AB0889 NEURAL NETWORKS IN PREDICTION OF KIDNEY DISEASE IN GOUT PATIENTS WITH OBESITY
Authors: Svitlana Smiyan, Maksym Franchuk
Journal: Annals of the Rheumatic Diseases (2019)

<h3>Background</h3> Nowadays, in addition to correctly verified diagnosis and effective therapy of gouty nephropathy in obese patients, there is a problem to predict the development of kidney disease in this cohort of patients. Gouty nephropathy may have subclinical course for years, that's why, with what probability it can occur? To solve such kind of problem we used neural networks or "artificial intellect". <h3>Objectives</h3> To predict gouty nephropathy. <h3>Methods</h3> A one-center cohort prospective study was conducted that included 117 patients with gouty arthritis, who were on scheduled in-patient treatment at the Rheumatologic Department of Ternopil University Hospital during 2015-2018 years. All patients had no history of any kidney disease. Statistical analysis was performed with STATISTICA software. <h3>Results</h3> To create a neural network, as input information, it is necessary to select data that have the greatest correlation with gouty nephropathy. Significant correlations between gouty nephropathy and hyperuricemia (r=0,85), uricosuria (r=-0,79), hypercholesterolemia (r=0,44), hypertriglyceridemia (r=0,84), an increase of low density lipoproteins (r=0,80), concomitant arterial hypertension (r=0,81), diabetes mellitus (r = 0,59) were established. Age of the patients, the number of involved joints and the duration of the disease were also included. Then the probability of development of gouty nephropathy can be calculated by the formula: <i>P</i>(I) – probability (Ill) of having gouty nephropathy in a patient. Accepts values from 0 to 1. 0 – absolutely (100%) healthy patient, 1 – absolutely (100%) ill patient; <i>W</i> – weight, vector of weights of synaptic arches of artificial neuron; <i>D</i> – patient's data scaled at intervals of 0 to 1, in such a way that the minimum possible value corresponds to 0, and to the maximum possible – 1; e – Euler's number. To facilitate mathematical calculations, a peculiar "calculator" for predicting kidney damage in obese gout patients has been developed. This formula was written in Microsoft Office Excel and automatically calculates the probability of development of gouty nephropathy in%. Approval of this method indicated that 15.6% of the patients without gouty nephropathy may have development of kidney disease in the range of 55.1 - 98.8%. <h3>Conclusion</h3> Consequently, having input data (levels of uric acid in the blood, uric acid in the urine, cholesterol, triglycerides, low density lipoproteins, the presence of concomitant hypertension, diabetes mellitus, the number of affected joints and the duration of the disease), it is easy to predict the probability of kidney disease in patients with gout and obesity using the proposed formula. <h3>References</h3> [1] Qeethara Al-Shayea. Artificial Neural Networks in Medical Diagnosis. IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 2, 2011, P.150; [2] Sarfaraz Ahmed. Medical Diagnosis using Neural Networks. International Journal of Engineering Development and Research. 2014. P.201. <h3>Disclosure of Interests</h3> Svitlana Smiyan Grant/research support from: Celltrion, Inc., Maksym Franchuk Grant/research support from: Celltrion, Inc.