Personalizing cholesterol treatment recommendations for primary cardiovascular disease prevention

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

PURPOSE OF REVIEW

This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology.

RECENT FINDINGS

AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.

Shared Decision-Making and Cardiovascular Health: A Scientific Statement From the American Heart Association

Shared decision-making is increasingly embraced in health care and recommended in cardiovascular guidelines. Patient involvement in health care decisions, patient-clinician communication, and models of patient-centered care are critical to improve health outcomes and to promote equity, but formal models and evaluation in cardiovascular care are nascent. Shared decision-making promotes equity by involving clinicians and patients, sharing the best available evidence, and recognizing the needs, values, and experiences of individuals and their families when faced with the task of making decisions. Broad endorsement of shared decision-making as a critical component of high-quality, value-based care has raised our awareness, although uptake in clinical practice remains suboptimal for a range of patient, clinician, and system issues. Strategies effective in promoting shared decision-making include educating clinicians on communication techniques, engaging multidisciplinary medical teams, incorporating trained decision coaches, and using tools (ie, patient decision aids) at appropriate literacy and numeracy levels to support patients in their cardiovascular decisions. This scientific statement shines a light on the limited but growing body of evidence of the impact of shared decision-making on cardiovascular outcomes and the potential of shared decision-making as a driver of health equity so that everyone has just opportunities. Multilevel solutions must align to address challenges in policies and reimbursement, system-level leadership and infrastructure, clinician training, access to decision aids, and patient engagement to fully support patients and clinicians to engage in the shared decision-making process and to drive equity and improvement in cardiovascular outcomes.

Artificial intelligence in clinical workflow processes in vascular surgery and beyond

In the past decade, artificial intelligence (AI)-based applications have exploded in health care. In cardiovascular disease, and vascular surgery specifically, AI tools such as machine learning, natural language processing, and deep neural networks have been applied to automatically detect underdiagnosed diseases, such as peripheral artery disease, abdominal aortic aneurysms, and atherosclerotic cardiovascular disease. In addition to disease detection and risk stratification, AI has been used to identify guideline-concordant statin therapy use and reasons for nonuse, which has important implications for population-based cardiovascular disease health. Although many studies highlight the potential applications of AI, few address true clinical workflow implementation of available AI-based tools. Specific examples, such as determination of optimal statin treatment based on individual patient risk factors and enhancement of intraoperative fluoroscopy and ultrasound imaging, demonstrate the potential promise of AI integration into clinical workflow. Many challenges to AI implementation in health care remain, including data interoperability, model bias and generalizability, prospective evaluation, privacy and security, and regulation. Multidisciplinary and multi-institutional collaboration, as well as adopting a framework for integration, will be critical for the successful implementation of AI tools into clinical practice.

Mechanism of Yiqi Huoxue Huatan recipe in the treatment of coronary atherosclerotic disease through network pharmacology and experiments

In recent years, with population aging and economic development, morbidity and mortality of atherosclerotic cardiovascular disease associated with atherosclerosis (AS) have gradually increased. In this study, a combination of network pharmacology and experimental verification was used to systematically explore the action mechanism of Yiqi Huoxue Huatan Recipe (YHHR) in the treatment of coronary atherosclerotic heart disease (CAD). We searched and screened the active ingredients of Coptis chinensis, Astragalus membranaceus, Salvia miltiorrhiza, and Hirudo. We also searched multiple databases for related target genes corresponding to the compounds and CAD. STRING was used to construct the protein-protein interaction (PPI) network of genes. Metascape was used to perform gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for common targets to analyze the main pathways, and finally, the molecular docking and main possible pathways were verified by experimental studies. Firstly, a total of 1480 predicted target points were obtained through the Swiss Target Prediction database. After screening, merging, and deleting duplicate values, a total of 768 targets were obtained. Secondly, "Coronary atherosclerotic heart disease" was searched in databases such as the OMIM, GeneCards, and TTD. 1844 disease-related targets were obtained. Among PPI network diagram of YHHR-CAD, SRC had the highest degree value, followed by AKT1, TP53, hsp90aa1 and mapk3. The KEGG pathway bubble diagram was drawn using Chiplot, the Signal pathways such as NF kappa B signaling pathway, Lipid and AS, and Apelin signaling pathway are closely related to the occurrence of CAD. The PCR and Western blot methods were used to detect the expression of NF-κB p65. When compared with that in the model group, the expression of NF-κB p65mRNA decreased in the low-concentration YHHR group, with P < .05, while the expression of NF-κB p65mRNA decreased significantly in the high-concentration YHHR group, with P < .01. On the other hand, when compared with that in the model group, the expression of NF-κB p65 decreased in the low-concentration YHHR group, but was not statistically significant, while the expression of NF-κB p65 was significant in the high-concentration YHHR group, and has statistical significance with P < .05. YHHR has been shown to resist inflammation and AS through the SRC/NF-κB signaling pathway.

Undertreatment or Overtreatment With Statins: Where Are We?

Statins, in addition to healthy lifestyle interventions, are the cornerstone of lipid-lowering therapy. Other low-density lipoprotein (LDL)-lowering drugs include ezetimibe, bile acid sequestrants, and PCSK9 inhibitors. As new evidence emerges from new clinical trials, therapeutic goals change, leading to renewed clinical guidelines. Nowadays, LDL goals are getting lower, leading to the "lower is better" paradigm in LDL-cholesterol (LDL-C) management. Several observational studies have shown that LDL-C control in real life is suboptimal in both primary and secondary preventions. It is critical to enhance the adherence to guideline recommendations through shared decision-making between clinicians and patients, with patient engagement in selecting interventions based on individual values, preferences, and associated conditions and comorbidities. This narrative review summarizes the evidence regarding the benefits of lipid-lowering drugs in reducing cardiovascular events, the pleiotropic effect of statins, real-world data on overtreatment and undertreatment of lipid-lowering therapies, and the changing LDL-C in targets in the clinical guidelines of dyslipidemias over the years.