Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up
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: ECG screening.
- Кому может быть актуально
- Состояния в реестре: Public Health, Public Health System Research, Multi-system Disease Diagnosis. Базовые параметры: Без ограничений · 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
- Китай
- Следующий шаг
- Сохраните исследование, покажите его лечащему врачу и уточните актуальный статус у исследовательского центра. Расходы, документы и поездка →
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Обзор
This registered multicenter study aims to investigate the diagnostic efficacy of artificial intelligence-enhanced electrocardiography (AI-ECG) in detecting multi-system diseases. The research will utilize prospectively collected data from inpatient, emergency, and outpatient populations to develop ECG-based diagnostic, screening, and predictive models for multi-system diseases.
Подробное описание
Recent advances in artificial intelligence (AI) have expanded the diagnostic capabilities of electrocardiography (ECG) beyond cardiovascular diseases. Emerging evidence demonstrates that AI-enhanced ECG analysis can provide valuable insights into age, gender, mortality risk, cardiac function, and systemic conditions such as electrolyte imbalances, renal dysfunction, and thyroid disorders. These findings position ECG as a promising tool for the identification and prediction of a broad spectrum of diseases.
To further investigate the underlying mechanisms linking ECG abnormalities with multi-system diseases and to develop ECG-based diagnostic, screening, and predictive models, we initiated a multi-center, prospective, observational registry study involving patients undergoing ECG examinations. The goals of the project are as follows:
1\. AI-ECG Foundation Model Development
1. Diagnosis of traditional cardiovascular diseases (e.g., arrhythmias, myocardial infarction). 2. Screening of multi-system disorders, including: Circulatory, digestive, respiratory, and nervous system diseases, Endocrine/metabolic disorders, urogenital diseases, hematologic conditions, Neoplasms and mental health disorders. 3. Prediction of new-onset conditions (e.g., atrial fibrillation, heart failure, valvular diseases, NSTEMI, ventricular tachycardia) and 1-year mortality risk.
2\. Clinical Utility \& Implementation
Leveraging the portability, cost-effectiveness, and non-invasiveness of ECG, our AI foundation model enables:
1. Rapid, large-scale screening in outpatient, inpatient, emergency, and community settings. 2. Early detection of multi-system diseases, guiding targeted diagnostic workups.
3\. Mechanistic \& Interpretability Research Elucidating the diagnostic, predictive, and risk-stratification logic of AI-ECG foundation models.
Вмешательства
- Другое ECG screening
Each subject is subjected to ECG assessment.
Первичные конечные точки
- Multi-system disease predicting based on ECG [Срок оценки: 1 month]
Критерии участия
Критерии включения
- Patients who visited the study hospital.
- Patients included should have both ECG data and discharge diagnosis codes (ICD-10) for inpatients and emergency patients.
Критерии исключения
1\. Patients who declined participation, cases with incomplete or missing clinical data, and pregnant individuals.
Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.
Здоровые добровольцы: Да
Дизайн исследования
- Модель наблюдения
- Случай-контроль
Центры проведения
Китай · 1 центр
- Ren Ji Hospital Afflited to School of Medicine, Shanghai Jiao Tong University — Шанхай
Идентификаторы
NCT: NCT06924580 · EARLY-ECG-PREDICTION Cohort