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      Using Artificial Intelligence to Predict the Equilibrated Postdialysis Blood Urea Concentration

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          Total dialysis dose (Kt/V) is considered to be a major determinant of morbidity and mortality in hemodialyzed patients. The continuous growth of the blood urea concentration over the 30- to 60-min period following dialysis, a phenomenon known as urea rebound, is a critical factor in determining the true dose of hemodialysis. The misestimation of the equilibrated (true) postdialysis blood urea or equilibrated Kt/V results in an inadequate hemodialysis prescription, with predictably poor clinical outcomes for the patients. The estimation of the equilibrated postdialysis blood urea (eqU) is therefore crucial in order to estimate the equilibrated (true) Kt/V. In this work we propose a supervised neural network to predict the eqU at 60 min after the end of hemodialysis. The use of this model is new in this field and is shown to be better than the currently accepted methods (Smye for eqU and Daugirdas for eqKt/V). With this approach we achieve a mean difference error of 0.22 ± 7.71 mg/ml (mean % error: 1.88 ± 13.46) on the eqU prediction and a mean difference error for eqKt/V of –0.01 ± 0.15 (mean % error: –0.95 ± 14.73). The equilibrated Kt/V estimated with the eqU calculated using the Smye formula is not appropriate because it showed a great dispersion. The Daugirdas double-pool Kt/V estimation formula appeared to be accurate and in agreement with the results of the HEMO study.

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          Most cited references 5

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          The urea reduction ratio and serum albumin concentration as predictors of mortality in patients undergoing hemodialysis.

          Among patients with end-stage renal disease who are treated with hemodialysis, solute clearance during dialysis and nutritional adequacy are determinants of mortality. We determined the effects of reductions in blood urea nitrogen concentrations during dialysis and changes in serum albumin concentrations, as an indicator of nutritional status, on mortality in a large group of patients treated with hemodialysis. We analyzed retrospectively the demographic characteristics, mortality rate, duration of hemodialysis, serum albumin concentration, and urea reduction ratio (defined as the percent reduction in blood urea nitrogen concentration during a single dialysis treatment) in 13,473 patients treated from October 1, 1990, through March 31, 1991. The risk of death was determined as a function of the urea reduction ratio and serum albumin concentration. As compared with patients with urea reduction ratios of 65 to 69 percent, patients with values below 60 percent had a higher risk of death during follow-up (odds ratio, 1.28 for urea reduction ratios of 55 to 59 percent and 1.39 for ratios below 55 percent). Fifty-five percent of the patients had urea reduction ratios below 60 percent. The duration of dialysis was not predictive of mortality. The serum albumin concentration was a more powerful (21 times greater) predictor of death than the urea reduction ratio, and 60 percent of the patients had serum albumin concentrations predictive of an increased risk of death (values below 4.0 g per deciliter). The odds ratio for death was 1.48 for serum albumin concentrations of 3.5 to 3.9 g per deciliter and 3.13 for concentrations of 3.0 to 3.4 g per deciliter. Diabetic patients had lower serum albumin concentrations and urea reduction ratios than nondiabetic patients. Low urea reduction ratios during dialysis are associated with increased odds ratios for death. These risks are worsened by inadequate nutrition.
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            Application of artificial neural networks to clinical medicine.

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              An adaptive backpropagation neural network for real-time ischemia episodes detection: development and performance analysis using the European ST-T database.

              A supervised neural network (NN)-based algorithm was used for automated detection of ischemic episodes resulting from ST segment elevation or depression. The performance of the method was measured using the European ST-T database. In particular, the performance was measured in terms of beat-by-beat ischemia detection and in terms of the detection of ischemic episodes. The algorithm used to train the NN was an adaptive backpropagation (BP) algorithm. This algorithm drastically reduces training time (tenfold decrease in our case) when compared to the classical BP algorithm. The recall phase of the NN is then extremely fast, a fact that makes it appropriate for real-time detection of ischemic episodes. The resulting NN is capable of detecting ischemia independent of the lead used. It was found that the average ischemia episode detection sensitivity is 88.62% while the ischemia duration sensitivity is 72.22%. The results show that NN can be used in electrocardiogram (ECG) processing in cases where fast and reliable detection of ischemic episodes is desired as in the case of critical care units (CCU's).

                Author and article information

                Blood Purif
                Blood Purification
                S. Karger AG
                26 February 2001
                : 19
                : 3
                : 271-285
                aFavaloro University and bRTC Dialysis Centers Adrogue and Monte Grande, Buenos Aires, Argentina
                46955 Blood Purif 2001;19:271–285
                © 2001 S. Karger AG, Basel

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                Page count
                Figures: 9, Tables: 10, References: 18, Pages: 15
                Self URI (application/pdf): https://www.karger.com/Article/Pdf/46955
                Original Paper


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