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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Sepsis with MODS | Patients with sepsis occurred MODS. |
| |
| Sepsis without MODS | Patients with sepsis did not occur MODS. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| All intervention of real world | Other | We analyzed all data we can obtain from our databases |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| The Incidence rate of MODS in sepsis patients | The Incidence rate of MODS in Chinese sepsis patients | 90 days |
| The mortality of MODS in sepsis patients | The mortality of MODS in Chinese sepsis patients |
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Inclusion Criteria:
Exclusion Criteria:
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Patients with sepsis
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Xiangdong Guan, Dr | Contact | 020-87755766 | 8454 | guanxd@mail.sysu.edu.cn |
| Jianfeng Wu, Dr | Contact | 020-87755766 | 8454 | wujianf@mail.sysu.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Chinese PLA General Hospital | Not yet recruiting | Beijing | Beijing Municipality | 100000 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 31804299 | Background | 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. | |
| 26903338 |
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Serum, Urine
| 90 days |
| Peking Union Medical College Hospital | Not yet recruiting | Beijing | Beijing Municipality | 100000 | China |
|
| Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University | Recruiting | Guangzhou | Guangdong | 510000 | China |
|
| The First Affiliated Hospital of Guangzhou Medical University | Recruiting | Guangzhou | Guangdong | 510000 | China |
|
| The First Affiliated Hospital, Sun Yat-sen University | Recruiting | Guangzhou | Guangdong | 510080 | China |
|
| Qingyuan People's Hospital | Not yet recruiting | Qingyuan | Guangdong | China |
|
| Peking University Shenzhen Hospital | Not yet recruiting | Shenzhen | Guangdong | China |
|
| Union Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology | Not yet recruiting | Wuhan | Hubei | China |
|
| Nanjing General Hospital of Nanjing Military Commend | Not yet recruiting | Nanjing | Jiangsu | 210000 | China |
|
| The First Affiliated Hospital of Xi 'an Jiaotong University | Not yet recruiting | Xi'an | Shaanxi | China |
|
| Shandong Provincial Hospital | Not yet recruiting | Jinan | Shandong | 250014 | China |
|
| Shanghai Ruijin Hospital | Not yet recruiting | Shanghai | Shanghai Municipality | 200000 | China |
|
| Shanghai Zhongshan Hospital, Fudan University | Not yet recruiting | Shanghai | Shanghai Municipality | 200000 | China |
|
| West China Hospital, Sichuan University | Not yet recruiting | Chengdu | Sichuan | 610000 | China |
|
| The Second Affiliated Hospital of Zhejiang University School of Medicine | Not yet recruiting | Hangzhou | Zhejiang | 310000 | China |
|
| Zhejiang Hospital | Not yet recruiting | Hangzhou | Zhejiang | 310000 | China |
|
| Zhejiang Provincial People's Hospital | Not yet recruiting | Hangzhou | Zhejiang | 310000 | China |
|
| Beijing Friendship Hospital, Capital Medical University | Not yet recruiting | Beijing | China |
|
| 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. |
| 31511662 | Background | 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. |
| 31039813 | Background | 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. |
| ID | Term |
|---|---|
| D018805 | Sepsis |
| D009102 | Multiple Organ Failure |
| ID | Term |
|---|---|
| D007239 | Infections |
| D018746 | Systemic Inflammatory Response Syndrome |
| D007249 | Inflammation |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D012769 | Shock |
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