Acta cardiologica最新文献

Background: Contrast-induced acute kidney injury (CI-AKI) occurs as a result of the use of contrast media during coronary interventions and can lead to serious complications.

Aim: To investigate the predictive value of the pre-procedural aggregate index of systemic inflamation (AISI) for the development of CI-AKI in patients with chronic coronary artery disease suspicion who underwent coronary angiography (CAG) or percutaneous coronary intervention (PCI).

Methods: This retrospective cohort study conducted on 166 patients with chronic coronary artery disease suspicion who underwent CAG or PCI. The neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII) and AISI levels were calculated. The relationship between these parameters and the development of CI-AKI within 72 h after intervention was analysed.

Results: CI-AKI occurred in 25 patients (15.1%). Upon conducting a likelihood ratio test to compare full and reduced models, it was found that in the reduced model, variables such as NLR, SII and AISI were independently predictors of CI-AKI, The NLR model (Odds ratio (OR) =1.32, 95% CI [1.16-1.52]), SII model (OR =3.41, 95% CI [1.92-6.08]), and AISI model (OR =4.81, 95% CI [2.42-9.60]). An increase in AISI was linearly associated with CI-AKI and showed the highest prediction for CI-AKI.

Conclusion: These findings demonstrate that AISI is a significant independent predictor for CI-AKI in patients undergoing CAG or PCI.

Background: Reduced cardiovascular event risk is observed with eicosapentaenoic acid (EPA), but EPA mixed with docosahexaenoic acid (EPA/DHA) does not show consistent benefit. Comparative effects of EPA versus EPA/DHA on coronary plaque remain unclear.

Methods: We systematically reviewed trials measuring coronary plaque volume in patients randomised to statin + EPA or statin + EPA/DHA therapy compared to statin monotherapy, and used network meta-analysis to compare percent change in total and lipid coronary plaque volumes on these treatments.

Results: Among 553 articles, ten trials comprising 860 patients met inclusion criteria. Among statin, statin + EPA, and statin + EPA/DHA respectively, random effects analysis yielded changes of +1.9% [-3.4%, +7.2%], -10.0% [-17.5%, -2.5%], and -3.3% [-14.2%, +7.5%] in total plaque volume and +1.3% [-4.7%, +7.4%], -21.5% [-32.1%, -10.8%], and -6.1% [-18.9%, +6.7%] in lipid volume. Compared with statin, statin + EPA achieved greater percent reduction in coronary plaque volumes (total volume: SMD = 0.60, p < 0.0001; lipid volume: SMD = 1.1, p = 0.0017) but statin + EPA/DHA showed no difference (total volume: SMD = 0.19, p = 0.19; lipid volume: SMD = 0.43, p = 0.38).

Conclusions: EPA but not EPA/DHA is associated with reductions in coronary plaque burden when given as adjunct to statins in patients with coronary artery disease.

Cardiovascular diseases (CVD) remain a significant global health challenge, necessitating innovative approaches. The emergence of digital twin technology, which creates virtual replicas of real-world objects or systems, has shown great promise in various fields, including healthcare. In the context of CVD, digital twins offer a unique opportunity for personalised medicine and risk assessment by integrating diverse data sources and generating patient-specific computational models. This viewpoint explores the potential applications and benefits of digital twins in CVD management, including personalised risk assessment, disease modelling, treatment optimisation, and remote patient monitoring. Additionally, it discusses the challenges and limitations associated with implementing digital twins in the context of cardiovascular diseases. Digital twins have the potential to revolutionise CVD management by providing a dynamic and individualised approach to risk assessment, treatment optimisation, and proactive care. Collaborative efforts between healthcare professionals, researchers, and technology developers are necessary to overcome these challenges and fully realise the potential of digital twins in improving patient outcomes and revolutionising cardiovascular healthcare. Future directions include advancements in artificial intelligence, integration of omics data, real-time monitoring, virtual clinical trials, patient empowerment, and integration with healthcare systems. Digital twins can foster a more personalised approach to managing CVD.

In this paper, AI-enabled handheld ultrasound is used in point-of-care or at home, and evaluate the accuracy of it for left ventricular ejection fraction (LVEF) evaluation. It provides a simple, convenient, and practical tool for the patients with heart disease, especially those with heart failure. The AI model used for this AI-enabled handheld ultrasound is a machine learning model trained with tens of thousands of ultrasound four-chamber cardiograms. The LVEF evaluation accuracy of the AI model was compared by the experts performing ultrasound four-chamber cardiogram detection in 100 patients on high-end ultrasound in the hospital. In the 100 clinical trials, the sensitivity, specificity, and accuracy of the AI model were 91%, 95%, and 98%, respectively. Then 10 cases were used to compare the LVEF results of hospital tests with the predicted results of the AI model. The difference between the two is less than 10%. Finally, over the course of one month, the AI-enabled handheld ultrasound was employed to conduct regular evaluations of left LVEF for point-of-care purposes on a group of 10 patients diagnosed with heart failure. The LVEF evaluation accuracy of AI-enabled handheld ultrasound is more than 96%, which was higher than that of experts in high-end ultrasound in hospitals. The easy-to-use AI-enabled handheld ultrasound can evaluate the LVEF in the point of care or at home and get the same accuracy as the high-end ultrasound equipment in the hospital. It may play an important role in monitoring cardiac function at home for the ambulatory heart failure patients.