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

NCT05533593 | Completed

• 1 other identifier: LCYY-LX-20220102
• Study Type: observational
• Target: 8,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Aminoglycoside Toxicity; Acute Kidney Injury

Keywords

Aminoglycosides; Acute kidney injury; Pharmacoepidemiology; Miss diagnosis; Risk factors; Logic regression

Outcome Measures

Primary Outcomes (1)

• The incidence of acute kidney injury in hospitalized patients treated with aminoglycosides

  To analyze the incidence of acute kidney injury in hospitalized patients after using aminoglycosides and to build a prediction model.

  Through study completion，up to half a year.

Study Arms (2)

AKI Group

Drug: Aminoglycoside

Non-AKI Group

Drug: Aminoglycoside

Interventions

Aminoglycoside DRUG

Inpatients using aminoglycoside

AKI Group; Non-AKI Group

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The patients were included if received treatment with aminoglycosides and discharged from the hospital between January 1, 2018 and December 31, 2020.

You may qualify if:

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

You may not qualify if:

• 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

Sponsors & Collaborators

• Qianfoshan Hospital: lead

Study Sites (1)

Xiao Li，MD

Jinan, Shandong, 250014, China

Location

Related Publications (1)

• 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.

  PMID: 40487395 DERIVED

MeSH Terms

Conditions

Acute Kidney Injury

Interventions

Aminoglycosides

Condition Hierarchy (Ancestors)

Renal Insufficiency; Kidney Diseases; Urologic Diseases; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Male Urogenital Diseases

Intervention Hierarchy (Ancestors)

Glycosides; Carbohydrates

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate professor of pharmacy

Study Record Dates

• First Submitted: September 2, 2022
• First Posted: September 9, 2022
• Study Start: July 1, 2022
• Primary Completion: October 31, 2023
• Study Completion: October 31, 2023
• Last Updated: November 18, 2023

Record last verified: 2023-11

Locations

CN (1)