Clinical trial NCT04904289: Early Recognition and Dynamic Risk Warning System of Multiple Organ Dysfunction Syndrome Caused by Sepsis
Official title: Early Recognition and Dynamic Risk Warning System of Multiple Organ Dysfunction Syndrome Caused by Sepsis
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Sepsis
MODS
Keywords: Sepsis
MODS
Big data
Artificial intelligence
Lead sponsor: Sun Yat-sen University
Sponsor class: OTHER
First posted: 2021-05-27
Enrollment: 60000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with sepsis

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.


Eligibility criteria: Inclusion Criteria:

* Patients diagnosed with sepsis3.0

Exclusion Criteria:

* Patients' data missing is greater than 20%

Primary outcomes:
measure: Sensitivity of the MODS recognized system
timeFrame: 90 days
measure: Specificity of the MODS recognized system
timeFrame: 90 days
measure: The AUC of the MODS recognized system ROC
timeFrame: 90 days
Secondary outcomes:
measure: The Incidence rate of MODS in sepsis patients
timeFrame: 90 days
measure: The mortality of MODS in sepsis patients
timeFrame: 90 days
Other outcomes:
Locations:
city: Beijing
country: China
facility: Chinese PLA General Hospital
state: Beijing
city: Beijing
country: China
facility: Peking Union Medical College Hospital
state: Beijing
city: Guangzhou
country: China
facility: Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
state: Guangdong
city: Guangzhou
country: China
facility: The First Affiliated Hospital of Guangzhou Medical University
state: Guangdong
city: Guangzhou
country: China
facility: The First Affiliated Hospital, Sun Yat-sen University
state: Guangdong
city: Qingyuan
country: China
facility: Qingyuan People's Hospital
state: Guangdong
city: Shenzhen
country: China
facility: Peking University Shenzhen Hospital
state: Guangdong
city: Wuhan
country: China
facility: Union Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology
state: Hubei
city: Nanjing
country: China
facility: Nanjing General Hospital of Nanjing Military Commend
state: Jiangsu
city: Xi'an
country: China
facility: The First Affiliated Hospital of Xi 'an Jiaotong University
state: Shaanxi
city: Jinan
country: China
facility: Shandong Provincial Hospital
state: Shandong
city: Shanghai
country: China
facility: Shanghai Ruijin Hospital
state: Shanghai
city: Shanghai
country: China
facility: Shanghai Zhongshan Hospital, Fudan University
state: Shanghai
city: Chengdu
country: China
facility: West China Hospital, Sichuan University
state: Sichuan
city: Hangzhou
country: China
facility: The Second Affiliated Hospital of Zhejiang University School of Medicine
state: Zhejiang
city: Hangzhou
country: China
facility: Zhejiang Hospital
state: Zhejiang
city: Hangzhou
country: China
facility: Zhejiang Provincial People's Hospital
state: Zhejiang
city: Beijing
country: China
facility: Beijing Friendship Hospital, Capital Medical University
Study officials:
References:
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.
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.
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.
citation: 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.