A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth.
Hector Borboa-OlivaresMaria Jose Rodríguez-SibajaAurora Espejel-NuñezArturo Flores-PliegoJonatan A Mendoza-OrtegaIgnacio Camacho-ArroyoRamón González-CamarenaJuan Carlos Echeverría-ArjonillaGuadalupe Estrada GutierrezPublished in: International journal of molecular sciences (2023)
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.
Keyphrases
- preterm birth
- machine learning
- heavy metals
- pregnant women
- gestational age
- low birth weight
- rheumatoid arthritis
- preterm infants
- birth weight
- immune response
- risk assessment
- big data
- real time pcr
- loop mediated isothermal amplification
- artificial intelligence
- systemic lupus erythematosus
- pregnancy outcomes
- systemic sclerosis