Gender differences in acute aortic syndromes.

PURPOSE OF REVIEW: Acute aortic syndromes include acute aortic dissection, intramural hematoma, and penetrating aortic ulcer, and are associated with high mortality and morbidity. This review focuses on recent findings and current understanding of gender-related and sex-related differences in acute aortic syndromes. RECENT FINDINGS: Large international and national registries, population studies, and multicentre national prospective cohort studies show evidence of sex differences in acute aortic syndromes. Recent studies of risk factors, aorta remodelling, and genetics provide possible biological basis for sex differences. The 2022 American College of Cardiology/American Heart Association Guidelines for the Diagnosis and Management of Aortic Disease revise recommendations for surgical management for aortic root and ascending aorta dilatation, which could impact outcome differences between the sexes. SUMMARY: Acute aortic syndromes affect men more frequently than women. The prevalence of acute aortic syndromes and prevalence of many risk factors rise sharply with age in women leading to higher age at presentation for women. Times from symptom onset to presentation and presentation to diagnosis are delayed in female patients. Females with type A dissection are also more commonly treated conservatively than male counterparts. These factors likely contribute to higher early mortality and complications in women.

Evaluating a novel accelerated free-breathing late gadolinium enhancement imaging sequence for assessment of myocardial injury.

INTRODUCTION: Cardiac magnetic resonance imaging (MRI), including late gadolinium enhancement (LGE), plays an important role in the diagnosis and prognostication of ischemic and non-ischemic myocardial injury. Conventional LGE sequences require patients to perform multiple breath-holds and require long acquisition times. In this study, we compare image quality and assessment of myocardial LGE using an accelerated free-breathing sequence to the conventional standard-of-care sequence. METHODS: In this prospective cohort study, a total of 41 patients post Coronavirus 2019 (COVID-19) infection were included. Studies were performed on a 1.5 Tesla scanner with LGE imaging acquired using a conventional inversion recovery rapid gradient echo (conventional LGE) sequence followed by the novel accelerated free-breathing (FB-LGE) sequence. Image quality was visually scored (ordinal scale from 1 to 5) and compared between conventional and free-breathing sequences using the Wilcoxon rank sum test. Presence of per-segment LGE was identified according to the American Heart Association 16-segment myocardial model and compared across both conventional LGE and FB-LGE sequences using a two-sided chi-square test. The perpatient LGE extent was also evaluated using both sequences and compared using the Wilcoxon rank sum test. Interobserver variability in detection of per-segment LGE and per-patient LGE extent was evaluated using Cohen's kappa statistic and interclass correlation (ICC), respectively. RESULTS: The mean acquisition time for the FB-LGE sequence was 17 s compared to 413 s for the conventional LGE sequence (P < 0.001). Assessment of image quality was similar between both sequences (P = 0.19). There were no statistically significant differences in LGE assessed using the FB-LGE versus conventional LGE on a per-segment (P = 0.42) and per-patient (P = 0.06) basis. Interobserver variability in LGE assessment for FB-LGE was good for per-segment (= 0.71) and per-patient extent (ICC = 0.92) analyses. CONCLUSIONS: The accelerated FB-LGE sequence performed comparably to the conventional standard-of-care LGE sequence in a cohort of patients post COVID-19 infection in a fraction of the time and without the need for breath-holding. Such a sequence could impact clinical practice by increasing cardiac MRI throughput and accessibility for frail or acutely ill patients unable to perform breath-holding.

Machine learning and decision making in aortic arch repair.

BACKGROUND: Decision making during aortic arch surgery regarding cannulation strategy and nadir temperature are important in reducing risk, and there is a need to determine the best individualized strategy in a data-driven fashion. Using machine learning (ML), we modeled the risk of death or stroke in elective aortic arch surgery based on patient characteristics and intraoperative decisions. METHODS: The study cohort comprised 1323 patients from 9 institutions who underwent an elective aortic arch procedure between 2002 and 2021. A total of 69 variables were used in developing a logistic regression and XGBoost ML model trained for binary classification of mortality and stroke. Shapely additive explanations (SHAP) values were studied to determine the importance of intraoperative decisions. RESULTS: During the study period, 3.9% of patients died and 5.4% experienced stroke. XGBoost (area under the curve [AUC], 0.77 for death, 0.87 for stroke) demonstrated better discrimination than logistic regression (AUC, 0.65 for death, 0.75 for stroke). From SHAP analysis, intraoperative decisions are 3 of the top 20 predictors of death and 6 of the top 20 predictors of stroke. Predictor weights are patient-specific and reflect the patient's preoperative characteristics and other intraoperative decisions. Patient-level simulation also demonstrates the variable contribution of each decision in the context of the other choices that are made. CONCLUSIONS: Using ML, we can more accurately identify patients at risk of death and stroke, as well as the strategy that better reduces the risk of adverse events compared to traditional prediction models. Operative decisions made may be tailored based on a patient's specific characteristics, allowing for maximized, personalized benefit.

Trends in sex-specific differences following aortic arch repair: results from the Canadian Thoracic Aortic Collaborative.

Background: Previous data have shown that sex-related differences exist in aortic arch surgery, with female patients experiencing worse outcomes. Over time, as surgical techniques and strategies have improved, these improvements have benefitted female patients. Using a multicenter national aortic registry from the Canadian Thoracic Aortic Collaborative (CTAC), we aimed to determine the relationship between sex and outcomes following aortic arch repair and to examine how these have changed over time. Methods: The multicenter prospective CTAC database of all aortic procedures performed under circulatory arrest from participating centers across Canada (n=9) was used. Patients were included who underwent elective or urgent/emergency arch reconstruction under circulatory arrest from 2002 to 2021. The primary composite endpoint was defined as the occurrence of one of the following endpoints: in-hospital mortality, stroke, dialysis-dependent renal failure, deep sternal wound infection, reoperation, or prolonged ventilation of >40 hours. Secondary endpoints included in-hospital mortality, in-hospital stroke, and a modified version of the Society of Thoracic Surgeons-defined composite endpoint for mortality and major morbidity (MMOM). Results: A total of 2,592 patients who underwent aortic arch repair between 2002 and 2021 (31.4% female and 68.6% male patients). Operative mortality decreased through the study period for female patients. No change in operative mortality was observed in male patients or following elective repair. The composite endpoint improved for female patients over time in both elective and urgent surgery, while for male patients, rates improved for elective surgery and remained stable for urgent. Ultimately, female sex was not an independent predictor of adverse outcomes following aortic arch repair. Conclusions: Our results are congruent with existing data and are highly encouraging. It shows that multilevel improvements in our approach to aortic arch surgery have helped to serve female patients who were previously disadvantaged.

Sex differences in long-term outcomes following surgery for acute type A aortic dissection: a systematic review and meta-analysis.

Background: Recent reports on sex differences in long-term outcomes after surgery for acute type A aortic dissection (ATAAD) are conflicting. We aimed to aggregate updated data on long-term survival and reoperation stratified by sex. Methods: A literature search was conducted using Medline, Embase, and Cochrane Central. Studies reporting sex-stratified long-term survival and/or reoperation following surgery for ATAAD between January 1, 2000, to March 15, 2023 were included. Preoperative characteristics, intraoperative variables, and early perioperative outcomes were meta-analyzed using a random effects model and pooled risk ratio (RR) with men as the reference group. Individual patient-level data for long-term outcomes was reconstructed to generate sex-specific pooled Kaplan-Meier curves to assess long-term survival and freedom from reoperation. Results: A total of 15 studies with 7,608 male and 3,989 female patients were included in this analysis. Female patients were older, had higher rates of hypertension, and had less previous cardiac surgery. Intraoperatively, women received less extensive repairs with lower rates of aortic valve replacement and total arch replacement, and higher rates of hemiarch replacement. There were no sex differences for in-hospital/30-day mortality [risk ratio (RR), 1.18; 95% confidence interval (CI): 0.96, 1.45; P=0.12], stroke (RR, 1.07; 95% CI: 0.90, 1.28; P=0.46), and early reoperation (RR, 0.90; 95% CI: 0.75, 1.09; P=0.28). Female patients had lower long-term survival overall (P<0.001) and amongst survivors at 1-year (P=0.014). Overall survival at 5-year was 82.4% in men and 78.1% in women, and at 10-year was 68.1% for men and 63.4% in women. Male patients had higher rates of long-term reoperation (P<0.001). Freedom for reoperation at 5-year was 88.4% in men vs. 93.1% in women. Conclusions: Though perioperative early outcomes have equalized between the sexes following surgery for ATAAD, differences remain in long-term survival and reoperation.

A Deep Learning Segmentation Pipeline for Cardiac T1 Mapping Using MRI Relaxation-based Synthetic Contrast Augmentation.

Purpose: To design and evaluate an automated deep learning method for segmentation and analysis of cardiac MRI T1 maps with use of synthetic T1-weighted images for MRI relaxation-based contrast augmentation. Materials and Methods: This retrospective study included MRI scans acquired between 2016 and 2019 from 100 patients (mean age ± SD, 55 years ± 13; 72 men) across various clinical abnormalities with use of a modified Look-Locker inversion recovery, or MOLLI, sequence to quantify native T1 (T1native), postcontrast T1 (T1post), and extracellular volume (ECV). Data were divided into training (n = 60) and internal (n = 40) test subsets. "Synthetic" T1-weighted images were generated from the T1 exponential inversion-recovery signal model at a range of optimal inversion times, yielding high blood-myocardium contrast, and were used for contrast-based image augmentation during training and testing of a convolutional neural network for myocardial segmentation. Automated segmentation, T1, and ECV were compared with experts with use of Dice similarity coefficients (DSCs), correlation coefficients, and Bland-Altman analysis. An external test dataset (n = 147) was used to assess model generalization. Results: Internal testing showed high myocardial DSC relative to experts (0.81 ± 0.08), which was similar to interobserver DSC (0.81 ± 0.08). Automated segmental measurements strongly correlated with experts (T1native, R = 0.87; T1post, R = 0.91; ECV, R = 0.92), which were similar to interobserver correlation (T1native, R = 0.86; T1post, R = 0.94; ECV, R = 0.95). External testing showed strong DSC (0.80 ± 0.09) and T1native correlation (R = 0.88) between automatic and expert analysis. Conclusion: This deep learning method leveraging synthetic contrast augmentation may provide accurate automated T1 and ECV analysis for cardiac MRI data acquired across different abnormalities, centers, scanners, and T1 sequences.Keywords: MRI, Cardiac, Tissue Characterization, Segmentation, Convolutional Neural Network, Deep Learning Algorithms, Machine Learning Algorithms, Supervised Learning Supplemental material is available for this article. © RSNA, 2022.

Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks.

Lung nodules are commonly missed in chest radiographs. We propose and evaluate P-AnoGAN, an unsupervised anomaly detection approach for lung nodules in radiographs. P-AnoGAN modifies the fast anomaly detection generative adversarial network (f-AnoGAN) by utilizing a progressive GAN and a convolutional encoder-decoder-encoder pipeline. Model training uses only unlabelled healthy lung patches extracted from the Indiana University Chest X-Ray Collection. External validation and testing are performed using healthy and unhealthy patches extracted from the ChestX-ray14 and Japanese Society for Radiological Technology datasets, respectively. Our model robustly identifies patches containing lung nodules in external validation and test data with ROC-AUC of 91.17% and 87.89%, respectively. These results show unsupervised methods may be useful in challenging tasks such as lung nodule detection in radiographs.

Technical Note: Fully automatic segmental relaxometry (FASTR) for cardiac magnetic resonance T1 mapping.

PURPOSE: Cardiac relaxometry techniques, particularly T1 mapping, have recently gained clinical importance in various cardiac pathologies. Myocardial T1 and extracellular volume are usually calculated from manual identification of left ventricular epicardial and endocardial regions. This is a laborious process, particularly for large volume studies. Here we present a fully automated relaxometry framework (FASTR) for segmental analysis of T1 maps (both native and postcontrast) and partition coefficient (λ). METHODS: Patients (N = 11) were imaged postacute myocardial infarction on a 1.5T clinical scanner. The scan protocol involved CINE-SSFP imaging, native, and post-contrast T1 mapping using the Modified Look-Locker Inversion (MOLLI) recovery sequence. FASTR consisted of automatic myocardial segmentation of spatio-temporally coregistered CINE images as an initial guess, followed by refinement of the contours on the T1 maps to derive segmental T1 and λ. T1 and λ were then compared to those obtained from two trained expert observers. RESULTS: Robust endocardial and epicardial contours were achieved on T1 maps despite the presence of infarcted tissue. Relative to experts, FASTR resulted in myocardial Dice coefficients (native T1: 0.752 ± 0.041; postcontrast T1: 0.751 ± 0.057) that were comparable to interobserver Dice (native T1: 0.803 ± 0.045; postcontrast T1: 0.799 ± 0.054). There were strong correlations observed for T1 and λ derived from experts and FASTR (native T1: r = 0.83; postcontrast T1: r = 0.87; λ: r = 0.78; P < 0.0001), which were comparable to inter-expert correlation coefficients (native T1: r = 0.90; postcontrast T1: r = 0.93; λ: r = 0.80; P < 0.0001). CONCLUSIONS: Our fully automated framework, FASTR, can generate accurate myocardial segmentations for native and postcontrast MOLLI T1 analysis without the need for manual intervention. Such a design is appealing for high volume clinical protocols.