Venu Velagapudi

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

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.

Long-term Mortality Outcomes of Hyperkalemic Patients in the Emergency Department Setting: A Case Control Study

Background: The impact of an episode of hyperkalemia on long term mortality is not well studied. Such a study is vital for development of a utilization strategy for new drugs for the treatment of hyperkalemia. To study the long term impact of hyperkalemia on mortality, we conducted a retrospective, observational casecontrol study in a tertiary care emergency department. We compared cases of non-hemolyzed serum potassium >5.3 mEq/L with normokalemic controls. Allcause mortality and causes of death were determined from social security death index, national death index, and medical record review over a follow-up period of 490 patient years. Methods and Findings: A total of 287 consecutive subjects (223 cases, 64 controls) were identified among 14,483 patients. Cox proportional hazard regression showed that for each 1 mEq/L increase in admit potassium, all-cause mortality Hazard Ratio was 1.54, 95% Confidence Interval: 1.22-1.96. Subgroup analyses showed higher mortality in acute kidney injury and heart failure patients. Conclusion: Hyperkalemia predicted all-cause mortality, when adjusted for age, sex, Charlson comorbidity index, and significant covariates (heart failure and end stage renal disease). The deleterious effect of admit hyperkalemia persisted in survivors from index episode throughout the study period. Cardiovascular mortality was statistically more prevalent. Future studies are needed for risk stratification strategies and mortality impact of newer drugs.