Acta cardiologica最新文献

Artificial intelligence (AI) is transforming the medical landscape, offering unprecedented advancements in diagnostic precision, treatment planning, and decision-making across various specialties. However, its adoption also raises complex questions, particularly when AI-generated recommendations conflict with human expertise. This article explores through an institutional survey the challenges faced by physicians when introducing a unique AI application for the interpretation of the ECG across several medical specialties.

Background: This study aimed to evaluate the predictive value of the triglyceride-glucose (TyG) index in combination with inflammatory markers-systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and high-sensitivity C-reactive protein (hsCRP)-for the severity of coronary artery disease (CAD) and the incidence of major adverse cardiovascular events (MACEs) following percutaneous coronary intervention (PCI).

Methods: A retrospective cohort study was conducted with 759 acute coronary syndrome (ACS) patients who underwent PCI from September 2022 to December 2023. The primary outcome was the occurrence of MACEs within one year, defined as a composite of all-cause death, myocardial infarction, stroke, and angina. TyG index was calculated at admission, and multivariable regression models were used to explore the associations between TyG, inflammatory markers, and coronary lesion severity. Cox proportional hazards models evaluated the predictive ability for MACEs.

Results: Compared with non-MACEs patients, the MACEs group had significantly elevated hsCRP, NLR, and SII levels, and a higher TyG index (8.8 vs. 8.9, p = 0.007). The interaction between TyG and hsCRP remained an independent predictor of MACEs (HR = 1.014, p < 0.001). ROC analysis showed the highest predictive accuracy for MACEs with the TyG-hsCRP combination (AUC = 0.708).

Conclusion: The TyG index-inflammation composite is an independent and effective predictor of post-PCI MACEs and may aid in refining current cardiovascular risk prediction models.

Background: Heterozygous familial hypercholesterolaemia (HeFH) confers a high risk of cardiovascular disease (CVD) due to elevated levels of LDL-cholesterol (LDL-C). The aim of this study is to assess the influence of clinical and genetic factors on coronary artery calcium (CAC) score in patients with HeFH.

Methods: CAC score were obtained for 129 genetically confirmed HeFH patients from NAGENCOL project, who had no CVD. Association of CAC with clinical and genetic variables as well as with the SAFEHEART-risk equation (SAFEHEART-RE) was evaluated.

Results: 65 patients had CAC = 0 (50.4%), 24 had CAC score between 1 and 99 (18.6%), and 40 had a score of ≥100 (31%). Individuals with a CAC score ≥100 were older (56.5 years vs. 39 years for those with a CAC score of 0, p < 0.001). They also had a higher prevalence of classical risk factors and history of CAD in the family. In addition, this group had higher maximum LDL-C levels (308.8 mg/dL vs. 262.5 mg/dL, p < 0.001), higher age at genetic diagnosis (45 years vs. 31.3, p < 0.001) and at the beginning of Treatment (34.5 years vs. 26.6, p = 0.002). Consequently, the cumulative LDL-C throughout their life was higher (13745.3 mg/dL vs. 8693.2 mg/dL, p < 0.001). There was a correlation between the results of the SAFEHEART-RE and CAC score.

Conclusions: Early diagnosis and early initiation of appropriate treatment are essential for reducing the accumulated cholesterol burden in patients with HeFH. Given the heterogeneity in CVD in patients with HeFH, tools such as the SAFEHEART-RE and CAC score may be useful for better risk stratification.

This editorial highlights key advances featured in the current issue of Acta Cardiologica, reflecting the shift toward more precise, personalized, and evidencebased cardiovascular care. Contributions span artificial intelligence, bedside clinical assessment, biomarker-driven risk stratification, lifestyle determinants, advanced imaging, and interventional cardiology. Together, they illustrate how emerging technologies can be effectively integrated with rigorous clinical judgment, while maintaining a pragmatic focus on real-world applicability. The collected studies emphasize the importance of human oversight, simple yet powerful diagnostic tools, and individualized risk assessment to optimize patient outcomes in contemporary cardiology practice.

Background: Cardiac output (CO) is the product of the heart rate (HR) and the stroke volume (SV).over time. It is a direct marker of myocardial function. In healthy subjects, the myocard normally responds well and CO improves after exercise training. However, the effect of exercise on CO in patients with heart failure with preserved ejection fraction (HFpEF) is less clear. Therefore, this study aimed to systematically summarise the current evidence on the effects of an exercise intervention on CO in subjects with HFpEF.

Material and methods: A literature search in Medline, Embase, CENTRAL, and SportDiscus was performed. Included were all RCTs that compared the effect of exercise training on CO in patients with HFpEF and were published before 11 April 2024. Risk of bias assessment was performed by using Cochrane's RoB 2 tool. The review was reported according to preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines.

Results: We identified 750 abstracts that were screened for eligibility. A total of 11 selected full texts were analysed. One study fulfilled our inclusion criteria. No significant difference in the change of CO between the intervention and control groups at rest or during maximal exercise after the intervention was detected.

Discussion: The identification of only one RCT hallmarks the sparse evidence on alterations of CO prior to post an exercise intervention in subjects with HFpEF. This might be because other markers such as V̇O₂max are much easier to measure.