MO419THE ROLE OF SHORT-TERM, HIGH-DOSE ATORVASTATIN FOR PREVENTION OF CONTRAST-INDUCED ACUTE KIDNEY INJURY (CI-AKI) IN PATIENTS WITH CARDIOVASCULAR DISEASES UNDERGOING COMPUTED TOMOGRAPHY WITH INTRAVENOUS CONTRAST ADMINISTRATION

Background and Aims Computed tomography with intravenous contrast media is widely used in hospitals. The incidence of CI-AKI due to intravenous contrast media administration in high-risk patients remains not studied as well as CI-AKI after intraarterial contrast media administration is. According to other researchers, the use of statins in the prevention of AKI after intra-arterial administration of a contrast agent is currently considered an efficient preventive measure. The aim of our study is to assess the incidence of contrast-induced acute kidney injury in patients with cardiovascular diseases during CT scan with intravenous contrast media and analyze the efficacy and safety of various statin dosing regimens for prevention of CI-AKI. Method A randomized controlled open prospective study is planned. Statin naive patients with cardiovascular diseases will be divided into 3 groups. Patients in the first group will receive atorvastatin 80mg 24 hours and 40mg 2 hours before CT scans and 40 mg after. The second group – 40 mg 2 hours before CT scans and 40 mg after. A third group is a control group. Exclusion criteria were current or previous statin treatment, contraindications to statins, severe renal failure, acute coronary syndrome, administration of nephrotoxic drugs. The primary endpoint will the development of CI-AKI, defined as an increase in serum Cr concentration 0.5 mg/dl (44.2 mmol/l) or 25% above baseline at 72 h after exposure to the contrast media. Results We assume a higher incidence of contrast-induced acute kidney injury in the group of patients not receiving statin therapy (about 5-10%). At the same time, it is unlikely to get a significant difference between statin dosing regimens. Risk factors such as age over 75 years, the presence of chronic kidney disease, diabetes mellitus, and chronic heart failure increase the risk of contrast-induced acute kidney injury. Conclusion Despite the significantly lower incidence of CI-AKI with intravenous contrast compared to intra-arterial, patients with CVD have a greater risk of this complication even with intravenous contrast. Therefore, the development of prevention methods and scales for assessing the likelihood of CI-AKI is an important problem. As a result of the study, we expect to conclude the benefits of statins in CI-AKI prevention and the optimal dosage regimen. This information will help us to reduce the burden of CI-AKI after CT scanning in statin naive patients with cardiovascular diseases in everyday clinical practice. ClinicalTrials.gov ID: NCT04666389

Use of deep learning to predict acute kidney injury after intravenous contrast media administration (Preprint)

BACKGROUND Precise prediction of contrast media-induced acute kidney injury (CIAKI) is an important issue because of its relationship with worse outcomes. OBJECTIVE Herein, we examined whether a deep learning algorithm could predict the risk of intravenous CIAKI better than other machine learning and logistic regression models in patients undergoing computed tomography. METHODS A total of 14,185 cases that underwent intravenous contrast media for computed tomography under the preventive and monitoring facility in Seoul National University Hospital were reviewed. CIAKI was defined as an increase in serum creatinine ≥0.3 mg/dl within 2 days and/or ≥50% within 7 days. Using both time-varying and time-invariant features, machine learning models, such as the recurrent neural network (RNN), light gradient boosting machine, extreme boosting machine, random forest, decision tree, support vector machine, κ-nearest neighboring, and logistic regression, were developed using a training set, and their performance was compared using the area under the receiver operating characteristic curve (AUROC) in a test set. RESULTS CIAKI developed in 261 cases (1.8%). The RNN model had the highest AUROC value of 0.755 (0.708–0.802) for predicting CIAKI, which was superior to those obtained from other machine learning models. Although CIAKI was defined as an increase in serum creatinine ≥0.5 mg/dl and/or ≥25% within 3 days, the highest performance was achieved in the RNN model with an AUROC of 0.716 (0.664–0.768). In the feature ranking analysis, albumin level was the most highly contributing factor to RNN performance, followed by time-varying kidney function. CONCLUSIONS Application of a deep learning algorithm improves the predictability of intravenous CIAKI after computed tomography, representing a basis for future clinical alarming and preventive systems.

Contrast-associated acute kidney injury in elderly; a mini-review on nursing prospectives

Contrast medium is used daily for accurate and effective diagnostic procedures. They are often necessary to provide an exact diagnosis and are almost safe and effective when used correctly. However contrast media can sometimes be life-threatening especially in elderly, since the elderly suffer from several problems. Aging is frequently accompanied by chronic diseases like chronic renal failure, comorbidity, frailty and disability. Therefore, it is important to know how reactions to contrast agents manifest and how to manage them immediately. Decreased kidney functions are sometimes seen after intravenous contrast media injection therefore radiologists should monitor all stages of the contrast injection. Accordingly, the medical team should have information on the complications, prevention and care of the patients under contrast media.