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

Sponsor

Mayo Clinic (Other)

Overall Status

Enrolling by invitation

CT.gov ID

NCT05441852

Collaborator

(none)

1,200

Enrollment

Location

Anticipated Duration (Months)

100.1

Patients Per Site Per Month

Study Details

Study Description

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 .

Condition or Disease	Intervention/Treatment	Phase
Hyperkalemia

Detailed Description

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.

Study Design

Study Type:

Observational

Anticipated Enrollment :

1200 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

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

Actual Study Start Date :

Mar 31, 2022

Anticipated Primary Completion Date :

Mar 31, 2023

Anticipated Study Completion Date :

Mar 31, 2023

Arms and Interventions

Arm	Intervention/Treatment
Ambulatory Emergency Department Patients at risk for hyperkalemia 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

Outcome Measures

Primary Outcome Measures

Hyperkalemia detection by AI enhanced ECG [12 months]
Understanding model's ability to predict hyperkalemia as determined by the area under the receiver operating characteristic

Secondary Outcome Measures

Performance metrics for the detection of hyperkalemia by AI enhanced ECG [12 months]
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.

Other Outcome Measures

Time to laboratory confirmed hyperkalemia diagnosis [12 months]
Following the detection of hyperkalemia by AI enhanced ECG time to initial hyperkalemia diagnosis (in minutes) by laboratory analysis following ambulatory emergency department presentation will be assessed.
Time to first treatment of hyperkalemia in Emergency Department [12 months]
Following outcome measure 3 for patients determined to have hyperkalemia, time to first treatment intervention of hyperkalemia (in minutes) will be assessed since presentation to the emergency department.
Total time spent in Emergency Department [12 Months]
Patients who underwent AI enhanced screening for hyperkaliemia, and have a diagnosis of hyperkalemia by laboratory confirmation, will also be assessed for total time spent in the emergency department in hours.
Hospital Admission Rate for Hyperkalemia patients [12 months]
Patients who underwent AI enhanced screening for hyperkaliemia, and have a diagnosis of hyperkalemia by laboratory confirmation, will have the frequency of hospital admission assessed.
One year survival for hyperkalemic patients [12 months]
Patients who underwent AI enhanced screening for hyperkaliemia, and have a diagnosis of hyperkalemia by laboratory confirmation, will have evaluation of survival at one year.
Rate of Adverse Events related to hyperkalemia [12 months]
Patients who underwent AI enhanced screening for hyperkaliemia, and have a diagnosis of hyperkalemia by laboratory confirmation, will have evaluation of frequency of adverse events related to treatment of hyperkalemia (cardiac arrest, hypoglycemia, complications related to dialysis etc).
Exploratory AI enhanced ECG analysis for heart failure [12 months]
A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo screening for hyperkaliemia in the ED with 6L Kardia ECG device. This neural network uses PQRST complexes to yield a probability of heart failure which may not be readily apparent via manual review. Each recorded 6L Kardia ECG will undergo evaluation by this neural network and will produce a probability of heart failure (0-100%) for each individual patient.
Exploratory AI enhanced ECG analysis for silent/paroxysmal atrial fibrillation [12 months]
A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo screening for hyperkaliemia in the ED with 6L Kardia ECG device. This neural network uses PQRST complexes to yield a probability of silent/paroxysmal atrial fibrillation which may not be readily apparent via manual review. Each recorded 6L Kardia ECG will undergo evaluation by this neural network and will produce a probability of silent/paroxysmal atrial fibrillation (0-100%) for each individual patient.
Exploratory AI enhanced ECG analysis for aortic stenosis [12 months]
A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo screening for hyperkaliemia in the ED with 6L Kardia ECG device. This neural network uses PQRST complexes to yield a probability of aortic stenosis which may not be readily apparent via manual review. Each recorded 6L Kardia ECG will undergo evaluation by this neural network and will produce a probability of aortic stenosis (0-100%) for each individual patient.
Exploratory AI enhanced ECG analysis for amyloidosis [12 months]
A previously developed AI algorithm to predict potential underlying cardiac pathology assess from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo screening for hyperkaliemia in the ED with 6L Kardia ECG device. This neural network uses PQRST complexes to yield a probability of amyloidosis which may not be readily apparent via manual review. Each recorded 6L Kardia ECG will undergo evaluation by this neural network and will produce a probability of amyloidosis (0-100%) for each individual patient.
Exploratory AI enhanced ECG analysis to determine age [12 months]
A previously developed AI algorithm to predict patient age from 12 lead ECG via convolutional neural network will be adapted applied to the ECGs for patients who undergo screening for hyperkaliemia in the ED with 6L Kardia ECG device. This neural network uses PQRST complexes to yield an ECG-predicted age. Each recorded 6L Kardia ECG will undergo evaluation by this neural network and determine "ECG age" for each individual patient.

Eligibility Criteria

Criteria

Ages Eligible for Study:

50 Years to 89 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

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.