Computer-assisted image processing 12 lead ECG model to diagnose hyperkalemia

Abstract Background We sought to develop an improved 12 lead ECG model to diagnose hyperkalemia by use of traditional and novel parameters. Methods We retrospectively analyzed ECGs in consecutive hyperkalemic patients (serum potassium (K) > 5.3 mEq/L) by blinded investigators with normokalemic ECGs as internal controls. Potassium levels were modeled using general linear mixed models followed by refit with standardized variables. Optimum sensitivity and specificity were determined using cut point analysis of ROC- AUC. Results The training set included 236 ECGs (84 patients) and validation set 97 ECGs (23 patients). Predicted K = (5.2354) + (0.03434*descending T slope) + (−0.2329*T width) + (−0.9652*reciprocal of new QRS width > 100 msec). ROC-AUC in the validation set was 0.78 (95% CI 0.69–0.88). Maximum specificity of the model was 84% for K > 5.91 with sensitivity of 63%. Conclusion ECG model incorporating T-wave width, descending T-wave slope and new QRS prolongation improved hyperkalemia diagnosis over traditional ECG analysis.