Computer-assisted image processing 12 lead ECG model to diagnose hyperkalemia.
We sought to develop an improved 12 lead ECG model to diagnose hyperkalemia by use of traditional and novel parameters.



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.



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%.



ECG model incorporating T-wave width, descending T-wave slope and new QRS prolongation improved hyperkalemia diagnosis over traditional ECG analysis.
Division of Renal Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA. Electronic address: velagapv@gmail.com.
Venu
V
Velagapudi
Division of Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN.
John C
JC
O'Horo
Division of Cardiovascular Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA.
Anu
A
Vellanki
Division of Quantitative Health Sciences, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA.
Stephen P
SP
Baker
Division of Cardiovascular Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA.
Rahul
R
Pidikiti
Division of Renal Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA.
Jeffrey S
JS
Stoff
Division of Cardiovascular Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA.
Dennis A
DA
Tighe
D000368:Aged; D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D006947:Hyperkalemia / Q000097:blood* / Q000175:diagnosis*; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods; D011188:Potassium / Q000097:blood*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D011188:Potassium
ECG; Hyperkalemia; QRS prolongation; T wave slope; T wave width