Predicting Fall Risk in Stroke Patients Using a Machine Learning Model and Multi-Sensor Data
Ориентир для пациента и семьи
Простыми словами
Автоматическая сводка по структурированным данным реестра. Она помогает сориентироваться, но не заменяет официальный протокол или оценку врача.
- Что изучают
- В протоколе указаны: EMG Analysis Software.
- Кому может быть актуально
- Состояния в реестре: Stroke, Fall. Базовые параметры: от 19 лет · Все.
- Что важно проверить
- Возраст, диагноз и пол — только базовые ориентиры. Предыдущее лечение, анализы и другие обязательные условия указаны ниже в критериях участия.
- Где проводится
- South Korea
- Следующий шаг
- Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →
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Официальное название
Development and Validation of a Machine Learning-based Model to Predict a High-risk Group for Falls Using Multi-sensor Signals in Stroke Patients
Обзор
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.
Подробное описание
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.
Вмешательства
- Устройство EMG Analysis Software
Surface electromyography devices are non-invasive tools that measure electrical activity produced by skeletal muscles through sensors placed on the skin.
Первичные конечные точки
- Sensitivity of the Machine Learning Model [Срок оценки: At the time of the single visit]
Вторичные конечные точки (1)
- Specificity of the Machine Learning Model [Срок оценки: At the time of the single visit]
Критерии участия
Stroke Participants
Критерии включения
- 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
Критерии исключения
- 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
Критерии включения
- 19 years and older
- Individuals who fully understand the necessity of the study and have voluntarily consented to participate as subjects
Критерии исключения
- 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
Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.
Здоровые добровольцы: Да
Дизайн исследования
- Модель наблюдения
- Другое
Центры проведения
South Korea · 1 центр
- Seoul National University Hospital — Seoul
Идентификаторы
NCT: NCT06380049 · 0720242110 · 20240012366