Pervasive Sensing and AI in Intelligent ICU
Study Details
Study Description
Brief Summary
Important information related to the visual assessment of patients, such as facial expressions, 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, delirious state, and impending clinical deterioration, often cannot be incorporated into clinical status. The overall objectives of this project are to sense, quantify, and communicate patients' clinical conditions in an autonomous and precise manner, and develop a pervasive intelligent sensing system that combines deep learning algorithms with continuous data from inertial, color, and depth image sensors for autonomous visual assessment of critically ill patients. The central hypothesis is that deep learning models will be superior to existing acuity clinical scores by predicting acuity in a dynamic, precise, and interpretable manner, using autonomous assessment of pain, emotional distress, and physical function, together with clinical and physiologic data.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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Detailed Description
The under-assessment of pain is one of the primary barriers to the adequate treatment of pain in critically ill patients, and is associated with many negative outcomes such as chronic pain after discharge, prolonged mechanical ventilation, longer ICU stay, and increased mortality risk. Many ICU patients cannot self-report their pain intensity due to their clinical condition, ventilation devices, and altered consciousness. The monitoring of patients' pain status is yet another task for over-worked nurses, and due to pain's subjective nature, those assessments may vary among care staff. These challenges point to a critical need for developing objective and autonomous pain recognition systems. Delirium is another common complication of patient hospitalization, which is characterized by changes in cognition, activity level, consciousness, and alertness and has rates of up to 80% in surgical patients. The risk factors that have been associated with delirium include age, preexisting cognitive dysfunction, vision and hearing impairment, severe illness, dehydration, electrolyte abnormalities, overmedication, alcohol abuse, and disruptions in sleep patterns. Estimates show that about one third of delirium cases can benefit from drug and non-drug prevention and intervention. However, detecting and predicting pain and delirium is still very limited in practice.
The aim of this study is to evaluate the ability of the investigators' proposed model to leverage accelerometer, environmental, circadian rhythm biomarkers, and video data in autonomously quantifying pain, characterizing functional activities, and delirium status. The Autonomous Delirium Monitoring and Adaptive Prevention (ADAPT) system will use novel pervasive sensing and deep learning techniques to autonomously quantify patients' mobility and circadian dyssynchrony in terms of nightly disruptions, light intensity, and sound pressure level. This will allow for the integration of these risk factors into a dynamic model for predicting delirium trajectories. Commercially available cameras will be used to monitor patients' facial expressions and contextualize patients' actions by providing imaging data to provide additional patient movement information. Commercially available environmental sensors will be used to provide data on illumination, decibel level, and air quality. Patient blood samples will help determine their circadian rhythm and compare and validate the pervasive sensing system's capabilities of autonomously monitoring circadian dyssynchrony. Electronic health record data will also be collected.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| adult ICU patients adult patients aged 18 or older admitted to University of Florida Health Shands Gainesville ICU wards |
Other: Video Monitoring
continuous video monitoring
Other: Accelerometer Monitoring
continuous accelerometer monitoring of patient movements
Other: Noise Level Monitoring
continuous environmental noise monitoring
Other: Light Level Monitoring
continuous environmental light monitoring
Other: Air Quality Monitoring
continuous environmental air quality monitoring
Other: EKG Monitoring
continuous EKG monitoring
Other: Vitals Monitoring
continuous vitals monitoring (heart rate, oxygen saturation)
Other: Biosample Collection
blood and urine samples collected once on Day 1 and once on Day 2
Other: Delirium Motor Subtyping Scale 4 (DMSS-4)
done daily on delirious patients to subtype delirium
|
Outcome Measures
Primary Outcome Measures
- Algorithmic Activity Labeling [Image frames collected continuously for up to 7 days maximum.]
The algorithm's output will report on which activity the patient is performing in the corresponding image data.
- Algorithmic Pain Labeling [Image frames collected continuously for up to 7 days maximum.]
The algorithm's output will report on whether the patient is experiencing pain in the corresponding image data.
- Decibel Levels [Noise sensor data collected continuously for up to 7 days maximum.]
Determine relative decibel (noise loudness) levels in study patient's ICU room to alert for abnormalities in decibel level (noisiness of environment).
- Lux Levels [Light sensor data collected continuously for up to 7 days maximum.]
Determine relative lux (light illumination) levels in study patient's ICU room to alert for abnormalities in illumination level.
- Air Quality [Air quality sensor data collected continuously for up to 7 days maximum.]
Determines relative air quality pollution levels in study patient's ICU room to alert for abnormalities in room air quality.
- Circadian Dyssynchrony Index [Change in internal circadian profile from Day 1 to Day 2.]
Blood and urine samples will be collected and processed to determine the presence of dyssynchrony in a subject's internal circadian clock.
- Algorithmic Delirium Recognition Profile [Data collected for up to 7 days maximum.]
The algorithm's output will report on whether patient is likely to be delirious or at-risk of delirium based on activity, facial expression, and circadian dyssynchrony index data collected from study devices and biosamples.
- Delirium Motor Subtyping Scale 4 (DMSS-4) [Changes from baseline up to a maximum of 7 days]
Determines which subtype of delirium a subject is experiencing. This subtyping scale has 13 symptom items (5 hyperactive and 8 hypoactive) derived from the 30-item Delirium Motor Checklist. To subtype a delirious subject, at least 2 symptoms are required to be present from either the hyperactive or hypoactive checklist to meet the subtyping criteria for 'hyperactive delirium' or 'hypoactive delirium'. Patients who meet both hyperactive and hypoactive criteria are determined as 'mixed subtype', while patients meeting neither hyperactive or hypoactive criteria are labeled as 'no subtype'.
Secondary Outcome Measures
- Mortality [From baseline (study enrollment) up to a maximum of 7 days]
Status of alive or deceased
Eligibility Criteria
Criteria
Inclusion Criteria:
-
aged 18 or older
-
admitted to UF Health Shands Gainesville ICU ward
Exclusion Criteria:
-
under the age of 18
-
on contact/isolation precautions
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | University of Florida Health Shands Hospital | Gainesville | Florida | United States | 32610 |
Sponsors and Collaborators
- University of Florida
- National Institute of Neurological Disorders and Stroke (NINDS)
- National Institute for Biomedical Imaging and Bioengineering (NIBIB)
Investigators
- Principal Investigator: Azra Bihorac, MD, MS, University of Florida
Study Documents (Full-Text)
None provided.More Information
Publications
- IRB-202101013
- R01NS120924
- R01EB029699