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

NCT06093217 | Completed

• 2 other identifiers: 2720311735
• Study Type: observational
• Target: 3,872
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Pulmonary Embolism

Keywords

Artificial Intelligence; Pulmonary Embolism; CTPA; Venous Thrombo-embolic disease

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (16)

• Rate of acute PE detection with AI technology

  24 months

• Rate of discordant acute PE cases

  24 months

• AI failure rate for acute PE detection

  24 months

• Rate of RV:LV detection with AI technology

  24 months

• Rate of discordant RV:LV detection

  24 months

Other Outcomes (1)

• Exploratory outcomes

  12 months

Study Arms (2)

Prospective Cohort: 'Live' Introduction of AI technology

Consecutive CTPAs, for patients with suspected acute PE, which have their imaging interpreted 'live' by AI technology. The radiologist will have ultimate responsibility for the report generated.

Device: Artificial Intelligence

Comparator Cohort: Standard Radiology reporting

Retrospective CTPAs, for patients with suspected acute PE, which have been reported by a human radiologist only. These CTPAs will not be interpreted by AI technology 'live' BUT undergo analysis to help assess the sensitivity, specificity, false negative, false positive rates of AI technology.

Device: Artificial Intelligence

Interventions

Artificial Intelligence DEVICE

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

Comparator Cohort: Standard Radiology reporting; Prospective Cohort: 'Live' Introduction of AI technology

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients who attend the Royal United Hospital Bath NHS Trust and require CTPA to exclude or diagnose acute PE.

You may qualify if:

• Patients over 18 years of age
• Patient requiring CTPA to exclude or diagnose acute PE

You may not qualify if:

• 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

Sponsors & Collaborators

• Royal United Hospitals Bath NHS Foundation Trust: lead
• University of Bath: collaborator
• University of Bristol: collaborator
• London School of Hygiene and Tropical Medicine: collaborator

Study Sites (1)

Royal United Hospitals, Bath NHS Foundation Trust

Bath, BA1 3NG, United Kingdom

Location

MeSH Terms

Conditions

Pulmonary Embolism

Condition Hierarchy (Ancestors)

Lung Diseases; Respiratory Tract Diseases; Embolism; Embolism and Thrombosis; Vascular Diseases; Cardiovascular Diseases

Study Officials

• Jonathan Rodrigues, MBBS FRCR

  Royal United Hospitals Bath NHS Foundation Trust

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: October 4, 2023
• First Posted: October 23, 2023
• Study Start: January 8, 2024
• Primary Completion: January 8, 2025
• Study Completion: February 6, 2025
• Last Updated: February 13, 2025

Record last verified: 2025-02

Locations

GB (1)