InfectionNCT07493798
Serum Potassium Prediction Using Machine Learning and Single-lead ECG
An observational study of Infection and Heart Failure, run by Brigham and Women's Hospital at 2 sites in United States.
Trial summary
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Infection, Heart Failure, Chronic Obstructive Pulmonary Disease
- What is being tested?
- Potassium estimation algorithm
- How many people
- 0 participants
- Where
- 2 sitesUnited States
- Study dates
- 20 Mar 2021to 1 Dec 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Was a subject in the Brigham and Women's Home Hospital study and has a completed record in the study's database.
Trial locations and contacts
2 sites in 1 country.
United States2 sites
Brigham and Women's Hospital
Boston, Massachusetts, 02115
Brigham and Women's Faulkner Hospital
Boston, Massachusetts, 02130
About this study
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.
- Conditions
- Infection, Heart Failure, Chronic Obstructive Pulmonary Disease, Asthma, Gout Flare, Chronic Kidney Diseases
- Being tested
- Potassium estimation algorithm
- Medical subject headings
- Infections, Heart Failure, Pulmonary Disease, Chronic Obstructive, Asthma, Renal Insufficiency, Chronic, Hypertensive Crisis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Potassium estimation algorithmOther
Apply a machine learning algorithm to estimate a patient's potassium.
| Study group | Role | What they receive |
|---|---|---|
| Training | A subset of patients that are used to train the machine learning algorithm. Receives: Other: Potassium estimation algorithm | |
| Validation | A subset of patients that are "held back" and used to validate the algorithm's accuracy. Receives: Other: Potassium estimation algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Serum potassium concentration
Serum potassium, measured in millimol per liter
Measured: From date of admission to date of discharge, through study completion on average 7 days.
Secondary outcomes (4)
Hyperkalemia
Serum potassium greater than 5.1 millimol per liter
Measured: From date of admission to date of discharge, through study completion on average 7 days.
Hypokalemia
Serum potassium less than 3.4 millimol per liter
Measured: From date of admission to date of discharge, through study completion on average 7 days.
Normokalemia
Serum potassium between 3.4 and 5.1 millimol per liter
Measured: 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
Serum potassium falling either less than versus greater than or equal to 4 millimol per liter
Measured: From date of admission to date of discharge, through study completion on average 7 days.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Mar 2026
- Study start20 Mar 2021
- Primary completion1 Aug 2021 (estimated)
- Study completion1 Dec 2021 (estimated)
Why it stopped: Lack of resources
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for InfectionStudies of the same condition, open and under way.
- More trials run by Brigham and Women's HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- Brigham and Women's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Biofourmis Inc.
- Responsible party
- Principal investigator, David Levine, Brigham and Women's Hospital
- Investigators
- David Levine, MD MPH MA, Principal investigator, Associate Physician
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07493798
- Sponsor's protocol ID
- 2017P002583c
- Status last verified
- Mar 2026
- First submitted
- 14 Apr 2021
- Last updated
- 25 Mar 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Yasin OZ, Attia Z, Dillon JJ, DeSimone CV, Sapir Y, Dugan J, Somers VK, Ackerman MJ, Asirvatham SJ, Scott CG, Bennet KE, Ladewig DJ, Sadot D, Geva AB, Friedman PA. Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone. J Electrocardiol. 2017 Sep-Oct;50(5):620-625. doi: 10.1016/j.jelectrocard.2017.06.008. Epub 2017 Jun 8.
PubMed 28641860Dillon JJ, DeSimone CV, Sapir Y, Somers VK, Dugan JL, Bruce CJ, Ackerman MJ, Asirvatham SJ, Striemer BL, Bukartyk J, Scott CG, Bennet KE, Mikell SB, Ladewig DJ, Gilles EJ, Geva A, Sadot D, Friedman PA. Noninvasive potassium determination using a mathematically processed ECG: proof of concept for a novel "blood-less, blood test". J Electrocardiol. 2015 Jan-Feb;48(1):12-8. doi: 10.1016/j.jelectrocard.2014.10.002. Epub 2014 Oct 18.
PubMed 25453193Krogager ML, Kragholm K, Skals RK, Mortensen RN, Polcwiartek C, Graff C, Nielsen JB, Kanters JK, Holst AG, Sogaard P, Pietersen A, Torp-Pedersen C, Hansen SM. The relationship between serum potassium concentrations and electrocardiographic characteristics in 163,547 individuals from primary care. J Electrocardiol. 2019 Nov-Dec;57:104-111. doi: 10.1016/j.jelectrocard.2019.09.005. Epub 2019 Sep 4.
PubMed 31629993Corsi C, Cortesi M, Callisesi G, De Bie J, Napolitano C, Santoro A, Mortara D, Severi S. Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients. Sci Rep. 2017 Feb 15;7:42492. doi: 10.1038/srep42492.
PubMed 28198403Rafique Z, Aceves J, Espina I, Peacock F, Sheikh-Hamad D, Kuo D. Can physicians detect hyperkalemia based on the electrocardiogram? Am J Emerg Med. 2020 Jan;38(1):105-108. doi: 10.1016/j.ajem.2019.04.036. Epub 2019 Apr 22.
PubMed 31047740Attia ZI, DeSimone CV, Dillon JJ, Sapir Y, Somers VK, Dugan JL, Bruce CJ, Ackerman MJ, Asirvatham SJ, Striemer BL, Bukartyk J, Scott CG, Bennet KE, Ladewig DJ, Gilles EJ, Sadot D, Geva AB, Friedman PA. Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG. J Am Heart Assoc. 2016 Jan 25;5(1):e002746. doi: 10.1161/JAHA.115.002746.
PubMed 26811164Galloway CD, Valys AV, Shreibati JB, Treiman DL, Petterson FL, Gundotra VP, Albert DE, Attia ZI, Carter RE, Asirvatham SJ, Ackerman MJ, Noseworthy PA, Dillon JJ, Friedman PA. Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram. JAMA Cardiol. 2019 May 1;4(5):428-436. doi: 10.1001/jamacardio.2019.0640.
PubMed 30942845Lin CS, Lin C, Fang WH, Hsu CJ, Chen SJ, Huang KH, Lin WS, Tsai CS, Kuo CC, Chau T, Yang SJ, Lin SH. A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development. JMIR Med Inform. 2020 Mar 5;8(3):e15931. doi: 10.2196/15931.
PubMed 32134388
Study at a glance
From the registry record, last updated by the sponsor on 25 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Serum Potassium Prediction Using Machine Learning and Single-lead ECG
- ClinicalTrials.gov ID
- NCT07493798
- Acronym / sponsor ID
- 2017P002583c
- Conditions
- Infection, Heart Failure, Chronic Obstructive Pulmonary Disease, Asthma, Gout Flare, Chronic Kidney Diseases
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: Lack of resources
- Enrolment
- 0 participants (actual)
- Lead sponsor
- Brigham and Women's HospitalUniversity, hospital or other
- Collaborators
- Biofourmis Inc.
- Study start
- 20 Mar 2021 (actual)
- Primary completion
- 1 Aug 2021 (estimated)
- Study completion
- 1 Dec 2021 (estimated)
- First posted
- 25 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Training | – | Other: Potassium estimation algorithmA subset of patients that are used to train the machine learning algorithm. |
| Validation | – | Other: Potassium estimation algorithmA subset of patients that are "held back" and used to validate the algorithm's accuracy. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Potassium estimation algorithmApply a machine learning algorithm to estimate a patient's potassium. | Other | Training, Validation |
Primary outcome
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
4 secondary outcomes
Hyperkalemia
Time frame: 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.
Normokalemia
Time frame: 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.
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Mar 2026.
- Background: PubMed 28641860Yasin OZ, Attia Z, Dillon JJ, DeSimone CV, Sapir Y, Dugan J, Somers VK, Ackerman MJ, Asirvatham SJ, Scott CG, Bennet KE, Ladewig DJ, Sadot D, Geva AB, Friedman PA. Noninvasive blood potassium measurement using signal-processed, single-lead ecg acquired from a handheld smartphone. J Electrocardiol. 2017 Sep-Oct;50(5):620-625. doi: 10.1016/j.jelectrocard.2017.06.008. Epub 2017 Jun 8.
- Background: PubMed 25453193Dillon JJ, DeSimone CV, Sapir Y, Somers VK, Dugan JL, Bruce CJ, Ackerman MJ, Asirvatham SJ, Striemer BL, Bukartyk J, Scott CG, Bennet KE, Mikell SB, Ladewig DJ, Gilles EJ, Geva A, Sadot D, Friedman PA. Noninvasive potassium determination using a mathematically processed ECG: proof of concept for a novel "blood-less, blood test". J Electrocardiol. 2015 Jan-Feb;48(1):12-8. doi: 10.1016/j.jelectrocard.2014.10.002. Epub 2014 Oct 18.
- Background: PubMed 31629993Krogager ML, Kragholm K, Skals RK, Mortensen RN, Polcwiartek C, Graff C, Nielsen JB, Kanters JK, Holst AG, Sogaard P, Pietersen A, Torp-Pedersen C, Hansen SM. The relationship between serum potassium concentrations and electrocardiographic characteristics in 163,547 individuals from primary care. J Electrocardiol. 2019 Nov-Dec;57:104-111. doi: 10.1016/j.jelectrocard.2019.09.005. Epub 2019 Sep 4.
- Background: PubMed 28198403Corsi C, Cortesi M, Callisesi G, De Bie J, Napolitano C, Santoro A, Mortara D, Severi S. Noninvasive quantification of blood potassium concentration from ECG in hemodialysis patients. Sci Rep. 2017 Feb 15;7:42492. doi: 10.1038/srep42492.
- Background: PubMed 31047740Rafique Z, Aceves J, Espina I, Peacock F, Sheikh-Hamad D, Kuo D. Can physicians detect hyperkalemia based on the electrocardiogram? Am J Emerg Med. 2020 Jan;38(1):105-108. doi: 10.1016/j.ajem.2019.04.036. Epub 2019 Apr 22.
- Background: PubMed 26811164Attia ZI, DeSimone CV, Dillon JJ, Sapir Y, Somers VK, Dugan JL, Bruce CJ, Ackerman MJ, Asirvatham SJ, Striemer BL, Bukartyk J, Scott CG, Bennet KE, Ladewig DJ, Gilles EJ, Sadot D, Geva AB, Friedman PA. Novel Bloodless Potassium Determination Using a Signal-Processed Single-Lead ECG. J Am Heart Assoc. 2016 Jan 25;5(1):e002746. doi: 10.1161/JAHA.115.002746.
- Background: PubMed 30942845Galloway CD, Valys AV, Shreibati JB, Treiman DL, Petterson FL, Gundotra VP, Albert DE, Attia ZI, Carter RE, Asirvatham SJ, Ackerman MJ, Noseworthy PA, Dillon JJ, Friedman PA. Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram. JAMA Cardiol. 2019 May 1;4(5):428-436. doi: 10.1001/jamacardio.2019.0640.
- Background: PubMed 32134388Lin CS, Lin C, Fang WH, Hsu CJ, Chen SJ, Huang KH, Lin WS, Tsai CS, Kuo CC, Chau T, Yang SJ, Lin SH. A Deep-Learning Algorithm (ECG12Net) for Detecting Hypokalemia and Hyperkalemia by Electrocardiography: Algorithm Development. JMIR Med Inform. 2020 Mar 5;8(3):e15931. doi: 10.2196/15931.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
David Levine, MD MPH MA principal investigator
Associate Physician
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Infection and Heart Failure, run by Brigham and Women's Hospital at 2 sites in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 25 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Serum potassium concentration (time frame: From date of admission to date of discharge, through study completion on average 7 days.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07493798, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Mar 2026.