CVIA Journal

Announcing a new article publication for Cardiovascular Innovations and Applications journal. Sodium-glucose cotransporter-2 (SGLT2) inhibitors have been found to ameliorate major adverse cardiovascular events in patients with heart failure with preserved ejection fraction (HFpEF), but the exact mechanism is unknown. Ferroptosis is a form of programmed necrosis. In this article the authors verified that canagliflozin (CANA) ameliorates heart function in HFpEF rats, partly by regulating ferroptosis, which may be activated by AMPK/PGC-1α/Nrf2 signaling.

An HFpEF model was established and subjected to CANA treatment. Blood pressure was monitored, and echocardiography was performed at the 12^th week. Pathological examination was performed, and expression of ferroptosis-associated proteins and AMPK/PGC-1α/Nrf2 signaling related proteins was detected.

CANA had an antihypertensive effect and increased E/A ratios in HFpEF rats. Myocardial pathology was ameliorated, based on decreased cross-sectional area and intercellular fibrosis. Acyl-CoA synthetase long-chain family member 4 (ACSL4) expression increased, whereas ferritin heavy chain 1 (FTH1) expression decreased in HFpEF rats, which showed iron overload. CANA reversed changes in ACSL4 and FTH1, and decreased iron accumulation, but did not alter glutathione peroxidase 4 (GPX4) expression. The expression of AMPK/PGC-1α/Nrf2 signaling related proteins and heme oxygenase 1 (HO-1) in the HFpEF group decreased but was reverted after CANA treatment.

CANA regulates ferroptosis, potentially via activating AMPK/PGC-1α/Nrf2 signaling in HFpEF rats.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2022.0024

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. CVIA is indexed in the EMBASE, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ and Index Copernicus Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Article reference: Sai Ma, Lili He and Qingjuan Zuo et al. Canagliflozin Regulates Ferroptosis, Potentially via Activating AMPK/PGC-1α/Nrf2 Signaling in HFpEF Rats. CVIA. Vol. 7(1). DOI: 10.15212/CVIA.2022.0024

Announcing a new article publication for Cardiovascular Innovations and Applications journal. Coronary no-reflow phenomenon (NRP), a common adverse complication in patients with ST-segment elevation myocardial infarction (STEMI) treated by percutaneous coronary intervention (PCI), is associated with poor patient prognosis. In this study, the correlation between the systemic immune-inflammation index (SII) and NRP in older patients with STEMI was studied, to provide a basis for early identification of high-risk patients and improve their prognosis.

Between January 2017 and June 2020, 578 older patients with acute STEMI admitted to the Department of Cardiology of Hebei General Hospital for direct PCI treatment were selected for this retrospective study. Patients were divided into an NRP group and normal-flow group according to whether NRP occurred during the operation. Clinical data and the examination indexes of the two groups were collected. Logistic regression was used to analyze the independent predictors of NRP, and the receiver operating characteristic curve was used to further analyze the ability of SII to predict NRP in older patients with STEMI.

Multivariate logistic analysis indicated that hypertension (OR=2.048, 95% CI:1.252–3.352, P=0.004), lymphocyte count (OR=0.571, 95% CI:0.368–0.885, P=0.012), platelet count (OR=1.009, 95% CI:1.005–1.013, P<0.001), hemoglobin (OR=1.015, 95% CI:1.003–1.028, P=0.018), multivessel disease (OR=2.237, 95% CI:1.407–3.558, P=0.001), and SII≥1814 (OR=3.799, 95% CI:2.190–6.593, P<0.001) were independent predictors of NRP after primary PCI in older patients with STEMI. Receiver operating characteristic curve analysis demonstrated that SII had a high predictive value for NRP (AUC=0.738; 95% CI:0.686–0.790), with the best cut-off value of 1814, a sensitivity of 52.85% and a specificity of 85.71%.

For older patients with STEMI undergoing primary PCI, SII is a valid predictor of NRP.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0005

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. CVIA is indexed in the EMBASE, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ and Index Copernicus Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Article reference: Jiaqi Wang, Feifei Zhang and Man Gao et al. The Systemic Immune Inflammatory Index Predicts No-Reflow Phenomenon after Primary Percutaneous Coronary Intervention in Older Patients with STEMI. CVIA. Vol. 7(1). DOI: 10.15212/CVIA.2023.0005

Announcing a new article publication for Cardiovascular Innovations and Applications journal.   Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major complication that increases morbidity and mortality after cardiac surgery. Most established predictive models are limited to the analysis of nonlinear relationships and do not adequately consider intraoperative variables and early postoperative variables. Nonextracorporeal circulation coronary artery bypass grafting (off-pump CABG) remains the procedure of choice for most coronary surgeries, and refined CSA-AKI predictive models for off-pump CABG are notably lacking. Therefore, this study used an artificial intelligence-based machine learning approach to predict CSA-AKI from comprehensive perioperative data.

In total, 293 variables were analysed in the clinical data of patients undergoing off-pump CABG in the Department of Cardiac Surgery at the First Affiliated Hospital of Guangxi Medical University between 2012 and 2021. According to the KDIGO criteria, postoperative AKI was defined by an elevation of at least 50% within 7 days, or 0.3 mg/dL within 48 hours, with respect to the reference serum creatinine level. Five machine learning algorithms—a simple decision tree, random forest, support vector machine, extreme gradient boosting and gradient boosting decision tree (GBDT)—were used to construct the CSA-AKI predictive model. The performance of these models was evaluated with the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) values were used to explain the predictive model.

The three most influential features in the importance matrix plot were 1-day postoperative serum potassium concentration, 1-day postoperative serum magnesium ion concentration, and 1-day postoperative serum creatine phosphokinase concentration.

GBDT exhibited the largest AUC (0.87) and can be used to predict the risk of AKI development after surgery, thus enabling clinicians to optimise treatment strategies and minimise postoperative complications.

https://www.scienceopen.com/hosted-document?doi=10.15212/CVIA.2023.0006

CVIA is available on the ScienceOpen platform and at Cardiovascular Innovations and Applications. Submissions may be made using ScholarOne Manuscripts. There are no author submission or article processing fees. CVIA is indexed in the EMBASE, ESCI, OCLC, Primo Central (Ex Libris), Sherpa Romeo, NISC (National Information Services Corporation), DOAJ and Index Copernicus Databases. Follow CVIA on Twitter @CVIA_Journal; or Facebook.

Article reference: Sai Zheng, Yugui Li and Cheng Luo et al. Machine Learning for Predicting the Development of Postoperative Acute Kidney Injury After Coronary Artery Bypass Grafting Without Extracorporeal Circulation. CVIA. Vol. 7(1). DOI: 10.15212/CVIA.2023.0006