Establishment of a Biomarkers-based Early Warning System of Acute Respiratory Distress Syndrome (ARDS)
Overview
- Phase
- N/A
- Intervention
- Not specified
- Conditions
- Respiratory Distress Syndrome, Adult
- Sponsor
- Mingdong Hu
- Enrollment
- 376
- Locations
- 1
- Primary Endpoint
- Analysis of gene polymorphism
- Last Updated
- 6 years ago
Overview
Brief Summary
The aim of the investigators 'study is to investigate the relationship between the biomarkers (e.g. protein markers, genetic polymorphisms and epigenetic markers) and the onset of ARDS. In this study, the participants were divided into case group (with ARDS) and control group (without ARDS), based on a nested case-control study method. During the diagnosis and treatment, the clinical data of subjects are collected at the given time point. And the clinical data are extracted from plasma, blood and bronchoalveolar lavage fluid of participants. These data will be analyzed based on statistical methods. In the end ,the investigators can build a multi index early warning model based on the biomarkers,which is meaningful for the early diagnosis of the patient with high-risk for ARDS and provide evidence for the early treatment.
Detailed Description
The investigators studied patients at high risk of acute respiratory distress syndrome (ARDS) and ARDS patients. The patients with ARDS were the case group, and the patients without ARDS were the control group .Sample size estimate :set alpha =0.05,1- beta =0.8, estimated cases exposure rate was 50%, the control group estimated exposure rate was 35%, according to a case-control study of sample size estimation formula for sample size calculation, and considering the loss rate is 10%, the sample size for each group of 188 cases, two groups of 376 cases. The plasma, blood and bronchoalveolar lavage fluid will be collected during the diagnosis and treatment，to study biomarkers related to the onset of ARDS, such as protein markers,genetic polymorphisms and epigenetic markers.The observation data of two groups will be compared .The clinical data are collected at the given time point. The stepwise regression (forward-conditional) will be used for establishing a multivariate unconditional logistic regression model that will contribute to screened the main risk factor and protective factors that affect the ARDS. And these factor will help to established the early warning model and the risk function of ARDS in high-risk patients, which will contribute to predict the risk of ARDS in high-risk patients.All information about the subjects is strictly confidential, and the results of the study may be reported at medical conferences and published in scientific journals, but any individual who can identify subjects will not be able to use.
Investigators
Mingdong Hu
associate chief physician
Third Military Medical University
Eligibility Criteria
Inclusion Criteria
- •High-risk cases ARDS inclusion criteria:
- •Acute onset (within 1 weeks)
- •Pneumonia,Aspiration,Sepsis,Acute pancreatitis,Shock,High-risk trauma and other unexpected severe diseases.
- •Pao2/Fio2\>300mmhg
- •18 to 80 years old
- •The subjects agreed to sign the informed consent
- •ARDS Inclusion criteria:
- •Acute onset (within 1 weeks)
- •Pneumonia,Aspiration,Sepsis,Acute pancreatitis,Shock,High-risk trauma and other unexpected severe diseases.
- •Pao2/Fio2\<300mmhg
Exclusion Criteria
- •patients who developed ARDS before initial evaluation or blood collection
- •patients who were rehospitalized
- •the hospital stay was shorter than 7 days, and it was unfeasible to determine the clinical outcome
- •patients who died within 6h of admission
- •patients had a history of chronic interstitial lung disease
- •patients with an age of less than 18 years old
- •Patients were immunodeficiency (eg, eukaemia) or treated with cytotoxic drugs
- •patients who were pregnant
- •patients who were refused to join.
Outcomes
Primary Outcomes
Analysis of gene polymorphism
Time Frame: Day 0
Detecting the genetic polymorphisms,it include mannose binding lectin-2 gene (The Single Nucleotide Polymorphism database (dbSNP) identification number (ID): rs1800450) and lipopolysaccharide-binding peptide (LBP) gene（The Single Nucleotide Polymorphism database (dbSNP) ID: rs2232618）
Detection of methylation of occludin (OCLN) gene
Time Frame: Day 0,Day 1,Day 2,Day 3,Day 4,Day 7 and Day 14 post-enrollment.
Detecting the OCLN gene methylation is based on microarray methylation pattern analysis.The subjects will be detected OCLN gene methylation at day0,1,2,3,4,7. Among participants, for case group patients ,the observation time point day14 will be added.
the change of protein biomarkers expression
Time Frame: Day 0,Day 1,Day 2,Day 3,Day 4,Day 7 and Day 14 post-enrollment.
The protein biomarkers include surfactant protein(SP-D)，Clara Cell Protein 16(CC-16)，Angiopoietin-2(Ang-2)，von Willebrand factor(vWF)，Lipopolysaccharide-Binding Protein(LBP) and plasminogen activator inhibitor-1(PAI-1).The subjects will be observed for the content and expression changes of the items mentioned above at day0,1,2,3,4,7. Among participants, for case group patients ,the observation time point,day14 will be added.
the change of microRNA-126(miR-126) expression
Time Frame: Day 0,Day 1,Day 2,Day 3,Day 4,Day 7 and Day 14 post-enrollment.
The subjects will be observed for the content and expression changes of the miR-126 at day0,1,2,3,4,7. Among participants, for case group patients ,the observation time point,day14 will be added.
the change of microRNA-146a(miR-146a) expression
Time Frame: Day 0,Day 1,Day 2,Day 3,Day 4,Day 7 and Day 14 post-enrollment.
The subjects will be observed for the content and expression changes of the miR-146a at day0,1,2,3,4,7. Among participants, for case group patients ,the observation time point,day14 will be added.