Sepsis is a life-threatening organ dysfunction caused by a dysregulated host immune response to infection, and remains the leading cause of death in critical care medicine worldwide. According to the 2024 Global Burden of Disease data, there are 48.9 million new cases and 11 million deaths annually worldwide, with 11 million deaths accounting for 19.7% of all global deaths. In China, the incidence of sepsis continues to rise due to population aging, increased invasive procedures, overuse of antimicrobials, the prevalence of multidrug-resistant organisms, and a growing number of patients with chronic diseases. Regional studies indicate that the incidence of sepsis in Chinese ICUs ranges from 15% to 30%, with mortality rates as high as 40% to 60%-far exceeding those in developed countries. Among critically ill patients admitted to the ICU-such as those with severe trauma, major surgery, acute respiratory distress syndrome, severe pancreatitis, and advanced malignancies-hospital-acquired infections leading to secondary sepsis represent the most critical trigger for clinical deterioration, multiple organ dysfunction syndrome (MODS), and death. This creates a vicious cascade of "primary disease exacerbation → nosocomial infection → sepsis → MODS → death", resulting in skyrocketing costs, prolonged hospital stays, and heavy burdens on families and society. To address this challenge, this project aims to: (1) establish the largest and internationally leading multimodal dataset for severe sepsis in China, covering 19 tertiary ICUs nationwide over a 5-year period, with 5,300 critically ill patients including 800 sepsis cases, integrating clinical data, immunological indicators, biomarkers, microbiological data, imaging, longitudinal biospecimens, and long-term follow-up information into a standardized, shareable, and sustainable national sepsis database; (2) systematically elucidate the three core pathophysiological mechanisms of severe sepsis-identifying risk factors, pathogen profiles, antimicrobial resistance patterns, and early warning indicators; revealing the dynamic dysregulation patterns of cellular immunity, humoral immunity, and innate immunity to establish immunophenotyping standards; and clarifying the risk factors, mechanisms, and subtype characteristics of multiple organ injury; (3) foster interdisciplinary collaboration between medical and engineering sciences to develop a series of precision diagnostic and therapeutic tools, including AI-assisted early infection warning systems, rapid immunotyping assays, multi-organ injury prediction models, and individualized prognostic calculators for real-time, accurate, and non-invasive bedside assessment; (4) establish a comprehensive precision management system for sepsis, forming an integrated "prevention-early warning-diagnosis-immunotyping-stratified treatment-prognostic evaluation-rehabilitation" care pathway; (5) drive clinical translation to improve patient outcomes, aiming to reduce ICU sepsis incidence, mortality, and healthcare costs, while improving long-term quality of life, cognitive function, and psychological status of survivors and reducing readmission rates; and (6) build a national-level sepsis research platform and cultivate talent by establishing a nationwide collaborative research network, and training professionals with integrated clinical-research-translational competencies.
Age range
18 Years
Sex
ALL
See this in plain English?
AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.
Bring these to your next appointment. They're a starting point for a shared conversation — not a sign you qualify or a recommendation to enrol.
Generated to help you prepare — always confirm anything about your own eligibility and care with the study team and your doctor.
The trial coordinator is the person who runs the study day to day. These cover the practical side — logistics, costs, and what taking part would actually mean for your life. The study team confirms whether you meet the criteria; these are questions to ask, not a sign you qualify.
A starting point for the conversation — always confirm anything about your own eligibility, costs, and care with the study team and your doctor.
Annual incidence of sepsis in ICU
Timeframe: 90 days after enrollment.
Prevalence of sepsis in ICU
Timeframe: 90 days after enrollment
Distribution of infection site (lung/abdominal/bloodstream/urinary tract)
Timeframe: 90 days after enrollment
Distribution of infection type (community-acquired/hospital-acquired/secondary)
Timeframe: 90 days after enrollment
Pathogen distribution (Gram-negative/Gram-positive/fungal)
Timeframe: 90 days after enrollment
Antimicrobial resistance rate (CRE/CRAB/MRSA)
Timeframe: 90 days after enrollment
ICU length of stay
Timeframe: Through ICU discharge, up to 90 days
Total hospital length of stay
Timeframe: Through hospital discharge, up to 90 days
Duration of mechanical ventilation
Timeframe: Through 90 days
Duration of vasoactive agent use
Timeframe: Through 90 days
Duration of renal replacement therapy
Timeframe: Through 90 days
Daily ICU cost
Timeframe: Through ICU discharge, up to 90 days
Total hospitalization cost
Timeframe: Through hospital discharge, up to 90 days
ICU mortality
Timeframe: Through ICU discharge, an average of 28 days
In-hospital mortality
Timeframe: Through hospital discharge, up to 90 days
28-day all-cause mortality
Timeframe: 28 days after enrollment
90-day all-cause mortality
Timeframe: 90 days after enrollment
Discriminative Performance of the Infection Risk-Prediction Model
Timeframe: 90 days after enrollment
Sensitivity and Specificity of the Infection Risk-Prediction Score
Timeframe: 90 days after enrollment
Hand Hygiene Compliance Rate
Timeframe: 90 days after enrollment
Catheter Care Bundle Compliance Rate
Timeframe: 90 days after enrollment
Diagnostic Accuracy of Procalcitonin (PCT)
Timeframe: At enrollment (baseline), and at 72 hours after enrollment
Diagnostic Accuracy of C-Reactive Protein (CRP)
Timeframe: At enrollment (baseline), and at 72 hours after enrollment
Diagnostic Accuracy of Soluble Triggering Receptor Expressed on Myeloid Cells-1 (sTREM-1)
Timeframe: At enrollment (baseline), and at 72 hours after enrollment
Diagnostic Accuracy of Presepsin
Timeframe: At enrollment (baseline), and at 72 hours after enrollment
Diagnostic Accuracy of Soluble Urokinase Plasminogen Activator Receptor (suPAR)
Timeframe: At enrollment (baseline), and at 72 hours after enrollment
Diagnostic Accuracy of Interleukin-6 (IL-6)
Timeframe: At enrollment (baseline), and at 72 hours after enrollment
Diagnostic Accuracy of Interleukin-8 (IL-8)
Timeframe: At enrollment (baseline), and at 72 hours after enrollment
Diagnostic Accuracy of Pro-Adrenomedullin (Pro-ADM)
Timeframe: At enrollment (baseline), and at 72 hours after enrollment
AUC of Combined Multi-Parameter Early-Warning Model
Timeframe: From 24 hours before to 72 hours after infection onset
Diagnostic Accuracy of AI-Based Automated Warning System
Timeframe: From 24 hours before to 72 hours after infection onset
Lead Time of AI-Based Warning System
Timeframe: Up to 24 hours before clinical diagnosis
Time to First Effective Antibiotic Administration
Timeframe: Within 6 hours of infection onset
Rate of Appropriate Empirical Antibiotic Therapy
Timeframe: 90 days after enrollment
Rate of Antibiotic Coverage of Resistant Organisms
Timeframe: 90 days after enrollment
Antibiotic De-escalation Rate
Timeframe: 90 days after enrollment
Duration of Antibiotic Therapy
Timeframe: 90 days after enrollment
Time to Source Control
Timeframe: 90 days after enrollment
Compliance Rate with SSC Bundle Elements
Timeframe: 90 days after enrollment