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Serum Potassium Prediction Using Machine Learning and Single-lead ECG

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.

Sponsor

Brigham and Women's Hospital

Enrollment

—

Sites

2 locations

Start Date

March 2021

Completion

August 2021

InfectionHeart FailureChronic Obstructive Pulmonary DiseaseAsthmaGout Flare

Enrollment & Timeline

Withdrawn

Sites

locations

Duration

9 months

total trial span

Mar 2021Month 63 of 9Aug 2021

100% of trial timeline elapsed

View on ClinicalTrials.gov

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Primary Outcomes

Serum potassium concentration

From date of admission to date of discharge, through study completion on average 7 days.

Serum potassium, measured in millimol per liter

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Multi-Source Intelligencefrom PubMed · FDA FAERS · WHO ICTRP

Noninvasive potassium determination using a mathematically processed ECG: proof of concept for a novel "blood-less, blood test".

Dillon John J, DeSimone Christopher V, Sapir Yehu et al.Journal of electrocardiology2016

Serum Potassium Prediction Using Machine Learning and Single-lead ECG

Enrollment & Timeline

Primary Outcomes

Multi-Source Intelligencefrom PubMed · FDA FAERS · WHO ICTRP

Serum Potassium Prediction Using Machine Learning and Single-lead ECG

Enrollment & Timeline

Primary Outcomes

Multi-Source Intelligencefrom PubMed · FDA FAERS · WHO ICTRP

Eligibility

Trial Sites (2)