Clinical trial NCT06380049: Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data
Official title: Development and Validation of a Machine Learning-based Model to Predict a High-risk Group for Falls Using Multi-sensor Signals in Stroke Patients
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Stroke
Fall
Keywords: Predict model
Machin leanning
Electromyography
Lead sponsor: Seoul National University Hospital
Sponsor class: OTHER
Collaborators: name: Ministry of Trade, Industry & Energy, Republic of Korea
First posted: 2024-04-23
Enrollment: 90
Sex: ALL
Minimum age (years): 19
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
Sampling method: PROBABILITY_SAMPLE
Study population: The study aims to enroll approximately 80 stroke patients and 10 healthy adults to facilitate a comprehensive analysis of the EMG-based machine learning model's effectiveness.

Brief summary: The study assesses a machine learning model developed to predict fall risk among stroke patients using multi-sensor signals. This prospective, multicenter, open-label, sponsor-initiated confirmatory trial aims to validate the safety and efficacy of the model which utilizes electromyography (EMG) signals to categorize patients into high-risk or low-risk fall categories. The innovative approach hopes to offer a predictive tool that enhances preventative strategies in clinical settings, potentially reducing fall-related injuries in stroke survivors.

Detailed description: Objective: The primary objective is to develop and validate a machine learning-based model that uses multi-sensor (EMG) signals to identify stroke patients at high risk of falls. This model aims to improve on traditional fall risk assessments which rely heavily on physical assessments and patient history.

Study Design: This is a prospective, multicenter, open-label, confirmatory clinical trial. It involves collecting EMG data from stroke patients and applying machine learning techniques to predict fall risk. The study will compare the predictive accuracy of the machine learning model against conventional fall risk assessment tools.

Methods:

1. Participants:

   • Sample Size: 80 stroke patients and 10 healthy adults to establish baseline EMG readings.
2. Interventions:

   • Participants will undergo EMG signal collection from key lower limb muscles while performing standardized movements.
3. Outcome Measures:

   * Primary Outcome: Sensitivity and specificity of the machine learning model in predicting high-risk fall patients.
   * Secondary Outcomes: Comparison of the machine learning model's predictive performance with traditional fall risk assessment tools (e.g., Berg Balance Scale).

Data Collection:

* EMG sensors will be attached to the patients' muscles of the lower limbs. Sensors will record muscle activity during movement, which will then be analyzed using the machine learning model.
* The predictive model will be trained using features extracted from the EMG signals, and its performance will be validated against actual fall incidents reported during the follow-up period.

Statistical Analysis:

* The machine learning model's efficacy will be measured through its sensitivity (ability to correctly identify high-risk patients) and specificity (ability to correctly identify low-risk patients).
* Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) statistics will be used to assess model performance.

Eligibility criteria: Stroke Participants

Inclusion Criteria:

* 19 years and older
* the onset of the stroke is less than 3months ago
* Lower extremity weakness due to stroke (MMT =\< 4 grade)
* Cognitive ability to follow commands

Exclusion Criteria:

* stroke recurrence
* other neurological abnormalities (e.g. parkinson's disease).
* severely impaired cognition
* serious and complex medical conditions(e.g. active cancer)
* cardiac pacemaker or other implanted electronic system

Health Participants

Inclusion Criteria:

* 19 years and older
* Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects

Exclusion Criteria:

* other neurological abnormalities (e.g. parkinson's disease).
* severely impaired cognition
* serious and complex medical conditions(e.g. active cancer)
* cardiac pacemaker or other implanted electronic system

Primary outcomes:
measure: Sensitivity of the Machine Learning Model
timeFrame: At the time of the single visit
Secondary outcomes:
measure: Specificity of the Machine Learning Model
timeFrame: At the time of the single visit
Other outcomes:
measure: Area Under the Receiver Operating Characteristic Curve
timeFrame: At the time of the single visit
measure: Matthews Correlation Coefficient
timeFrame: At the time of the single visit
Locations:
city: Seoul
country: Korea, Republic of
facility: Seoul National University Hospital
state: Jongno
Study officials:
affiliation: Seoul National University Hospital
name: Woo Hyung Lee, prof
role: PRINCIPAL_INVESTIGATOR
affiliation: Seoul National University Hospital
name: Byung-Mo Oh, prof
role: STUDY_DIRECTOR
affiliation: Seoul National University Hospital
name: Han Gil Seo, prof
role: STUDY_DIRECTOR
affiliation: Seoul National University Hospital
name: Sung Eun Hyun, prof
role: STUDY_DIRECTOR
affiliation: National Traffic Injury Rehabilitation Hospital
name: Hyunmi Oh, prof
role: STUDY_DIRECTOR
affiliation: National Traffic Injury Rehabilitation Hospital
name: Sumin Oh, B.S.
role: STUDY_DIRECTOR
affiliation: Seoul National University Hospital
name: SO YEON JEON, B.S.
role: STUDY_DIRECTOR
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