AI-Powered ECG could spot dangerous potassium shifts without blood draws
NCT ID NCT07493798
First seen Jun 27, 2026 · Last updated Jun 27, 2026
Summary
This study planned to use a machine learning algorithm to estimate blood potassium levels from a single-lead ECG in hospitalized patients. It was designed as a retrospective analysis of existing data from a home hospital program. However, the study was withdrawn before enrolling any participants, so no results or conclusions are available.
This is an AI summary of the original study and may miss details. Read our disclaimer.
Get updates
Get notified about this study
Sign up to get updates when this study changes or when new studies for ANTICOAGULANTS; INCREASED are added.
By submitting, you agree to our Terms of use
Conditions
The condition(s) this trial relates to.
As listed by the trial registrant
The condition terms exactly as the trial's registrant entered them.
Contacts and locations
Locations
-
Brigham and Women's Faulkner Hospital
Boston, Massachusetts, 02130, United States
-
Brigham and Women's Hospital
Boston, Massachusetts, 02115, United States
More trials for these conditions
Other studies related to the condition(s) this trial covers.
- Smart breathing machines at home: a new way to predict COPD crises?
- Can tracking severe asthma over time unlock better treatments?
- Digital nurse companion: could an app ease the burden of chronic illness?
- Could continuing a common heart drug before surgery save lives?
- Poles apart? nordic walking may outpace standard rehab for heart failure
- Could a gentler tug on the Heart's reins spark recovery?