Early Recognition and Dynamic Risk Warning System of Multiple Organ Dysfunction Syndrome Caused by Sepsis
- Conditions
- SepsisMODS
- Interventions
- Other: All intervention of real world
- Registration Number
- NCT04904289
- Lead Sponsor
- Sun Yat-sen University
- Brief Summary
Background Sepsis still the main challenge of ICU patients, because of its high morbidity and mortality. The proportion of sepsis, severe sepsis, and septic shock in china were 3.10%, 43.6%, and 53.3% with a 2.78%, 17.69%, and 51.94%, of 90-day mortality, respectively.
Besides, according to the latest definition of sepsis- "a life-threatening organ dysfunction caused by a dysregulated host response to infection. ", it is a disease with intrinsic heterogeneity. Sepsis as a syndrome with such great heterogeneity, there will be significant differences in the severity of sepsis. As a result, there will be significant differences in the treatment and monitoring intensity required by patients with severe sepsis and mild sepsis. No matter from the economic perspective or from the risk of treatment, a proper level of treatment will be the best chose of patient. However, the evaluation of the sepsis severity was not satisfied. Such of SOFA, the AUC of predict patients' mortality was only 69%. Weather these patients occurred multiple organ dysfunction syndrome (MODS) may had totally different outcome and needed totally different treatment. All these treatments need early interference, in order to achieve a good prognosis. Hence, early recognition of MODS caused by sepsis became an imperious demand.
Study design On the base of regional critical medicine clinical information platform, a multi-center, sepsis big data platform (including clinical information database and biological sample database) and a long-term follow-up database will be established. Thereafter, an early identification, risk classification and dynamic early warning system of sepsis induced MODS will be established. This system was based on the real-time dynamic vital signs and clinical information, combined with biomarker and multi-omics information. And this system was evaluated sepsis patients via artificial intelligence, machine learning, bioinformatics analysis techniques.
Finally, optimize the early diagnosis of sepsis induced MODS, standardized the treatment strategy, reduce the morbidity and mortality of MODS through this system.
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- RECRUITING
- Sex
- All
- Target Recruitment
- 60000
- Patients diagnosed with sepsis3.0
- Patients' data missing is greater than 20%
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Arm && Interventions
Group Intervention Description Sepsis without MODS All intervention of real world Patients with sepsis did not occur MODS. Sepsis with MODS All intervention of real world Patients with sepsis occurred MODS.
- Primary Outcome Measures
Name Time Method Sensitivity of the MODS recognized system 90 days Specificity of the MODS recognized system 90 days The AUC of the MODS recognized system ROC 90 days
- Secondary Outcome Measures
Name Time Method The mortality of MODS in sepsis patients 90 days The mortality of MODS in Chinese sepsis patients
The Incidence rate of MODS in sepsis patients 90 days The Incidence rate of MODS in Chinese sepsis patients
Trial Locations
- Locations (18)
Qingyuan People's Hospital
π¨π³Qingyuan, Guangdong, China
Peking Union Medical College Hospital
π¨π³Beijing, Beijing, China
The Second Affiliated Hospital of Zhejiang University School of Medicine
π¨π³Hangzhou, Zhejiang, China
Chinese PLA General Hospital
π¨π³Beijing, Beijing, China
The First Affiliated Hospital of Xi 'an Jiaotong University
π¨π³Xi'an, Shaanxi, China
The First Affiliated Hospital, Sun Yat-sen University
π¨π³Guangzhou, Guangdong, China
Zhejiang Hospital
π¨π³Hangzhou, Zhejiang, China
Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
π¨π³Guangzhou, Guangdong, China
Peking University Shenzhen Hospital
π¨π³Shenzhen, Guangdong, China
West China Hospital, Sichuan University
π¨π³Chengdu, Sichuan, China
Shandong Provincial Hospital
π¨π³Jinan, Shandong, China
Union Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology
π¨π³Wuhan, Hubei, China
Nanjing General Hospital of Nanjing Military Commend
π¨π³Nanjing, Jiangsu, China
Zhejiang Provincial People's Hospital
π¨π³Hangzhou, Zhejiang, China
Beijing Friendship Hospital, Capital Medical University
π¨π³Beijing, China
The First Affiliated Hospital of Guangzhou Medical University
π¨π³Guangzhou, Guangdong, China
Shanghai Ruijin Hospital
π¨π³Shanghai, Shanghai, China
Shanghai Zhongshan Hospital, Fudan University
π¨π³Shanghai, Shanghai, China