Applying Artificial Intelligence to the 12 Lead ECG for the Diagnosis of Pulmonary Hypertension: an Observational Study
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 入组人数
- 600
- 试验地点
- 1
- 主要终点
- Pulmonary Hypertension diagnosis
研究概览
简要总结
The goal of this observational study is to apply Artificial Intelligence (AI) and machine learning technology to the resting 12-lead electrocardiogram (ECG) and assess whether it can assist doctors in the early diagnosis of Pulmonary Hypertension (PH). Early and accurate diagnosis is an important step for patients with PH. It helps provide effective treatments early which improve prognosis and quality of life. The main questions our study aims to answer are:
- Can AI technology in the 12-lead ECG accurately predict the presence of PH?
- Can AI technology in the 12-lead ECG identify specific sub-types of PH?
- Can AI technology in the 12-lead ECG predict mortality in patients with PH?
In this study, the investigators will recruit 12-lead ECGs from consenting participants who have undergone Right heart Catheterisation (RHC) as part of their routine clinical care. AI technology will be applied to these ECGs to assess whether automated technology can predict the presence of PH and it's associated sub-types.
详细描述
This study will be led by Royal United Hospital Bath NHS Trust and Liverpool John Moore's University. The aim of this study is to utilise Artificial Intelligence (AI) and machine learning technology to assist clinicians in the early diagnosis of Pulmonary Hypertension (PH). We hypothesise that the AI technologies can improve the quantification and interpretation of the parameters involved in detecting PH. This is either through highlighting significant abnormalities in the 12-lead ECG, or by rapidly providing fully automated measures of the features on the 12-lead ECG which indicate PH. The combination of these electrocardiographic features with clinical data may provide highly accurate predictive tools.
This observational study will have a retrospective and prospective arm with a 3 year follow-up period. Participants will not require any additional tests or procedures at any point during the study. Any ECGs performed within the 12 months prior to a participant's right heart catheterisation (RHC) will undergo Artificial Intelligence analysis to establish if early indicators of PH are identifiable.
For all recruited participants, an anonymised clinician case report form will be used to capture details relating to their demographics and routine clinical care. Follow-up times and outcomes including mortality and morbidity will also be recorded.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Other
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •prospective cohort: From July 2023, all patients aged 18 or over who are referred to the Bath Pulmonary Hypertension shared care service with clinical suspicion of PH and, who through their routine clinical care, undergo a RHC and 12-lead ECG.
- •Retrospective cohort: All patients aged 18 or over who were referred to the local Pulmonary Hypertension shared care service between 2007 and June 2023, and through their routine clinical care, have undergone RHC within a year of a 12-lead ECG. This cohort will also include patients who are deceased.
排除标准
- •Patient's less than 18 years-old
- •Patients who do not give valid consent (except deceased patients; REC approved)
- •Patients who have not undergone RHC to assess for PH
- •Patients who have not had an ECG within 12 months of their RHC
结局指标
主要结局
Pulmonary Hypertension diagnosis
时间窗: baseline
The investigators will calculate the area under the receiver operating characteristic curve (AUROC) for PH diagnosis by artificial intelligence technology and compare this to RHC (the gold standard)
次要结局
- Pulmonary Hypertension sub-type(baseline)
- Mortality(3 years)
- Morbidity(baseline)