Ignite Creation Date:
2024-05-06 @ 8:26 PM
Last Modification Date:
2024-10-26 @ 3:27 PM
Study NCT ID:
NCT06380049
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-04-23
First Post:
2024-04-15
Brief Title:
Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data
Sponsor:
Seoul National University Hospital
Organization:
Seoul National University Hospital
Study Overview
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
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-04
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 models predictive performance with traditional fall risk assessment tools eg 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 models 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 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 models predictive performance with traditional fall risk assessment tools eg 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 models 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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 20240012366 | OTHER | Ministry of Food and Drug Safety | None |