Prediction of hyperkalemia in dogs from electrocardiographic parameters using an artificial neural network

To predict severe hyperkalemia from single electrocardiogram (ECG) tracings.

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 > or =20% in < or =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.

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).

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.