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

Study Overview
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Brief Summary
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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 .

Detailed Description
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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. Those who consent will undergo 30 second 6 L ECG recording with a portable, mobile-enhanced device (AliveCor Kardia). This ECG data is subsequently evaluated by our artificial intelligence algorithm to detect hyperkalemia, and the estimated probability of hyperkalemia is recorded. 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.

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

Conditions
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Hyperkalemia

Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 50 Years
Maximum Age: 89 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Ambulatory Emergency Department Patients at risk for hyperkalemia<br>Patients who are at elevated risk for hyperkalemia identified during a visit to the emergency department. Elevated risk individuals are defined in this study as: >50 years of age, eGFR <45, or prior K >5.2 | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Hyperkalemia detection by AI enhanced ECG | Understanding model's ability to predict hyperkalemia as determined by the area under the receiver operating characteristic | 12 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Performance metrics for the detection of hyperkalemia by AI enhanced ECG | Detailed performance metrics of the algorithm (sensitivity, specificity, positive predictive value and negative predictive value) will be calculated using an optimized cutoff threshold determined from the primary outcome. | 12 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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electrocardiogram (ECG), Artificial Intelligence (AI), neural network, hyperkalemia, Emergency Department (ED), Smartphone, AliveCor Kardia 6 L ECG device