Analysis of ascending aortic diameter and long-term prognosis in patients with ascending aortic dissection

Diagnostic accuracy of transthoracic echocardiography for the identification of proximal aortic dissection: a systematic review and meta-analysis

This systematic review and meta-analysis evaluated the performance of transthoracic echocardiography (TTE) for diagnosis of proximal aortic dissections based on the identification of specific sonographic features. A systematic literature search of major databases was conducted on human studies investigating the diagnostic accuracy of TTE for proximal aortic dissection. The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. The quality of studies was evaluated using Quality Assessment of Diagnostic Accuracy Studies 2 tool. Data were gathered for the following sonographic findings: intimal flap, tear, or intramural hematoma; enlargement of aortic root or widening of aortic walls; aortic valve regurgitation; or pericardial effusion. Sensitivity, specificity, diagnostic odds ratio, number needed to diagnose values, and likelihood ratios were determined. Fourteen studies were included in our final analysis. More than half of the included studies demonstrated low risk of bias. The identification of intimal flap, tear, or intramural hematoma was shown to have an exceptional ability as a diagnostic tool to rule in proximal aortic dissections. TTE should be considered during the initial evaluation of patients presenting to the emergency department with suspected proximal aortic dissection. Positive sonographic findings on TTE may aid in rapid assessment, coordination of care, and treatment of individuals awaiting advanced imaging.

Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study

OBJECTIVES

To conduct a comprehensive analysis of demographic information, medical history, and blood pressure (BP) and heart rate (HR) variability during hospitalisation so as to establish a predictive model for preoperative in-hospital mortality of patients with acute aortic dissection (AD) by using machine learning techniques.

DESIGN

Retrospective cohort study.

SETTING

Data were collected from the electronic records and the databases of Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the First Affiliated Hospital of Anhui Medical University between 2004 and 2018.

PARTICIPANTS

380 inpatients diagnosed with acute AD were included in the study.

PRIMARY OUTCOME

Preoperative in-hospital mortality rate.

RESULTS

A total of 55 patients (14.47%) died in the hospital before surgery. The results of the areas under the receiver operating characteristic curves, decision curve analysis and calibration curves indicated that the eXtreme Gradient Boosting (XGBoost) model had the highest accuracy and robustness. According to the SHapley Additive exPlanations analysis of the XGBoost model, Stanford type A, maximum aortic diameter >5.5 cm, high variability in HR, high variability in diastolic BP and involvement of the aortic arch had the greatest impact on the occurrence of in-hospital deaths before surgery. Moreover, the predictive model can accurately predict the preoperative in-hospital mortality rate at the individual level.

CONCLUSION

In the current study, we successfully constructed machine learning models to predict the preoperative in-hospital mortality of patients with acute AD, which can help identify high-risk patients and optimise the clinical decision-making. Further applications in clinical practice require the validation of these models using a large-sample, prospective database.

TRIAL REGISTRATION NUMBER

ChiCTR1900025818.

Toward standard abbreviations and acronyms for use in articles on aortic disease

Objectives

Academic medical literature is fraught with complex article-specific acronyms and abbreviations that can impair communication and make reading arduous. Our goal is to ease frustration with bespoke, inconsistent, and variable sets of abbreviations that currently exist for common aorta-related terminology (eg, anatomy, imaging, disease, and therapy). We hope to ease reading and improve communication in the aortic sphere of cardiovascular literature.

Methods

We reviewed a total of 205 published references related to aortic disease, including a systematic review of aorta-related articles in the Journal of Thoracic and Cardiovascular Surgery from the years 2020 and 2021. The array of variable definitions, abbreviations, and acronyms encountered in different papers that refer to the same terminology was striking, revealing that there were few standardized abbreviations in the aortic literature. We cataloged these terms, their associated abbreviations, and their frequency of use, and compiled a list of proposed standard abbreviations for commonly used terms that could be implemented uniformly in articles written about aortic diseases.

Results

We present suggested acronyms and abbreviations for common terminology related to the aorta. It is anticipated that this standard list will evolve over time as the literature and technology of the field grows and develops.

Conclusions

A proposed standard set of acronyms and abbreviations for aorta-related terminology is provided that, if found useful, could be implemented broadly in the aortic literature.

Association of NFE2L2 Gene Polymorphisms with Risk and Clinical Characteristics of Acute Type A Aortic Dissection in Han Chinese Population

The present study is aimed at investigating the association of NFE2L2 gene polymorphisms with risk and clinical characteristics of acute type A aortic dissection (AAAD) in a Han Chinese population. Six SNPs (rs1806649, rs13001694, rs2364723, rs35652124, rs6721961, and rs2706110) in NFE2L2 were genotyped using SNaPshot Multiplex Kit in 94 adult patients diagnosed with AAAD at our hospital, and 208 healthy Han Chinese subjects from the 1000 Genomes Project were served as the control group. The CC genotype of rs2364723 (CC versus (GC+GG), OR = 2.069, 95% CI: 1.222-3.502, p = 0.006) and CC genotype of rs35652124 (CC versus (CT+TT), OR = 1.889, 95% CI: 1.112-3.210, p = 0.018) were identified as risk factors for AAAD. Multivariable linear regression analysis revealed that the CC genotype of rs2364723 (β = 5.031, 95% CI: 1.878-8.183, p = 0.002) and CC genotype of rs35652124 (β = 4.751, 95% CI: 1.544-7.958, p = 0.004) were associated with increased maximum ascending aorta diameter of AAAD. Patients carrying rs2364723 CC genotype had a higher incidence of coronary artery involvement (31% vs. 12%, p = 0.027), while patients carrying rs35652124 CC genotype had a higher incidence of brain ischemia (9% vs. 0%, p = 0.045). In conclusion, NFE2L2 gene polymorphisms were correlated with risk and severity of AAAD in Han Chinese population.