Ana García-Blanco1, Vicente Diago2, Verónica Serrano De La Cruz2, David Hervás3, Consuelo Cháfer-Pericás4, Máximo Vento5. 1. Neonatal Research Unit, Health Research Institute La Fe, Valencia, Spain; University of Valencia, Valencia, Spain. Electronic address: ana.garcia-blanco@uv.es. 2. Division of Obstetrics and Gynecology, Hospital Universitari i Politècnic La Fe, Valencia, Spain. 3. Biostatistics Unit, Health Research Institute La Fe, Valencia, Spain. 4. Neonatal Research Unit, Health Research Institute La Fe, Valencia, Spain. Electronic address: m.consuelo.chafer@uv.es. 5. Neonatal Research Unit, Health Research Institute La Fe, Valencia, Spain.
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 (α-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 α-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.
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 (α-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 α-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.
Authors: Kuldeep Dhama; Shyma K Latheef; Maryam Dadar; Hari Abdul Samad; Ashok Munjal; Rekha Khandia; Kumaragurubaran Karthik; Ruchi Tiwari; Mohd Iqbal Yatoo; Prakash Bhatt; Sandip Chakraborty; Karam Pal Singh; Hafiz M N Iqbal; Wanpen Chaicumpa; Sunil Kumar Joshi Journal: Front Mol Biosci Date: 2019-10-18
Authors: Robert C Johnston; Megan Faulkner; Philip M Carpenter; Ali Nael; Dana Haydel; Curt A Sandman; Deborah A Wing; Elysia Poggi Davis Journal: Reprod Sci Date: 2020-03-26 Impact factor: 3.060
Authors: Laura Campos-Berga; Alba Moreno-Giménez; Máximo Vento; Ana García-Blanco; Rosa Sahuquillo-Leal; David Hervás; Vicente Diago; Pablo Navalón Journal: Eur Child Adolesc Psychiatry Date: 2021-02-14 Impact factor: 4.785