Background: Delirium is a common postoperative complication among patients who undergo Stanford Type A aortic dissection (TAAD). It is associated with increased mortality, as well as other serious surgical outcomes. This study aimed to analyze the risk factors for delirium in TAAD patients. Methods: Pubmed, Web of science, Embase, the Cochrane Library and CINAHL were searched by computer to collect literatures on risk factors for postoperative delirium (POD) after TAAD. The retrieval period was from the establishment of the database to September 2022. After literature screening, two reviewers independently assessed the quality of the included studies using the Newcastle–Ottawa Scale (NOS). Data were extracted according to standard protocols, and then meta-analysis was performed using Revman 5.3 software. Results: A total of 9 articles, comprising 7 case–control studies and 2 cohort studies, were included in this analysis. The sample size consisted of 2035 patients. POD was associated with increased length of ICU stay (MD 3.24, 95% CI 0.18–6.31, p = 0.04) and length of hospital stay (MD 9.34, 95% CI 7.31–11.37, p < 0.0001) in TAAD patients. Various perioperative risk factors were identified, including age (MD 4.40, 95% CI 2.06–6.73, p = 0.0002), preoperative low hemoglobin levels (MD − 4.44, 95% CI − 7.67 to − 1.20, p = 0.007), body mass index (MD 0.92, 95% CI 0.22–1.63, p = 0.01), history of cardiac surgery (OR 3.06, 95% CI 1.20–7.83, p = 0.02), preoperative renal insufficiency (OR 2.50, 95% CI 1.04–6.04, p = 0.04), cardiopulmonary bypass (CPB) duration (MD 19.54, 95% CI 6.34–32.74, p = 0.004), surgery duration (MD 44.88, 95% CI 5.99–83.78, p = 0.02), mechanical ventilation time (SMD 1.14, 95% CI 0.34–1.94, p = 0.005), acute physiology and chronic health evaluation (APACHE II) score (MD 2.67, 95% CI 0.37–4.98, p = 0.02), postoperative renal insufficiency (OR 2.82, 95% CI 1.40–5.68, p = 0.004), electrolyte disturbance (OR 6.22, 95% CI 3.08–12.54, p < 0.0001) and hypoxemia (OR 3.56, 95% CI 1.70–7.44, p = 0.0007). Conclusions: POD can prolong ICU stay and hospital stay in TAAD patients. This study identified a number of risk factors for POD after TAAD, suggesting the possibility of early identification of high-risk patients using relevant data.

Background: Mesenteric malperfusion (MMP) is an uncommon but devastating complication of acute aortic dissection (AAD) that combines 2 life-threatening conditions—aortic dissection and acute mesenteric ischemia. The complex pathophysiology of MMP poses substantial diagnostic and management challenges. Currently, delayed diagnosis remains a critical contributor to poor outcomes because of the absence of reliable individualized risk assessment tools. Objective: This study aims to develop and validate a deep learning–based model that integrates multimodal data to identify patients with AAD at high risk of MMP. Methods: This multicenter retrospective study included 525 patients with AAD from 2 hospitals. The training and internal validation cohort consisted of 450 patients from Beijing Anzhen Hospital, whereas the external validation cohort comprised 75 patients from Nanjing Drum Tower Hospital. Three machine learning models were developed: the benchmark model using laboratory parameters, the multiorgan feature–based AAD complicating MMP (MAM) model based on computed tomography angiography images, and the integrated model combining both data modalities. Model performance was assessed using the area under the curve, accuracy, sensitivity, specificity, and Brier score. To improve interpretability, gradient-weighted class activation mapping was used to identify and visualize discriminative imaging features. Univariate and multivariate regression analyses were used to evaluate the prognostic significance of the risk score generated by the optimal model. Results: In the external validation cohort, the integrated model demonstrated superior performance, with an area under the curve of 0.780 (95% CI 0.777-0.785), which was significantly greater than those of the benchmark model (0.586, 95% CI 0.574-0.586) and the MAM model (0.732, 95% CI 0.724-0.734). This highlights the benefits of multimodal integration over single-modality approaches. Additional classification metrics revealed that the integrated model had an accuracy of 0.760 (95% CI 0.758-0.764), a sensitivity of 0.667 (95% CI 0.659-0.675), a specificity of 0.783 (95% CI 0.781-0.788), and a Brier score of 0.143 (95% CI 0.143-0.145). Moreover, gradient-weighted class activation mapping visualizations of the MAM model revealed that during positive predictions, the model focused more on key anatomical areas, particularly the superior mesenteric artery origin and intestinal regions with characteristic gas or fluid accumulation. Univariate and multivariate analyses also revealed that the risk score derived from the integrated model was independently associated with inhospital mortality risk among patients with AAD undergoing endovascular or surgical treatment (odds ratio 1.030, 95% CI 1.004-1.056; P=.02). Conclusions: Our findings demonstrate that compared with unimodal approaches, an integrated deep learning model incorporating both imaging and clinical data has greater diagnostic accuracy for MMP in patients with AAD. This model may serve as a valuable tool for early risk identification, facilitating timely therapeutic decision-making. Further prospective validation is warranted to confirm its clinical utility. Trial Registration: Chinese Clinical Registry Center ChiCTR2400086050; http://www.chictr.org.cn/showproj.html?proj=226129

Aortic dissection (AD) is a life-threatening cardiovascular disease with a dismal prognosis. Inflammation plays an important role in AD. Oxylipins are bioactive lipids involved in the modulation of inflammation and may be involved in the pathogenesis and progression of AD. This study aims to identify possible metabolites related to AD. A total of 10 type A Aortic dissection (TAAD) patients, 10 type B Aortic dissection (TBAD) patients and 10 healthy controls were included in this study. Over 100 oxylipin species were identified and quantified by liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis. Our investigation demonstrated substantial alterations in 91 oxylipins between AD and healthy individuals. Patients with TAAD had 89 entries accessible compared to healthy controls. According to orthogonal partial least squares discriminant analysis (OPLS-DA), fitness (R²X = 0.362 and R²Y = 0.807, p = 0.03) and predictability (Q² = 0.517, p = 0.005) are the validation parameters between the two groups. Using multivariate logistic regression, 13-HOTrE and 16(17)-EpDPE were the risk factors in the aortic patients group compared to healthy people (OR = 2.467, 95%CI:1.256–7.245, p = 0.035; OR = 0.015, 95%CI:0.0002–0.3240, p = 0.016, respectively). In KEGG enrichment of differential metabolites, the arachidonic acid metabolism pathway has the most metabolites involved. We established a diagnostic model in distinguishing between AD and healthy people. The AUC was 0.905. Oxylipins were significantly altered in AD patients, suggesting oxylipin profile is expected to exploit a novel, non-invasive, objective diagnosis for AD.

Acute type A aortic dissection (ATAAD) is a devastating cardiovascular disease with extraordinary morbidity and mortality. Prolonged mechanical ventilation (PMV) is a common complication following ATAAD surgery, leading to adverse outcomes. This study aimed to investigate the correlation between mechanical ventilation time (MVT) and prognosis and to devise a nomogram for predicting PMV after ATAAD surgery. This retrospective study enrolled 1049 ATAAD patients from 2011 to 2019. Subgroups were divided into < 12 h, 12 h to < 24 h, 24 h to < 48 h, 48 h to < 72 h, and ≥ 72 h according to MVT. Clinical characteristics and outcomes were compared among the groups. Using multivariable logistic regression analyses, we investigated the relationship between each stratification of MVT and mortality. A nomogram was constructed based on the refined multivariable logistic regression model for predicting PMV. The total mortality was 11.8% (124/1049). The results showed that the groups with MVT 48 h to < 72 h and ≥ 72 h had significantly higher operative mortality compared to other MVT categories. Multivariate logistic regression analysis showed that MVT ≥72 h was significantly associated with higher short-term mortality. Thus, a nomogram was presented to elucidate the association between PMV (MVT ≥72 h) and risk factors including advanced age, preoperative cerebral ischemia, ascending aorta replacement, concomitant coronary artery bypass grafting (CABG), longer cardiopulmonary bypass (CPB), and large-volume intraoperative fresh frozen plasma (FFP) transfusion. The nomogram exhibited strong predictive performance upon validation. Safely extubating patients within 72 h after ATAAD surgery is crucial for achieving favorable outcomes. The developed and validated nomogram provides a valuable tool for predicting PMV and optimizing postoperative care to improve patient prognosis. This novel nomogram has the potential to guide clinical decision-making and resource allocation in the management of ATAAD patients.

Background: Gastrointestinal bleeding is a serious adverse event of coronary artery bypass grafting and lacks tailored risk assessment tools for personalized prevention. Objective: This study aims to develop and validate predictive models to assess the risk of gastrointestinal bleeding after coronary artery bypass grafting (GIBCG) and to guide personalized prevention. Methods: Participants were recruited from 4 medical centers, including a prospective cohort and the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. From an initial cohort of 18,938 patients, 16,440 were included in the final analysis after applying the exclusion criteria. Thirty combinations of machine learning algorithms were compared, and the optimal model was selected based on integrated performance metrics, including the area under the receiver operating characteristic curve (AUROC) and the Brier score. This model was then developed into a web-based risk prediction calculator. The Shapley Additive Explanations method was used to provide both global and local explanations for the predictions. Results: The model was developed using data from 3 centers and a prospective cohort (n=13,399) and validated on the Drum Tower cohort (n=2745) and the MIMIC cohort (n=296). The optimal model, based on 15 easily accessible admission features, demonstrated an AUROC of 0.8482 (95% CI 0.8328-0.8618) in the derivation cohort. In external validation, the AUROC was 0.8513 (95% CI 0.8221-0.8782) for the Drum Tower cohort and 0.7811 (95% CI 0.7275-0.8343) for the MIMIC cohort. The analysis indicated that high-risk patients identified by the model had a significantly increased mortality risk (odds ratio 2.98, 95% CI 1.784-4.978; P<.001). For these high-risk populations, preoperative use of proton pump inhibitors was an independent protective factor against the occurrence of GIBCG. By contrast, dual antiplatelet therapy and oral anticoagulants were identified as independent risk factors. However, in low-risk populations, the use of proton pump inhibitors (χ21=0.13, P=.72), dual antiplatelet therapy (χ21=0.38, P=.54), and oral anticoagulants (χ21=0.15, P=.69) were not significantly associated with the occurrence of GIBCG. Conclusions: Our machine learning model accurately identified patients at high risk of GIBCG, who had a poor prognosis. This approach can aid in early risk stratification and personalized prevention. Trial Registration: Chinese Clinical Registry Center ChiCTR2400086050; http://www.chictr.org.cn/showproj.html?proj=226129

Cancer stem cells have been found to play important roles in carcinoma. Although thy-1 has been identified as a potential stem cell marker of liver cancer, whether the Wnt/β-catenin signaling pathway plays an important role in regulating hepatocarcinoma proliferation and apoptosis mediated by thy-1 remains unknown. Our results showed that high thy-1 expression caused hepG2 cells transfected with a pReceiver-M29/thy-1 eukaryotic expression vector to exhibit obvious heteromorphism, featuring double or multiple nuclei and weaker apoptosis. A high expression of β-catenin, as a critical signaling protein of Wnt/β-catenin, and its downstream transcription factor, cyclinD1, were detected in transfected hepG2 cells. We also used aspirin as an inhibitor of the Wnt signaling pathway in the treatment of hepG2 cells transfected with the pReceiver-M29/thy-1 expression vector to make detailed observations of apoptosis in hepG2 cells as well as the differential expression of β-catenin, cyclinD1, and thy-1. An increasing apoptosis rate was detected in the hepG2 cells and downregulated expression of the three proteins was detected. Hence, we suggest that thy-1 upregulation promotes the proliferation and inhibits apoptosis of hepG2 cells, and that these processes are regulated by the Wnt/β- catenin signaling pathway.