InfectionNCT05045742
Prediction of Patient Deterioration Using Machine Learning
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 observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict patient deterioration 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?
- Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2
- How many people
- 526 participants
- Where
- 2 sitesUnited States
- Study dates
- 20 Mar 2021to 16 Feb 2026
- 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 geographic residence requirements and are enrolled in home hospital.
Who may be able to join
- Cared for in the Brigham and Women's Home Hospital study
Who cannot join
- Incomplete continuous monitoring data
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 observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict patient deterioration 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
- Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2
- 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.
Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2Other
We will retrospectively compare the alarms produced from traditional vital sign alarms (thresholds set by clinicians) versus the BioVitals Index vs the National Early Warning Score 2
| Study group | Role | What they receive |
|---|---|---|
| Training | A subset of patients that are used to train the machine learning algorithm. Receives: Other: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2 | |
| Validation | A subset of patients that are "held back" and used to validate the algorithm's accuracy. Receives: Other: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2 |
What is being measured
The primary outcome is the main question the study is designed to answer.
Alarm burden
The number of alarms fired per patient per hour
Measured: From admission to discharge, measured in hours, on average 5 days
Secondary outcomes (5)
Sensitivity for recognition of a safety composite
The sensitivity (true positives divided by condition positives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).
Measured: From admission to discharge, on average 5 days
Specificity for recognition of a safety composite
The specificity (true negatives divided by condition negatives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).
Measured: From admission to discharge, on average 5 days
Positive predictive value for recognition of a safety composite
The positive predictive value (true positives divided by the sum of true positives plus false positives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).
Measured: From admission to discharge, on average 5 days
Negative predictive value for recognition of a safety composite
The negative predictive value (true negatives divided by the sum of true negatives plus false negatives) for detection of a safety composite (overnight visit, extra unplanned visit, transfer back to the hospital, death during admission, delirium, loss of consciousness, or other major event).
Measured: From admission to discharge, on average 5 days
Rate of alarms with clinical utility
We will use general estimating equations (GEE) with three outcomes per patient (the number of clinically important alarms for BioVitals, NEWS2, and traditional vital signs); the GEE will account for the clustering between the three outcomes on a patient. The GEE will use a negative binomial marginal model with a log-link for the number of alarms with clinical utility and an offset for log length-of stay (in hours); with this model, we model the rate per hour of number of alarms with clinical utility with BI, NEWS2, and traditional vital signs. The main covariate in the negative binomial model will be a three-level covariate for method: BI vs NEWS2 vs traditional vital signs, and the exponential of the effect of this covariate will be a pair-wise rate ratio for BI vs NEWS2 vs traditional vital signs.
Measured: From admission to discharge, on average 5 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Sep 2021
- Study start20 Mar 2021
- Primary completion20 Mar 2025
- Study completion16 Feb 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- 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
- 526 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
- NCT05045742
- Sponsor's protocol ID
- 2017P002583d
- Status last verified
- Mar 2026
- First submitted
- 14 Apr 2021
- Last updated
- 17 Mar 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prediction of Patient Deterioration Using Machine Learning
- ClinicalTrials.gov ID
- NCT05045742
- Acronym / sponsor ID
- 2017P002583d
- Conditions
- Infection, Heart Failure, Chronic Obstructive Pulmonary Disease, Asthma, Gout Flare, Chronic Kidney Diseases
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 526 participants (actual)
- Lead sponsor
- Brigham and Women's HospitalUniversity, hospital or other
- Collaborators
- Biofourmis Inc.
- Study start
- 20 Mar 2021 (actual)
- Primary completion
- 20 Mar 2025 (actual)
- Study completion
- 16 Feb 2026 (actual)
- First posted
- 16 Sep 2021 (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 observational study drawing on data from the Brigham and Women's Home Hospital database. Sociodemographic and clinic data from a training cohort were used to train a machine learning algorithm to predict patient deterioration 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: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2A subset of patients that are used to train the machine learning algorithm. |
| Validation | – | Other: Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2A 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 geographic residence requirements and are enrolled in home hospital.
Key inclusion criteria (1 listed)
- Cared for in the Brigham and Women's Home Hospital study
Key exclusion criteria (1 listed)
- Incomplete continuous monitoring data
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 |
|---|---|---|
| Traditional vital sign alarms versus the BioVitals Index vs the National Early Warning Score 2We will retrospectively compare the alarms produced from traditional vital sign alarms (thresholds set by clinicians) versus the BioVitals Index vs the National Early Warning Score 2 | Other | Training, Validation |
Primary outcome
Alarm burden
Time frame: From admission to discharge, measured in hours, on average 5 days
The number of alarms fired per patient per hour
5 secondary outcomes
Sensitivity for recognition of a safety composite
Time frame: From admission to discharge, on average 5 days
Specificity for recognition of a safety composite
Time frame: From admission to discharge, on average 5 days
Positive predictive value for recognition of a safety composite
Time frame: From admission to discharge, on average 5 days
Negative predictive value for recognition of a safety composite
Time frame: From admission to discharge, on average 5 days
Rate of alarms with clinical utility
Time frame: From admission to discharge, on average 5 days
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Mar 2026.
- 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?
The sponsor lists 1 inclusion and 1 exclusion criteria. 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: finished, as of the sponsor's last update on 17 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 is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Alarm burden (time frame: From admission to discharge, measured in hours, on average 5 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 17 Mar 2026.