Abstract. 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 Ն20% in Յ5 minutes. Serum potassium levels (K ϩ ) 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 ϩ 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 ϩ = 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 (ϮSD) difference between predicted and actual K ϩ values was 0.4 Ϯ 2.0 (95% CI = Ϫ0.2 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. Key words: hyperkalemia; neural network; diagnosis; ECG. ACADEMIC EMERGENCY MEDICINE 2001; 8:599-603 H YPERKALEMIA is commonly seen in emergency department (ED) patients and may be associated with lethal dysrhythmias. While hyperkalemia is easily diagnosed by laboratory testing, the result may be unavailable for 45 to 120 minutes or more, unless the ED has the capability of bedside measurement of electrolytes, a practice that is still not widespread. During this time, the undiagnosed hyperkalemic patient may decompensate suddenly and without warning. Unanticipated decompensation of the clinically occult hyperka- Because of its key role in cardiac electrophysiology, hyperkalemia often results in surface electrocardiogram (ECG) changes such as peaked Twaves, low-amplitude P-waves, and increases in PR or QRS intervals. Since the ECG may be obtained at the bedside much more quickly than the results of blood testing, ECG findings have been used for rapid bedside diagnosis of hyperkalemia, 1 but there are limitations. Since hyperkalemic ECG abnormalities are nonspecific, diagnostic accuracy may be decreased by many false-positive results. Additionally, sensitivity acceptable for screening for a life-threatening condition has not been demonstrated. Wrenn et al.2 tested two experienced ED clinicians in evaluating 220 ECGs for the diagnosis of hyperkalemia. In this group there were 87 subjects (40%) with hyperkalemia, defined as K ϩ > 5.0 mmol/L. Twenty-nine of those 87 subjects had K ϩ > 6.5 mmol/L....