A Machine Learning Approach to Continuous Vital Sign Data Analysis

This study is enrolling participants by invitation only.
Sponsor:
Information provided by (Responsible Party):
University of Colorado, Denver
ClinicalTrials.gov Identifier:
NCT01448161
First received: October 5, 2011
Last updated: March 8, 2013
Last verified: March 2013

October 5, 2011
March 8, 2013
December 2011
December 2013   (final data collection date for primary outcome measure)
Relevant Clinical Features [ Time Frame: 2 years ] [ Designated as safety issue: No ]
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.
Same as current
Complete list of historical versions of study NCT01448161 on ClinicalTrials.gov Archive Site
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A Machine Learning Approach to Continuous Vital Sign Data Analysis
A Machine Learning Approach to Continuous Vital Sign Data Analysis

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
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Observational
Observational Model: Case-Only
Time Perspective: Prospective
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Non-Probability Sample

Pediatric and Adult ICU patients

Vital Signs
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Pediatric and Adult ICU patients
Pediatric and Adult ICU patients
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*   Includes publications given by the data provider as well as publications identified by ClinicalTrials.gov Identifier (NCT Number) in Medline.
 
Enrolling by invitation
250
March 2014
December 2013   (final data collection date for primary outcome measure)

Inclusion Criteria:

  1. Age: 31 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)
Both
31 Days to 89 Years
No
Contact information is only displayed when the study is recruiting subjects
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NCT01448161
11-0858
No
University of Colorado, Denver
University of Colorado, Denver
Not Provided
Principal Investigator: Steve Moulton, MD Children's Hospital Colorado
University of Colorado, Denver
March 2013

ICMJE     Data element required by the International Committee of Medical Journal Editors and the World Health Organization ICTRP