Acta cardiologica最新文献

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.

Background: In 2012, the randomised trial IABP SHOCK II demonstrated that IABP did not reduce 30-day mortality in patients with acute myocardial infarction complicating cardiogenic shock (CS). However, SCAI proposed a new standardised classification of cardiogenic shock in 2019. It is reasonably hypothesised that the use of IABP may have different benefits for patients in different stages of CS.

Methods: AMI patients ≥18 years who had received treatment of implantation of IABP during emergency setting in Beijing Chaoyang Hospital between December 2010 and July 2021 were enrolled in this study. CS stages were classified at admission for all eligible cases. During the same period, AMI patients without implantation of IABP were matched with each stage of CS above using propensity score matching (PSM). Data were as follows: Stage B (n = 302), Stage C (n = 290), Stage D (n = 68), and Stage E (n = 32). In-hospital mortality was compared between using and without using IABP at each stage.

Results: In stage C, the in-hospital mortality was significantly lower in the IABP group (31/145, 21.4%) than in the group without IABP (47/145, 32.4%) (OR 0.567, 95% CI, p = .034). In-hospital mortality had no significant difference between use and no-use of IABP in stage B (16.6% vs 14.6%, p = .634), stage D (47.1% vs 38.2%, p = .462), and in stage E (81.3%vs 68.8%, p = .414).

Conclusions: For patients with acute myocardial infarction complicating cardiogenic shock, IABP has no effect on in-hospital mortality in SCAI stages B, D, and E. However, in SCAI stage C, IABP can reduce in-hospital mortality.