Fundamental Intelligent Building Blocks of the Intensive Care Unit (ICU) of the Future: Intelligent ICU of the Future

NCT ID: NCT03905668

Last Updated: 2025-06-03

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Total Enrollment: 71 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2016-02-03
• Study Completion Date: 2028-07-31

Brief Summary

The objective of this project is to create deep learning and machine learning models capable of recognizing patient visual cues, including facial expressions such as pain and functional activity. Many important details related to the visual assessment of patients, such as facial expressions like pain, head and extremity movements, posture, and mobility are captured sporadically by overburdened nurses or are not captured at all. Consequently, these important visual cues, although associated with critical indices, such as physical functioning, pain, and impending clinical deterioration, often cannot be incorporated into clinical status. The study team will develop a sensing system to recognize facial and body movements as patient visual cues. As part of a secondary evaluation method the study team will assess the models ability to detect delirium.

Detailed Description

Pain is a critical national health problem with nearly 50% of critical care patients experience significant pain in the Intensive Care Unit (ICU). The under-assessment of pain response is one of the primary barriers to the adequate treatment of pain in critically ill patients, associated with many negative outcomes such as chronic pain after discharge, prolonged mechanical ventilation, longer ICU stay, and increased mortality risk. Nonetheless, many ICU patients are unable to self-report pain intensity due to clinical conditions, ventilation devices, and altered consciousness. Currently, behavioral pain scales are used to assess pain in nonverbal patients. Unfortunately, these scales require repetitive manual administration by overburdened nurses. Moreover, prior work suggests that nurses caring for quasi-sedated patients in critical care settings have considerable variability in pain intensity ratings. Furthermore, manual pain assessment tools lack the capability to monitor pain continuously and autonomously. Together, these challenges point to a critical need for developing objective and autonomous pain recognition systems.

Delirium is another common complication of hospitalization that poses significant health problems in hospitalized patients. It is most prevalent in surgical ICU patients with diagnosis rates up to 80%. It is characterized by changes in cognition, activity level, consciousness, and alertness. Delirium typically leads to changes in activity level and alertness that pose additional health risks including risk of fall, inadequate mobilization, disturbed sleep, inadequate pain control, and negative emotions. All of these effects are difficult to monitor in real-time and further contribute to worsening of patient's cognitive abilities, inhibit recovery, and slow down the rehabilitation process. Though about a third of delirium cases can benefit from intervention, detecting and predicting delirium is still very limited in practice. Current Delirium assessments need to be performed by trained healthcare staff, are time consuming, and resource intensive. Due to the resources necessary to complete the assessment, delirium is often assessed twice per day, despite the transient nature of the disease state which can come and go undetected between the assessments. Jointly these obstacles demonstrate a dire need for real-time autonomous delirium detection.

The investigators hypothesize that the proposed model would be able to leverage accelerometer, electromyographic, and video data for the purpose of autonomously quantifying patient facial expressions such as pain, characterizing functional activities, and delirium status. Rationalizing that autonomous visual cue quantification and delirium detection can reduce nurse workload and can enable real-time pain and delirium monitoring. Early detection of delirium offers patients the best chance for good delirium treatment outcomes.

Conditions

Pain; Delirium

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: PROSPECTIVE

Study Groups

ICU Patients

Adults in admitted to an ICU at University of Florida Health Gainesville with an expected length of stay greater than 24 hours which are not on any form of contact precaution or isolation. Patients will have continuous video, accelerometer, and electromyographic monitoring for up to seven days while in the ICU.

Video Monitoring

Intervention Type: OTHER

Patients may have video monitoring for up to seven days while in the ICU. The video system will be placed in an unobtrusive area in the patient's ICU room.

Accelerometer Monitoring

Intervention Type: OTHER

Patients may have accelerometer monitoring for up to seven days while in the ICU. Commercially available accelerometer units, which have been validated in previous clinical studies, will be used.

Electromyographic Monitoring

Intervention Type: OTHER

Patients may have electromyographic monitoring for up to seven days while in the ICU.

Noise Level Monitoring

Intervention Type: OTHER

Patients may have noise level monitoring (in decibels) for up to seven days while in the ICU.

Light Level Monitoring

Intervention Type: OTHER

Patients may have light level monitoring for up to seven days while in the ICU.

ICU Patient Friends/Family Members

Adult visitors of participating ICU patients that are willing to provide feedback to the learning algorithms.

No interventions assigned to this group

Interventions

Video Monitoring

Patients may have video monitoring for up to seven days while in the ICU. The video system will be placed in an unobtrusive area in the patient's ICU room.

Intervention Type: OTHER

Accelerometer Monitoring

Patients may have accelerometer monitoring for up to seven days while in the ICU. Commercially available accelerometer units, which have been validated in previous clinical studies, will be used.

Intervention Type: OTHER

Electromyographic Monitoring

Patients may have electromyographic monitoring for up to seven days while in the ICU.

Intervention Type: OTHER

Noise Level Monitoring

Patients may have noise level monitoring (in decibels) for up to seven days while in the ICU.

Intervention Type: OTHER

Light Level Monitoring

Patients may have light level monitoring for up to seven days while in the ICU.

Intervention Type: OTHER

Other Intervention Names

EMG monitoring

Eligibility Criteria

Inclusion Criteria

• patient admitted to University of Florida (UF) Health Gainesville ICU

• Individual has their name designated on a patient's informed consent form giving them permission to view and modify facial and activity data collected about that patient

Exclusion Criteria

• Anticipated ICU stay is less than one day
• Patient is on any form of contact precaution or isolation
• Patient is unable to wear a Shimmer3 unit

ICU Patient Friends/Family:

• Age < 18
• They are unable to answer short questions on a touch screen display
• They are unable to wear a proximity sensor
• They were not on the listed of designated individuals specified in their friend/family members informed consent form

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Institute for Biomedical Imaging and Bioengineering (NIBIB)

NIH

Sponsor Role: collaborator

U.S. National Science Foundation

FED

Sponsor Role: collaborator

National Institutes of Health (NIH)

NIH

Sponsor Role: collaborator

National Institute of Neurological Disorders and Stroke (NINDS)

NIH

Sponsor Role: collaborator

University of Florida

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Azra Bihorac, MD

Role: PRINCIPAL_INVESTIGATOR

University of Florida

Locations

UF Health Shands Hospital

Gainesville, Florida, United States

Countries

United States

Other Identifiers

1R21EB027344-01

Identifier Type: NIH

Identifier Source: secondary_id

View Link

1750192

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

OCR20330

Identifier Type: OTHER

Identifier Source: secondary_id

R01NS120924-01

Identifier Type: NIH

Identifier Source: secondary_id

View Link

IRB201900354 -N

Identifier Type: -

Identifier Source: org_study_id