Cargando…
A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth
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...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530929/ https://www.ncbi.nlm.nih.gov/pubmed/37762154 http://dx.doi.org/10.3390/ijms241813851 |
_version_ | 1785111600904011776 |
---|---|
author | Borboa-Olivares, Hector Rodríguez-Sibaja, Maria Jose Espejel-Nuñez, Aurora Flores-Pliego, Arturo Mendoza-Ortega, Jonatan Camacho-Arroyo, Ignacio Gonzalez-Camarena, Ramón Echeverria-Arjonilla, Juan Carlos Estrada-Gutierrez, Guadalupe |
author_facet | Borboa-Olivares, Hector Rodríguez-Sibaja, Maria Jose Espejel-Nuñez, Aurora Flores-Pliego, Arturo Mendoza-Ortega, Jonatan Camacho-Arroyo, Ignacio Gonzalez-Camarena, Ramón Echeverria-Arjonilla, Juan Carlos Estrada-Gutierrez, Guadalupe |
author_sort | Borboa-Olivares, Hector |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10530929 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-105309292023-09-28 A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth Borboa-Olivares, Hector Rodríguez-Sibaja, Maria Jose Espejel-Nuñez, Aurora Flores-Pliego, Arturo Mendoza-Ortega, Jonatan Camacho-Arroyo, Ignacio Gonzalez-Camarena, Ramón Echeverria-Arjonilla, Juan Carlos Estrada-Gutierrez, Guadalupe Int J Mol Sci Article 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. MDPI 2023-09-08 /pmc/articles/PMC10530929/ /pubmed/37762154 http://dx.doi.org/10.3390/ijms241813851 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Borboa-Olivares, Hector Rodríguez-Sibaja, Maria Jose Espejel-Nuñez, Aurora Flores-Pliego, Arturo Mendoza-Ortega, Jonatan Camacho-Arroyo, Ignacio Gonzalez-Camarena, Ramón Echeverria-Arjonilla, Juan Carlos Estrada-Gutierrez, Guadalupe A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth |
title | A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth |
title_full | A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth |
title_fullStr | A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth |
title_full_unstemmed | A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth |
title_short | A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth |
title_sort | novel predictive machine learning model integrating cytokines in cervical-vaginal mucus increases the prediction rate for preterm birth |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530929/ https://www.ncbi.nlm.nih.gov/pubmed/37762154 http://dx.doi.org/10.3390/ijms241813851 |
work_keys_str_mv | AT borboaolivareshector anovelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT rodriguezsibajamariajose anovelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT espejelnunezaurora anovelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT florespliegoarturo anovelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT mendozaortegajonatan anovelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT camachoarroyoignacio anovelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT gonzalezcamarenaramon anovelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT echeverriaarjonillajuancarlos anovelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT estradagutierrezguadalupe anovelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT borboaolivareshector novelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT rodriguezsibajamariajose novelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT espejelnunezaurora novelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT florespliegoarturo novelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT mendozaortegajonatan novelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT camachoarroyoignacio novelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT gonzalezcamarenaramon novelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT echeverriaarjonillajuancarlos novelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth AT estradagutierrezguadalupe novelpredictivemachinelearningmodelintegratingcytokinesincervicalvaginalmucusincreasesthepredictionrateforpretermbirth |