Clinical trial NCT06765551: AI Based Muscular Ultrasound to Assess Intensive Care Unit-acquired Weakness
Official title: Artificial IntelligenCe Based UlTrasonographic Assessment of IntensiVe CAre UniT-acquired WEakness (ACTIVATE)
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Intensive Care Unit-acquired Weakness
Artifical Intelligence
Ultrasound
Keywords: weakness
neuromuscular
critical illness polyneuropathy
critical illness myopathy
neuromuscular impairment
Lead sponsor: Jena University Hospital
Sponsor class: OTHER
Collaborators: name: University of Rostock
First posted: 2025-01-09
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients with critical illness fulfilling the inclusion criteria should be screened for the study on two surgical ICUs at the department of anesthesiology and intensive care medicine of Jena University Hospital, Germany.

Brief summary: The aim of this observational case-control study is to investigate, whether artificial intelligence can detect ultrasound-derived imaging characteristics typical for intensive care unit-acquired weakness. The main questions it aims to answer are:

1. Is the evaluation of specific parameters of neuromuscular ultrasound using AI-based image analysis suitable for detecting and monitoring critically ill ICU patients with ICUAW?
2. Do the results of AI-based ultrasound image analysis correlate with:

(A) the severity of ICUAW (B) the visual grading of muscle echogenicity (C) the 30- and 90-day-outcome?

Detailed description: Intensive Care Unit-Acquired Weakness (ICUAW) is one of the most common neuromuscular complications in patients treated in intensive care. With increasing disease severity and especially in analgosedated, ventilated and delirious patients with limited ability to cooperate during the clinical examination, the detection and follow-up of ICUAW is limited to impossible. The clinical diagnosis and severity assessment of ICUAW is usually carried out with the help of established diagnostic methods (e.g. clinical-neurological examination, Medical Research Council-Sum Score, electrophysiological examinations), which, however, cannot be carried out regularly if the patient does not cooperate, thus delaying the diagnosis of ICUAW and making follow-up more difficult. Neuromuscular ultrasound (NMUS), on the other hand, is an easy-to-use, non-invasive examination option that is largely independent of patient compliance and is increasingly being investigated in patients with ICUAW. It was shown that NMUS can detect ICUAW and is helpful in assessing the severity of muscular weakness. However, the standardized recording and follow-up by means of scoring procedures (e.g. the 4-stage Heckmatt Scala) is assessed as partially subjective by the examiner and each individual ultrasound image must be taken with the human eye, taking into account various image parameters. To overcome these diagnostic limitations, artificial intelligence (AI) could be a useful extension or even an alternative.

AI is already being used in a variety of ways in medical diagnostics (e.g. in the detection of tumors and organ assessment), and increasingly also in the analysis of ultrasound images. In this study, the investigators aim to use AI, specifically Convolutional Neural Networks (CNNs), to classify ultrasound images into different categories based on muscle weakness. The main benefit of using AI for such tasks lies in the automation it provides. Once an AI model has been trained on an initial set of images, it can quickly categorize new, unseen images, significantly reducing the time and human effort required for diagnosis. AI models can analyze large amounts of data quickly and consistently, which is especially beneficial in a clinical intensive care setting. By applying AI, this study aims to train the detection and classification of muscle weakness in patients treated in intensive care. However, one challenge with AI models is their "black box" nature, where the decision-making process is not transparent. To solve this problem, the investigators will use explainable AI techniques (XAI) such as Grad-CAM (Gradient-weighted Class Activation Mapping) to visualize the specific areas of the ultrasound images that the AI model focuses on in its analysis. This not only helps validate the AI decisions, but also provides insights into the morphological changes in the muscles that come with different degrees of weakness.

By integrating AI and XAI, the study team aims to not only automate the detection and categorization of muscle weakness, but also improve our understanding of the underlying morphological changes in muscles. This dual approach could lead to more accurate and reliable diagnostics and ultimately improve outcomes for patients in intensive care.

Eligibility criteria: Inclusion Criteria:

* Patients aged 18 years or above
* Major elective surgery, e.g. cardiothoracic or abdominal surgery
* Expected ICU stay \>1 day postoperatively
* Healthy, age-machted subjects without ICUAW (recruited from staff of the department of anesthesiology and intensive care medicine)

Exclusion Criteria:

* No informed consent
* Emergency surgery
* Previous participation in the same study
* Preexisting neuromuscular disease
* Preexisting central nervous system disease with residual neuromuscular impairment (e.g. cerebral haemorrhage, stroke, brain tumor)
* High-dose glucocorticoid therapy (\>300 mg hydrocortisone or equivalent per day) before or during particiation in the study

Primary outcomes:
measure: Incidence of abnormalities of muscle echogenicity assesed by muscular ultrasound
timeFrame: Day 14
Secondary outcomes:
measure: Incidence of ICUAW severity assessed by Heckmatt Scale
timeFrame: Day 14
measure: Incidence of ICUAW severity assessed by Medical Research Council Sum Score
timeFrame: Day 14
measure: Days on ventilation
timeFrame: Day 30
measure: Days on vasopressors
timeFrame: Day 30
measure: Number of days in Hospital
timeFrame: Day 30
measure: Number of patients with survival
timeFrame: Day 30
measure: Frailty assessed by FRAIL-Scale
timeFrame: Day 30
measure: Global outcome assessed by Bathel Index
timeFrame: Day 30
measure: Number of patients with survival
timeFrame: Day 90
measure: Frailty assessed by FRAIL-Scale
timeFrame: Day 90
measure: Global outcome assessed by Bathel Index
timeFrame: Day 90
Other outcomes:
Locations:
city: Jena
country: Germany
facility: Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital
state: Thüringen
Study officials:
affiliation: Department of Anesthesiology and Intensive Care Medicine, Jena University Hospital
name: PD Dr. Johannes Ehler, M.D.
role: PRINCIPAL_INVESTIGATOR
References: