Addis Urea Coefficient

Addis urea ratio is one of the most important parameters used in medicine to assess kidney function and predict the development of renal failure. This parameter was proposed by the American physician Addis in 1935.

Addis urea ratio (AUC) is the ratio of the concentration of urea in the blood to creatinine. Urea is a protein breakdown product that is excreted by the kidneys. Creatinine is a substance that is formed in the muscles and is also excreted by the kidneys in small quantities.

BUN helps assess kidney function, as high urea levels indicate kidney dysfunction and may be a sign of kidney failure or other diseases. However, to get an accurate result, other factors such as age, gender, weight, etc. must be taken into account.

ABA is currently used in clinical trials to evaluate the effectiveness of the treatment of kidney diseases. This parameter can also be used to diagnose kidney disease in patients with elevated urea levels.

However, it should be noted that BUN is not the only parameter used to assess renal function. There are other diagnostic methods, such as kidney ultrasound, computed tomography and others, which can give a more accurate picture of the condition of the kidneys.

Addis urea ratio (AMC) is a measure used in medicine to assess kidney function in patients with kidney disease. It was developed by the American physician Addis in 1920 and named after him.

Addis urea ratio is measured by determining the concentration of urea in the patient's blood and comparing it with the norm. The normal AMC value is between 30 and 60 mg/dL. If the value is higher than normal, this may indicate impaired renal function and the need for additional examination.

The AMC indicator is used to diagnose and monitor kidney diseases such as chronic pyelonephritis, renal failure, nephrotic syndrome and others. It can also be used to evaluate the effectiveness of treatments for kidney disease.

However, it should be noted that AMC measurement results are not conclusive and require additional analysis and interpretation. Therefore, when interpreting the results, the patient’s clinical picture, age, gender, weight and other factors should be taken into account.