Venu Velagapudi1, John C O'Horo2, Anu Vellanki3, Stephen P Baker4, Rahul Pidikiti3, Jeffrey S Stoff5, Dennis A Tighe3. 1. Division of Renal Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA. Electronic address: velagapv@gmail.com. 2. Division of Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN. 3. Division of Cardiovascular Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA. 4. Division of Quantitative Health Sciences, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA. 5. Division of Renal Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA.
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)>5.3mEq/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>100msec). 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.
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 hyperkalemicpatients (serum potassium (K)>5.3mEq/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>100msec). 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.
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