跳至主要内容
临床试验/NCT06145880
NCT06145880撤回不适用

Artificial Intelligence: Improving Early Detection of Pulmonary Hypertension by Transthoracic Echocardiography: ADOPT

Royal United Hospitals Bath NHS Foundation Trust1 个研究点 分布在 1 个国家目标入组 2,500 人开始时间: 2023年12月1日最近更新:
适应症

试验速览

阶段
不适用
状态
撤回
入组人数
2,500
试验地点
1
主要终点
Compare the artificial intelligence tool (AIT) performance for detecting pulmonary hypertension (PH) with the current probability criteria

研究概览

简要总结

Pulmonary Hypertension (PH) is a condition caused by high blood pressure in the blood vessels that carry blood to the lungs. It can cause severe breathlessness and failure of the right side of the heart. Sadly it is often fatal, and life expectancy ranges from months to years. For some subtypes of PH, effective treatments exist which can improve life expectancy and quality-of-life. Accurate tools for the assessment of PH are therefore essential so that life-saving medications can be started earlier.

In existing diagnostic pathways, evidence for the suspicion of PH is frequently overlooked, significantly delaying the time to diagnosis. Echocardiography (echo) is a quick, safe and well-tolerated test requested to investigate breathless patients, and which can provide useful information about the suspicion of PH. However, outside of specialist PH centres, doctors may not routinely look for and comment on the presence of clues to possible PH.

The investigators think that using Artificial Intelligence (AI) techniques to read echo's could make their interpretation faster and more reliable. There may also be subtle clues to the presence or severity of PH on echo, less recognisable to the human eye, which AI can identify.

In this study the investigators will gather echo images from 5 specialist PH hospitals across the UK which have all been anonymised (patient's name and personal details removed). These will all be historic scans (i.e. have already taken place) and will be grouped into those with PH present (including PH sub-type) or absent. These anonymised echo images will be used to develop and train an AI tool to identify scans where PH is present, including which specific type of PH may be present. The developed AI tool will then be tested on a separate group of scans (not used in the training stage) to validate its performance.

详细描述

In this study the investigators will gather retrospective echo images from 5 specialist PH hospitals across the UK (Royal Free Hospital NHS FT; Sheffield Teaching Hospitals NHS FT; Royal Papworth Hospital NHS FT; NHS Golden Jubilee National Hospital Glasgow; Royal United Hospitals Bath NHS FT).

These will all be historic scans (i.e. have already taken place) and will be grouped into those with PH present (including PH sub-type) or PH absent. Inclusion criteria involve patients aged ≥18 who have undergone both a transthoracic echo (TTE) and a right heart catheter (RHC) as part of their clinical care. Exclusion Criteria will involve patients aged <18, known or suspected congenital heart disease and patients who have opted out of allowing their information to be used for research and planning (via the national data opt-out choice). A clinical case report form (CRF) will be used to capture patient demographics, clinical data with regards to the PH assessment including previous TTE results. Where available, mortality data will be recorded within 5 years of the RHC.

These anonymised echo images will be collated and labelled centrally in a core lab at the RUH Bath, who will work with Janssen to develop and train an AI tool to identify scans where PH is present, including which specific type of PH may be present.

AI tool training will be based on 5 groups (each group anticipated to contain 415 echocardiograms): mild pre-capillary PH; moderate pre-capillary PH; severe pre-capillary PH; post capillary PH; no PH. The tool will then be validated in a separate pool made up of 425 echocardiograms (a combination of pre-capillary, post capillary PH and no PH). The validation cohort will not have been used in the training stage.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Retrospective

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • Patients aged ≥18
  • Have undergone a transthoracic echo and right heart catheter as part of their routine clinical care.

排除标准

  • Patients aged <18
  • Known or suspected congenital heart disease
  • Patient opted out of allowing their information to be used for research and planning (via the national data opt-out choice).

结局指标

主要结局

Compare the artificial intelligence tool (AIT) performance for detecting pulmonary hypertension (PH) with the current probability criteria

时间窗: Month 24

Compare the proportion of patients identified by the AI tool as having PH with the current guideline criteria for diagnosing PH from a TTE.

Detect patients with pre-capillary pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT)

时间窗: Month 24

Measure the proportion of patients the developed AIT correctly identifies as having pre-capillary PH.

Evaluate early detection capabilities of the artificial intelligence tool (AIT) compared to standard of care clinical diagnosis

时间窗: Month 24

Compare the proportion of patients identified by the AI tool as having PH with current standard clinical practice

Detect patients with pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT)

时间窗: Month 24

Measure the proportion of patients the developed AIT correctly identifies as having PH.

Detect patients with post-capillary pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT)

时间窗: Month 24

Measure the proportion of patients the developed AIT correctly identifies as having post-capillary PH.

Detect patients without pulmonary hypertension (PH) with the novel artificial intelligence tool (AIT)

时间窗: Month 24

Measure the proportion of patients the developed AIT correctly identifies as not having PH.

次要结局

  • The artificial intelligence tool (AIT) is able to predict mortality(Month 24)
  • The novel artificial intelligence tool (AIT) is able to assess the severity of pulmonary hypertension (PH)(Month 24)

研究者

申办方类型
Other
责任方
Sponsor

研究点 (1)

Loading locations...

相似试验

ADOPT: Improving Diagnosis of Pulmonary Hypertension... | 临床试验