Artificial Intelligence to Improve Detection and Risk Stratification of Acute Pulmonary Embolism (AID-PE)

Pulmonary Embolism Clinical Trial

— AID-PE  

Official title:

Developing Artificial Intelligence Solutions to Improve Diagnosis and Risk Stratification in Acute Pulmonary Embolism

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06093217
• Other study ID #: 2720
• Secondary ID: 311735
• Status: Recruiting
• Start date: January 8, 2024
• Est. completion date: December 31, 2025

Study information

• Verified date: October 2023
• Source: Royal United Hospitals Bath NHS Foundation Trust
• Contact: Jonathan Rodrigues, MBBS FRCR
• Phone: 01225 821896
• Email: ruh-tr.researchatruh[@]nhs.net
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The goal of this exploratory observational study is to assess the feasibility and real-world clinical impact of implementing Artificial Intelligence (AI) software for the detection of acute Pulmonary Embolism (PE) in patients who undergo Computed Tomography Pulmonary Angiogram (CTPA). The main questions that this study aims to answer are:

[Question 1] What is the real-world impact of AI on the clinical outcomes and decision making by radiologists and clinicians in the management of acute PE?

[Question 2] Is AI software for the detection of acute PE acceptable to use in clinical practice and do they have a favourable impact on clinical workload?

[Question 3] Is it cost-effective to implement AI software for the detection of acute PE in clinical practice?

Patients having a CTPA for the detection of acute PE will have their imaging analysed by AI software in combination with a human radiologist. Researchers will aim to compare the clinical and radiology specific outcomes with a retrospective cohort of patients who have had standard routine radiology reporting.

Description:

Acute Pulmonary Embolism (PE) results from partial or total occlusion of the pulmonary blood vessels by thrombus, which can cause right ventricular failure and death if not diagnosed and treated early. Acute PE is a common condition with rising mortality. Patients with acute PE are often poorly risk stratified despite clear guidelines. In fact, the 2019 National Confidential Inquiry into Patient related Outcome and Death (NCEPOD) for acute PE highlighted the need to address worsening mortality rates through appropriate risk stratification of the condition.

ESC/ERS guidelines for the diagnosis and management of acute PE also advise on the importance of risk stratification. An increased right ventricle: left ventricle (RV:LV) ratio >1.0 on Computed Tomography Pulmonary Angiogram (CTPA) is associated 2.5-fold increased risk of all-cause mortality, and 5-fold risk for PE-related mortality. This metric is intended to help clinicians distinguish between patients with high and low risk acute PE. Patients stratified as high risk (RV:LV ratio >1.0) necessitate closer monitoring within an inpatient setting. Whereas, patients stratified as low risk (RV:LV ratio <1.0) are suitable for early discharge through ambulatory pathways.

Therefore, the provision of RV:LV metrics within radiology reporting has potentially important clinical implications. If clinicians are not provided with any quantifiable evidence of RV dysfunction on which to base their treatment decisions, patients with high risk acute PE may be unintentionally considered 'low risk' and discharged home. Furthermore, patients with low risk acute PE may be subject to longer, and potentially unnecessary, inpatient stays which undoubtedly contributes to the cost of healthcare. The integration of Artificial Intelligence (AI) technology within radiology reporting of CTPAs for acute PE could be a potential solution to address this challenge.

AI is an increasingly attractive technology within healthcare. It describes a number of computer software techniques which mimic human cognitive function. AI shows promise in ability to detect and risk stratify acute PE. However, most studies have been conducted in retrospective cohorts. Furthermore, no study current has addressed the health economic impact of implementing AI technology within the real-world reporting of acute PE.

This observational study will be led by Royal United Hospital Bath NHS Trust (RUH). The aim of this study is to integrate Artificial Intelligence and machine learning technology within the reporting of CTPAs for acute PE. The investigators hypothesise that AI technology can improve the prompt diagnosis, risk stratification, and management of acute PE within a real-world clinical setting. The investigators also hypothesis that integration of AI technology is cost-effective, and acceptable to radiologists and clinicians.

Patients whose scans will be included in the study will be all those consecutively presenting to the RUH with a possible diagnosis of acute PE for 12 months before (comparator cohort) and 12 months after (intervention cohort) 'live' introduction of integrated AI technology reporting. For all recruited participants, an anonymised clinician case report form will be used to capture details relating to their demographics, clinical-radiological PE severity, their management, and outcomes including mortality at 12 months.

At the point of analysis, the investigators will perform adjustments/matching between the two cohorts for patient baseline characteristics. The investigators will also adjust for calendar time of recruitment, to account for temporal trends. Analysis between both cohorts will also allow development of a decision analysis model to assess the cost-effectiveness of integrated AI technology within CTPA report for acute PE. Clinician and radiologist questionnaires will be used to assess user acceptability.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 2500
• Est. completion date: December 31, 2025
• Est. primary completion date: January 31, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients over 18 years of age

• Patient requiring CTPA to exclude or diagnose acute PE

Exclusion Criteria:

• Patients under 18 years of age

• Patients who have registered with the national opt-out scheme for research

• CTPA performed for reasons other than acute PE

• CTPA performed for acute PE but reported by external radiologists

• Incomplete or discontinued CTPA scans

• Insufficient quality CTPA to allow for analysis by a radiologist

Related Conditions & MeSH terms

• Embolism
• Pulmonary Embolism

Intervention

Device:
• Artificial Intelligence
AI technology will generate a report with relevant key slice imaging identifying the presence of an acute pulmonary embolism and RV:LV ratio measurements to the radiologist

Locations

Country	Name	City	State
United Kingdom	Royal United Hospitals, Bath NHS Foundation Trust	Bath

Sponsors (4)

Lead Sponsor	Collaborator
Royal United Hospitals Bath NHS Foundation Trust	London School of Hygiene and Tropical Medicine, University of Bath, University of Bristol

Country where clinical trial is conducted

United Kingdom, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Exploratory outcomes	Given the exploratory nature of this observational non-randomised feasibility study, there may be patterns/outcomes which emerge/develop during the study period. The investigators will report on any patterns which may emerge following introduction of AI reporting.	12 months
Primary	Proportion of patient decisions made in line with evidence based best practice guidelines after introducing AI technology within CTPA reporting	Comparison before and after AI introduction	12 months
Secondary	Rate of acute PE detection with AI technology	True positives and True negatives	24 months
Secondary	Rate of discordant acute PE cases	False positive and false negative rate with acute PE detection	24 months
Secondary	AI failure rate for acute PE detection	Proportion of scans unable to be interpreted by AI despite suitable CTPA acquisition	24 months
Secondary	Rate of RV:LV detection with AI technology	True positive and true negative	24 months
Secondary	Rate of discordant RV:LV detection	False positive and false negative	24 months
Secondary	Failure rate for automated RV:LV ratio	Proportion of scans unable to calculate automated RV:LV ratio despite suitable CTPA acquisition	24 months
Secondary	30 day mortality	Patient mortality (death) at 30-days post-PE diagnosis. Comparison before and after AI introduction.	12 months
Secondary	12 month mortality	Patient mortality (death) at 12-months post-PE diagnosis. Comparison before and after AI introduction.	12 months
Secondary	Hospital admission and bed days for acute PE	Comparison before and after AI introduction	12 months
Secondary	Time to anticoagulation in PE cases	Comparison before and after AI introduction	12 months
Secondary	Time from CTPA to discharge	Comparison before and after AI introduction	12 months
Secondary	PE risk stratification rates (low, intermediate low, intermediate high and high risk)	Comparison before and after AI introduction	12 months
Secondary	Cost to NHS for acute PE	Comparison before and after AI introduction	12 months
Secondary	End-user (clinician and radiologist) acceptability of AI technology	Quantified metrics from a non-validated questionnaire to evaluate end-use experience of integrated AI radiology reporting.	12 months
Secondary	Referral rates to outpatient follow-up (respiratory, thrombosis, haematology)	Comparison before and after AI introduction	12 months
Secondary	Diagnostic rate of Chronic thromboembolic pulmonary hypertension (CTEPH)	Comparison before and after AI introduction	12 months