Detection Of Aortic Dissection Research Articles

Early matrix softening contributes to vascular smooth muscle cell phenotype switching and aortic dissection through down-regulation of microRNA-143/145

Thoracic aortic dissection (TAD) is characterized by extracellular matrix (ECM) dysregulation. Aberrations in the ECM stiffness can lead to changes in cellular functions. However, the mechanism by which ECM softening regulates vascular smooth muscle cell (VSMCs) phenotype switching remains unclear. To understand this mechanism, we cultured VSMCs in a soft extracellular matrix and discovered that the expression of microRNA (miR)-143/145, mediated by activation of the AKT signalling pathway, decreased significantly. Furthermore, overexpression of miR-143/145 reduced BAPN-induced aortic softening, switching the VSMC synthetic phenotype and the incidence of TAD in mice. Additionally, high-throughput sequencing of immunoprecipitated RNA indicated that the TEA domain transcription factor 1 (TEAD1) is a common target gene of miR-143/145, which was subsequently verified using a luciferase reporter assay. TEAD1 is upregulated in soft ECM hydrogels in vitro, whereas the switch to a synthetic phenotype in VSMCs decreases after TEAD1 knockdown. Finally, we verified that miR-143/145 levels are associated with disease severity and prognosis in patients with thoracic aortic dissection. ECM softening, as a result of promoting the VSMCs switch to a synthetic phenotype by downregulating miR-143/145, is an early trigger of TAD and provides a therapeutic target for this fatal disease. miR-143/145 plays a role in the early detection of aortic dissection and its severity and prognosis, which can offer information for future risk stratification of patients with dissection.

Low D-dimer in acute coronary syndrome and heart failure: Screening for large vessel diseases in patients with chest symptoms

BackgroundDistinguishing between large-vessel diseases such as acute aortic syndrome (AAS) and pulmonary embolism (PE), and non-large-vessel diseases, such as acute coronary syndrome (ACS), heart failure (HF), and neurogenic diseases, in patients presenting with chest symptoms remains a challenge, which can result in a significant number of misdiagnoses. Simultaneously distinguishing both AAS and PE is essential because large-vessel diseases require angio-computed tomography (CT) during initial presentation whereas, non-large-vessel diseases do not. This study aimed to determine the optimal method for differentiating between large-vessel and non-large-vessel diseases using D-dimer, troponin I, and pretest probability scores. MethodsFrom the 11683 patients who presented with chest symptoms including chest pain, discomfort, or dyspnea, this retrospective observational study included 1817 patients who had complete data for essential biomarkers; 105 with AAS, 139 with PE, 1093 with ACS, 451 with HF, and 83 with neurogenic diseases. ResultsD-dimer, D-dimer/troponin I ratio (DT ratio), and troponin I results distinguished the 2 groups: D-dimer (>2.38 μg/mL), AUC 0.935; DT ratio, AUC 0.827; and troponin I, AUC 0.653. For predicting AAS, the performances of D-dimer level and aortic dissection detection risk score (ADD-RS) were AUCs of 0.915 (p < 0.0001) and 0.67 (p = 0.0004), respectively; for predicting PE, the AUCs of D-dimer level and modified Wells score were 0.95 (p = 0.0001) and 0.857 (p < 0.0001), respectively. ConclusionsThe D-dimer levels proved to be a crucial discriminator for identifying AAS and PE, even when compared with the ADD-RS and modified Wells scores. Moderately elevated D-dimer levels suggest the need to consider AAS and PE diagnoses via angio-CT for patients with chest symptoms.

Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.

This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p < 0.05), similar in obese but significantly different in non-obese patients (p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.

Identifying patients with acute aortic dissection using an electrocardiogram with convolutional neural network

BackgroundThe potential of utilizing artificial intelligence with electrocardiography (ECG) for initial screening of aortic dissection (AD) is promising. However, achieving a high positive predictive rate (PPR) remains challenging. Methods and resultsThis retrospective analysis of a single-center, prospective cohort study (Shinken Database 2010–2017, N = 19,170) used digital 12-lead ECGs from initial patient visits. We assessed a convolutional neural network (CNN) model's performance for AD detection with eight-lead (I, II, and V1-6), single-lead, and double-lead (I, II) ECGs via five-fold cross-validation. The mean age was 63.5 ± 12.5 years for the AD group (n = 147) and 58.1 ± 15.7 years for the non-AD group (n = 19,023). The CNN model achieved an area under the curve (AUC) of 0.936 (standard deviation [SD]: 0.023) for AD detection with eight-lead ECGs. In the entire cohort, the PPR was 7 %, with 126 out of 147 AD cases correctly diagnosed (sensitivity 86 %). When applied to patients with D-dimer levels ≥1 μg/dL and a history of hypertension, the PPR increased to 35 %, with 113 AD cases correctly identified (sensitivity 86 %). The single V1 lead displayed the highest diagnostic performance (AUC: 0.933, SD: 0.03), with PPR improvement from 8 % to 38 % within the same population. ConclusionsOur CNN model using ECG data for AD detection achieved an over 30% PPR when applied to patients with elevated D-dimer levels and hypertension history while maintaining sensitivity. A similar level of performance was observed with a single-lead V1 ECG in the CNN model.

Image detection of aortic dissection complications based on multi-scale feature fusion

BackgroundAortic dissection refers to the true and false two-lumen separation of the aortic wall, in which the blood in the aortic lumen enters the aortic mesomembrane from the tear of the aortic intima to separate the mesomembrane and expand along the long axis of the aorta. PurposeIn view of the problems of individual differences, complex complications and many small targets in clinical aortic dissection detection, this paper proposes a convolution neural network MFF-FPN (Multi-scale Feature Fusion based Feature Pyramid Network) for the detection of aortic dissection complications. MethodsThe proposed model uses Resnet50 as the backbone for feature extraction and builds a pyramid structure to fuse low-level and high-level feature information. We add an attention mechanism to the backbone network, which can establish inter-dependencies between feature graph channels and enhance the representation quality of CNN. ResultsThe proposed method has a mean average precision (MAP) of 99.40% in the task of multi object detection for aortic dissection and complications, which is higher than the accuracy of 96.3% on SSD model and 99.05% on YoloV7 model. It greatly improves the accuracy of small target detection such as cysts, making it more suitable for clinical focus detection. ConclusionsThe proposed deep learning model achieves feature reuse and focuses on local important information. By adding only a small number of model parameters, we are able to greatly improve the detection accuracy, which is effective in detecting small target lesions commonly found in clinical settings, and also performs well on other medical and natural datasets.

Improved detection of aortic dissection in non-contrast-enhanced chest CT using an attention-based deep learning model

Rationale and objectivesThis study investigated the effects of implementing an attention-based deep learning model for the detection of aortic dissection (AD) using non-contrast-enhanced chest computed tomography (CT). Materials and methodsWe analysed the records of 1300 patients who underwent contrast-enhanced chest CT at 2 medical centres between January 2015 and February 2023. We considered an internal cohort of 200 patients with AD and 200 patients without AD and an external test cohort of 40 patients with AD and 40 patients without AD. The internal cohort was divided into training and test sets, and a deep learning model was trained using 9600 CT images. A convolutional block attention module (CBAM) and a traditional deep learning architecture (namely, You Only Look Once version 5 [YOLOv5]) were combined into an attention-based model (i.e., YOLOv5-CBAM). Its performance was measured against the unmodified YOLOv5 model, and the accuracy, sensitivity, and specificity of the algorithm were evaluated by two independent radiologists. ResultsThe CBAM-based model outperformed the traditional deep learning model. In the external testing set, YOLOv5-CBAM achieved an area under the curve (AUC) of 0.938, accuracy of 91.5 %, sensitivity of 90.0 %, and specificity of 92.9 %, whereas the unmodified model achieved an AUC of 0.844, accuracy of 83.6 %, sensitivity of 71.2 %, and specificity of 96.0 %. The sensitivity results of the unmodified algorithms were not significantly different from those of the radiologists; however, the proposed YOLOv5-CBAM algorithm outperformed the unmodified algorithms in terms of detection. ConclusionsIncorporating the CBAM attention mechanism into a deep learning model can significantly improve AD detection in non-contrast-enhanced chest CT. This approach may aid radiologists in the timely and accurate diagnosis of AD, which is important for improving patient outcomes.

Analysis of clinical characteristics and imagological features of the aortic dissection patients with negative D-dimer results.

D-dimer (DD) is a vital biomarker to rule out the diagnosis of aortic dissection (AD). However, the DD level in some patients with AD is not high in clinical practice, which often leads to missed diagnosis; therefore, understanding the characteristics of patients with AD and negative DD is of great clinical value. From May 2015 to October 2020, 286 patients with AD who visited the first medical contact (FMC) within 24 h of symptom onset and were hospitalized in the Xiamen Cardiovascular Hospital of Xiamen University were enrolled in this study. Clinical characteristics and outcomes of patients were assessed. Among them, 13 cases (approximately 4.5%) had negative DD results. Compared to patients with positive DD results, patients with negative DD results had significantly higher platelet counts and lower aortic dissection detection risk scores (ADD-RS). The imagological analysis showed that patients with AD and negative DD had lower extension scores and milder damage to the mesenteric artery and three branches of the aortic arch. Furthermore, the results of the multivariable analysis showed that white blood cell count (WBC) [odds ratio (OR): 1.379, P = 0.028], FMC (OR: 0.904, P = 0.028), and extension score (OR: 1.623, P = 0.046) were associated with negative DD result. Patients with AD and negative DD results had longer FMC and lower WBC. Imaging showed a smaller tear extension range and less damage to the mesenteric artery and three branches of the aortic arch. A negative DD result could not completely rule out AD even if the ADD-RS was zero.

Diagnosis of Acute Aortic Syndrome in the Emergency Department (DAShED) study: an observational cohort study of people attending the emergency department with symptoms consistent with acute aortic syndrome

BackgroundThe diagnosis of acute aortic syndrome (AAS) is commonly delayed or missed in the ED. We describe characteristics of ED attendances with symptoms potentially associated with AAS, diagnostic performance of...

Predicting acute aortic syndrome using aortic dissection detection risk score, D-dimer, and X-ray

ObjectiveAcute aortic syndrome (AAS) is a fatal disease with high mortality. There were previous studies using aortic dissection detection risk score (ADD-RS) and D-dimer (DD) to screen AAS. There were screening failures in previous studies, suggesting the need for a more accurate tool. This study investigated the effect of combining ADD-RS and age adjusted D-dimer (DDage-adj) with abnormal findings on chest radiographs on the diagnosis of AAS in patients admitted to emergency department (ED). MethodsThis single-center retrospective case-control study included 93 patients with AAS and 465 with chest pain (CP), diagnosis other than AAS. We attempted to compare the initial clinical presentation and laboratory examination findings. ResultsAge-adjusted DD (DDage-adj), defined as age x 0.01 mg/L in patients ≥50 years, showed sensitivity of 92.5% and specificity of 76.3% for patients with AAS (p < 0.001). Positive chest radiography findings were significant with AAS group; sensitivity was 89.2% with a specificity of 80.9% (p < 0.001). Multivariate logistic regression analysis was used; widened mediastinum, widening of aortic contour and aortic kinking indicates the probability of AAS in patients with CP (p < 0.05).ADD-RS was used to evaluate the risk of AAS. For low risk group, ADD-RS ≤1, combined use of chest radiography and DDage-adj showed meaningful result. Sensitivity and specificity were 100% and 67.1% with failure rate of 0% (p < 0.001). Multivariate logistic regression analysis were made; widening of the mediastinum (p = 0.035), widening of the aortic contour (p < 0.001) and aortic kinking (p < 0.001) showed significant p-value. Combining DDage-adj and these three chest radiography findings in ADD-RS≤1 patients resulted 0% failure rate with 67.8% specificity (p < 0.001). ConclusionsThe combination of ADD-RS, DDage-adj and chest radiography could lower the failure rate of AAS exclusion strategy. This combination strategy satisfies low failure rate (<3%) and yields relatively high specificity of 67.8%.

Low and intermediate risk aortic dissection detection risk score and negative D-dimer: a word of caution.

A low or intermediate aortic dissection detection risk score coupled with a negative D-dimer has been proposed as a reliable rule-out strategy for acute aortic syndrome (AAS) in the emergency department. Locally, its use has crept into the work-up of patients with suspected AAS. This opinion piece offers a word of caution-the stakes are high for missing AAS. Although the rule-out strategy does show exciting potential, it remains to be validated, especially for Australasian patients. Patients with suspected AAS should continue to be investigated with timely advanced imaging such as contrast-enhanced computed tomographic aortography.

A Fatal Disaster: Accompanying Aortic Dissection Obscured by Acute Coronary Syndrome.

The present study investigated the characteristics of rare but often fatal cases of acute coronary syndrome and aortic dissection and the factors affecting mortality. HEART score, aortic dissection detection risk score, and Global Registry of Acute Coronary Event (GRACE) Score were calculated by evaluating biochemical tests and symptoms/signs of the 20 patients who were admitted to our clinic with a preliminary diagnosis of acute myocardial infarction and subsequently diagnosed with aortic dissection. The assumption of normal distribution was checked with the Shapiro-Wilk test, and independent group comparisons were made with the t test or the Mann-Whitney U test. Relationships between qualitative variables were analyzed with chi-square tests. Relationships between quantitative variables were analyzed with Pearson or Spearman correlation coefficients. Patients with conditions like hypertension and diabetes are at higher risk for aortic dissection. We recommend the use of the GRACE Score with the risk parameters we have determined for the prediction of operative mortality. We suggest that the axillary region and antegrade cerebral perfusion may be preferred as the cannulation area in dissection repair. We emphasize preliminary risk assessment according to the aortic dissection detection risk score and remind physicians that ECG ST-T and troponin changes may be misleading.

Type A Aortic Dissection and Non-Contrast Computed Tomography.

Non-contrast enhanced chest computed tomography (CT) carries a low sensitivity for acute aortic dissection; CT Angiography remains the gold standard. We highlight the potential utility of non-contrast CT for detection of aortic dissection in a case of a young, immunocompromised man presenting with acute abdominal pain and renal injury. Given elevated creatinine, an initial non-contrast chest CT demonstrated subtle findings suggestive of aortic dissection (aneurysmal dilation of the proximal ascending aorta as well as displaced calcified intimal flap/intraluminal high linear density in the thoracic descending and distal abdominal aorta). Subsequent CT angiography confirmed the presence of an extensive type A aortic dissection. He underwent emergent exploratory laparotomy and hemiarch repair. Displaced calcified intimal flaps, intraluminal high-densities, intramural hematoma, and aneurysmal aortic dilation are common non-contrast computed tomography imaging findings that suggest aortic dissection.

Abstract 275: A New, Novel Shape Feature Space Classifier For Acute Type B Aortic Dissection

Background: Early detection and treatment of acute type B aortic dissection (TBAD) is associated with improved morbidity and mortality. Artificial intelligence and machine-learning (ML) based approaches for expedited detection of TBAD have made strides in recent years with improving predictive power and accuracy. These deep-learning neural networks are trained with iterative exposure to patient imaging and clinical information of patients with aortic pathology to identify consistent patterns. AI-decision support tools can be improved with more focused feature selection, variables of particular interest. Here, we identify a novel shape variable to describe the highly complex topography of the aorta. Methods: 46 patients with acute, complicated TBAD were identified from a now closed multi-center clinical trial of a conformable thoracic stent graft. An independent cohort of 87 patients without aortic pathology was obtained from a single center, retrospective database. Two-dimensional surface segmentations were generated from pre-operative CT angiography. Variance in total curvature, a size-independent shape measure, and inverse Casorati curvature, a size scalar, were calculated from the segmentations. Additional size measurements, including maximum diameter, were calculated using centerline methods. Results: 44 pre-operative scans of acute TBAD were analyzed. Mean maximum diameter in the acute TBAD cohort was 39.20 ± 6.75 mm compared to 21.44 ± 3.28 mm in the non-pathologic aorta cohort (p = 2.50E-17). Mean fluctuation in total curvature, normalized, was 7.09 ± 3.58 versus 4.18 ± 2.32 in the non-pathologic aorta cohort (p = 4.36E-13). Mean inverse Casorati curvature was 0.043 ± 0.005 in acute TBAD compared to 0.06 ± 0.01 in non-pathologic aortas (p = 4.67E-21). Logistic regression classification accuracy was 91.6 ± 2.4%. Conclusions: Acute TBAD demonstrates a unique shape signature defined by fluctuation in total curvature, in addition to traditional size measures. This easily quantifiable and reproducible dimension of aortic geometry classification has significant potential as a new, anatomically derived feature space to improve AI/ML-based aortic dissection detection.

Pre-Test Probability Assessment and d-Dimer Based Evaluation in Patients with Previous Acute Aortic Syndrome.

Background and Objectives. Acute aortic syndromes (AASs) are emergencies burdened by high morbidity and mortality. Guideline-recommended diagnostic workup is based on pre-test probability assessment (PPA) and d-dimer testing. However, the performance of PPA and d-dimer has never been studied in individuals with previous AAS (pAAS), which represent a challenging population. Materials and Methods. We analyzed a registry of patients with pAAS evaluated in two Emergency Departments (EDs) for suspected novel AAS (nAAS). Enrolment criteria were history of pAAS and the presence of truncal pain, syncope or perfusion deficit. All patients underwent advanced imaging. Clinical data were registered prospectively and PPA was performed by applying the aortic dissection detection (ADD) and an aorta simplified (AORTAs) score. Results. A total of 128 patients were enrolled, including 77 patients with previous Stanford type A aortic dissection and 45 patients with previous Stanford type B aortic dissection. The final diagnosis was nAAS in 40 (31%) patients. Clinical variables associated with nAAS were: aortic valve disease, thoracic aortic aneurysm, severe pain, sudden pain, ripping/tearing pain and hypotension/shock. ADD score ≥ 2 had a sensitivity of 65% and a specificity of 83% for nAAS; AORTAs score ≥ 2 had a sensitivity of 48% and a specificity of 88%. d-dimer (cutoff ≥ 500 ng/mL or age-adjusted cutoff) had a sensitivity of 97% and a specificity of 13%/14.7%, for diagnosis of nAAS. Patients that were candidates for guideline-compliant PPA/d-dimer integrated rule-out were: 5 (4.9%) with ADD ≤ 1/d-dimer and 8 (7.8%) with AORTAs ≤ 1/d-dimer < age-adjusted cutoff. None of them had a nAAS. Conclusions. Patients with pAAS evaluated in the ED for red-flag symptoms showed intermediate-to-high pre-test probability of nAAS. The ADD score had lower sensitivity and specificity than in unselected patients. d-dimer, alone and integrated with PPA, was highly sensitive for nAAS, but very unspecific. PPA/d-dimer integrated strategies are unlikely to significantly reduce the number of patients with pAAS undergoing advanced imaging.

Aortic Dissection Detection Risk Score and D-Dimer for Acute Aortic Syndromes in the Chinese Population: Exploration of Optimal Thresholds and Integrated Diagnostic Value.

This study aimed to assess the diagnostic performance of the aortic dissection detection risk score (ADD-RS) plus D-dimer for acute aortic syndrome (AAS) in Chinese patients. Two hundred and sixty-two and 200 patients with suspected AAS symptoms were enrolled as exploration cohort and validation cohort, respectively. In exploration cohort, ADD-RS plus D-dimer (AUC = 0.929, 95%CI: 0.887-0.971) presented a better diagnostic value for AAS than ADD-RS or D-dimer alone. Meanwhile, ADD-RS > 1 and D-dimer > 2000ng/mL were the optimal thresholds. Then, a diagnostic model integrating ADD-RS > 1 plus D-dimer > 2000ng/mL was established, presenting sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 92.5%, 70.3%, 34.9%, and 98.2%, respectively. In validation cohort, the established diagnostic model exhibited a sensitivity, specificity, PPV, and NPV of 93.1%, 70.2%, 34.6%, and 98.4%, respectively, for diagnosing AAS. Summarily, ADD-RS > 1 and D-dimer > 2000ng/mL are optimal thresholds for diagnosing AAS in the Chinese population. However, confirmative MSCT results are necessary.

Aortic dissection detection and thrombus structure quantification using volumetric ultrasound, histology, and scanning electron microscopy

Aortic dissection occurs when a weakened portion of the intima tears, and a separation of layers propagates along the aortic wall to form a false lumen filled with active blood flow or intramural thrombus. The unpredictable nature of aortic dissection formation and need for immediate intervention leaves limited serial human image data to study the formation and morphological changes that follow dissection. We used volumetric ultrasound examination, histology, and scanning electron microscopy (SEM) to examine intramural thrombi at well-defined timepoints after dissection occurs in apolipoprotein E-deficient mice infused with angiotensin II (n = 71). Stratification of red blood cell (RBC) morphologies (biconcave, intermediate biconcave, intermediate polyhedrocyte, and polyhedrocyte) in the thrombi with scanning electron microscopy (n = 5) was used to determine degree of thrombus deposition/contraction. Very few biconcave RBCs (1.2 ± 0.6%) were in the thrombi, and greater amounts of intermediate biconcave RBCs (25.8 ± 6.7%) were located in the descending thoracic portion of the dissection while more polyhedrocytes (14.6 ± 5.1%) and fibrin (42.3 ± 4.5%; P < .05) were found in the distal suprarenal aorta. Thrombus deposition likely plays some role in patient outcomes, and this multimodality technique can help investigate thrombus deposition and characteristics in experimental animal models and human tissue samples.

Detection of acute thoracic aortic dissection based on plain chest radiography and a residual neural network (Resnet)

Acute thoracic aortic dissection is a life-threatening disease, in which blood leaking from the damaged inner layer of the aorta causes dissection between the intimal and adventitial layers. The diagnosis of this disease is challenging. Chest x-rays are usually performed for initial screening or diagnosis, but the diagnostic accuracy of this method is not high. Recently, deep learning has been successfully applied in multiple medical image analysis tasks. In this paper, we attempt to increase the accuracy of diagnosis of acute thoracic aortic dissection based on chest x-rays by applying deep learning techniques. In aggregate, 3,331 images, comprising 716 positive images and 2615 negative images, were collected from 3,331 patients. Residual neural network 18 was used to detect acute thoracic aortic dissection. The diagnostic accuracy of the ResNet18 was observed to be 90.20% with a precision of 75.00%, recall of 94.44%, and F1-score of 83.61%. Further research is required to improve diagnostic accuracy based on aorta segmentation.

Ethnic Disparities in CT Aortography Use for Diagnosing Acute Aortic Syndrome.

To determine whether CT aortography was performed in proportion to patient risk for acute aortic syndrome (AAS) and incidence of AAS for different ethnic groups. All atraumatic thoracic aorta CT aortographic examinations performed in adults (age > 15 years) suspected of having AAS between January 2009 and December 2019 at Auckland City Hospital (New Zealand) were included. Patients were risk stratified using the aortic dissection detection risk score (ADD-RS). The primary outcomes were the ratio of CT aortography rates to rates of positive CT aortographic examinations and the incidence of AAS. Population census data were used to determine age-standardized incidence of AAS in the emergency department (ED). In total, 1646 CT aortographic examinations were performed in 1543 patients (mean age, 62 years ± 16 [SD]; 877 male patients). Māori (34% [68 of 203]) and Pacific Islanders (35% [80 of 229]) were more likely to be at high risk of AAS (ADD-RS > 1) compared with patients from other ethnic groups (25% [308 of 1214]); in the ED catchment population, age-standardized AAS incidence was significantly higher in Māori (6.9 per 100 000 person-years [95% CI: 4.3, 10.4]) and Pacific Islanders (5.3 [95% CI: 3.4, 7.8]) than in other ethnic groups (2.3 [95% CI: 1.8, 2.8]). Despite this higher incidence, disproportionately fewer CT aortographic examinations were requested in the ED for Māori (9.2 CT aortographic examinations per AAS diagnosis) and Pacific Islanders (9.2 CT aortographic examinations per AAS diagnosis) compared with other ethnic groups (13.8 CT aortographic examinations per AAS diagnosis). Māori and Pacific Islanders were at disproportionately higher risk of AAS but had fewer requested CT aortographic examinations compared with other ethnic groups. This increased risk of AAS in Pacific Islander and indigenous Māori patients should be considered by clinicians when investigating AAS.Keywords: Ethnicity, Māori, Pacific Islander, Aortic Dissection Detection Risk Score, Acute Aortic Syndrome, Aortic Dissection, CT Angiography Supplemental material is available for this article. © RSNA, 2022.

Abstract 11285: Synthetic-Contrast-Enhanced Computed Tomography Creating by Machine Learning Without Contrast Media

Introduction: Contrast-enhanced computed tomography (CECT) is the gold standard for the diagnosis of aortic dissection (AD), at the same time, CECT has risks of nephropathy and allergic reactions caused by contrast media. Hypothesis: Synthetic-contrast-enhanced computed tomography (SECT) created from non-contrast-enhanced computed tomography (NECT) by machine learning without contrast agents could be an alternative to CECT in the diagnosis of AD. Methods: 50 patients with type A AD and 200 patients without AD who underwent NECT and CECT in pairs at Kobe university hospital were randomly selected and used for model training and testing. 3120 CT slices were used for training and 379 slices were used for testing. AD detection algorithms for NECT or CECT were developed by convolutional neural networks using NECT datasets or CECT datasets respectively. A SECT creating algorithm was developed by u-net using the pairs of CT datasets. To provide visual explanations of diagnosis, the gradient-weighted class activation mapping (Grad-CAM) method was adapted. Results: The provided sensitivities of AD detection algorithms for NECT or SECT were 90 % and 97 %, and the specificities were 100 % and 100 % respectively. When the developed AD detection algorithms for CECT were adapted to SECT, the provided sensitivity of 100 %, and sensitivity of 95 %. Most slices SECT of patients with AD demonstrated intimal flap and/or thrombosed false lumen as the almost same as CECT. The Grad-CAM visualization revealed that the AD detection algorithm using SECT images identified intimal flap or thrombosed false lumen in images more clearly than the AD detection algorithm using NECT images. Conclusions: The SECT creating algorithm improved diagnostic sensitivity for detecting AD using NECT datasets, which suggests the potential of the proposed method to support the clinical practice by reducing complications of contrast agents.

What can be done to improve diagnosis of aortic dissection?

Aortic dissection (AD) is rare. Missed AD is a common reason for coronial investigations and civil claims for medical negligence. Recommendations include improved education, supervision and information transfer, reminders in chest pain pathways and higher rates of investigation for AD. Higher investigation rates pose risks to patients and the health system which may not be in balance with the likelihood of AD. The appropriate diagnostic yield of investigation to balance risk and benefit has not been defined. The AD detection risk score pathway has been proposed as a useful diagnostic tool but concerns about its derivation, validation and utility remain. In this paper, we try to draw together published literature and local audit data to develop recommendations about what might be done to reduce the number of missed AD cases in EDs and what the impact of higher investigation rates might be.