Overview

Establishment and Evaluation of Prenatal Prevention and Treatment Strategy for NARDS

Status:
Not yet recruiting
Trial end date:
2025-12-31
Target enrollment:
0
Participant gender:
Female
Summary
1. A predictive model for NARDS was established based on perinatal risk factors. Multivariate Logistic regression analysis was used to screen the independent prenatal risk factors for NARDS. A Logistic regression model was constructed using the above independent risk factors and quantified in a nomogram to construct a visualization model for prenatal prediction of NARDS. 2. The role of ACS in the prevention and treatment of ARDS in near-term/full-term infants. For neonates with a probability greater than 80% in the prediction model of ARDS, at least one ACS was given before the termination of pregnancy. The GC level of cord blood (taken at birth) and the mRNA levels of α-ENaC, Na-K-atpase and SGK1 in nasal epithelium were measured within 2 hours and 1 day after birth in the ACS intervention group and the control group. The occurrence and severity of pulmonary edema, the occurrence and severity of ARDS, and the mortality rate of NARDS were evaluated by lung ultrasound. The indexes of the two groups were compared horizontally and longitudinally.
Phase:
N/A
Accepts Healthy Volunteers?
No
Details
Lead Sponsor:
The Second Affiliated Hospital of Chongqing Medical University
Collaborators:
Children's Hospital of Chongqing Medical University
Chongqing Medical Center for Women and Children
University-Town Hospital of Chongqing Medical University
Treatments:
Dexamethasone
Criteria
A predictive model for neonatal acute respiratory distress syndrome was established based
on perinatal risk factors.

Inclusion Criteria:

1. The pregnant women with a probability greater than 80% in the prediction model of
neonatal acute respiratory distress syndrome and agreed to ACS intervention.

2. Obtaining patient consent.

Exclusion Criteria:

1. the pregnant women with a probability of less than 80% in the neonatal acute
respiratory distress syndrome prediction model.

2. The patient refuses.