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 However, the study was withdrawn before enrolling any participants, so no results or conclusions are available.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.
Study facts
What this study's own registry entry says, in plain language.
- Started
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Mar 2021
- Expected to finish
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Dec 2021
An estimate. End dates often move.
- Lead sponsor
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Other sponsor
The registry's catch-all category, for sponsors it does not file as a company, a government agency, or a research network.
Who can take part
This study's own entry requirements. Only the study team can say for certain whether you qualify.
Who is studied
Subjects admitted at Brigham and Women's Hospital and Brigham and Women's Faulkner Hospital who meet primary diagnosis, age, and residence within 5 mile requirements and are enrolled in home hospital.
- Ages
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18 years and older
- Sex
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Anyone
- Healthy volunteers
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Not accepted
This study is not open to healthy volunteers. The entry requirements below say who it is open to.
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Copied word for word from the study's registry entry, so the wording is the study team's rather than ours.
Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.
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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
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Brigham and Women's Faulkner Hospital
Boston, Massachusetts, 02130, United States
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Brigham and Women's Hospital
Boston, Massachusetts, 02115, United States
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