A Machine Learning Approach to Continuous Vital Sign Data Analysis

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT01448161
Recruitment Status : Enrolling by invitation
First Posted : October 7, 2011
Last Update Posted : January 4, 2019
Information provided by (Responsible Party):
University of Colorado, Denver

Brief 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 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:

  1. 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.
  2. 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.
  3. 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

Condition or disease
Vital Signs

Study Type : Observational
Estimated Enrollment : 1200 participants
Observational Model: Case-Only
Time Perspective: Prospective
Official Title: A Machine Learning Approach to Continuous Vital Sign Data Analysis
Study Start Date : December 2011
Estimated Primary Completion Date : July 2019
Estimated Study Completion Date : December 2019

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Vital Signs

Pediatric and Adult ICU patients
Pediatric and Adult ICU patients

Primary Outcome Measures :
  1. 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.

Information from the National Library of Medicine

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Ages Eligible for Study:   up to 89 Years   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Pediatric and Adult ICU patients

Inclusion Criteria:

  1. Age: 0 days - 89 years
  2. 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

    1. Hemodynamic instability
    2. Febrile >38.5
    3. Respiratory distress
    4. Requiring mechanical ventilation
    5. Requiring central access
    6. Requiring vasoactive medications As well as the time that any of these patients might be in the operating rooms at Children's Hospital Colorado.

Exclusion Criteria:

  1. Pregnant
  2. Incarcerated
  3. Limited access to or compromised monitoring sites for non-invasive finger and forehead sensors
  4. Brain death (GCS 3 with fixed, dilated pupils)), unless patient is actively being resuscitated (see CPR specific details in protocol and application)

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT01448161

Sponsors and Collaborators
University of Colorado, Denver
Principal Investigator: Steve Moulton, MD Children's Hospital Colorado

Responsible Party: University of Colorado, Denver Identifier: NCT01448161     History of Changes
Other Study ID Numbers: 11-0858
First Posted: October 7, 2011    Key Record Dates
Last Update Posted: January 4, 2019
Last Verified: January 2019

Keywords provided by University of Colorado, Denver: