Incidence and Risk Factors of PostopeRativE Delirium in ICU in China
NCT ID: NCT03704324
Last Updated: 2022-01-03
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
1000 participants
OBSERVATIONAL
2018-11-01
2023-01-01
Brief Summary
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Detailed Description
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The PREDICt study aims to find out risk factors, especially any are modifiable, and any have value for developing prediction model. Our primary aim is to determine the incidence and severity of Post-Operative Delirium (POD) in ICU after surgery, and identify the associated outcomes and burdens of POD in ICU by evaluating the impact on postoperative outcome, ICU and hospital length of stay, medical expenses. Our secondary aim is to investigate the modifiable and non-modifiable risk factors for the occurrence of POD during ICU stay and develop delirium prediction model for ICU patients. Our final aim is to comprehensively and deeply explore the etiology of POD to guide prevention of delirium among postoperative patients.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
* Surgical patients
* Admitted to ICU after surgery
Exclusion Criteria
* Unable to fully participate in delirium testing, including blind, deaf, illiterate or inability to understand Chinese
* Undergoing surgery procedures do not require admission to SICU
* Transfer to SICU from wards after surgery
18 Years
ALL
No
Sponsors
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Shanghai Zhongshan Hospital
OTHER
Responsible Party
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Principal Investigators
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Yuxia Zhang
Role: PRINCIPAL_INVESTIGATOR
Fudan University
Locations
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180 Fenglin Road
Shanghai, , China
Countries
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Central Contacts
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Facility Contacts
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References
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Cai S, Cui H, Pan W, Li J, Lin X, Zhang Y. Two-stage prediction model for postoperative delirium in patients in the intensive care unit after cardiac surgery. Eur J Cardiothorac Surg. 2022 Dec 2;63(1):ezac573. doi: 10.1093/ejcts/ezac573.
Wang H, Zhao QY, Luo JC, Liu K, Yu SJ, Ma JF, Luo MH, Hao GW, Su Y, Zhang YJ, Tu GW, Luo Z. Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model. BMC Pulm Med. 2022 Aug 8;22(1):304. doi: 10.1186/s12890-022-02096-7.
Other Identifiers
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2018ZSLC08
Identifier Type: -
Identifier Source: org_study_id
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