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.