TIARA-IMPACT: Immunological and microbiome-based predictors of postoperative infections
Postoperative infections following major abdominal surgery remain common and difficult to predict. A key reason is the incomplete understanding of how individual differences in the gut microbiome, host immunity, and microbial metabolites shape infection susceptibility. Following completion of patient enrollment in the TIARA cohort study, "TIARA-IMPACT" conducts in-depth analyses of biospecimens and clinical data from approximately 870 patients. The project aims to identify predictive biomarkers for postoperative infections, multidrug-resistant organism (MDRO) colonization, and long-term clinical outcomes by integrating multi-omics approaches across the microbiome–metabolome–immune axis with machine-learning methods.
Between 2022 and 2025, the TIARA cohort study enrolled 872 patients following major abdominal surgery at eight German university hospitals. Biospecimens, including stool, blood, urine, and swab samples, were collected alongside comprehensive clinical data. The translational follow-up project, TIARA-IMPACT, builds on the TIARA study and focuses on advanced laboratory and data analyses. Six DZIF partner sites are integrating complementary multi-omics technologies across three major fields of research to achieve this.
1. Microbiome, resistome, and pathogens:
TIARA-IMPACT uses metagenomic sequencing of stool and wound samples, metabolomic analyses, and targeted cultivation of multidrug-resistant ESKAPE pathogens to investigate the composition and function of the gut microbiome, as well as the occurrence of multidrug-resistant organisms (MDROs), before and after surgery.
2. Host immune responses:
Advanced immunological methods, including multiplex cytokine profiling, flow cytometry, and single-cell RNA sequencing, are used to characterize individual patient immune responses. These analyses aim to improve our understanding of the factors that protect against infections, promote their development, and influence long-term clinical outcomes following surgery.
3. Biomarkers and predictive modelling:
Advanced biostatistical approaches and machine-learning methods integrate multimodal datasets to identify biomarkers and develop individualized risk profiles for postoperative infections, sepsis, MDRO colonization, and long-term clinical outcomes.
TIARA-IMPACT aims to enable personalized risk prediction, support targeted antibiotic stewardship, and improve perioperative care for high-risk surgical patients.