Potential Clinical Value of Biomarker-Guided Emergency Triage for Thoracic Aortic Dissection

Aortic Aneurysm with and without Dissection and Concomitant Atherosclerosis-Differences in a Retrospective Study

BACKGROUND

Thoracic aortic aneurysm is a latent disease with a high risk of death. Today, as data are accumulating, an estimation of the differences in thoracic aneurysm in men and women of different age groups is required. The present study evaluated the type of atherosclerotic aortic lesions in males and females at different ages regarding the presence or absence of aortic dissection.

METHODS

A retrospective analysis of clinical and morphological data of 43 patients with thoracic aortic aneurysm was carried out. Patients were divided into groups based on the presence or absence of thoracic aneurysm dissection.

RESULTS

Our results of a comparative analysis of the age of study participants showed that patients with aneurysm dissection were younger than patients without dissection. In the subgroup of patients with aortic dissection, the mean age was 50.6 years old, and in patients without aortic dissection, the mean age was 55.0 years old. When conducting a frequency analysis using Fisher's exact test, it was found that in men and women aneurysm dissection was not associated with atherosclerotic lesions of the aorta.

CONCLUSIONS

In women and men, aneurysm dissection was not associated with stage of atherosclerotic lesions of the aorta regardless of age; no statistically significant differences were found between the groups with and without aneurysm dissection (p > 0.05). Dissection of the thoracic aneurysm developed in the absence of severe atherosclerosis of the thoracic aorta. Only 18.6% men and women possessed atherosclerotic plaques of types IV and V.

Anti-inflammatory response-based risk assessment in acute type A aortic dissection: A national multicenter cohort study

Background

Early identification of patients at high risk of operative mortality is important for acute type A aortic dissection (TAAD). We aimed to investigate whether patients with distinct risk stratifications respond differently to anti-inflammatory pharmacotherapy.

Methods

From 13 cardiovascular hospitals, 3110 surgically repaired TAAD patients were randomly divided into a training set (70%) and a test set (30%) to develop and validate a risk model to predict operative mortality using extreme gradient boosting. Performance was measured by the area under the receiver operating characteristic curve (AUC). Subgroup analyses were performed by risk stratifications (low versus middle-high risk) and anti-inflammatory pharmacotherapy (absence versus presence of ulinastatin use).

Results

A simplified risk model was developed for predicting operative mortality, consisting of the top ten features of importance: platelet-leukocyte ratio, D-dimer, activated partial thromboplastin time, urea nitrogen, glucose, lactate, base excess, hemoglobin, albumin, and creatine kinase-MB, which displayed a superior discrimination ability (AUC: 0.943, 95 % CI 0.928-0.958 and 0.884, 95 % CI 0.836-0.932) in the derivation and validation cohorts, respectively. Ulinastatin use was not associated with decreased risk of operative mortality among each risk stratification, however, ulinastatin use was associated with a shorter mechanical ventilation duration among patients with middle-high risk (defined as risk probability >5.0 %) (β -1.6 h, 95 % CI [-3.1, -0.1] hours; P = 0.048).

Conclusion

This risk model reflecting inflammatory, coagulation, and metabolic pathways achieved acceptable predictive performances of operative mortality following TAAD surgery, which will contribute to individualized anti-inflammatory pharmacotherapy.

Machine learning-based prognostic model for in-hospital mortality of aortic dissection: Insights from an intensive care medicine perspective

Objective

Aortic dissection (AD) is a severe emergency with high morbidity and mortality, necessitating strict monitoring and management. This retrospective study aimed to identify prognostic factors and establish predictive models for in-hospital mortality among AD patients in the intensive care unit (ICU).

Methods

We retrieved ICU admission records of AD patients from the Medical Information Mart for Intensive Care (MIMIC)-IV critical care data set and the eICU Collaborative Research Database. Functional data analysis was further applied to estimate continuous vital sign processes, and variables associated with in-hospital mortality were identified through univariate analyses. Subsequently, we employed multivariable logistic regression and machine learning techniques, including simple decision tree, random forest (RF), and eXtreme Gradient Boosting (XGBoost) to develop prognostic models for in-hospital mortality.

Results

Given 643 ICU admissions from MIMIC-IV and 501 admissions from eICU, 29 and 28 prognostic factors were identified from each database through univariate analyses, respectively. For prognostic model construction, 507 MIMIC-IV admissions were divided into 406 (80%) for training and 101 (20%) for internal validation, and 87 eICU admissions were included as an external validation group. Of the four models tested, the RF consistently exhibited the best performance among different variable subsets, boasting area under the receiver operating characteristic curves of 0.870 and 0.850. The models highlighted the mean 24-h fluid intake as the most potent prognostic factor.

Conclusions

The current prognostic models effectively forecasted in-hospital mortality among AD patients, and they pinpointed noteworthy prognostic factors, including initial blood pressure upon ICU admission and mean 24-h fluid intake.

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.