Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning

Environmental Factors and Cardiovascular Susceptibility: Toward Personalized Prevention Mediated by the Role of Artificial Intelligence-A Narrative Review

Background and Purpose

Cardiovascular diseases (CVD) represent a significant global health challenge due to high morbidity and mortality rates, that necessitate approaching the intricate relation between cardiovascular susceptibility and environmental factors, highlighting the importance of creating personalized cardiovascular prevention plans. Furthermore, as it is becoming integrated with the various aspects of healthcare, the role of artificial intelligence in cardiovascular and precision medicine is driving innovations towards personalized care. This review dives into the complex connection between cardiovascular susceptibility and environmental risk highlighting the importance of creating personalized cardiovascular preventive strategies in light of the upcoming artificial intelligence.

Methods

An in-depth review was conducted using PubMed and ScienceDirect, to collect data from all articles that handled environmental factors and cardiovascular susceptibility with special emphasis on the up-to-date emerging role of artificial intelligence in preventive strategies.

Results

The review revealed high heritability estimates and highlighted the significance of modifiable risk factors which are pivotal determinants affecting CVD susceptibility. The integration of artificial intelligence is implementing the power of precision preventive medicine that can be directed toward specific environmental factors, shifting the whole healthcare system to superior outcomes.

Conclusion

Recognizing the preventability of CVD through personalized environmental modifications, this review advocates tailored prevention plans that account for individual characteristics. Despite its proven efficacy in managing modifiable risk factors, achieving optimal cardiovascular health remains challenging, necessitating innovative strategies and the integration of artificial intelligence in personalized healthcare.

Personalized statin therapy: Targeting metabolic processes to modulate the therapeutic and adverse effects of statins

Statins are widely used for treating lipid disorders and cardiovascular diseases. However, the therapeutic efficiency and adverse effects of statins vary among different patients, which numerous clinical and epidemiological studies have attributed to genetic polymorphisms in statin-metabolizing enzymes and transport proteins. The metabolic processes of statins are relatively complex, involving spontaneous or enzyme-catalyzed interconversion between more toxic lactone metabolites and active acid forms in the liver and bloodstream, influenced by multiple factors, including the expression levels of many metabolic enzymes and transporters. Addressing the variable statin therapeutic outcomes is a pressing clinical challenge. Transcription factors and epigenetic modifications regulate the metabolic enzymes and transporters involved in statin metabolism and disposition and, therefore, hold promise as 'personalized' targets for achieving optimized statin therapy. In this review, we explore the potential for customizing therapy by targeting the metabolism of statin medications. The biochemical bases of adverse reactions to statin drugs and their correlation with polymorphisms in metabolic enzymes and transporters are summarized. Next, we mainly focus on the regulatory roles of transcription factors and epigenetic modifications in regulating the gene expression of statin biochemical machinery. The recommendations for future therapies are finally proposed by targeting the central regulatory factors of statin metabolism.

Implementing and evaluating shared decision-making before transcatheter aortic valve implantation with a dedicated pathway and questionnaire

Aims

Transcatheter aortic valve implantation (TAVI) is an alternative to surgical aortic valve replacement for patients with aortic valve stenosis. The choice between TAVI, surgery, or a conservative approach should be based upon multiple factors including clinical considerations, technical feasibility, and informed patient preference. In this context, engaging patients in a shared decision-making (SDM) process becomes essential, but this practice is generally underused.

Methods and results

To comply with the European and UK national guidelines, in January 2023 we established a structured SDM pathway in which patients are offered virtual/physical decision aids and after 1 week are invited to a meeting to reach a shared decision. From December 2022 to June 2023, a custom-developed questionnaire was prospectively administered to 23 patients prior to, and 38 patients after, the implementation of the SDM pathway. The answers to 12 core questions were recorded on a Likert scale (1-5). Global satisfaction, as measured by mean Likert score, was significantly higher for the post-SDM group than for the pre-SDM group (4.46 ± 0.14 vs. 3.78 ± 0.30, P < 0.001). The percentage of positive (Likert 4-5) responses was significantly higher in the post-SDM group (289/312, 92.6% vs. 155/234, 66.2%, P < 0.001). The percentage of negative (Likert 1-2) responses was significantly lower in the post-SDM group (5/312, 1.6% vs. 53/234, 22.6%, P < 0.001).

Conclusion

The SDM pathway proved effective in delivering SDM in compliance with national and international guidance. A similar approach leveraging digital technology to minimize cost and enhance patient convenience could be implemented for other treatments and across other institutions.

Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review

The explosive growth of biomedical Big Data presents both significant opportunities and challenges in the realm of knowledge discovery and translational applications within precision medicine. Efficient management, analysis, and interpretation of big data can pave the way for groundbreaking advancements in precision medicine. However, the unprecedented strides in the automated collection of large-scale molecular and clinical data have also introduced formidable challenges in terms of data analysis and interpretation, necessitating the development of novel computational approaches. Some potential challenges include the curse of dimensionality, data heterogeneity, missing data, class imbalance, and scalability issues. This overview article focuses on the recent progress and breakthroughs in the application of big data within precision medicine. Key aspects are summarized, including content, data sources, technologies, tools, challenges, and existing gaps. Nine fields-Datawarehouse and data management, electronic medical record, biomedical imaging informatics, Artificial intelligence-aided surgical design and surgery optimization, omics data, health monitoring data, knowledge graph, public health informatics, and security and privacy-are discussed.

A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care

BACKGROUND

 With the rapid development of science and technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis of various spine diseases. It has been proved that AI has a broad prospect in accurate diagnosis and treatment of spine disorders.

METHODS

 On May 7, 2022, the Web of Science (WOS) Core Collection database was used to identify the documents on the application of AI in the field of spine care. HistCite and VOSviewer were used for citation analysis and visualization mapping.

RESULTS

 A total of 693 documents were included in the final analysis. The most prolific authors were Karhade A.V. and Schwab J.H. United States was the most productive country. The leading journal was Spine. The most frequently used keyword was spinal. The most prolific institution was Northwestern University in Illinois, USA. Network visualization map showed that United States was the largest network of international cooperation. The keyword "machine learning" had the strongest total link strengths (TLS) and largest number of occurrences. The latest trends suggest that AI for the diagnosis of spine diseases may receive widespread attention in the future.

CONCLUSIONS

 AI has a wide range of application in the field of spine care, and an increasing number of scholars are committed to research on the use of AI in the field of spine care. Bibliometric analysis in the field of AI and spine provides an overall perspective, and the appreciation and research of these influential publications are useful for future research.

Personalized statin treatment plan using counterfactual approach with multi-objective optimization over benefits and risks

Background

Statins are a class of drugs that lower cholesterol levels in the blood by inhibiting an enzyme called 3-hydroxy-3-methyl-glutaryl-coenzyme A (HMG-CoA) reductase. High cholesterol levels can lead to plaque buildup in the arteries, which can cause Atherosclerotic Cardiovascular Disease(ASCVD). Statins can reduce the risk of ASCVD events by about 25-35% but they might be associated with symptoms such as muscle pain, liver damage, or diabetes. As a result, this leads to a strong reason to discontinue statin therapy, which increases the risk of cardiovascular events and mortality and becomes a public-health problem.To solve this problem, in the previous work, we proposed a framework to produce a proactive strategy, called a personalized statin treatment plan (PSTP) to minimize the risks of statin-associated symptoms and therapy discontinuation when prescribing statin. In our previous PSTP framework, three limitations remain, and they can influence PSTP usability: (1) Not taking the counterfactual predictions and confounding bias into account. (2) The balance between multiple drug-prescribing objectives (especially trade-off objectives), such as tradeoff between benefits and risks. (3) Evaluating PSTP in retrospective data.

Objectives

This manuscript aimed to provide solutions for the three abovementioned problems to improve PSTP robustness to produce a proactive strategy for statin prescription that can maximize the benefits (low-density lipoprotein cholesterol (LDL-C) reduction) and minimize risks (statin-associated symptoms and therapy discontinuation) at the same time.

Methods

We applied overlapping weighting counterfactual survival risk prediction (CP), multiple objective optimization (MOO), and clinical trial simulation (CTS) which consists of Random Arms, Clinical Guideline arms, PSTP Arms, and Practical Arms to improve the PSTP framework and usability.

Results

In addition to highly balanced covariates, in the CTS, the revised PSTP showed improvements in lowering the SAS risks overall compared to other arms across all time points by at most 7.5% to at least 1.0% (Fig. 8(a)). It also has the better flexibility of identifying the optimal Statin across all time points within one year.

Conclusion

We demonstrated feasibility of robust and trustworthy counterfactual survival risk prediction model. In CTS, we also demonstrated the PSTP with Pareto optimization can personalize optimal balance between Statin benefits and risks.

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.

Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review

Artificial intelligence (AI) plays a more and more important role in our everyday life due to the advantages that it brings when used, such as 24/7 availability, a very low percentage of errors, ability to provide real time insights, or performing a fast analysis. AI is increasingly being used in clinical medical and dental healthcare analyses, with valuable applications, which include disease diagnosis, risk assessment, treatment planning, and drug discovery. This paper presents a narrative literature review of AI use in healthcare from a multi-disciplinary perspective, specifically in the cardiology, allergology, endocrinology, and dental fields. The paper highlights data from recent research and development efforts in AI for healthcare, as well as challenges and limitations associated with AI implementation, such as data privacy and security considerations, along with ethical and legal concerns. The regulation of responsible design, development, and use of AI in healthcare is still in early stages due to the rapid evolution of the field. However, it is our duty to carefully consider the ethical implications of implementing AI and to respond appropriately. With the potential to reshape healthcare delivery and enhance patient outcomes, AI systems continue to reveal their capabilities.