Risk Factors and Machine Learning Model for Aminoglycines... | NCT05533593

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

NCT05533593

Sponsor

Qianfoshan Hospital

Status

Completed

Last Update Posted

Nov 18, 2023Actual

Enrollment

8,000Actual

Phase

Not provided

Conditions

Aminoglycoside Toxicity
Acute Kidney Injury

Interventions

Aminoglycoside

Countries

China

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Nov 2023

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jul 1, 2022Actual

Primary Completion Date

Oct 31, 2023Actual

Completion Date

Oct 31, 2023Actual

First Submitted Date

Sep 2, 2022

First Submission Date that Met QC Criteria

Sep 8, 2022

First Posted Date

Sep 9, 2022Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Nov 15, 2023

Last Update Posted Date

Nov 18, 2023Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Xiao Li，MD, Associate professor of pharmacy, Qianfoshan HospitalPrincipal Investigator

Lead Sponsor

Qianfoshan HospitalOTHER

Collaborators

Not provided

Oversight Module

Has Data Monitoring Committee (DMC)

Not provided

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

8,000Actual

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
AKI Group			Drug: Aminoglycoside
Non-AKI Group			Drug: Aminoglycoside

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Aminoglycoside	Drug	Inpatients using aminoglycoside	AKI Group Non-AKI Group

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
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.

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

100 Years

Standard Ages

AdultOlder Adult

Study Population

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

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Xiao Li，MD		Jinan	Shandong	250014	China

References Module

Citations

PubMed Identifier	Type	Citation	Retractions
40487395	Derived	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.

ID	Term
D051437	Renal Insufficiency
D007674	Kidney Diseases
D014570	Urologic Diseases
D052776	Female Urogenital Diseases

ID	Term
D006027	Glycosides
D002241	Carbohydrates