Clinical trial NCT05441852: A Study to Detect Hyperkalemia Using Smartphone-enabled Electrocardiogram (EKG)
Official title: Rapid dEtection of HyperkAlemia (K+) in the EmergenCy Department Using a SmarTphone-enabled Single-lead EKG (REACT)
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Hyperkalemia
Keywords: electrocardiogram (ECG)
Artificial Intelligence (AI)
neural network
hyperkalemia
Emergency Department (ED)
Smartphone
AliveCor Kardia 6 L ECG device
Lead sponsor: Mayo Clinic
Sponsor class: OTHER
First posted: 2022-07-01
Enrollment: 1151
Sex: ALL
Minimum age (years): 50
Maximum age (years): 89
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients in the emergency department who meet the above inclusion criteria. Patients with the above inclusion criteria experience hyperkalemia more frequently than the general population at a prevalence near 10% compared to 2-4%, respectively.

Brief summary: 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: 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.

Eligibility criteria: 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.

Primary outcomes:
measure: Hyperkalemia detection by AI enhanced ECG
timeFrame: 12 months
Secondary outcomes:
measure: Performance metrics for the detection of hyperkalemia by AI enhanced ECG
timeFrame: 12 months
Other outcomes:
measure: Time to laboratory confirmed hyperkalemia diagnosis
timeFrame: 12 months
measure: Time to first treatment of hyperkalemia in Emergency Department
timeFrame: 12 months
measure: Total time spent in Emergency Department
timeFrame: 12 Months
measure: Hospital Admission Rate for Hyperkalemia patients
timeFrame: 12 months
measure: One year survival for hyperkalemic patients
timeFrame: 12 months
measure: Rate of Adverse Events related to hyperkalemia
timeFrame: 12 months
measure: Exploratory AI enhanced ECG analysis for heart failure
timeFrame: 12 months
measure: Exploratory AI enhanced ECG analysis for silent/paroxysmal atrial fibrillation
timeFrame: 12 months
measure: Exploratory AI enhanced ECG analysis for aortic stenosis
timeFrame: 12 months
measure: Exploratory AI enhanced ECG analysis for amyloidosis
timeFrame: 12 months
measure: Exploratory AI enhanced ECG analysis to determine age
timeFrame: 12 months
Locations:
city: Rochester
country: United States
facility: Mayo Clinic
state: Minnesota
Study officials:
affiliation: Mayo Clinic
name: John Dillon, MD
role: PRINCIPAL_INVESTIGATOR
References: