Pervasive Sensing and AI in Intelligent ICU

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

400 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-05-24

Study Completion Date

2026-12-31

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Critical Illness Pain Delirium Confusion

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Study Groups

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adult ICU patients

adult patients aged 18 or older admitted to University of Florida Health Shands Gainesville ICU wards

Video Monitoring

Intervention Type OTHER

continuous video monitoring

Accelerometer Monitoring

Intervention Type OTHER

continuous accelerometer monitoring of patient movements

Noise Level Monitoring

Intervention Type OTHER

continuous environmental noise monitoring

Light Level Monitoring

Intervention Type OTHER

continuous environmental light monitoring

Air Quality Monitoring

Intervention Type OTHER

continuous environmental air quality monitoring

EKG Monitoring

Intervention Type OTHER

continuous EKG monitoring

Vitals Monitoring

Intervention Type OTHER

continuous vitals monitoring (heart rate, oxygen saturation)

Biosample Collection

Intervention Type OTHER

blood and urine samples collected once on Day 1 and once on Day 2

Delirium Motor Subtyping Scale 4 (DMSS-4)

Intervention Type OTHER

done daily on delirious patients to subtype delirium

Interventions

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Video Monitoring

continuous video monitoring

Intervention Type OTHER

Accelerometer Monitoring

continuous accelerometer monitoring of patient movements

Intervention Type OTHER

Noise Level Monitoring

continuous environmental noise monitoring

Intervention Type OTHER

Light Level Monitoring

continuous environmental light monitoring

Intervention Type OTHER

Air Quality Monitoring

continuous environmental air quality monitoring

Intervention Type OTHER

EKG Monitoring

continuous EKG monitoring

Intervention Type OTHER

Vitals Monitoring

continuous vitals monitoring (heart rate, oxygen saturation)

Intervention Type OTHER

Biosample Collection

blood and urine samples collected once on Day 1 and once on Day 2

Intervention Type OTHER

Delirium Motor Subtyping Scale 4 (DMSS-4)

done daily on delirious patients to subtype delirium

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* aged 18 or older
* admitted to UF Health Shands Gainesville ICU ward
* expected to remain in ICU ward for at least 24 hours at time of screening

Exclusion Criteria

* under the age of 18
* on any contact/isolation precautions
* expected to transfer or discharge from the ICU in 24 hours or less
* unable to provide self-consent or has no available proxy/LAR

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No