Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth

Hector Borboa-Olivares
* ,
Maria Jose Rodríguez-Sibaja
,
Aurora Espejel-Nuñez
,
Arturo Flores-Pliego
ORCID logo ,
Jonatan Mendoza-Ortega
ORCID logo ,
Ramón González-Camarena
,
Juan Carlos Echeverría-Arjonilla
,
Guadalupe Estrada-Gutierrez
*
Version 1 : Received: 24 August 2023 / Approved: 28 August 2023 / Online: 29 August 2023 (08:56:23 CEST)
Version 2 : Received: 6 September 2023 / Approved: 7 September 2023 / Online: 8 September 2023 (04:07:19 CEST)

A peer-reviewed article of this Preprint also exists.

Borboa-Olivares, H.; Rodríguez-Sibaja, M.J.; Espejel-Nuñez, A.; Flores-Pliego, A.; Mendoza-Ortega, J.; Camacho-Arroyo, I.; González-Camarena, R.; Echeverría-Arjonilla, J.C.; Estrada-Gutierrez, G. A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth. Int. J. Mol. Sci. 2023, 24, 13851. Borboa-Olivares, H.; Rodríguez-Sibaja, M.J.; Espejel-Nuñez, A.; Flores-Pliego, A.; Mendoza-Ortega, J.; Camacho-Arroyo, I.; González-Camarena, R.; Echeverría-Arjonilla, J.C.; Estrada-Gutierrez, G. A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth. Int. J. Mol. Sci. 2023, 24, 13851.

Abstract

Preterm birth (PB) is a leading cause of perinatal morbidity and mortality. PB prediction is per-formed 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, no screening method implemented in clinical practice includes cytokine levels as a predictor varia-ble. 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, 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: the full model with 12 clinical variables and cyto-kine values, and the adjusted model, including the most significant 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 integrat-ing cytokines showed a detection rate 8 points higher than the gold standard calculator, which may allow us to identify PB risk more accurately and implement strategies for preventive in-terventions

Keywords

Preterm delivery; screening; artificial intelligence; inflammatory response; interleukin-2; cervical length

Subject

Medicine and Pharmacology, Obstetrics and Gynaecology

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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