Background

Acute aortic syndrome (AAS) includes a heterogenous group of patients with interlinked aortic pathologies, including penetrating atherosclerotic aortic ulcer (PAU), intramural haematoma (IMH), and aortic dissection (AD). AAS is an emergency with an incidence of 6/100,000 per year [1] and results in high peri-hospital mortality rates [2]. AD accounts for over 90% of AAS cases [3]. There are multiple risk factors for AD including those that increase intimal shear stress, such as hypertension, and those which cause weakening of the vessel wall, such as atherosclerosis or connective tissue disorders comprising Marfan, Ehlers- Danlos and Loeys-Dietz syndromes. A number of patients will also have a family history of aortic dissection, with 13–22% of patients affected having a first degree relative with previous AD [4].

There are approximately 2500 cases per year in England although numerous groups have suggested the condition is under-reported [5,6]. Clinical presentation of AD can be varied, with symptoms such as chest and back pain that can mimic a range of other more common conditions. To make a timely diagnosis, a high degree of clinical suspicion alongside a low threshold for cross-sectional imaging is required. Prompt diagnosis is key to the successful management of patients with acute AD, however in 16–40% of cases there is a delay in diagnosis [6].

At present there is limited understanding of the current pathways used to diagnose AAS. Clinical red-flag symptoms, such as truncal pain and syncope, have historically demonstrated limited accuracy [7]. Although literature suggests that one of the more sensitive (62–78% [8]) symptoms for AAS is truncal pain, it is a very common presenting complaint and may represent several other differential diagnoses. Pain characteristics should therefore be explored in greater detail; however, these are still unreliable at ruling out AAS (sensitivity<90%). Furthermore, there are several factors that delay diagnosis such as female sex, age >70, diabetes mellitus, and painless presentation [9].

Currently, D-dimer is a promising point-of-care biomarker available for the testing of AD [10]. Previous literature has demonstrated high sensitivity if used to rule out AD within the first six hours of symptom onset. Despite this, D-dimer lacks specificity since it may be elevated in several other conditions. Transthoracic echocardiography (TEE) and computed tomography angiography (CTA) continue to be the gold standard diagnostic techniques for AAS, however, there are new biomarkers on the horizon [11] that could prove to be specific in diagnosing AAS [12]. Suitable patient selection for imaging still constitutes a major diagnostic dilemma, with concerns for radiation exposure risks in low-suspicion cases and busy radiology departments with finite imaging capacity playing a role in the decision to proceed to cross-sectional imaging.

Rationale

Significant variation exist in AAS diagnostic pathways across regions with the majority of emergency departments not having a formal work-up pathway for AAS [1]. This likely affects the time to diagnosing AAS, which is time-sensitive, given the need for urgent management and potential treatment. These diagnostic delays could impact patient outcomes. CAASP has been developed to characterise and understand the current patient pathways for those presenting with suspected AAS, and to highlight clinical factors or diagnostic/ management delays that might impact on patient outcomes.

Objectives

The primary objective of CAASP is to outline the current national diagnostic and management pathways for patients presenting with suspected AAS and assess the impact of this on early mortality, and to inform and facilitate improvements in clinical practice.

Secondary objectives include:

• To assess the variation in positive yield rate of AAS from all imaging referrals performed to investigate for AAS at each centre.
• To identify any clinical and demographic factors associated with delays in admission, diagnosis and treatment
• To compare differences in diagnostic pathways and outcomes between tertiary aortic centres and other hospitals.
• To identify geographical and socioeconomic variation in AAS detection and outcomes.
• To identify the impact of COVID-19 on AAS pathways
• To understand differences between the UK AAS pathways with international sites (USA/ New Zealand).
• Promote collaborative working between multi-disciplinary trainee-led research collaboratives.

Study design

This is a multicentre, retrospective service evaluation project of adult patients diagnosed on imaging with acute aortic syndrome (AAS). The summary diagram for CAASP is shown in Fig 1. The study will be delivered through the Vascular and Endovascular Research Network (VERN), UK National Interventional Radiology Trainee Research (UNITE) network, the British Society of Interventional Radiology Trainees (BSIRT) network, and the Society of Interventional Radiology Resident and Fellow Section Research and Innovation (SIR RFS R&I) Committee (a collaboration with Massachusetts General Hospital, irlab.mgh.harvard.edu). VERN, UNITE, and SIR RFS R&I group are all trainee-led national research collaboratives that are run by, and engage with, research-active vascular and interventional radiology trainees, and allied health professionals. The co-investigators have experience in designing and delivering multi-centre collaborative studies.

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Fig 1. CAASP summary diagram.

https://doi.org/10.1371/journal.pone.0297782.g001

Eligibility criteria

Patients may be eligible for the study if they meet all inclusion criteria and none of the exclusion criteria apply. Adult patients with a diagnosis of AAS (AD Type A, AD Type B, AD non-A non-B, PAU, IMH) on imaging between 1-Jan-2018 and 1-June-2021 will be eligible to be included. Exclusion criteria are non-AAS pathology (e.g. traumatic or iatrogenic aortic injury), chronic AAS presentation and age <18 years.

Primary outcomes

The primary outcome will be to assess the association of recorded time delays in AAS pathways to admission, imaging diagnosis and treatment planning with patient mortality within 30, 90 days and 6 months

Secondary outcomes

• The proportion of positive scans from all imaging referrals performed to investigate for AAS and the proportion of AAS cases diagnosed incidentally per site.
• The clinical (presentation symptoms, signs, observations, vital signs, comorbidities and blood test results) and demographic (age, gender, ethnicity, distance to hospital, deprivation score) factors associated with delays in admission, diagnosis and treatment.
• Differences in time durations (to admission, diagnosis and treatment) and mortality outcomes between tertiary aortic centres and other hospitals.
• Compare the impact of geographical (distance to hospital) and socioeconomic (deprivation score) on the AAS diagnosis times and mortality outcomes between sites.
• Evaluate the incidence of AAS and mortality rates before and during COVID-19.
• Compare the UK AAS pathways with USA/ New Zealand pathways and associated time and mortality outcomes.

Sample size

This project is designed to evaluate current AAS pathways therefore no minimum sample size is required to show effect. Centres with less than 5 cases will be excluded.

Patient identification

Using a focused search strategy on electronic radiology information systems (S1 File) at each enrolled centre, cases AAS will be identified out of all the CT scans where AAS is queried. Data will be retrospectively collected for cases of AAS diagnosed between 1-Jan-2018 to 1-June-2021. From the first imaging diagnosis of AAS, follow-up data will be collected for a duration of six months. The start date for centres is variable to allow for local registration and approval however the proposed start date is August 2022 and end date March 2023. Prospectively maintained local databases of acute aortic syndrome cases will also be interrogated to ensure identification of all possible cases.

Data collection

Data collected will include patient demographics, details regarding timing of presentation, symptoms at presentation, risk factors for AD, physical examination findings, investigations including laboratory analyses and imaging, treatment, details regarding hospital stay, and mortality (S1 File). These data will be collected at each hospital site and pseudo-anonymised using a unique study ID which will include the first two letters of the site name and a number. The local site lead will keep a log that contains the key to the study IDs, i.e. a record of the personal identification data linked to each patient study ID number. This record will be filed at the investigational site and only accessed by the local site team. The pseudo-anonymised information will be collected at each participating centre onto a pre-piloted spreadsheet provided by the CAASP study team. The data collection proforma will be circulated to site leads across all centres. Only the pseudo-anonymised data will be transferred to the central study team leads via the secure NHS email server.

Statistical considerations

Descriptive and summary statistics will be used to present data regarding time to presentation/ time to CT scan/ time to treatment, mortality and complications for AAS patients within the first six months of imaging diagnosis. We will explore significant predictive factors leading to both mortality and delay using univariate and multivariate analysis. Data will be analysed in multiple ways, such as, stepwise multiple linear regression to calculate coefficients of predicted delay times (or delay time ratios) for individual factors that contribute to delays in diagnosis and management as well as multiway ANOVA (analysis of variance). Additionally, we will explore the possibility of utilising different machine learning algorithms such as MLR (multinomial logistic regression), SVM (support vector machines) and Random forests to form predictive models of AAS patient outcomes that later might guide the generation of predictive tools. Sub analysis will also look to focus on differences in diagnostic pathways between specialist cardiovascular centres and non-cardiovascular centres, geographical and socioeconomic (using index of multiple deprivation scores) variation to provide an insight in the variability of management. Statistical significance will be measured at p<0.05 with bonferroni correction to allow for multiple comparisons.

Ethics and dissemination