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

Overview

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.

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