搜索结果：Cardiology in review

BACKGROUND: A high-quality search strategy is considered an essential component of systematic reviews but many do not contain reproducible search strategies. It is unclear if low reproducibility spans medical disciplines, is affected by librarian/search specialist involvement or has improved with increased awareness of reporting guidelines. OBJECTIVES: To examine the reporting of search strategies in systematic reviews published in Pediatrics, Surgery or Cardiology journals in 2012 and determine rates and predictors of including a reproducible search strategy. METHODS: We identified all systematic reviews published in 2012 in the ten highest impact factor journals in Pediatrics, Surgery and Cardiology. Each search strategy was coded to indicate what elements were reported and whether the overall search was reproducible. Reporting and reproducibility rates were compared across disciplines and we measured the influence of librarian/search specialist involvement, discipline or endorsement of a reporting guideline on search reproducibility. RESULTS: 272 articles from 25 journals were included. Reporting of search elements ranged widely from 91% of articles naming search terms to 33% providing a full search strategy and 22% indicating the date the search was executed. Only 22% of articles provided at least one reproducible search strategy and 13% provided a reproducible strategy for all databases searched in the article. Librarians or search specialists were reported as involved in 17% of articles. There were strong disciplinary differences on the reporting of search elements. In the multivariable analysis, only discipline (Pediatrics) was a significant predictor of the inclusion of a reproducible search strategy. CONCLUSIONS: Despite recommendations to report full, reproducible search strategies, many articles still do not. In addition, authors often report a single strategy as covering all databases searched, further decreasing reproducibility. Further research is needed to determine how disciplinary culture may encourage reproducibility and the role that journal editors and peer reviewers could play.

OBJECTIVE: The aim of this article is to evaluate the clinical utility of brain natriuretic peptide in pediatric patients, examining the diagnostic value, management, and prognostic relevance, by critical assessment of the literature. DATA SOURCES: In December 2015, a literature search was performed (PubMed access to MEDLINE citations; http://www.ncbi.nlm.nih.gov/PubMed/) and included these Medical Subject Headings and text terms for the key words: "brain natriuretic peptide," "amino-terminal pro-brain natriuretic peptide," "children," "neonate/s," "newborn/s," "infant/s," and "echocardiography." STUDY SELECTION: Each article title and abstract was screened to identify relevant studies. The search strategy was limited to published studies in English language concerning brain natriuretic peptide/amino-terminal pro-brain natriuretic peptide in pediatric patients. DATA EXTRACTION: Data on age, gender, type of clinical condition, brain natriuretic peptide assay method, cardiac function variables evaluated by echocardiography, and prognosis were extracted. DATA SYNTHESIS: Brain natriuretic peptide reference values in healthy newborns, infants, and children are presented. Brain natriuretic peptide diagnostic accuracy in newborns, infants, and children suspected to have congenital heart defects is discussed, and brain natriuretic peptide prognostic value reviewed. The data suggest that the determination of brain natriuretic peptide levels improves the diagnostic accuracy in the assessment of heart disease in the pediatric population. Brain natriuretic peptide assay may increase the accuracy of neonatal screening programs for diagnosing congenital heart defects. Echocardiographic variables correlated to brain natriuretic peptide levels. Additionally, brain natriuretic peptide levels predicted adverse outcomes in the postoperative period. CONCLUSIONS: Brain natriuretic peptide assessment is a reliable test to diagnose significant structural or functional cardiovascular disease in children. In the integrated follow-up of these cases, several physiologic and clinical variables must be considered; brain natriuretic peptide may be an additional helpful marker. Nevertheless, larger prospective studies are warranted to elucidate the true prognostic value of brain natriuretic peptide in pediatric patients.

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, necessitating comprehensive management and prevention strategies. Rehabilitative cardiology, also known as cardiac rehabilitation (CR), is a multidisciplinary approach aimed at enhancing recovery, reducing the risk of recurrent cardiac events, and improving patients' quality of life. This review explores the critical role of cardiovascular nursing in CR, highlighting its contributions to patient education, psychosocial support, and care coordination. Through an analysis of current evidence, we outline the core components of CR, including exercise training, risk factor modification, and behavioral interventions. Cardiovascular nurses play a pivotal role in optimizing patient outcomes by conducting assessments, providing tailored education, and addressing psychological challenges such as depression and anxiety, which often accompany CVDs. Despite the well-documented benefits of CR, participation rates remain low due to barriers such as inadequate referral systems, accessibility challenges, and socioeconomic disparities. Emerging solutions, including telemedicine and home-based CR, offer promising alternatives to improve adherence and accessibility. The review underscores the need for expanded nursing roles, interdisciplinary collaboration, and policy advancements to bridge existing gaps in CR utilization. By integrating innovative care models, cardiovascular nursing can further enhance the effectiveness of rehabilitative cardiology and contribute to improved long-term patient outcomes.

The review article considers key applications of artificial intelligence (AI) in cardiology. The review includes subsections devoted to weak and strong AI used in clinical practice and cardiology health provision. The article describes the application options for AI in the analysis of electrocardiography, echocardiography, sonography, computed tomography, magnetic resonance imaging, and positron emission tomography of the heart data. The article briefly describes the aspects of using machine learning and artificial intelligence to process ambulance calls from patients with cardiac complaints, and considers AI applications in preventive cardiology. The review considers the potential of AI in the analysis of data arrays obtained during tonometry, pulse wave velocity measurement, and in biochemical studies. The paper also formulates the principles of strong AI (large language models) in cardiology health provision, identifies the main problems and difficulties in implementing the latest technology, and provides a conceptual scheme for implementing AI technology in a cardiology center. This paper highlights the key limitations of the large language model technology, such as the lack of standard algorithms for collecting and reviewing data, lack of understanding of the context, the inability of models to form expert conclusions, and the emergence of many problematic ethical characteristics when using large language models.

BACKGROUND: Additive manufacturing (AM) has emerged as a serious planning, strategy, and education tool in cardiovascular medicine. This review describes and illustrates the application, development and associated limitation of additive manufacturing in the field of cardiology by studying research papers on AM in medicine/cardiology. METHODS: Relevant research papers till August 2018 were identified through Scopus and examined for strength, benefits, limitation, contribution and future potential of AM. With the help of the existing literature & bibliometric analysis, different applications of AM in cardiology are investigated. RESULTS: AM creates an accurate three-dimensional anatomical model to explain, understand and prepare for complex medical procedures. A prior study of patient's 3D heart model can help doctors understand the anatomy of the individual patient, which may also be used create training modules for institutions and surgeons for medical training. CONCLUSION: AM has the potential to be of immense help to the cardiologists and cardiac surgeons for intervention and surgical planning, monitoring and analysis. Additive manufacturing creates a 3D model of the heart of a specific patient in lesser time and cost. This technology is used to create and analyse 3D model before starting actual surgery on the patient. It can improve the treatment outcomes for patients, besides saving their lives. Paper summarised additive manufacturing applications particularly in the area of cardiology, especially manufacturing of a patient-specific artificial heart or its component. Model printed by this technology reduces risk, improves the quality of diagnosis and preoperative planning and also enhanced team communication. In cardiology, patient data of heart varies from patient to patient, so AM technologies efficiently produce 3D models, through converting the predesigned virtual model into a tangible object. Companies explore additive manufacturing for commercial medical applications.

Heart failure (HF) is growing to a modern epidemic and despite
\nadvances in therapy, it still carries an ominous prognosis and a significant socioeconomic burden. Many novel agents that emerged as
\npromising HF drugs failed to improve residual morbidity and mortality.2,3 Since developing and testing new agents has become increasingly costly,4 the concept of repurposing existing drugs for new
\nindications has gained considerable importance.
\nConceptually, comorbidities such as type 2 diabetes mellitus
\n(T2DM), obesity or chronic kidney disease, all highly prevalent in HF
\npopulations, have shifted from being innocent bystanders to drivers
\nof HF. This applies especially to HF with preserved ejection fraction
\n(HFpEF), a phenotype that accounts for more than 50% of HF
\npatients and for which no effective therapy exists thus far.5,6 In particular, the prevalence of T2DM, thereby its combination with HF is
\nrapidly increasing, mainly due to the obesity epidemic.
\nCardiovascular (CV) outcomes are addressed by an increasing
\nnumber of clinical studies in T2DM, mainly as safety endpoints for
\nanti-diabetic agents. Some of those drugs have beneficial CV effects
\nindependent of their glucose-lowering action. Consequently, antidiabetic agents have gained interest for their potential repurposing in
\nHF treatment. In this context, the Translational Research Committee
\nof the Heart Failure Association (HFA) of the European Society of
\nCardiology (ESC) organized a workshop on HF and T2DM, focusing
\non the pathophysiological and therapeutic aspects of this relationship.
\nHere, we summarize the main points raised during this workshop,
\nproviding an overview of current evidence and open issues.

Natural language processing (NLP) is a set of automated methods to organise and evaluate the information contained in unstructured clinical notes, which are a rich source of real-world data from clinical care that may be used to improve outcomes and understanding of disease in cardiology. The purpose of this systematic review is to provide an understanding of NLP, review how it has been used to date within cardiology and illustrate the opportunities that this approach provides for both research and clinical care. We systematically searched six scholarly databases (ACM Digital Library, Arxiv, Embase, IEEE Explore, PubMed and Scopus) for studies published in 2015-2020 describing the development or application of NLP methods for clinical text focused on cardiac disease. Studies not published in English, lacking a description of NLP methods, non-cardiac focused and duplicates were excluded. Two independent reviewers extracted general study information, clinical details and NLP details and appraised quality using a checklist of quality indicators for NLP studies. We identified 37 studies developing and applying NLP in heart failure, imaging, coronary artery disease, electrophysiology, general cardiology and valvular heart disease. Most studies used NLP to identify patients with a specific diagnosis and extract disease severity using rule-based NLP methods. Some used NLP algorithms to predict clinical outcomes. A major limitation is the inability to aggregate findings across studies due to vastly different NLP methods, evaluation and reporting. This review reveals numerous opportunities for future NLP work in cardiology with more diverse patient samples, cardiac diseases, datasets, methods and applications.

Cardiovascular disorders are the primary cause of death on a global scale. The World Health Organization report indicates that approximately 18 million people die from CVD each year. Major cardiac risks include arrhythmia and coronary artery disease, among others. Recent advancements in Artificial Intelligence play a pivotal role for life-saving interventions in CVD treatment. This survey examines the latest progress in Machine Learning, Deep Learning and Pre-trained transfer learning models for classifying and predicting CVD using a survey of 159 articles, including 36 image datasets, 41 signal data and 52 clinical data from various sources. The survey investigates cardiac risk factors, cardiac dysfunction classification, various modalities and medical image processing techniques, performance metrics and hybrid techniques. Surveys on traditional neural networks such as Convolutional Neural Networks, Artificial Neural Networks, and Recurrent Neural Networks often achieve an accuracy rate of 70% to 95%. Leveraging pre-trained architectures such as ResNet, DenseNet, AlexNet, MobileNet, EfficientNet and GoogLeNet, transfer learning models consistently outperform other approaches frequently achieving accuracy levels greater than 96%. Researchers utilize various hybrid optimization algorithms to improve overall accuracy rate. The outcome of the survey supports a precise prognosis for patients with comorbidities. The survey findings indicate limitations in the incorporation of multimodal data for real-time risk assessment. The study results possess the capacity to bridge gaps in research on cardiovascular disease prediction, thereby assisting medical practitioners in early identification and subsequent prognosis.

In cardiology, optical coherence tomography (OCT) is an invasive imaging technique based on the principle of light coherence. This system was developed to obtain three-dimensional high resolution images to examine coronary artery normal and/or pathological structure. This technique replaces the ultrasound used by its main alternative procedure, intravascular ultrasound, by a near-infrared light source. Acute coronary syndromes due to atherosclerotic vascular disease are the leading cause of mortality in developed and developing countries. As a consequence, intravascular imaging systems became an important area of research and 1991 marks the first use of OCT in coronary artery observations. Since its first appearance in invasive cardiology, OCT maintains a strong presence in the research environments for the identification of vulnerable plaques, as it is able to overcome difficulties presented by other techniques such as virtual intravascular ultrasound, near-infrared spectroscopy, and histology. Moreover, OCT is increasingly being used in the clinical practice as a guide during coronary interventions and in the assessment of vascular response after coronary stent implantation. This review focuses on the relevance of OCT in research and clinical applications in the field of invasive cardiology and discusses the future directions of the field.

The evolution of AI and data science has aided in mechanizing several aspects of medical care requiring critical thinking: diagnosis, risk stratification, and management, thus mitigating the burden of physicians and reducing the likelihood of human error. AI modalities have expanded feet to the specialty of pediatric cardiology as well. We conducted a scoping review searching the Scopus, Embase, and PubMed databases covering the recent literature between 2002-2022. We found that the use of neural networks and machine learning has significantly improved the diagnostic value of cardiac magnetic resonance imaging, echocardiograms, computer tomography scans, and electrocardiographs, thus augmenting the clinicians' diagnostic accuracy of pediatric heart diseases. The use of AI-based prediction algorithms in pediatric cardiac surgeries improves postoperative outcomes and prognosis to a great extent. Risk stratification and the prediction of treatment outcomes are feasible using the key clinical findings of each CHD with appropriate computational algorithms. Notably, AI can revolutionize prenatal prediction as well as the diagnosis of CHD using the EMR (electronic medical records) data on maternal risk factors. The use of AI in the diagnostics, risk stratification, and management of CHD in the near future is a promising possibility with current advancements in machine learning and neural networks. However, the challenges posed by the dearth of appropriate algorithms and their nascent nature, limited physician training, fear of over-mechanization, and apprehension of missing the 'human touch' limit the acceptability. Still, AI proposes to aid the clinician tomorrow with precision cardiology, paving a way for extremely efficient human-error-free health care.

Artificial intelligence (AI) is an integral part of clinical decision support systems (CDSS), offering methods to approximate human reasoning and computationally infer decisions. Such methods are generally based on medical knowledge, either directly encoded with rules or automatically extracted from medical data using machine learning (ML). ML techniques, such as Artificial Neural Networks (ANNs) and support vector machines (SVMs), are based on mathematical models with parameters that can be optimally tuned using appropriate algorithms. The ever-increasing computational capacity of today's computer systems enables more complex ML systems with millions of parameters, bringing AI closer to human intelligence. With this objective, the term deep learning (DL) has been introduced to characterize ML based on deep ANN (DNN) architectures with multiple layers of artificial neurons. Despite all of these promises, the impact of AI in current clinical practice is still limited. However, this could change shortly, as the significantly increased papers in AI, machine learning and deep learning in cardiology show. We highlight the significant achievements of recent years in nearly all areas of cardiology and underscore the mounting evidence suggesting how AI will take a central stage in the field.

BACKGROUND: Cardiovascular diseases are the deadliest diseases worldwide, with 17.3 million deaths in 2008 alone. Among them, heart-related deaths are of the utmost relevance; a fact easily proven by the 7.25 million deaths caused by ischemic heart disease alone in that year. The latest advances in smartphones and mHealth have been used in the creation of thousands of medical apps related to cardiology, which can help to reduce these mortality rates. OBJECTIVE: The aim of this paper is to study the literature on mobile systems and applications currently available, as well as the existing apps related to cardiology from the leading app stores and to then classify the results to see what is available and what is missing, focusing particularly on commercial apps. METHODS: Two reviews have been developed. One is a literature review of mobile systems and applications, retrieved from several databases and systems such as Scopus, PubMed, IEEE Xplore, and Web of Knowledge. The other is a review of mobile apps in the leading app stores, Google play for Android and Apple's App Store for iOS. RESULTS: Search queries up to May 2013 located 406 papers and 710 apps related to cardiology and heart disease. The most researched section in the literature associated with cardiology is related to mobile heart (and vital signs) monitoring systems and the methods involved in the classification of heart signs in order to detect abnormal functions. Other systems with a significant number of papers are mobile cardiac rehabilitation systems, blood pressure measurement, and systems for the detection of heart failure. The majority of apps for cardiology are heart monitors and medical calculators. Other categories with a high number of apps are those for ECG education and interpretation, cardiology news and journals, blood pressure tracking, heart rate monitoring using an external device, and CPR instruction. There are very few guides on cardiac rehabilitation and apps for the management of the cardiac condition, and there were no apps that assist people who have undergone a heart transplant. CONCLUSIONS: The distribution of work in the field of cardiology apps is considerably disproportionate. Whereas some systems have significant research and apps are available, other important systems lack such research and lack apps, even though the contribution they could provide is significant.

Medication adherence is directly associated with health outcomes. Adherence has been reviewed extensively; however, most studies provide a narrow scope of the problem, covering a specific disease or treatment. This project's objective was to identify risk factors for non-adherence in the fields of rheumatology, oncology, and cardiology as well as potential interventions to improve adherence and their association with the risk factors. The project was developed in three phases and carried out by a Steering Committee made up of experts from the fields of rheumatology, oncology, cardiology, general medicine, and hospital and community pharmacy. In phase 1, a bibliographic review was performed, and the articles/reviews were classified according to the authors' level of confidence in the results and their clinical relevance. In phase 2, 20 risk factors for non-adherence were identified from these articles/reviews and agreed upon in Steering Committee meetings. In phase 3, potential interventions for improving adherence were also identified and agreed upon. The results obtained show that adherence is a dynamic concept that can change throughout the course of the disease, the treatments, and other factors. Educational interventions are the most studied ones and have the highest level of confidence in the authors' opinion. Information and education are essential to improve adherence in all patients.

Artificial intelligence (AI) and language models such as ChatGPT-4 (Generative Pretrained Transformer) have made tremendous advances recently and are rapidly transforming the landscape of medicine. Cardiology is among many of the specialties that utilize AI with the intention of improving patient care. Generative AI, with the use of its advanced machine learning algorithms, has the potential to diagnose heart disease and recommend management options suitable for the patient. This may lead to improved patient outcomes not only by recommending the best treatment plan but also by increasing physician efficiency. Language models could assist physicians with administrative tasks, allowing them to spend more time on patient care. However, there are several concerns with the use of AI and language models in the field of medicine. These technologies may not be the most up-to-date with the latest research and could provide outdated information, which may lead to an adverse event. Secondly, AI tools can be expensive, leading to increased healthcare costs and reduced accessibility to the general population. There is also concern about the loss of the human touch and empathy as AI becomes more mainstream. Healthcare professionals would need to be adequately trained to utilize these tools. While AI and language models have many beneficial traits, all healthcare providers need to be involved and aware of generative AI so as to assure its optimal use and mitigate any potential risks and challenges associated with its implementation. In this review, we discuss the various uses of language models in the field of cardiology.

Cardiovascular diseases exert a significant burden on the healthcare system worldwide. This narrative literature review discusses the role of artificial intelligence (AI) in the field of cardiology. AI has the potential to assist healthcare professionals in several ways, such as diagnosing pathologies, guiding treatments, and monitoring patients, which can lead to improved patient outcomes and a more efficient healthcare system. Moreover, clinical decision support systems in cardiology have improved significantly over the past decade. The addition of AI to these clinical decision support systems can improve patient outcomes by processing large amounts of data, identifying subtle associations, and providing a timely, evidence-based recommendation to healthcare professionals. Lastly, the application of AI allows for personalized care by utilizing predictive models and generating patient-specific treatment plans. However, there are several challenges associated with the use of AI in healthcare. The application of AI in healthcare comes with significant cost and ethical considerations. Despite these challenges, AI will be an integral part of healthcare delivery in the near future, leading to personalized patient care, improved physician efficiency, and anticipated better outcomes.

Increasing incidence of cardiovascular disease and their mortality rate render them as second leading cause of death worldwide. Artificial Intelligence (AI) is used in many fields of science and industry, but also has found its use in medicine for diagnosis, treatment and prediction of diseases. This paper presents the review of AI application in cardiology. The review is based on research papers published in Medline database. Findings of the review indicate that, according to accuracy parameter, the overall performance of AI based models for cardiovascular application is above 83%. Based on the results, AI algorithms and deep learning can be rendered as accurate, hence showing possibility to be used as a diagnostic tool now and in the future. New era of modern diagnosing is coming and Artificial Intelligence has the potential to change the way in which medicine is practiced.

With the rate of cardiovascular diseases in the U.S increasing throughout the years, there is a need for developing more advanced treatment plans that can be tailored to specific patients and scenarios. The development of 3D printing is rapidly gaining acceptance into clinical cardiology. In this review, key technologies used in 3D printing are briefly summarized, particularly, the use of artificial intelligence (AI), open-source tools like MeshLab and MeshMixer, and 3D printing techniques such as fused deposition molding (FDM) and polyjet are reviewed. The combination of 3D printing, multiple image integration, and augmented reality may greatly enhance data visualization during diagnosis, treatment planning, and surgical procedures for cardiology.