#2534 Classification of patients with acute kidney damage on the base of blood pressure value as a risk factor using a neural network
Authors: Tasic Danijela , Stoisavljevic Zorana , Galovic Slobodanka , Djordjević Katarina 
Published: May 2024

Abstract

Background and Aims
In acute kidney damage, the monitoring of hemodynamic status is best achieved by determining the blood pressure value. It is considered that the values of diastolic blood pressure in young and healthy people are constant from the aorta to the periphery, and the values of systolic and pulse pressure increase from the periphery to the aorta. The frequency of hypertension correlates with the presence of a cardiovascular precipitating risk factor in acute kidney injury, hypertension is also a modifying risk factor in the second and third stages of acute kidney injury, while the presence of hypotension on admission can be a clinical indicator of its cause. In patients with acute kidney damage and in patients with sepsis, the mechanism of autoregulation of mean arterial pressure, which is affected by stroke volume and heart rate, is not effective. The aim of the paper is to examine the classification of hospitalized patients with acute kidney injury based on blood pressure as a risk factor.

Method
The study included 86 patients over 18 years of age of both sexes (39 men 45.3% and 47 women 54.7%) with an average age of 66.85 ± 15.30 years who were diagnosed with acute kidney injury on admission to hospital treatment. Patients were divided into groups according to the stages of renal insufficiency. The study was designed as a cross-sectional comparative study. The assessment of the existence of acute kidney damage, as well as the determination of the stage of the disease, was based on the diagnostic criteria proposed by the K/DIGO expert group for acute kidney damage. The study did not include patients who had disease progression after acute kidney failure and were treated with dialysis for more than three months. Basic clinical, biochemical and hemodynamic parameters were determined in all subjects at the beginning of treatment, except for the analysis of nitrogen status, which was determined both before and at the end of treatment.
During the formation of the neural network (pattern recognition Mat Lab), the parameters of systolic arterial pressure, diastolic arterial pressure normalized by min-max normalization to values from 0 to 1 were included. The data were randomly distributed into three categories: 80% for training, 10% for test, 10% for validation, which means that the parameters of 68 patients were used for training, 9 patients for validation and another 9 patients for the test. The number of input parameters is 33 to 39, while the number of output parameters is 2 in relation to treatment. Formed neural networks with different included blood pressure parameters, structures of 25 neurons in the hidden layer, with 55 and 15 epochs and with accuracy of 97.7% and 90.7% of patient classification.

Results
Our results show that 40.3% of patients in the third stage of acute kidney injury have hypertension. The lowest average values of systolic (108.44 ± 22.81 mmHg) and diastolic (65.56 ± 15.29 mmHg) blood pressure were experienced by patients in the first stage of acute kidney damage, and the lowest values of mean arterial pressure existed in the second stage of acute kidney damage (68.20 ± 44.96 mmHg) .Statistically significant risk factors for the development of the third stage of acute kidney damage in the multivariate analysis were patients older than 65 years, and mean arterial pressure values less than 65 mmHg.
The confusion matrices showed that the classifiers adapted to the minimum and maximum values of systolic, diastolic and mean arterial blood pressure are well adapted and achieve satisfactory accuracy with 97.7% and 90.7%.

Conclusion
By comparing blood pressure parameters in hospitalized patients in relation to the stage of acute kidney damage, no statistically significant deviations were found. Univariate logistic regression analysis showed that age over 65 was a significant independent risk factor for stage three acute kidney injury, while the multivariate model showed that risk factors for the development of stage three acute kidney injury were patients same age and middle arterial blood pressure less than 65 mmHg. Satisfactory accuracy of formed neural networks when classifying patients during validation training and test was also demonstrated.