Evidence map›Paper›PMID 39587571›Full record

ArticleBMC pregnancy and childbirth2024

Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling.

Yaqi Zhang, Karl G Sylvester, Ronald J Wong, Yair J Blumenfeld, Kuo Yuan Hwa, C James Chou, Sheeno Thyparambil, Weili Liao, Zhi Han, James Schilling and 14 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Article
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

24 authors.

Yaqi Zhang *College of Automation, Guangdong Polytechnic Normal University, Guangzhou, 510665, China.
Karl G Sylvester *Department of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Ronald J WongDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Yair J BlumenfeldDepartment of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Kuo Yuan HwaCenter for Biomedical Industry, National Taipei University of Technology, Taipei, 10608, Taiwan.
C James ChouDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Sheeno ThyparambilmProbe Inc., Palo Alto, CA, 94303, USA.
Weili LiaomProbe Inc., Palo Alto, CA, 94303, USA.
Zhi HanDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
James SchillingmProbe Inc., Palo Alto, CA, 94303, USA.
Bo JinmProbe Inc., Palo Alto, CA, 94303, USA.
Ivana MarićDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Nima AghaeepourDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Martin S AngstDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94303, USA.
Brice GaudilliereDepartment of Anesthesiology, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, CA, 94303, USA.
Virginia D WinnDepartment of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Gary M ShawDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Lu TianDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Ruben Y LuoDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Gary L DarmstadtDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Harvey J CohenDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
David K StevensonDepartment of Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Doff B McElhinneyDepartments of Cardiothoracic Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Xuefeng B LingDepartment of Surgery, Stanford University School of Medicine, Stanford, CA, 94305, USA. bxling@stanford.edu.

Funding

March of Dimes Prematurity Research Center at Stanford University 22-FY-169-126
6 · The paper itself

Abstract

backgroundPreterm birth (PTB) is a serious health problem. PTB complications is the main cause of death in infants under five years of age worldwide. The ability to accurately predict risk for PTB during early pregnancy would allow early monitoring and interventions to provide personalized care, and hence improve outcomes for the mother and infant.

objectiveThis study aims to predict the risks of early preterm (< 35 weeks of gestation) or very early preterm (≤ 26 weeks of gestation) deliveries by using high-resolution maternal urinary metabolomic profiling in early pregnancy.

designA retrospective cohort study was conducted by two independent preterm and term cohorts using high-density weekly urine sampling. Maternal urine was collected serially at gestational weeks 8 to 24. Global metabolomics approaches were used to profile urine samples with high-resolution mass spectrometry. The significant features associated with preterm outcomes were selected by Gini Importance. Metabolite biomarker identification was performed by liquid chromatography tandem mass spectrometry (LCMS-MS). XGBoost models were developed to predict early or very early preterm delivery risk. SETTING AND

participantsThe urine samples included 329 samples from 30 subjects at Stanford University, CA for model development, and 156 samples from 24 subjects at the University of Alabama, Birmingham, AL for validation.

results12 metabolites associated with PTB were selected and identified for modelling among 7,913 metabolic features in serial-collected urine samples of pregnant women. The model to predict early PTB was developed using a set of 12 metabolites that resulted in the area under the receiver operating characteristic (AUROCs) of 0.995 (95% CI: [0.992, 0.995]) and 0.964 (95% CI: [0.937, 0.964]), and sensitivities of 100% and 97.4% during development and validation testing, respectively. Using the same metabolites, the very early PTB prediction model achieved AUROCs of 0.950 (95% CI: [0.878, 0.950]) and 0.830 (95% CI: [0.687, 0.826]), and sensitivities of 95.0% and 60.0% during development and validation, respectively.

conclusionModels for predicting risk of early or very early preterm deliveries were developed and tested using metabolic profiling during the 1st and 2nd trimesters of pregnancy. With patient validation studies, risk prediction models may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights of preterm birth.

Indexed as

BiomarkersGestational AgeMetabolomicsPremature BirthAdultChromatography, LiquidFemaleHumansInfant, NewbornPredictive Value of TestsPregnancyRetrospective StudiesRisk AssessmentTandem Mass SpectrometryBiomarkersBiomarkerEarly pregnancyLC-MS/MSPreterm risk predictionSpontaneous preterm birthUrinary metabolite

Identifiers

PMID39587571
PMCPMC11587579

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.