Risk Factors and Machine Learning Model for Aminoglycines Related Acute Kidney Injury
NCT ID: NCT05533593
Last Updated: 2023-11-18
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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COMPLETED
8000 participants
OBSERVATIONAL
2022-07-01
2023-10-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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OTHER
RETROSPECTIVE
Study Groups
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AKI Group
Aminoglycoside
Inpatients using aminoglycoside
Non-AKI Group
Aminoglycoside
Inpatients using aminoglycoside
Interventions
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Aminoglycoside
Inpatients using aminoglycoside
Eligibility Criteria
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Inclusion Criteria
* Hospital stay ≥ 48h
* Age ≥18 years
* There are two or more blood creatinine tests during hospitalization
Exclusion Criteria
* Age \<18 years
* Glomerular filtration rate (GFR) \< 30ml/min/1.73m2 within 48 hours after admission
* AKI was diagnosed on admission
* Less than two Scr test results during hospitalization
* The Scr values were always lower than 40 μmol/L during hospitalization
* Cases with incomplete medical history information
18 Years
100 Years
ALL
No
Sponsors
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Qianfoshan Hospital
OTHER
Responsible Party
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Xiao Li,MD
Associate professor of pharmacy
Locations
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Xiao Li,MD
Jinan, Shandong, China
Countries
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References
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Zhang P, Chen Q, Lao J, Shi J, Cao J, Li X, Huang X. Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin. Front Pharmacol. 2025 May 22;16:1538074. doi: 10.3389/fphar.2025.1538074. eCollection 2025.
Other Identifiers
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LCYY-LX-20220102
Identifier Type: -
Identifier Source: org_study_id
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