A Study to Detect Hyperkalemia Using Smartphone-enabled Electrocardiogram (EKG)

Rapid dEtection of HyperkAlemia (K+) in the EmergenCy Department Using a SmarTphone-enabled Single-lead EKG (REACT)

The purpose of this study is to validate the real-world performance of a previously developed Artificial Intelligence - Electrocardiogram (AI-ECG) algorithm for identification of hyperkalemia with a six-lead mobile-enhanced device .

1. Ambulatory adult patients in the Emergency Department (ED) at increased risk for hyperkalemia (due to age ≥ 50 years, and one or more criteria including estimated Glomerular filtration rate (eGFR) (from serum creatinine) < 45 ml/minute and/or a history of serum potassium > 5.2 milliequivalents per liter (mEq/l) who present to the emergency department will be approached to consent for the rapid screening process.
2. Those who consent will undergo 30 second 6 L ECG recording with a portable, mobile-enhanced device (AliveCor Kardia).
3. This ECG data is subsequently evaluated by our artificial intelligence algorithm to detect hyperkalemia, and the estimated probability of hyperkalemia is recorded.
4. The research team notifies supervising Emergency Department staff of patients whose probability of hyperkalemia is significantly elevated above the optimized cutoff point according to the AI-ECG algorithm.

Inclusion Criteria:

• Age greater than/equal to 50 years and able to provide consent.
• Patients with eGFR (from serum creatinine) < 45 ml/minute and/or a history of serum potassium > 5.2 mEq/l.

Exclusion Criteria:

• Patients underage < 50.
• Do not meet inclusion criteria.
• Unstable patients requiring emergent resuscitation.
• Patients unable to provide consent.