, California, USA.
Xuefeng B LingDepartment of Surgery, Stanford University School of Medicine, Stanford, California, USA bxling@stanford.edu karls@stanford.edu.ORCID 0000-0002-5386-3884

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe aim of this study was to develop a single blood test that could determine gestational age and estimate the risk of preterm birth by measuring serum metabolites. We hypothesised that serial metabolic modelling of serum analytes throughout pregnancy could be used to describe fetal gestational age and project preterm birth with a high degree of precision. STUDY

designA retrospective cohort study.

settingTwo medical centres from the USA.

participantsThirty-six patients (20 full-term, 16 preterm) enrolled at Stanford University were used to develop gestational age and preterm birth risk algorithms, 22 patients (9 full-term, 13 preterm) enrolled at the University of Alabama were used to validate the algorithms. OUTCOME MEASURES: Maternal blood was collected serially throughout pregnancy. Metabolic datasets were generated using mass spectrometry.

resultsA model to determine gestational age was developed (R

conclusionsIn this study, metabolic profiling was used to develop and test a model for determining gestational age during full-term pregnancy progression, and to determine risk of preterm birth. With additional patient validation studies, these algorithms may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights into the pathophysiology of preterm birth. Metabolic pathway-based pregnancy modelling is a novel modality for investigation and clinical application development.

Indexed as

Premature BirthFemaleGestational AgeHumansInfant, NewbornMass SpectrometryMetabolomicsPregnancyRetrospective Studieshealth informaticsobstetricsrisk management

Identifiers

PMID33268420
PMCPMC7713207

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.