Sepsis Clinical Trial
Official title:
Early Recognition and Dynamic Risk Warning System of Multiple Organ Dysfunction Syndrome Caused by Sepsis
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.
Status | Recruiting |
Enrollment | 60000 |
Est. completion date | December 31, 2023 |
Est. primary completion date | September 30, 2023 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years to 90 Years |
Eligibility | Inclusion Criteria: - Patients diagnosed with sepsis3.0 Exclusion Criteria: - Patients' data missing is greater than 20% |
Country | Name | City | State |
---|---|---|---|
China | Beijing Friendship Hospital, Capital Medical University | Beijing | |
China | Chinese PLA General Hospital | Beijing | Beijing |
China | Peking Union Medical College Hospital | Beijing | Beijing |
China | West China Hospital, Sichuan University | Chengdu | Sichuan |
China | Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University | Guangzhou | Guangdong |
China | The First Affiliated Hospital of Guangzhou Medical University | Guangzhou | Guangdong |
China | The First Affiliated Hospital, Sun Yat-sen University | Guangzhou | Guangdong |
China | The Second Affiliated Hospital of Zhejiang University School of Medicine | Hangzhou | Zhejiang |
China | Zhejiang Hospital | Hangzhou | Zhejiang |
China | Zhejiang Provincial People's Hospital | Hangzhou | Zhejiang |
China | Shandong Provincial Hospital | Jinan | Shandong |
China | Nanjing General Hospital of Nanjing Military Commend | Nanjing | Jiangsu |
China | Qingyuan People's Hospital | Qingyuan | Guangdong |
China | Shanghai Ruijin Hospital | Shanghai | Shanghai |
China | Shanghai Zhongshan Hospital, Fudan University | Shanghai | Shanghai |
China | Peking University Shenzhen Hospital | Shenzhen | Guangdong |
China | Union Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology | Wuhan | Hubei |
China | The First Affiliated Hospital of Xi 'an Jiaotong University | Xi'an | Shaanxi |
Lead Sponsor | Collaborator |
---|---|
Sun Yat-sen University |
China,
Liu Z, Meng Z, Li Y, Zhao J, Wu S, Gou S, Wu H. Prognostic accuracy of the serum lactate level, the SOFA score and the qSOFA score for mortality among adults with Sepsis. Scand J Trauma Resusc Emerg Med. 2019 Apr 30;27(1):51. doi: 10.1186/s13049-019-0609-3. — View Citation
Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA. 2016 Feb 23;315(8):801-10. doi: 10.1001/jama.2016.0287. — View Citation
Stanski NL, Wong HR. Prognostic and predictive enrichment in sepsis. Nat Rev Nephrol. 2020 Jan;16(1):20-31. doi: 10.1038/s41581-019-0199-3. Epub 2019 Sep 11. Review. — View Citation
Xie J, Wang H, Kang Y, Zhou L, Liu Z, Qin B, Ma X, Cao X, Chen D, Lu W, Yao C, Yu K, Yao X, Shang H, Qiu H, Yang Y; CHinese Epidemiological Study of Sepsis (CHESS) Study Investigators. The Epidemiology of Sepsis in Chinese ICUs: A National Cross-Sectional Survey. Crit Care Med. 2020 Mar;48(3):e209-e218. doi: 10.1097/CCM.0000000000004155. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Sensitivity of the MODS recognized system | 90 days | ||
Primary | Specificity of the MODS recognized system | 90 days | ||
Primary | The AUC of the MODS recognized system ROC | 90 days | ||
Secondary | The Incidence rate of MODS in sepsis patients | The Incidence rate of MODS in Chinese sepsis patients | 90 days | |
Secondary | The mortality of MODS in sepsis patients | The mortality of MODS in Chinese sepsis patients | 90 days |
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