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Can stress biomarkers predict preterm birth in women with threatened preterm labor?

Fecha de publicación:

Autores de IIS La Fe

Participantes ajenos a IIS La Fe

  • Serrano De La Cruz V

Grupos

Abstract

Background: Preterm birth is a major paediatric challenge difficult to prevent and with major adverse outcomes. Prenatal stress plays an important role on preterm birth; however, there are few stress-related models to predict preterm birth in women with Threatened Preterm Labor (TPL). Objective: The aim of this work is to study the influence of stress biomarkers on time until birth in TPL women. Methods: Eligible participants were pregnant women between 24 and 31 gestational weeks admitted to the hospital with TPL diagnosis (n = 166). Stress-related biomarkers (a-amylase and cortisol) were determined in saliva samples after TPL diagnosis. Participants were followed-up until labor. A parametric survival model was constructed based on a-amylase, cortisol), TPL gestational week, age, parity, and multiple pregnancy. The model was adjusted using a logistic distribution and it was implemented as a nomogram to predict the labor probability at 7- and 14-day term. Results: The time until labor was associated with cortisol (p = 0.001), gestational week at TPL diagnosis (p = 0.004), and age (p = 0.02). Importantly, high cortisol levels at TPL diagnosis were predictive of latency to labor. Validation of the model yielded an optimum corrected AUC value of 0.63. Conclusions: High cortisol levels at TPL diagnosis may have an important role in the preterm birth prediction. Our statistical model implemented as a nomogram provided accurate predictions of individual prognosis of pregnant women.

Datos de la publicación

ISSN/ISSNe:
0306-4530, 1873-3360

PSYCHONEUROENDOCRINOLOGY  PERGAMON-ELSEVIER SCIENCE LTD

Tipo:
Article
Páginas:
19-24
PubMed:
28558282
Factor de Impacto:
2,353 SCImago ℠
Cuartil:
Q1 SCImago ℠

Citas Recibidas en Web of Science: 32

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Keywords

  • alpha-amylase; Cortisol; Survival model; Threatened preterm labor

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