Perioperative changes of microbiome and metabolism in cardiac surgery patients

Conditions: I25.1
R65.2
F05.8
T81.1
R57.2
J95.1
K56.7
R65.1
Atherosclerotic heart disease
Systemic Inflammatory Response Syndrome of non-infectious origin without organ failure

Registration Number: DRKS00016493

Lead Sponsor: niversitätsklinikum Schleswig-Holstein Campus Kiel

Brief Summary: Not available

Detailed Description: Not available

Recruitment & Eligibility

Status: Complete

Sex: All

Target Recruitment: 100

Inclusion Criteria

Scheduled surgery using the use of cardio-pulmonary Bypass; male and female patients (age> 18 years); written informed consent of the patient.

Exclusion Criteria

Patients receiving antibiotic therapy; anal Stenosis; Perianal Bleeding or infections; Emergency indication for surgery; Endocarditis; Immunosuppression or therapy with glucocorticoids above the Cushing threshold (> 7.5 mg prednisolone equivalent); Existing chronic inflammatory bowel disease; Non-consenting Patient; Child Pugh Class C; Women during pregnancy and lactation; Participation in a clinical trial (intervention study) within the last 30 days; Current participation in another clinical trial (intervention study); Participation of the patient in this study earlier; Therapy restriction or setting (e.g., DNR order)

Study & Design

Study Type: observational

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The primary endpoint of the microbiome analysis is the quantitative change in the diversity of the four dominant bacterial phyla Firmicutes, Bacteroidetes, Proteobacteria and Actinobacteria between baseline and the other investigation times. An exploratory analysis will identify microorganisms that may be drivers, protectors or biomarkers of metabolic disorders associated with poor postoperative outcome or altered metabolic processes.

Secondary Outcome Measures

Name	Time	Method
Secondary endpoints include the determination of typical metabolic metabolites from patients' blood samples using mass spectrometry-based technologies and the correlation of metabolite concentration changes with clinical outcome parameters.