Dataset Information

Prediction of Preterm Birth: Maternal Characteristics, Ultrasound Markers, and Biomarkers: An Updated Overview.

ABSTRACT: There is not a single or combined screening method for preterm birth with high sensitivity which will truly identify the women at risk for preterm birth while also with high specificity to prevent unnecessary interventions and high treatment costs. Measurement of cervical length is the most cost-effective method that is used in clinical practice. Bedside tests have also been developed for detecting markers like fetal fibronectin, insulin-like growth factor binding protein-1 (IGFBP-1), interleukin-6, and placental alpha-macroglobulin-1. Taking the maternal history, health condition, and sociodemographical factors into consideration is recommended. Ultrasound markers apart from cervical length measurements as uterocervical angle and placental strain ratio are studied. Investigations on metabolomics, proteomics, and microRNA profiling have brought a new aspect on this subject. Maybe in the future, with clear identification of women at true risk for preterm birth, development of more effective preventive strategies will not be unfeasible.

SUBMITTER: Oskovi Kaplan ZA

PROVIDER: S-EPMC6199875 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prediction of Preterm Birth: Maternal Characteristics, Ultrasound Markers, and Biomarkers: An Updated Overview.

Oskovi Kaplan Zeynep Asli ZA Ozgu-Erdinc A Seval AS

Journal of pregnancy 20181010

There is not a single or combined screening method for preterm birth with high sensitivity which will truly identify the women at risk for preterm birth while also with high specificity to prevent unnecessary interventions and high treatment costs. Measurement of cervical length is the most cost-effective method that is used in clinical practice. Bedside tests have also been developed for detecting markers like fetal fibronectin, insulin-like growth factor binding protein-1 (IGFBP-1), interleuki ...[more]

PMID: 30405914

Similar Datasets

Project description:ObjectiveTo examine the relationship between maternal characteristics, serum biomarkers and preterm birth (PTB) by spontaneous and medically indicated subtypes.DesignPopulation-based cohort.SettingCalifornia, United States of America.PopulationFrom a total population of 1 004 039 live singleton births in 2009 and 2010, 841 665 pregnancies with linked birth certificate and hospital discharge records were included.MethodsCharacteristics were compared for term and preterm deliveries by PTB subtype using logistic regression and odds ratios adjusted for maternal characteristics and obstetric factors present in final stepwise models and 95% confidence intervals. First-trimester and second-trimester serum marker levels were analysed in a subset of 125 202 pregnancies with available first-trimester and second-trimester serum biomarker results.Main outcome measurePTB by subtype.ResultsIn fully adjusted models, ten characteristics and three serum biomarkers were associated with increased risk in each PTB subtype (Black race/ethnicity, pre-existing hypertension with and without pre-eclampsia, gestational hypertension with pre-eclampsia, pre-existing diabetes, anaemia, previous PTB, one or two or more previous caesarean section(s), interpregnancy interval ? 60 months, low first-trimester pregnancy-associated plasma protein A, high second-trimester ?-fetoprotein, and high second-trimester dimeric inhibin A). These risks occurred in 51.6-86.2% of all pregnancies ending in PTB depending on subtype. The highest risk observed was for medically indicated PTB <32 weeks in women with pre-existing hypertension and pre-eclampsia (adjusted odds ratio 89.7, 95% CI 27.3-111.2).ConclusionsOur findings suggest a shared aetiology across PTB subtypes. These commonalities point to targets for further study and exploration of risk reduction strategies.Tweetable abstractFindings suggest a shared aetiology across preterm birth subtypes. Patterns may inform risk reduction efforts.

Project description:IntroductionPrediction models may contribute to personalized risk-based management of women at high risk of spontaneous preterm delivery. Although prediction models are published frequently, often with promising results, external validation generally is lacking. We performed a systematic review of prediction models for the risk of spontaneous preterm birth based on routine clinical parameters. Additionally, we externally validated and evaluated the clinical potential of the models.Material and methodsPrediction models based on routinely collected maternal parameters obtainable during first 16 weeks of gestation were eligible for selection. Risk of bias was assessed according to the CHARMS guidelines. We validated the selected models in a Dutch multicenter prospective cohort study comprising 2614 unselected pregnant women. Information on predictors was obtained by a web-based questionnaire. Predictive performance of the models was quantified by the area under the receiver operating characteristic curve (AUC) and calibration plots for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation. Clinical value was evaluated by means of decision curve analysis and calculating classification accuracy for different risk thresholds.ResultsFour studies describing five prediction models fulfilled the eligibility criteria. Risk of bias assessment revealed a moderate to high risk of bias in three studies. The AUC of the models ranged from 0.54 to 0.67 and from 0.56 to 0.70 for the outcomes spontaneous preterm birth <37 weeks and <34 weeks of gestation, respectively. A subanalysis showed that the models discriminated poorly (AUC 0.51-0.56) for nulliparous women. Although we recalibrated the models, two models retained evidence of overfitting. The decision curve analysis showed low clinical benefit for the best performing models.ConclusionsThis review revealed several reporting and methodological shortcomings of published prediction models for spontaneous preterm birth. Our external validation study indicated that none of the models had the ability to predict spontaneous preterm birth adequately in our population. Further improvement of prediction models, using recent knowledge about both model development and potential risk factors, is necessary to provide an added value in personalized risk assessment of spontaneous preterm birth.

Project description:BackgroundPreterm birth is serious public health worldwide, and early prediction of preterm birth in pregnant women may provide assistance for timely intervention and reduction of preterm birth. This study aimed to develop a preterm birth prediction model that is readily available and convenient for clinical application.MethodsData used in this case-control study were extracted from the National Vital Statistics System (NVSS) database between 2018 and 2019. Univariate and multivariate logistic regression analyses were utilized to find factors associated with preterm birth. Odds ratio (OR) and 95% confidence interval (CI) were used as effect measures. The area under the curve (AUC), accuracy, sensitivity, and specificity were utilized as model performance evaluation metrics.ResultsData from 3,006,989 pregnant women in 2019 and 3,039,922 pregnant women in 2018 were used for the model establishment and external validation, respectively. Of these 3,006,989 pregnant women, 324,700 (10.8%) had a preterm birth. Higher education level of pregnant women [bachelor (OR = 0.82; 95%CI, 0.81-0.84); master or above (OR = 0.82; 95%CI, 0.81-0.83)], pre-pregnancy overweight (OR = 0.96; 95%CI, 0.95-0.98) and obesity (OR = 0.94; 95%CI, 0.93-0.96), and prenatal care (OR = 0.48; 95%CI, 0.47-0.50) were associated with a reduced risk of preterm birth, while age ≥ 35 years (OR = 1.27; 95%CI, 1.26-1.29), black race (OR = 1.26; 95%CI, 1.23-1.29), pre-pregnancy underweight (OR = 1.26; 95%CI, 1.22-1.30), pregnancy smoking (OR = 1.27; 95%CI, 1.24-1.30), pre-pregnancy diabetes (OR = 2.08; 95%CI, 1.99-2.16), pre-pregnancy hypertension (OR = 2.22; 95%CI, 2.16-2.29), previous preterm birth (OR = 2.95; 95%CI, 2.88-3.01), and plurality (OR = 12.99; 95%CI, 12.73-13.24) were related to an increased risk of preterm birth. The AUC and accuracy of the prediction model in the testing set were 0.688 (95%CI, 0.686-0.689) and 0.762 (95%CI, 0.762-0.763), respectively. In addition, a nomogram based on information on pregnant women and their spouses was established to predict the risk of preterm birth in pregnant women.ConclusionsThe nomogram for predicting the risk of preterm birth in pregnant women had a good performance and the relevant predictors are readily available clinically, which may provide a simple tool for the prediction of preterm birth.

Project description:Endogenous signaling molecules derived from lipids, peptides, and DNA, are important regulators of physiological processes during pregnancy. The effect of their collective impact on preterm birth (delivery < 37 weeks gestation) is understudied. We aimed to characterize the associations and predictive capacity of an extensive panel of eicosanoids, immune biomarkers, oxidative stress markers, and growth factors towards preterm birth and its subtypes. We conducted a cross-sectional study of pregnant women (recruited < 15 weeks gestation) in the LIFECODES birth cohort, which included 58 cases of preterm birth and 115 controls that delivered term. Among the cases there were 31 cases who had a spontaneous preterm birth (cases who had spontaneous preterm labor and/or preterm premature rupture of membranes) and 25 that had preterm birth associated with aberrant placentation (cases who had preeclampsia and/or intrauterine growth restriction) and 2 cases that could not be sufficiently categorized as either. We analyzed single biomarker associations with each preterm birth outcome using multiple logistic regression. Adaptive elastic-net was implemented to perform a penalized multiple logistic regression on all biomarkers simultaneously to identify the most predictive biomarkers. We then organized biomarkers into biological groups and by enzymatic pathways and applied adaptive elastic-net and random forest to evaluate the accuracy of each group for predicting preterm birth cases. The majority of associations we observed were for spontaneous preterm birth, and adaptive elastic-net identified 5-oxoeicosatetraenoic acid, resolvin D1, 5,6-epoxy-eicsatrienoic acid, and 15-deoxy-12,14-prostaglandin J2 as most predictive. Overall, lipid biomarkers performed the best at separating cases from controls compared to other biomarker categories (adaptive elastic-net AUC = 0.78 [0.62, 0.94], random forest AUC = 0.84 [0.72, 0.96]). Among the enzymatic pathways that differentiate eicosanoid metabolites, we observed the highest prediction of overall preterm birth by lipoxygenase metabolites using random forest (AUC = 0.83 [0.69, 0.96]), followed by cytochrome p450 metabolites using adaptive elastic-net (AUC = 0.74 [0.52, 0.96]). In this study we translate biological hypothesis into the language of modern machine learning. Many lipid biomarkers were highly associated with overall and spontaneous preterm birth. Among eicosanoids, lipoxygenase and cytochrome p450 products performed best in identifying overall and spontaneous preterm birth. The combination of lipid biomarkers may have good utility in clinical settings to predict preterm birth.

Dataset Information

Prediction of Preterm Birth: Maternal Characteristics, Ultrasound Markers, and Biomarkers: An Updated Overview.

Publications

Prediction of Preterm Birth: Maternal Characteristics, Ultrasound Markers, and Biomarkers: An Updated Overview.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets