Prediction of Hyperkalemia in Dogs from Electrocardiographic Parameters Using an Artificial Neural Network

. Objective: To predict severe hyperkalemia from single electrocardiogram (ECG) tracings. Methods: Ten conditioned dogs each underwent this protocol three times: Under isoflurane anesthesia, 2 mEq/kg/hr of potassium chloride was given intravenously until P‐waves were absent from the ECG and ventricular rates decreased ≥20% in ≤5 minutes. Serum potassium levels (K+) were measured at regular intervals with concurrent digital storage of lead II of the surface ECG. A three‐layer artificial neural network with four hidden nodes was trained to predict K+ from 15 separate elements of corresponding ECG data. Data were divided into a training set and a test set. Sensitivity, specificity, and diagnostic accuracy for recognizing hyperkalemia were calculated for the test set based on a prospectively defined K+= 7.5. Results: The model produced data for 189 events; 139 were placed in the training set and 50 in the test set. The test set had 37 potassium levels at or above 7.5 mmol/L. The neural network had a sensitivity of 89% (95% CI = 75% to 97%) and a specificity of 77% (95% CI = 46% to 95%) in recognizing these. The positive likelihood ratio was 3.87. Overall accuracy of this model was 86% (95% CI = 73% to 94%). Mean (±SD) difference between predicted and actual K+ values was 0.4 ± 2.0 (95% CI = ‐0.2 to 1.0). Conclusions: An artificial neural network can accurately diagnose experimental hyperkalemia using ECG parameters. Further work could potentially demonstrate its usefulness in bedside diagnosis of human subjects.