**Authors and Affiliations**
Authors: David M. Harmon MD, Chris K Heinrich MD, John J Dillon MD, Kianoush B Kashani MD, Zachi I Attia PhD, Paul A Friedman MD, Jacob C Jentzer MD
Affiliations: Deparmtnet of Cardiovascular Medicine, Deparmtnet of Internal Medicine, Division of Nephrology and Hypertension, Division of Pulmonary and Critical Care Medicine; Mayo Clinic, Rochester, MN, USA

**Background**
Hyperkalemia has been associated with increased mortality in cardiac intensive care unit (CICU) patients. An artificial intelligence (AI) enhanced electrocardiogram (ECG) can predict hyperkalemia, and other AI-ECG algorithms have predicted mortality in CICU patients. We hypothesized that the AI-ECG hyperkalemia algorithm could stratify mortality risk beyond laboratory serum potassium measurement alone.

**Methods**
We included 11,234 unique Mayo Clinic CICU patients admitted from 2007 to 2018 with a 12-lead ECG and blood potassium (K) level obtained at admission with K>5 mEq/L defining hyperkalemia. ECGs underwent AI evaluation for the probability of hyperkalemia (probability >0.5 defined as positive). Hospital mortality was analyzed using logistic regression, and survival to one year was estimated using Kaplan-Meier and Cox analysis.

**Results**
In the final cohort (n=11,234), the mean age was 69.6 (±10.5) years, 37.8% were females, and 92.4% were white. Chronic kidney disease was present in 20.2%. The mean laboratory potassium value for the cohort was 4.2±0.3 mEq/L. The AI-ECG predicted hyperkalemia in 33.9% (n=3,810) of CICU patients and 12.9% (n=1,451) of patients had laboratory-confirmed hyperkalemia (K ≥5 mEq/L). In-hospital mortality increased in false-positive, false-negative, and true-positive patients, respectively (p<0.001), and each of these patient groups had successively lower survival out to one year (p<0.0001).

**Conclusion**
AI-ECG-based prediction of hyperkalemia, even with a normal laboratory potassium value, was associated with higher in-hospital mortality and lower one-year survival in CICU patients. This study demonstrated that AI-ECG probability of hyperkalemia may enable rapid individualized risk stratification in critically ill patients beyond laboratory value alone.



Mortality risk-stratification utilizing the artificial intelligence enhanced electrocardiogram for the detection of hyperkalemia in cardiac intensive care unit patients