학술논문
A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth.
Document Type
Academic Journal
Author
Borboa-Olivares H; Community Interventions Research Branch, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico.; PhD Program in Biological and Health Sciences, Universidad Autónoma Metropolitana, Mexico City 09310, Mexico.; Rodríguez-Sibaja MJ; Department of Maternal-Fetal Medicine, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico.; Espejel-Nuñez A; Department of Immunobiochemistry, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico.; Flores-Pliego A; Department of Immunobiochemistry, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico.; Mendoza-Ortega J; Department of Bioinformatics and Statistical Analysis, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico.; Camacho-Arroyo I; Unidad de Investigación en Reproducción Humana, Instituto Nacional de Perinatología, Facultad de Química, Universidad Nacional Autónoma de Mexico, Mexico City 11000, Mexico.; González-Camarena R; Department of Health Sciences, Universidad Autónoma Metropolitana, Unidad Iztapalapa, Mexico City 09310, Mexico.; Echeverría-Arjonilla JC; Department of Electrical Engineering, Universidad Autónoma Metropolitana, Unidad Iztapalapa, Mexico City 09310, Mexico.; Estrada-Gutierrez G; Research Division, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, Mexico City 11000, Mexico.
Source
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101092791 Publication Model: Electronic Cited Medium: Internet ISSN: 1422-0067 (Electronic) Linking ISSN: 14220067 NLM ISO Abbreviation: Int J Mol Sci Subsets: MEDLINE
Subject
Language
English
Abstract
Preterm birth (PB) is a leading cause of perinatal morbidity and mortality. PB prediction is performed by measuring cervical length, with a detection rate of around 70%. Although it is known that a cytokine-mediated inflammatory process is involved in the pathophysiology of PB, none screening method implemented in clinical practice includes cytokine levels as a predictor variable. Here, we quantified cytokines in cervical-vaginal mucus of pregnant women (18-23.6 weeks of gestation) with high or low risk for PB determined by cervical length, also collecting relevant obstetric information. IL-2, IL-6, IFN-γ, IL-4, and IL-10 were significantly higher in the high-risk group, while IL-1ra was lower. Two different models for PB prediction were created using the Random Forest machine-learning algorithm: a full model with 12 clinical variables and cytokine values and the adjusted model, including the most relevant variables-maternal age, IL-2, and cervical length- (detection rate 66 vs. 87%, false positive rate 12 vs. 3.33%, false negative rate 28 vs. 6.66%, and area under the curve 0.722 vs. 0.875, respectively). The adjusted model that incorporate cytokines showed a detection rate eight points higher than the gold standard calculator, which may allow us to identify the risk PB risk more accurately and implement strategies for preventive interventions.