Identification of diagnostic biomarkers and immune cell infiltration in coronary artery disease by machine learning, nomogram, and molecular docking.

Background: Coronary artery disease (CAD) is still a lethal disease worldwide. This study aims to identify clinically relevant diagnostic biomarker in CAD and explore the potential medications on CAD. Methods: GSE42148, GSE180081, and GSE12288 were downloaded as the training and validation cohorts to identify the candidate genes by constructing the weighted gene co-expression network analysis. Functional enrichment analysis was utilized to determine the functional roles of these genes. Machine learning algorithms determined the candidate biomarkers. Hub genes were then selected and validated by nomogram and the receiver operating curve. Using CIBERSORTx, the hub genes were further discovered in relation to immune cell infiltrability, and molecules associated with immune active families were analyzed by correlation analysis. Drug screening and molecular docking were used to determine medications that target the four genes. Results: There were 191 and 230 key genes respectively identified by the weighted gene co-expression network analysis in two modules. A total of 421 key genes found enriched pathways by functional enrichment analysis. Candidate immune-related genes were then screened and identified by the random forest model and the eXtreme Gradient Boosting algorithm. Finally, four hub genes, namely, CSF3R, EED, HSPA1B, and IL17RA, were obtained and used to establish the nomogram model. The receiver operating curve, the area under curve, and the calibration curve were all used to validate the accuracy and usefulness of the diagnostic model. Immune cell infiltrating was examined, and CAD patients were then divided into high- and low-expression groups for further gene set enrichment analysis. Through targeting the hub genes, we also found potential drugs for anti-CAD treatment by using the molecular docking method. Conclusions: CSF3R, EED, HSPA1B, and IL17RA are potential diagnostic biomarkers for CAD. CAD pathogenesis is greatly influenced by patterns of immune cell infiltration. Promising drugs offers new prospects for the development of CAD therapy.

Short- and Long-term survival prediction in patients with acute type A aortic dissection undergoing open surgery.

BACKGROUND: Acute Type A aortic dissection (ATAAD) is a life-threatening cardiovascular disease associated with high mortality rates, where surgical intervention remains the primary life-saving treatment. However, the mortality rate for ATAAD operations continues to be alarmingly high. To address this critical issue, our study aimed to assess the correlation between preoperative laboratory examination, clinical imaging data, and postoperative mortality in ATAAD patients. Additionally, we sought to establish a reliable prediction model for evaluating the risk of postoperative death. METHODS: In this study, a total of 384 patients with acute type A aortic dissection (ATAAD) who were admitted to the emergency department for surgical treatment were included. Based on preoperative laboratory examination and clinical imaging data of ATAAD patients, logistic analysis was used to obtain independent risk factors for postoperative in-hospital death. The survival prediction model was based on cox regression analysis and displayed as a nomogram. RESULTS: Logistic analysis identified several independent risk factors for postoperative in-hospital death, including Marfan syndrome, previous cardiac surgery history, previous renal dialysis history, direct bilirubin, serum phosphorus, D-dimer, white blood cell, multiple aortic ruptures and age. A survival prediction model based on cox regression analysis was established and presented as a nomogram. The model exhibited good discrimination and significantly improved the prediction of death risk in ATAAD patients. CONCLUSIONS: In this study, we developed a novel survival prediction model for acute type A aortic dissection based on preoperative clinical features. The model demonstrated good discriminatory power and improved accuracy in predicting the risk of death in ATAAD patients undergoing open surgery.

Postoperative hyper-inflammation as a predictor of poor outcomes in patients with acute type A aortic dissection (ATAAD) undergoing surgical repair.

BACKGROUND: Postoperative hyper-inflammation is a frequent event in patients with acute Stanford type A aortic dissection (ATAAD) after surgical repair. This study's objective was to determine which inflammatory biomarkers could be used to make a better formula for identifying postoperative hyper-inflammation, and which risk factors were associated with hyper-inflammation. METHODS: A total of 405 patients were enrolled in this study from October 1, 2020 to April 1, 2023. Of these patients, 124 exhibited poor outcomes. In order to investigate the optimal cut-off values for poor outcomes, logistic and receiver operating characteristic analyses were performed on the following parameters on the first postoperative day: procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), and systemic immune-inflammation index (SII). These cut-off points were used to separate the patients into hyper-inflammatory (n = 52) and control (n = 353) groups. Finally, the logistic were used to find the risk factors of hyper-inflammatory. RESULTS: PCT, CRP, IL-6, and SII were independent risk factors of poor outcomes in the multivariate logistic model. Cut-off points of these biomarkers were 2.18 ng/ml, 49.76 mg/L, 301.88 pg/ml, 2509.96 × 109/L respectively. These points were used to define postoperative hyper-inflammation (OR 2.97, 95% CI 1.35-6.53, P < 0.01). Cardiopulmonary bypass (CPB) > 180 min, and deep hypothermia circulatory arrest (DHCA) > 40 min were the independent risk factors for hyper-inflammation. CONCLUSIONS: PCT > 2.18, CRP > 49.76, IL-6 > 301.88, and SII < 2509.96 could be used to define postoperative hyper-inflammation which increased mortality and morbidity in patients after ATAAD surgery. Based on these findings, we found that CPB > 180 min and DHCA > 40 min were separate risk factors for postoperative hyper-inflammation.

Sivelestat in Patients at a High Risk of Postoperative Acute Lung Injury After Scheduled Cardiac Surgery: A Prospective Cohort Study.

Background: Sivelestat, a neutrophil elastase inhibitor, is specifically developed to mitigate the occurrence of acute lung injury (ALI) in individuals who are undergoing cardiovascular surgery. However, its impact on patients who are at a heightened risk of developing ALI after scheduled cardiac surgery has yet to be determined. In order to address this knowledge gap, we undertook a study to assess the efficacy of sivelestat in protecting the lungs of these patients. Methods: We conducted a prospective cohort study involving 718 patients who were at high risk of developing postoperative acute lung injury (ALI) and underwent scheduled cardiac surgery between April 25th, 2022, and September 7th, 2023. Among them, 52 patients received sivelestat (administered at a dosage of 0.2mg/kg/h for 3 days), while 666 patients served as controls, not receiving sivelestat. The control conditions were the same for all patients, including ventilation strategy, extubating time, and fluid management. Subsequently, a propensity-score matched cohort was established, consisting of 40 patients in both the sivelestat and control groups. The primary outcome measure encompassed a composite of adverse outcomes, including 30-day mortality, ALI, acute respiratory distress syndrome (ARDS), and others. Secondary outcomes assessed included pneumonia, ventricular arrhythmias, mechanical ventilation (MV) time, and more. Results: After conducting propensity matching in our study, we observed that there were no significant differences in 30-day mortality between the sivelestat and control groups (0% vs 2.5%, P=0.32). However, the use of sivelestat exhibited a significant reduction in the incidence of acute lung injury/acute respiratory distress syndrome (ALI/ARDS) compared to the control group (0% vs 55%, P<0.01), pneumonia (0 vs 37.5%, P<0.01), MV time (median:8 hours, IQR:4-14.8 hours vs median: 15.2 hours, IQR:14-16.3 hours, P<0.01). Compared to the control group, the sivelestat could significantly decrease white cell count (P<0.01), neutrophile percentage (P<0.01) and C-reactive protein (P<0.01) in the period of postoperative 5 days. Conclusion: The prophylactic administration of sivelestat has shown promising results in reducing the occurrence of acute lung injury/acute respiratory distress syndrome (ALI/ARDS) in patients with a heightened risk of developing these conditions after elective cardiac surgery. Our study findings indicate that sivelestat may provide protective effects by suppressing inflammation triggered by neutrophil activation, thereby safeguarding pulmonary function. Registration: ChiCTR2200059102, https://www.chictr.org.cn/showproj.html?proj=166643.