Clinical trial NCT05527054: Risk Factors and Machine Learning Model for Diuretics Related Acute Kidney Injury
Official title: Analysis of Risk Factors of Diuretics Related Acute Kidney Injury in Hospitalized Patients and Development of Machine Learning Model
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Diuretic Toxicity
Acute Kidney Injury
Keywords: Diuretics
Aute kidney injury
Pharmacoepidemiology
Miss diagnosis
Risk factors
Logic regression
Lead sponsor: Qianfoshan Hospital
Sponsor class: OTHER
First posted: 2022-09-02
Enrollment: 18000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The patients were included if received treatment with diuretics and discharged from the hospital between January 1, 2018 and December 31, 2020.

Brief summary: Acute kidney injury (AKI) is a series of clinical syndromes in which serum creatinine (Scr) concentrations increase over a short period of time, or urine output decreases. It has become an increasing global concern.Drug-induced acute kidney injury (D-AKI) refers to kidney injury caused by drugs or their metabolites within 7 days after the use of one or more drugs. The kidneys are rich in blood flow and have the function of acidifying the urine, making them an easy target for drug toxicity. Besides, there are enzymes in the kidney that metabolize some drugs, and if these drugs are metabolized abnormally in the kidney, substances toxic to the kidney may be produced.It was found that about 20% of AKI in hospitalized patients was caused by medications.Diuretics are one of the well-known nephrotoxic drugs, since they can directly or indirectly cause a significant decrease in renal blood perfusion and glomerular filtration rate through the mechanism of affecting tubulobulb feedback, which leads to kidney ischemia and hypoxia.However, there are few real-world studies on the incidence of AKI in hospitalized patients received diuretics. In this study, we aimed to explore the incidence and risk factors analysis of AKI in hospitalized patients received diuretics and develop the machine learning model for diuretics related AKI based on electronic medical record data. With the individual characteristics of patients, the risk of AKI can be evaluated before receiving diuretics, which may provide useful information for clinical decision making to better prevent D-AKI.


Eligibility criteria: Inclusion Criteria:

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

Exclusion Criteria:

Hospital stay \< 48h Age \< 18 years 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: Acute kidney injury
timeFrame: Through study completion，up to half a year.
Secondary outcomes:
Other outcomes:
Locations:
city: Jinan
country: China
facility: Xiao Li，MD
state: 山东省
Study officials:
References: