Serum Potassium Prediction Using Machine Learning and Single-lead ECG

Part of paid clinical trials in Boston, Massachusetts.

Sponsor
Brigham and Women's Hospital
Study ID
NCT07493798
Status
Withdrawn

Conditions

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Not accepted

Interventions

  • Potassium estimation algorithm — OTHER
    Apply a machine learning algorithm to estimate a patient's potassium.

Study Details

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.

Key Dates

Start date
Mar 20, 2021
Status verified
Mar 2026
Primary completion
Aug 1, 2021
Completion
Dec 1, 2021

Study Design

Enrollment
0 participants (actual)

Arms

  • Arm: Training
    A subset of patients that are used to train the machine learning algorithm.
  • Arm: Validation
    A subset of patients that are "held back" and used to validate the algorithm's accuracy.

Primary Outcome Measure

Serum potassium concentration [ Time Frame: From date of admission to date of discharge, through study completion on average 7 days. ]

Locations (2)

FacilityCityStateZIPSite coordinators
Brigham and Women's Faulkner HospitalBostonMassachusetts02130-
Brigham and Women's HospitalBostonMassachusetts02115-

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