Risk Factors and Machine Learning Model for Aminoglycines Related Acute Kidney Injury

NCT ID: NCT05533593

Last Updated: 2023-11-18

Study Results

Results pending

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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Recruitment Status

COMPLETED

Total Enrollment

8000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-07-01

Study Completion Date

2023-10-31

Brief Summary

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Drug-induced acute kidney injury (D-AKI) can occur after treatment with aminoglycosides. Predicting the risk of D-AKI is important for a tailored prevention and palliation strategy. There are currently no studies to construct a model for predicting the risk of D-AKI associated with aminoglycosides. Therefore, the study aimed to develop a model to predict the risk of D-AKI that could be used in clinical practice. Clinical data of inpatients treated with aminoglycosides at the First Affiliated Hospital of Shandong First Medical University from January 2018 to December 2020, were collected. The primary endpoint was D-AKI, defined according to the 2012 Global Outcomes for Kidney Disease Improvement (KDIGO). Patient clinical information, including demographic information, admission and discharge information, disease history, medication information, and laboratory tests, was obtained through an in-hospital electronic medical record system. Independent risk factors associated with D-AKI will be screened by univariate and multifactorial analyses. Covariates with significant differences (P \< 0.05) were included in logistic regression models. The models were evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROC) obtained by ten-fold cross-validation. Future studies are needed to test the application of this model in clinical practice to determine whether D-AKI in this setting can be predicted and mitigated.

Detailed Description

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Conditions

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Aminoglycoside Toxicity Acute Kidney Injury

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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AKI Group

Aminoglycoside

Intervention Type DRUG

Inpatients using aminoglycoside

Non-AKI Group

Aminoglycoside

Intervention Type DRUG

Inpatients using aminoglycoside

Interventions

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Aminoglycoside

Inpatients using aminoglycoside

Intervention Type DRUG

Eligibility Criteria

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Inclusion Criteria

* All inpatients who used aminoglycosides during hospitalization
* Hospital stay ≥ 48h
* Age ≥18 years
* There are two or more blood creatinine tests during hospitalization

Exclusion Criteria

* Hospital stay \< 48h
* 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
Minimum Eligible Age

18 Years

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Qianfoshan Hospital

OTHER

Sponsor Role lead

Responsible Party

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Xiao Li,MD

Associate professor of pharmacy

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Xiao Li,MD

Jinan, Shandong, China

Site Status

Countries

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China

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.

Reference Type DERIVED
PMID: 40487395 (View on PubMed)

Other Identifiers

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LCYY-LX-20220102

Identifier Type: -

Identifier Source: org_study_id

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