Serum Potassium Prediction Using Machine Learning and Single-lead ECG

March 21, 2026 updated by: David Levine, Brigham and Women's Hospital
This is a retrospective study drawing on data from the Brigham and Women's Hospital Home Hospital Program's Database. Sociodemographic and clinical data from a training cohort were used to train a machine learning algorithm to predict blood potassium throughout a patient's admission. This algorithm was then validated in a validation cohort.

Study Overview

Status

Withdrawn

Conditions

Intervention / Treatment

Study Type

Observational

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • United States
    • Massachusetts
      • Boston, Massachusetts, United States, 02115
        • Brigham and Women's Hospital
      • Boston, Massachusetts, United States, 02130
        • Brigham and Women's Faulkner Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

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.

Description

Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Training
A subset of patients that are used to train the machine learning algorithm.
Other: Potassium estimation algorithm
Apply a machine learning algorithm to estimate a patient's potassium.
Validation
A subset of patients that are "held back" and used to validate the algorithm's accuracy.
Other: Potassium estimation algorithm
Apply a machine learning algorithm to estimate a patient's potassium.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Serum potassium concentration
Time Frame: From date of admission to date of discharge, through study completion on average 7 days.
Serum potassium, measured in millimol per liter
From date of admission to date of discharge, through study completion on average 7 days.

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Hyperkalemia
Time Frame: From date of admission to date of discharge, through study completion on average 7 days.
Serum potassium greater than 5.1 millimol per liter
From date of admission to date of discharge, through study completion on average 7 days.
Hypokalemia
Time Frame: From date of admission to date of discharge, through study completion on average 7 days.
Serum potassium less than 3.4 millimol per liter
From date of admission to date of discharge, through study completion on average 7 days.
Normokalemia
Time Frame: From date of admission to date of discharge, through study completion on average 7 days.
Serum potassium between 3.4 and 5.1 millimol per liter
From date of admission to date of discharge, through study completion on average 7 days.
Serum potassium less than versus greater than or equal to 4 millimol per liter
Time Frame: From date of admission to date of discharge, through study completion on average 7 days.
Serum potassium falling either less than versus greater than or equal to 4 millimol per liter
From date of admission to date of discharge, through study completion on average 7 days.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Brigham and Women's Hospital

Collaborators

Biofourmis Inc.

Investigators

  • Principal Investigator: David Levine, MD MPH MA, Associate Physician

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 20, 2021

Primary Completion (Estimated)

August 1, 2021

Study Completion (Estimated)

December 1, 2021

Study Registration Dates

First Submitted

April 14, 2021

First Submitted That Met QC Criteria

March 21, 2026

First Posted (Actual)

March 25, 2026

Study Record Updates

Last Update Posted (Actual)

March 25, 2026

Last Update Submitted That Met QC Criteria

March 21, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 2017P002583c

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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