Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data

Not yet recruiting

• Conditions: Fall; Stroke

• Registration Number: NCT06380049
• Lead Sponsor: Seoul National University Hospital

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.

Study Timeline

• Status Verified: 2024-04
• Study First Submitted: 2024-04-15
• Study First Submitted QC: 2024-04-17
• Last Update Submitted: 2024-04-17
• Study First Posted: 2024-04-23
• Last Update Posted: 2024-04-23
• Study Start: 2024-04-29
• Primary Completion: 2025-04-28
• Study Completion: 2026-04-28

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 90

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

Read More

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

Read More

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Sensitivity of the Machine Learning Model	At the time of the single visit	The primary outcome measure is the sensitivity of the machine learning model, which refers to its ability to correctly identify patients who are at high risk of falls. Sensitivity is defined as the proportion of actual positives that are correctly identified.

Secondary Outcome Measures

Name	Time	Method
Specificity of the Machine Learning Model	At the time of the single visit	Specificity measures the proportion of actual negatives that are correctly identified.