Devastating fungal endocarditis involving ascending aorta in a patient with a history of aortic valve replacement: a case report.

BACKGROUND: Fungal endocarditis is a rare but serious condition associated with high mortality rates. Various predisposing factors contribute to its occurrence, such as underlying cardiac abnormalities, cardiac surgeries, prosthetic cardiac devices, and central venous catheters. Diagnosing fungal endocarditis, particularly Aspergillus, poses challenges, often complicated by negative blood cultures. CASE PRESENTATION: This report details a case of extensive ascending aorta involvement in Aspergillus endocarditis (AE) in a 24-year-old man with a history of bioprosthesis aortic valve replacement (AVR). Three months post-AVR, he presented with pericardial effusion and aortic rupture, leading to a redo biological valved conduit aortic root replacement (Bentall surgery). Despite the intervention, the tubular graft exhibited extensive Aspergillus involvement, resulting in graft disruption and significant peri-aortic infection. A second redo procedure involving aortic homograft root replacement was performed. Unfortunately, the patient succumbed two days after the surgery. CONCLUSION: A combined approach of medical and surgical therapies is recommended to manage fungal endocarditis. Despite efforts, the mortality rate associated with Aspergillus endocarditis remains unacceptably high, with no significant difference observed between combination therapy and antifungal treatment alone. Further research is essential to explore novel therapeutic strategies and improve outcomes for patients with this challenging condition.

Mejora en el manejo de la sospecha del síndrome aórtico agudo en urgencias mediante un algoritmo clínico y el estudio de factores predictivos / Improvement in the management of suspected acute aortic syndrome in the emergency room through a clinical algorithm and study of predictive factors

Antecedentes y objetivo: El síndrome aórtico agudo (SAA) es poco frecuente y difícil de diagnosticar, con una gran variabilidad en su cuadro clínico inicial. Los objetivos son: 1) desarrollar un algoritmo informático, o un sistema de apoyo a las decisiones clínicas (SADC), para el manejo y la solicitud de estudios de diagnóstico por imagen en el servicio de Urgencias, en concreto de una tomografía computarizada (TC) de la aorta, ante la sospecha de SAA, 2) determinar el efecto de la implantación de este sistema, y 3) determinar los factores asociados a un diagnóstico radiológico positivo que mejoren la capacidad predictiva de los hallazgos de la TC de aorta. Material y métodos: Tras desarrollar e implementar un algoritmo basado en la evidencia, se estudiaron casos de sospecha de SAA. Se utilizó el test de la χ2 para analizar la asociación entre las variables incluidas en el algoritmo y el diagnóstico radiológico, con 3 categorías: sin hallazgos relevantes, positivo para SAA y diagnósticos alternativos. Resultados: Se identificaron 130 solicitudes; 19 (14,6%) tenían SAA y 34 (26,2%) tenían otra patología aguda. De las 19 con SAA, 15 habían sido estratificadas como de alto riesgo y 4 como de riesgo intermedio. La probabilidad de SAA era 3,4 veces mayor en los pacientes con aneurisma aórtico conocido (p=0,021, IC del 95%: 1,2-9,6) y 5,1 veces mayor en los pacientes con un soplo de insuficiencia vascular aórtica de novo(p=0,019, IC del 95 %: 1,3-20,1). La probabilidad de tener una enfermedad aguda grave alternativa fue 3,2 veces mayor en los pacientes con hipotensión o choque (p=0,02, IC del 95 %: 1,2-8,5). Conclusión: El uso de un SADC en el servicio de Urgencias puede ayudar a optimizar el diagnóstico del SAA. Se demostró que la presencia de un aneurisma aórtico conocido y de insuficiencia valvular aórtica de nueva aparición aumentan significativamente la probabilidad de SAA. Se necesitan más estudios para establecer una regla de predicción clínica.(AU)

Background and objective: Acute aortic syndrome (AAS) is uncommon and difficult to diagnose, with great variability in clinical presentation. To develop a computerized algorithm, or clinical decision support system (CDSS), for managing and requesting imaging in the emergency department, specifically computerized tomography of the aorta (CTA), when there is suspicion of AAS, and to determine the effect of implementing this system. To determine the factors associated with a positive radiological diagnosis that improve the predictive capacity of CTA findings. Materials and methods: After developing and implementing an evidence-based algorithm, we studied suspected cases of AAS. Chi-squared test was used to analyze the association between the variables included in the algorithm and radiological diagnosis, with 3 categories: no relevant findings, positive for AAS, and alternative diagnoses. Results: 130 requests were identified; 19 (14.6%) had AAS and 34 (26.2%) had a different acute pathology. Of the 19 with AAS, 15 had been stratified as high risk and 4 as intermediate risk. The probability of AAS was 3.4 times higher in patients with known aortic aneurysm (P=.021, 95% CI 1.2–9.6) and 5.1 times higher in patients with a new aortic regurgitation murmur (P=.019, 95% CI 1.3–20.1). The probability of having an alternative severe acute pathology was 3.2 times higher in patients with hypotension or shock (P=.02, 95% CI 1.2–8.5). Conclusion: The use of a CDSS in the emergency department can help optimize AAS diagnosis. The presence of a known aortic aneurysm and new-onset aortic regurgitation were shown to significantly increase the probability of AAS. Further studies are needed to establish a clinical prediction rule.(AU)

Improvement in the management of suspected acute aortic syndrome in the emergency room through a clinical algorithm and study of predictive factors.

BACKGROUND AND OBJECTIVE: Acute aortic syndrome (AAS) is uncommon and difficult to diagnose, with great variability in clinical presentation. To develop a computerized algorithm, or clinical decision support system (CDSS), for managing and requesting imaging in the emergency department, specifically computerized tomography of the aorta (CTA), when there is suspicion of AAS, and to determine the effect of implementing this system. To determine the factors associated with a positive radiological diagnosis that improve the predictive capacity of CTA findings. MATERIALS AND METHODS: After developing and implementing an evidence-based algorithm, we studied suspected cases of AAS. Chi-squared test was used to analyze the association between the variables included in the algorithm and radiological diagnosis, with 3 categories: no relevant findings, positive for AAS, and alternative diagnoses. RESULTS: 130 requests were identified; 19 (14.6%) had AAS and 34 (26.2%) had a different acute pathology. Of the 19 with AAS, 15 had been stratified as high risk and 4 as intermediate risk. The probability of AAS was 3.4 times higher in patients with known aortic aneurysm (P = .021, 95% CI 1.2-9.6) and 5.1 times higher in patients with a new aortic regurgitation murmur (P = .019, 95% CI 1.3-20.1). The probability of having an alternative severe acute pathology was 3.2 times higher in patients with hypotension or shock (P = .02, 95% CI 1.2-8.5). CONCLUSION: The use of a CDSS in the emergency department can help optimize AAS diagnosis. The presence of a known aortic aneurysm and new-onset aortic regurgitation were shown to significantly increase the probability of AAS. Further studies are needed to establish a clinical prediction rule.

Artificial intelligence-based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study.

OBJECTIVES: To further reduce the contrast medium (CM) dose of full aortic CT angiography (ACTA) imaging using the augmented cycle-consistent adversarial framework (Au-CycleGAN) algorithm. METHODS: We prospectively enrolled 150 consecutive patients with suspected aortic disease. All received ACTA scans of ultra-low-dose CM (ULDCM) protocol and low-dose CM (LDCM) protocol. These data were randomly assigned to the training datasets (n = 100) and the validation datasets (n = 50). The ULDCM images were reconstructed by the Au-CycleGAN algorithm. Then, the AI-based ULDCM images were compared with LDCM images in terms of image quality and diagnostic accuracy. RESULTS: The mean image quality score of each location in the AI-based ULDCM group was higher than that in the ULDCM group but a little lower than that in the LDCM group (all p < 0.05). All AI-based ULDCM images met the diagnostic requirements (score ≥ 3). Except for the image noise, the AI-based ULDCM images had higher attenuation value than the ULDCM and LDCM images as well as higher SNR and CNR in all locations of the aorta analyzed (all p < 0.05). Similar results were also seen in obese patients (BMI > 25, all p < 0.05). Using the findings of LDCM images as the reference, the AI-based ULDCM images showed good diagnostic parameters and no significant differences in any of the analyzed aortic disease diagnoses (all K-values > 0.80, p < 0.05). CONCLUSIONS: The required dose of CM for full ACTA imaging can be reduced to one-third of the CM dose of the LDCM protocol while maintaining image quality and diagnostic accuracy using the Au-CycleGAN algorithm. KEY POINTS: • The required dose of contrast medium (CM) for full ACTA imaging can be reduced to one-third of the CM dose of the low-dose contrast medium (LDCM) protocol using the Au-CycleGAN algorithm. • Except for the image noise, the AI-based ultra-low-dose contrast medium (ULDCM) images had better quantitative image quality parameters than the ULDCM and LDCM images. • No significant diagnostic differences were noted between the AI-based ULDCM and LDCM images regarding all the analyzed aortic disease diagnoses.

Spinal dural fistula evaluation using 4-Dimensional intra-aortic spinal CTAngiography in a hybrid angiography suite.

Intra-aortic CT Angiography (IA-CTA) is a relatively new technique that shows promise in efficient diagnosis and evaluation of the angioarchitecture of spinal dural arteriovenous fistulae(sdAVF). The authors document the first reported use of a 4D-CT and C-arm fluoroscope in a hybrid interventional suite to evaluate a sdAVF. Time resolved IA-CTA is clinically feasible in the evaluation of sdAVF, has higher temporal resolution as compared to standard IA-CTA and reduced contrast load, radiation dose and potential for procedural complications as compared to standard spinal angiography.