Dataset Information

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis.

ABSTRACT: The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospective cohort study, we collected 2741 urine samples from 187 healthy pregnant women, 23 patients with hypertensive disorders of pregnancy, and 14 patients with spontaneous preterm birth. Using gas chromatography-tandem mass spectrometry, we identified 184 urinary metabolites that showed dynamic systematic changes in healthy pregnant women according to gestational age. A model to predict gestational age during normal pregnancy progression was constructed; the correlation coefficient between actual and predicted weeks of gestation was 0.86. The predicted gestational ages of cases with hypertensive disorders of pregnancy exhibited significant progression, compared with actual gestational ages. This is the first study to predict gestational age in normal and complicated pregnancies by using urinary metabolite information. Minimally invasive urinary metabolomics might facilitate changes in the prediction of gestational age in various clinical settings.

SUBMITTER: Yamauchi T

PROVIDER: S-EPMC8423760 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis.

Yamauchi Takafumi T Ochi Daisuke D Matsukawa Naomi N Saigusa Daisuke D Ishikuro Mami M Obara Taku T Tsunemoto Yoshiki Y Kumatani Satsuki S Yamashita Riu R Tanabe Osamu O Minegishi Naoko N Koshiba Seizo S Metoki Hirohito H Kuriyama Shinichi S Yaegashi Nobuo N Yamamoto Masayuki M Nagasaki Masao M Hiyama Satoshi S Sugawara Junichi J

Scientific reports 20210907 1

The elucidation of dynamic metabolomic changes during gestation is particularly important for the development of methods to evaluate pregnancy status or achieve earlier detection of pregnancy-related complications. Some studies have constructed models to evaluate pregnancy status and predict gestational age using omics data from blood biospecimens; however, less invasive methods are desired. Here we propose a model to predict gestational age, using urinary metabolite information. In our prospect ...[more]

PMID: 34493809

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Project description:BackgroundPrediction models have shown promise in helping clinicians and patients engage in shared decision-making by providing quantitative estimates of individual risk of important clinical outcomes. Gestational diabetes mellitus is a common complication of pregnancy, which places patients at higher risk of primary CD. Suspected fetal macrosomia diagnosed on prenatal ultrasound is a well-known risk factor for primary CD in patients with gestational diabetes mellitus, but tools incorporating multiple risk factors to provide more accurate CD risk are lacking. Such tools could help facilitate shared decision-making and risk reduction by identifying patients with both high and low chances of intrapartum primary CD.ObjectiveThis study aimed to develop and internally validate a multivariable model to estimate the risk of intrapartum primary CD in pregnancies complicated by gestational diabetes mellitus undergoing a trial of labor.Study designThis study identified a cohort of patients with gestational diabetes mellitus derived from a large, National Institutes of Health-funded medical record abstraction study who delivered singleton live-born infants at ≥34 weeks of gestation at a large tertiary care center between January 2002 and March 2013. The exclusion criteria included previous CD, contraindications to vaginal delivery, scheduled primary CD, and known fetal anomalies. Candidate predictors were clinical variables routinely available to a practitioner in the third trimester of pregnancy found to be associated with an increased risk of CD in gestational diabetes mellitus. Stepwise backward elimination was used to build the logistic regression model. The Hosmer-Lemeshow test was used to demonstrate goodness of fit. Model discrimination was evaluated via the concordance index and displayed as the area under the receiver operating characteristic curve. Internal model validation was performed with bootstrapping of the original dataset. Random resampling with replacement was performed for 1000 replications to assess predictive ability. An additional analysis was performed in which the population was stratified by parity to evaluate the model's predictive ability among nulliparous and multiparous individuals.ResultsOf the 3570 pregnancies meeting the study criteria, 987 (28%) had a primary CD. Of note, 8 variables were included in the final model, all significantly associated with CD. They included large for gestational age, polyhydramnios, older maternal age, early pregnancy body mass index, first hemoglobin A1C recorded in pregnancy, nulliparity, insulin treatment, and preeclampsia. Model calibration and discrimination were satisfactory with the Hosmer-Lemeshow test (P=.862) and an area under the receiver operating characteristic curve of 0.75 (95% confidence interval, 0.74-0.77). Internal validation demonstrated similar discriminatory ability. Stratification by parity demonstrated that the model worked well among both nulliparous and multiparous patients.ConclusionUsing information routinely available in the third trimester of pregnancy, a clinically pragmatic model can predict intrapartum primary CD risk with reasonable reliability in pregnancies complicated by gestational diabetes mellitus and may provide quantitative data to guide patients in understanding their individual primary CD risk based on preexisting and acquired risk factors.

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Dataset Information

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis.

Publications

Machine learning approaches to predict gestational age in normal and complicated pregnancies via urinary metabolomics analysis.

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