GENECARD - the Use of Genetic, Epigenetic, Metabolomic, Proteomic and Microbiotic Markers, Image and Voice Biomarker Analyses, and Pre- and Intraoperative Clinical Data - to Predict Early Complications After Cardiac Surgery.
GENECARD - the Use of Genetic, Epigenetic, Metabolomic, Proteomic and Microbiotic Markers, Image and Voice Biomarker Analyses, and Pre- and Intraoperative Clinical Data - to Predict Early Complications After Cardiac Surgery.
The goal of this observational cohort study is to prove whether genetic, epigenetic, transcriptomic, proteomic, metabolomic, imaging, voice, and clinical markers can improve prediction of early complications after cardiac surgery in adult patients.
The main questions it aims to answer are:
Which biological and clinical markers are associated with: new-onset atrial fibrillation (NOAF), acute kidney injury (AKI), postoperative delirium (POD), vasoplegia, postoperative bleeding and 30-day mortality? Can combining these markers improve early prediction of postoperative complications compared with current clinical risk scores?
Researchers will analyze a wide range of data collected before, during, and after cardiac surgery and compare patients who develop early complications with those who do not to identify risk factors and early biomarkers.
Participants will:
Provide biological samples (blood, urine, stool) before and after surgery for genetic, epigenetic, transcriptomic, proteomic, metabolomic, microbiome, and laboratory testing.
Undergo standard preoperative and intraoperative imaging and clinical assessments.
Allow collection of clinical data related to postoperative outcomes (For some participants) have voice and video recordings performed to help identify early signs of postoperative delirium.
This study aims to improve early detection of postoperative complications and support development of personalized diagnostic and treatment strategies for patients undergoing cardiac surgery.
Single-center, prospective, translational, observational cohort study designed to identify markers that predict early postoperative complications in adult patients undergoing elective cardiac surgery. The study will analyze genetic, epigenetic, transcriptomic, proteomic, metabolomic, microbiome, imaging, voice, and detailed clinical data collected before, during, and after surgery. Approximately 2,000-3,000 participants will be enrolled between 2026 and 2029.
Study Objectives
The main objective is to determine whether selected biological and clinical markers can predict early postoperative complications, including:
New-onset atrial fibrillation (NOAF), Acute kidney injury (AKI), Postoperative delirium (POD), Vasoplegia, Postoperative bleeding 30-day mortality.
The study will use current clinical definitions: NOAF per ESC guidelines, AKI per KDIGO criteria, and POD per DSM-V, as well as validated delirium scales such as CAM-ICU or DOSS. Postoperative bleeding will be defined as >1000 mL drainage in 24 hours or the need for surgical re-exploration.
Secondary outcomes include in-hospital mortality, 30-day mortality, duration of mechanical ventilation, ICU length of stay, and total postoperative hospital length of stay.
Participants
Eligible participants are adult men and women undergoing elective cardiac surgery who provide informed consent. Exclusion criteria are age <18 years, lack of consent, and prior or planned organ or bone marrow transplantation.
Data and Sample Collection
The study will collect a broad set of data and biological materials, including:
Clinical data: detailed medical history, epidemiologic factors, disease history, physical exam parameters, perioperative clinical data, and postoperative complication data.
Genetic analysis: targeted sequencing of selected SNPs associated with primary outcomes using PCR-based arrays or NGS/WGS. DNA from PBMCs will be collected from all participants, with potential additional sequencing pending external funding. A replication cohort of 525 patients from the INFLACOR study will be used for confirmatory analyses.
Epigenetic profiling: genome-wide epigenetic marker profiling from PBMCs in matched case-control subgroups (approximately n=300 per group) for participants who develop primary outcomes. DNA methylation will be analyzed to develop an epigenetic risk index and integrated with genetic and clinical data.
Transcriptomics: RNA-seq of PBMCs collected before surgery, as well as short-chain RNA (scRNA) profiling from urine samples collected pre- and postoperatively to identify early markers of AKI.
Proteomics and metabolomics: untargeted and targeted analyses of plasma collected preoperatively and at two postoperative time points (6 hours and postoperative day 3). These analyses aim to identify and validate early biomarkers of primary complications.
Laboratory diagnostics: serial measurement of selected laboratory markers relevant to early complications, such as serum creatinine, NGAL, cystatin C, and novel biomarkers (e.g., KIM) using ELISA.
Microbiota and microbiome: metabolomic and metagenomic sequencing analyses on fractionated stool samples to characterize gut bacterial composition, extracellular vesicles, and metabolite profiles, using GC-MS and LC-MS/MS.
Imaging data: routine preoperative imaging including transthoracic echocardiography (TTE) and coronary angiography.
Voice and video biomarkers: for participants developing POD, continuous bedside-acquired video, audio, and sensor data will be analyzed to identify voice and image biomarkers (e.g., MFCC parameters) associated with prodromal delirium. Machine-learning models will be developed to support real-time detection of POD-related features.
Analytical Approach
The study will use multistage regression, machine-learning techniques, and AI-based modeling to identify predictors of both primary and secondary outcomes. Analyses will integrate genetic, environmental, preoperative, and intraoperative factors. One aim is to enhance existing clinical risk calculators for postoperative morbidity and mortality, such as STS-ACSD and EuroSCORE. The study expects improved discrimination of predictive models, targeting ROC-AUC values >0.9 for mortality and >0.8 for morbidity.
A polygenic risk score will also be developed to evaluate genetic contribution to variation in primary outcomes.
Expected Impact
The integrated multi-omics and clinical approach is expected to identify new pathophysiological mechanisms underlying early postoperative complications and potentially support development of novel preventive therapies. The study aims to facilitate personalized perioperative diagnostic and therapeutic strategies by improving early identification of high-risk patients undergoing cardiac surgery.
Inclusion Criteria:
Exclusion Criteria:
mkowalik@gumed.edu.pl+48585846109
mbrzez@gumed.edu.pl+585844200