A Machine Learning Approach to Continuous Vital Sign Data Analysis

NCT01448161 | Completed

• 1 other identifier: 11-0858
• Study Type: observational
• Target: 605
• Locations: 0 countries
• Sites: N/A

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:

• 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

Outcome Measures

Primary Outcomes (1)

• 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.

  2 years

Study Arms (1)

Pediatric and Adult ICU patients

Pediatric and Adult ICU patients

Eligibility Criteria

• Age: Up to 89 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Pediatric and Adult ICU patients

You may qualify if:

• 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.

You may not qualify if:

• 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)

Sponsors & Collaborators

• University of Colorado, Denver: lead

Study Officials

• Steve Moulton, MD

  Children's Hospital Colorado

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 5, 2011
• First Posted: October 7, 2011
• Study Start: September 1, 2011
• Primary Completion: May 19, 2022
• Study Completion: May 19, 2022
• Last Updated: September 28, 2023

Record last verified: 2023-09