Clinical trial NCT05533593: Risk Factors and Machine Learning Model for Aminoglycines Related Acute Kidney Injury
Official title: Analysis of Risk Factors of Aminoglycines Related Acute Kidney Injury in Hospitalized Patients and Development of Machine Learning Model
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Aminoglycoside Toxicity
Acute Kidney Injury
Keywords: Aminoglycosides
Acute kidney injury
Pharmacoepidemiology
Miss diagnosis
Risk factors
Logic regression
Lead sponsor: Qianfoshan Hospital
Sponsor class: OTHER
First posted: 2022-09-09
Enrollment: 8000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
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.

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.


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

Primary outcomes:
measure: The incidence of acute kidney injury in hospitalized patients treated with aminoglycosides
timeFrame: Through study completion，up to half a year.
Secondary outcomes:
Other outcomes:
Locations:
city: Jinan
country: China
facility: Xiao Li，MD
state: Shandong
Study officials:
References: