MO475PREDICTION OF CARDIORENAL SYNDROME BY ARTIFITIAL INTELLIGENCE. (29th May 2021)

• Record Type: Journal Article
• Authors: Tasic, Danijela; Djordjevic, Katarina; Galovic, Slobodanka; Milovancevic, Milos; Kocic, Gordana; Radenkovic, Sonja; Dimitrijevic, Zorica; Jancic, Nina; Vrecic, Tamara; Jovanovic, Andriana
• Is Part Of: Nephrology dialysis transplantation. Volume 36(2021)Supplement 1
• Publication Date: 2021-05-29
• Subjects: Nephrology -- Periodicals; Hemodialysis -- Periodicals; Kidneys -- Transplantation -- Periodicals; Hemodialysis; Kidneys -- Transplantation; Nephrology; Periodicals; 616.61
• DOI: 10.1093/ndt/gfab090.0037
• Languages: English
• ISSNs: 0931-0509

Abstract

Abstract: Background and Aims: Potassium excretion is a secretory phenomenon and levels are often abnormal in patients with heart failure. An abnormal sodium serum level is the most common electrolyte disorder and independent predictor of readmission for heart failure and post discharge death. Since different factors could affect balance of Potassium in Cardiorenal syndrome, in this study soft computing was used to predict most important factors for the detection of the severity of systolic heart failure by ejection fraction (EF), and a subclinical phase of the cardiorenal disease by EPI creatinine-cystatin C formula (Chronic Kidney Disease Epidemiology Collaboration). Method: The balance of potassium in Cardiorenal syndrome is analyzed by soft computing approach namely adaptive neuro fuzzy inference system or ANFIS. Results: The clinical group consisted of 79 patients, 40 of whom were men (50.63%) and 39 of whom were women (49.37%), in the average age of 70.72 ± 9.26 years. After comparing serum electrolytes (Na+, K+) did not differ significantly in the clinical group from those of the control group. The tested biomarkers showed significantly higher values in the clinical group than in the control group: BNP (p<0.001), cystatin C (p<0.001). Figures 1 and 2 shows flowcharts of the used inputs and outputs and how they are implemented in the ANFIS networks. There are two ANFIS networks since there are two outputs. ANFIS networks shold determine which input has the strongest influence on the given outputs nased on root mean squre errors or prediciton accuracy.Based on the training error (trn) one can determine the inputs influence on the given output. Checking error (chk) is used to track the results validity. In other words the checking errors could track training error. It was found that BNP (pg/mL) has the most influence on the - EPI creatinine-cystatin C formula. Serum sodium (Na) has the most influence on the ejection fraction (EF). Conclusion: Serum sodium-potassium disturbances are associated with advanced heart failure and reduced prognosis. ANFIS is suitable for nonlinear systems with highly redundant data. Although there are encouraging advances around this unsolved clinical problem, further investigation should consider the progressive inclusion of patients with advanced renal impairment to allow a better understanding of cardiorenal syndrome. The result of our research shows that if the values of BNP and Na significantly deviate from normal values, it is expected that EPI creatinine-cystatin C formula and EF indicate impaired organ function and that such patients are candidates for hospital treatment.