Vital SignsNCT01448161
A Machine Learning Approach to Continuous Vital Sign Data Analysis
An observational study of Vital Signs, run by University of Colorado, Denver.
Trial summary
Study hypothesis: Machine Learning algorithms and techniques previously developed for use in the robotics field can be applied to the field of medicine. These state-of-the-art, feature extraction and machine learning techniques can utilize patient vital sign data from bedside monitors to discover hidden relationships… Read more about this study
- Who is it for?
- Ages Up to 89 years
- Conditions the study is for
- Vital Signs
- How many people
- 605 participants
- Where
- No sites listed yet
- Study dates
- 1 Sep 2011to 19 May 2022
- 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 Up to 89 years
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Pediatric and Adult ICU patients
Who may be able to join
- Age: 0 days - 89 years
- Admitted to the surgical intensive care unit (SICU) at the University of Colorado Hospital or to the pediatric intensive care unit (PICU) or children's intensive care unit (CICU) at Children's Hospital Colorado or patients in the Childrens Hospital Colorado (CHC) emergency room with the following conditions
- Hemodynamic instability
- Febrile >38.5
- Respiratory distress
- Requiring mechanical ventilation
- Requiring central access
- Requiring vasoactive medications As well as the time that any of these patients might be in the operating rooms at Children's Hospital Colorado.
Who cannot join
- Pregnant
- Incarcerated
- Limited access to or compromised monitoring sites for non-invasive finger and forehead sensors
- Brain death (GCS 3 with fixed, dilated pupils)), unless patient is actively being resuscitated (see CPR specific details in protocol and application)
About this study
Study hypothesis: Machine Learning algorithms and techniques previously developed for use in the robotics field can be applied to the field of medicine. These state-of-the-art, feature extraction and machine learning techniques can utilize patient vital sign data from bedside monitors to discover hidden relationships within the physiological waveforms and identify physiological trends or concerning conditions that are predictive of various clinical events. These algorithms could potentially provide preemptive alerts to clinicians of a developing patient problem, well before any human could detect a worrisome combination of events or trend in the data.
Specific aims:
Read more
- Collect physiological waveform and numeric trend data from patient vital signs monitors in ICUs at the University of Colorado Hospital and Children's Hospital Colorado.
- Combine the physiological data from patient monitors with clinical data obtained from patient Electronic Medical Records including IV fluids, medications, ventilator settings, urine output, etc. for use in developing models of various clinical conditions.
- Apply Machine Learning techniques to these models to identify physiological waveform features and trend information, which are characteristic and predictive of common clinical conditions including but not limited to:
- Post-operative atrial fibrillation and other cardiac dysrhythmias
- Post-operative cardiac tamponade
- Tension pneumothorax
- Optimal post-operative and post-resuscitation fluid needs
- Intracranial hypertension and cerebral perfusion pressure
- Conditions
- Vital Signs
- Keywords
- Pediatric, Adult, ICU
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Pediatric and Adult ICU patients | Pediatric and Adult ICU patients |
What is being measured
The primary outcome is the main question the study is designed to answer.
Relevant Clinical Features
The Primary outcome utilized in this study will be the identification of the most relevant clinical features for detecting a chosen clinical event as determined by the Machine Learning feature-extraction techniques.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Oct 2011 (estimated)
- Study start1 Sep 2011
- Primary completion19 May 2022
- Study completion19 May 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Vital SignsStudies of the same condition, open and under way.
- More trials run by University of Colorado, DenverThe 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
- Case only
- Time perspective
- Prospective
- Enrolment
- 605 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Colorado, Denver
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Steve Moulton, MD, Principal investigator, Children's Hospital Colorado
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT01448161
- Sponsor's protocol ID
- 11-0858
- Status last verified
- Sep 2023
- First submitted
- 5 Oct 2011
- Last updated
- 28 Sep 2023
- Results posted
- No
- Data monitoring committee
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Sep 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Machine Learning Approach to Continuous Vital Sign Data Analysis
- ClinicalTrials.gov ID
- NCT01448161
- Acronym / sponsor ID
- 11-0858
- Conditions
- Vital Signs
- Keywords
- Pediatric, Adult, ICU
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 605 participants (actual)
- Lead sponsor
- University of Colorado, DenverUniversity, hospital or other
- Study start
- 1 Sep 2011 (actual)
- Primary completion
- 19 May 2022 (actual)
- Study completion
- 19 May 2022 (actual)
- First posted
- 7 Oct 2011 (estimated)
- 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.
Study hypothesis: Machine Learning algorithms and techniques previously developed for use in the robotics field can be applied to the field of medicine. These state-of-the-art, feature extraction and machine learning techniques can utilize patient vital sign data from bedside monitors to discover hidden relationships within the physiological waveforms and identify physiological trends or concerning conditions that are predictive of various clinical events. These algorithms could potentially provide preemptive alerts to clinicians of a developing patient problem, well before any human could detect a worrisome combination of events or trend in the data.
Specific aims:
- Collect physiological waveform and numeric trend data from patient vital signs monitors in ICUs at the University of Colorado Hospital and Children's Hospital Colorado.
- Combine the physiological data from patient monitors with clinical data obtained from patient Electronic Medical Records including IV fluids, medications, ventilator settings, urine output, etc. for use in developing models of various clinical conditions.
- Apply Machine Learning techniques to these models to identify physiological waveform features and trend information, which are characteristic and predictive of common clinical conditions including but not limited to:
- Post-operative atrial fibrillation and other cardiac dysrhythmias
- Post-operative cardiac tamponade
- Tension pneumothorax
- Optimal post-operative and post-resuscitation fluid needs
- Intracranial hypertension and cerebral perfusion pressure
Study design
- Observational model
- Case only
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Pediatric and Adult ICU patients | – | –Pediatric and Adult ICU patients |
Who may qualify
- Ages
- Up to 89 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Pediatric and Adult ICU patients
Key inclusion criteria (8 listed)
- Age: 0 days - 89 years
- Admitted to the surgical intensive care unit (SICU) at the University of Colorado Hospital or to the pediatric intensive care unit (PICU) or children's intensive care unit (CICU) at Children's Hospital Colorado or patients in the Childrens Hospital Colorado (CHC) emergency room with the following conditions
Key exclusion criteria (4 listed)
- Pregnant
- Incarcerated
- Limited access to or compromised monitoring sites for non-invasive finger and forehead sensors
- Brain death (GCS 3 with fixed, dilated pupils)), unless patient is actively being resuscitated (see CPR specific details in protocol and application)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Relevant Clinical Features
Time frame: 2 years
The Primary outcome utilized in this study will be the identification of the most relevant clinical features for detecting a chosen clinical event as determined by the Machine Learning feature-extraction techniques.
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 28 Sep 2023.
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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.
Steve Moulton, MD principal investigator
Children's Hospital Colorado
Questions about this trial
What is this clinical trial studying?
An observational study of Vital Signs, run by University of Colorado, Denver.
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 8 inclusion and 4 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 28 Sep 2023. Status can change between updates, so confirm with the study contact before making plans.
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?
Relevant Clinical Features (time frame: 2 years). 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 28 Sep 2023.