Role of Big Data in Cardiovascular Research

Evaluating the diagnostic and triage performance of digital and online symptom checkers for the presentation of myocardial infarction; A retrospective cross-sectional study

Online symptom checkers are increasingly popular health technologies that enable patients to input their symptoms to produce diagnoses and triage advice. However, there is concern regarding the performance and safety of symptom checkers in diagnosing and triaging patients with life-threatening conditions. This retrospective cross-sectional study aimed to evaluate and compare commercially available symptom checkers for performance in diagnosing and triaging myocardial infarctions (MI). Symptoms and biodata of MI patients were inputted into 8 symptom checkers identified through a systematic search. Anonymised clinical data of 100 consecutive MI patients were collected from a tertiary coronary intervention centre between 1st January 2020 to 31st December 2020. Outcomes included (1) diagnostic sensitivity as defined by symptom checkers outputting MI as the primary diagnosis (D1), or one of the top three (D3), or top five diagnoses (D5); and (2) triage sensitivity as defined by symptom checkers outputting urgent treatment recommendations. Overall D1 sensitivity was 48±31% and varied between symptom checkers (range: 6-85%). Overall D3 and D5 sensitivity were 73±20% (34-92%) and 79±14% (63-94%), respectively. Overall triage sensitivity was 83±13% (55-91%). 24±16% of atypical cases had a correct D1 though for female atypical cases D1 sensitivity was only 10%. Atypical MI D3 and D5 sensitivity were 44±21% and 48±24% respectively and were significantly lower than typical MI cases (p<0.01). Atypical MI triage sensitivity was significantly lower than typical cases (53±20% versus 84±15%, p<0.01). Female atypical cases had significantly lower diagnostic and triage sensitivity than typical female MI cases (p<0.01).Given the severity of the pathology, the diagnostic performance of symptom checkers for correctly diagnosing an MI is concerningly low. Moreover, there is considerable inter-symptom checker performance variation. Patients presenting with atypical symptoms were under-diagnosed and under-triaged, especially if female. This study highlights the need for improved clinical performance, equity and transparency associated with these technologies.

Building national patient registries in Mexico: insights from the MexOMICS Consortium

Objective

To introduce MexOMICS, a Mexican Consortium focused on establishing electronic databases to collect, cross-reference, and share health-related and omics data on the Mexican population.

Methods

Since 2019, the MexOMICS Consortium has established three electronic-based registries: the Mexican Twin Registry (TwinsMX), Mexican Lupus Registry (LupusRGMX), and the Mexican Parkinson's Research Network (MEX-PD), designed and implemented using the Research Electronic Data Capture web-based application. Participants were enrolled through voluntary participation and on-site engagement with medical specialists. We also acquired DNA samples and Magnetic Resonance Imaging scans in subsets of participants.

Results

The registries have successfully enrolled a large number of participants from a variety of regions within Mexico: TwinsMX (n = 2,915), LupusRGMX (n = 1,761) and MEX-PD (n = 750). In addition to sociodemographic, psychosocial, and clinical data, MexOMICS has collected DNA samples to study the genetic biomarkers across the three registries. Cognitive function has been assessed with the Montreal Cognitive Assessment in a subset of 376 MEX-PD participants. Furthermore, a subset of 267 twins have participated in cognitive evaluations with the Creyos platform and in MRI sessions acquiring structural, functional, and spectroscopy brain imaging; comparable evaluations are planned for LupusRGMX and MEX-PD.

Conclusions

The MexOMICS registries offer a valuable repository of information concerning the potential interplay of genetic and environmental factors in health conditions among the Mexican population.

Annual Increase of Acute Inpatients with Both Cancer and Cardiovascular Diseases in Japan 2011-2015: Analysis From National Database of Health Insurance Claims and Specific Health Checkups of Japan

BACKGROUND

Patients with cancer were able to live longer due to improvements in cancer treatment. Additionally, cardiovascular disease (CVD) is the second leading cause of mortality in cancer survivors. However, epidemiological data on onco-CVD have not been sufficiently provided. We aimed to investigate the clinical characteristics of cancer in CVD patients using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB).

METHOD AND RESULTS

The NDB sampling dataset used in this study was randomly sampled 10% from the whole Diagnosis Procedure Combination (DPC) records from every January, April, July, and October from 2011 to 2015. The significance of the increase trend in the percentage of records in each disease group to the total number of all DPC records from 2011 to 2015 was checked with Chi-square test with a Bonferroni correction. The percentage of records in cancer with the CVD group to the total number of all DPC records significantly increased over time, and their average age also increased since 2011. Their proportion over 75 years was approximately 56 % in 2015. There was no difference in the cancer sites. However, the prevalence of heart failure dramatically elevated.

CONCLUSION

We were able to assess the increase in cancer among CVD patients using DPC inpatient records obtained from the NDB. Both cardiologists and oncologists should be more aware of this phenomenon.

Artificial intelligence, big data and heart transplantation: Actualities

BACKGROUND

As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the supply chain of heart transplantation (HTx), allocation opportunities, correct treatments, and finally optimize HTx outcome. We explored available studies, and discussed opportunities and limits of medical application of AI to the field of HTx.

METHOD

A systematic overview of studies published up to December 31st, 2022, in English on peer-revied journals, have been identified through PUBMED-MEDLINE-WEB of Science, referring to HTx, AI, BD. Studies were grouped in 4 domains based on main studies' objectives and results: etiology, diagnosis, prognosis, treatment. A systematic attempt was made to evaluate studies by the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD).

RESULTS

Among the 27 publications selected, none used AI applied to BD. Of the selected studies, 4 fell in the domain of etiology, 6 in the domain of diagnosis, 3 in the domain of treatment, and 17 in that of prognosis, as AI was most frequently used for algorithmic prediction and discrimination of survival, but in retrospective cohorts and registries. AI-based algorithms appeared superior to probabilistic functions to predict patterns, but external validation was rarely employed. Indeed, based on PROBAST, selected studies showed, to some extent, significant risk of bias (especially in the domain of predictors and analysis). In addition, as example of applicability in the real-world, a free-use prediction algorithm developed through AI failed to predict 1-year mortality post-HTx in cases from our center.

CONCLUSIONS

While AI-based prognostic and diagnostic functions performed better than those developed by traditional statistics, risk of bias, lack of external validation, and relatively poor applicability, may affect AI-based tools. More unbiased research with high quality BD meant for AI, transparency and external validations, are needed to have medical AI as a systematic aid to clinical decision making in HTx.

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications

Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.

Big Data in cardiac surgery: real world and perspectives

Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.

Coronary perforation incidence, outcomes and temporal trends (COPIT): a systematic review and meta-analysis

Coronary perforation is a potentially life-threatening complication of percutaneous coronary intervention (PCI). We studied incidence, outcomes and temporal trends following PCI-related coronary artery perforation (CAP).

METHODS

Prospective systematic review and meta-analysis including meta-regression using MEDLINE and EMBASE to November 2020. We included 'all-comer' PCI cohorts including large PCI registries and randomised controlled trials and excluding registries or trials limited to PCI in high-risk populations such as chronic total occlusion PCI or cohorts treated only with atheroablative devices. Regression analysis and corresponding correlation coefficients were performed comparing perforation incidence, mortality rate, tamponade rate and the rate of Ellis III perforations against the midpoint (year) of data collection to determine if a significant temporal relationship was present.

RESULTS

3997 studies were screened for inclusion. 67 studies met eligibility criteria with a total of 5 568 191 PCIs included over a 38-year period (1982-2020). The overall pooled incidence of perforation was 0.39% (95% CI 0.34% to 0.45%) and remained similar throughout the study period. Around 1 in 5 coronary perforations led to tamponade (21.1%). Ellis III perforations are increasing in frequency and account for 43% of all perforations. Perforation mortality has trended lower over the years (7.5%; 95% CI 6.7% to 8.4%). Perforation risk factors derived using meta-regression were female sex, hypertension, chronic kidney disease and previous coronary bypass grafting. Coronary perforation was most frequently caused by distal wire exit (37%) followed by balloon dilation catheters (28%). Covered stents were used to treat 25% of perforations, with emergency cardiac surgery needed in 17%.

CONCLUSION

Coronary perforation complicates approximately 1 in 250 PCIs. Ellis III perforations are increasing in incidence although it is unclear whether this is due to reporting bias. Despite this, the overall perforation mortality rate (7.5%) has trended lower in recent years. Limitations of our findings include bias that may be introduced through analysis of multidesign studies and registries without pre-specified standardised perforation reporting CMore research into coronary perforation management including the optimal use of covered stents seems warranted.

PROSPERO REGISTRATION NUMBER

CRD42020207881.

Trend and prediction of citations on the topic of neuromuscular junctions in 100 top-cited articles since 2001 using a temporal bar graph: A bibliometric analysis

BACKGROUND

A neuromuscular junction (NMJ) (or myoneural junction) is a chemical synapse between a motor neuron (MN) and a muscle fiber. Although numerous articles have been published, no such analyses on trend or prediction of citations in NMJ were characterized using the temporal bar graph (TBG). This study is to identify the most dominant entities in the 100 top-cited articles in NMJ (T100MNJ for short) since 2001; to verify the improved TBG that is viable for trend analysis; and to investigate whether medical subject headings (MeSH terms) can be used to predict article citations.

METHODS

We downloaded T100MNJ from the PubMed database by searching the string ("NMJ" [MeSH Major Topic] AND ("2001" [Date - Modification]: "2021" [Date - Modification])) and matching citations to each article. Cluster analysis of citations was performed to select the most cited entities (e.g., authors, research institutes, affiliated countries, journals, and MeSH terms) in T100MNJ using social network analysis. The trend analysis was displayed using TBG with two major features of burst spot and trend development. Next, we examined the MeSH prediction effect on article citations using its correlation coefficients (CC) when the mean citations in MeSH terms were collected in 100 top-cited articles related to NMJ (T100NMJs).

RESULTS

The most dominant entities (i.e., country, journal, MesH term, and article in T100NMJ) in citations were the US (with impact factor [IF] = 142.2 = 10237/72), neuron (with IF = 151.3 = 3630/24), metabolism (with IF = 133.02), and article authored by Wagh et al from Germany in 2006 (with 342 citing articles). The improved TBG was demonstrated to highlight the citation evolution using burst spots, trend development, and line-chart plots. MeSH terms were evident in the prediction power on the number of article citations (CC = 0.40, t = 4.34).

CONCLUSION

Two major breakthroughs were made by developing the improved TBG applied to bibliographical studies and the prediction of article citations using the impact factor of MeSH terms in T100NMJ. These visualizations of improved TBG and scatter plots in trend, and prediction analyses are recommended for future academic pursuits and applications in other disciplines.

Using real-world data to monitor and improve quality of care in coronary artery disease: results from the Netherlands Heart Registration

Worldwide, quality registries for cardiovascular diseases enable the use of real-world data to monitor and improve the quality of cardiac care. In the Netherlands Heart Registration (NHR), cardiologists and cardiothoracic surgeons register baseline, procedural and outcome data across all invasive cardiac interventional, electrophysiological and surgical procedures. This paper provides insight into the governance and processes as organised by the NHR in collaboration with the hospitals. To clarify the processes, examples are given from the percutaneous coronary intervention and coronary artery bypass grafting registries. Physicians who are mandated by their hospital to instruct the NHR to process their data are united in registration committees. The committees determine standard sets of variables and periodically discuss the completeness and quality of data and patient-relevant outcomes. In the case of significant variation in outcomes, processes of healthcare delivery are discussed and good practices are shared in a non-competitive and safe setting. To create new insights for further improvement in patient-relevant outcomes, quality projects are initiated on, for example, multivessel disease treatment, cardiogenic shock and diagnostic intracoronary procedures. Moreover, possibilities are explored to expand the quality registries through additional relevant indicators, such as resource use before and after the procedure, by enriching NHR data with other existing data resources.

Big Data in Cardiology: State-of-Art and Future Prospects

Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.

A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac Disease Classification

The quality and acceptance of machine learning (ML) approaches in cardiovascular data interpretation depends strongly on model design and training and the interaction with the clinical experts. We hypothesize that a software infrastructure for the training and application of ML models can support the improvement of the model training and provide relevant information for understanding the classification-relevant data features. The presented solution supports an iterative training, evaluation, and exploration of machine-learning-based multimodal data interpretation methods considering cardiac MRI data. Correction, annotation, and exploration of clinical data and interpretation of results are supported through dedicated interactive visual analytics tools. We test the presented concept with two use cases from the ACDC and EMIDEC cardiac MRI image analysis challenges. In both applications, pre-trained 2D U-Nets are used for segmentation, and classifiers are trained for diagnostic tasks using radiomics features of the segmented anatomical structures. The solution was successfully used to identify outliers in automatic segmentation and image acquisition. The targeted curation and addition of expert annotations improved the performance of the machine learning models. Clinical experts were supported in understanding specific anatomical and functional characteristics of the assigned disease classes.

ANMCO POSITION PAPER: The reorganization of cardiology in times of the SARS-CoV-2 pandemic

The COVID-19 pandemic represents an unprecedented event that has brought deep changes in hospital facilities with reshaping of the health system organization, revealing inadequacies of current hospital and local health systems. When the COVID-19 emergency will end, further evaluation of the national health system, new organization of acute wards, and a further evolution of the entire health system will be needed to improve care during the chronic phase of disease. Therefore, new standards for healthcare personnel, more efficient organization of hospital facilities for patients with acute illnesses, improvement of technological approaches, and better integration between hospital and territorial services should be pursued. With experience derived from the COVID-19 pandemic,new models, paradigms, interventional approaches, values and priorities should be suggested and implemented.

Social Determinants of Health and Cardiovascular Disease: Current State and Future Directions Towards Healthcare Equity

PURPOSE OF REVIEW

We sought to examine the role of social and environmental conditions that determine an individual's behaviors and risk of disease-collectively known as social determinants of health (SDOH)-in shaping cardiovascular (CV) health of the population and giving rise to disparities in risk factors, outcomes, and clinical care for cardiovascular disease (CVD), the leading cause of death in the United States (US).

RECENT FINDINGS

Traditional CV risk factors have been extensively targeted in existing CVD prevention and management paradigms, often with little attention to SDOH. Limited evidence suggests an association between individual SDOH (e.g., income, education) and CVD. However, inequities in CVD care, risk factors, and outcomes have not been studied using a broad SDOH framework. We examined existing evidence of the association between SDOH-organized into 6 domains, including economic stability, education, food, neighborhood and physical environment, healthcare system, and community and social context-and CVD. Greater social adversity, defined by adverse SDOH, was linked to higher burden of CVD risk factors and poor outcomes, such as stroke, myocardial infarction (MI), coronary heart disease, heart failure, and mortality. Conversely, favorable social conditions had protective effects on CVD. Upstream SDOH interact across domains to produce cumulative downstream effects on CV health, via multiple physiologic and behavioral pathways. SDOH are major drivers of sociodemographic disparities in CVD, with a disproportionate impact on socially disadvantaged populations. Efforts to achieve health equity should take into account the structural, institutional, and environmental barriers to optimum CV health in marginalized populations. In this review, we highlight major knowledge gaps for each SDOH domain and propose a set of actionable recommendations to inform CVD care, ensure equitable distribution of healthcare resources, and reduce observed disparities.

Sex differences in health resource utilization, costs and mortality during hospitalization for infective endocarditis in the United States

Background

Few studies have assessed the association between sex and outcomes among patients with infective endocarditis. The aim of the study was to better understand the association between biologic sex, clinical outcomes and surgical treatment patterns among a contemporary cohort of patients admitted to hospital with infective endocarditis.

Methods

We used the National Inpatient Sample dataset from the Health Care Utilization Project to identify adult patients admitted for infective endocarditis between January and December 2016. We compared outcomes between men and women including inpatient hospital mortality, direct hospital costs, length of stay, and inpatient surgical treatment patterns. Multivariable analyses were performed with adjustment for age, socioeconomic status, and comorbidity burden.

Results

Among 18,702 patients with infective endocarditis, there were 8730 (46.7%) women and 1753 (8.4%) in-hospital deaths. In multivariable analysis, female sex was associated with a trend toward lower in-hospital mortality (adjusted odds ratio (OR) 0.90; 95% confidence interval (CI) 0.80 to 1.01, p = 0.06). Additionally, female sex was associated with significantly shorter hospital length of stay (-0.5 days; 95% CI -0.88 to -0.12, p = 0.009) and lower hospital costs (-$3035; 95% CI -$4277 to -$1792; p < 0.001). Notably, women were less likely to undergo surgical intervention (adjusted OR 0.59; 95% CI 0.52 to 0.67, p < 0.001).

Conclusions

In a contemporary, nationally representative cohort of patients admitted for IE in the United States, there were sex-specific differences in management and in-hospital outcomes. Possible sex-based bias in treatment patterns and access to inpatient surgical intervention for infective endocarditis warrants further study.

Data Sharing Under the General Data Protection Regulation: Time to Harmonize Law and Research Ethics?

Supplemental Digital Content is available in the text.

The General Data Protection Regulation (GDPR) became binding law in the European Union Member States in 2018, as a step toward harmonizing personal data protection legislation in the European Union. The Regulation governs almost all types of personal data processing, hence, also, those pertaining to biomedical research. The purpose of this article is to highlight the main practical issues related to data and biological sample sharing that biomedical researchers face regularly, and to specify how these are addressed in the context of GDPR, after consulting with ethics/legal experts. We identify areas in which clarifications of the GDPR are needed, particularly those related to consent requirements by study participants. Amendments should target the following: (1) restricting exceptions based on national laws and increasing harmonization, (2) confirming the concept of broad consent, and (3) defining a roadmap for secondary use of data. These changes will be achieved by acknowledged learned societies in the field taking the lead in preparing a document giving guidance for the optimal interpretation of the GDPR, which will be finalized following a period of commenting by a broad multistakeholder audience. In parallel, promoting engagement and education of the public in the relevant issues (such as different consent types or residual risk for re-identification), on both local/national and international levels, is considered critical for advancement. We hope that this article will open this broad discussion involving all major stakeholders, toward optimizing the GDPR and allowing a harmonized transnational research approach.

The use of forest plot to identify article similarity and differences in characteristics between journals using medical subject headings terms: A protocol for bibliometric study

BACKGROUND

Comparison of similarity and difference in research types among journals are concerned in literature. However, to date, none display the methodology seen in selecting similar journals related to the target journal, as similar articles did to a given article. Authors need 1 effective method not only to find similar journals for their studies but also to know the difference in methods. This study (1) shows the similar journals for the target journal online displayed, and (2) identifies the effect of similarity odds ratio compared to the counterparts using the forest plots in Meta-analysis and the major medical subject headings (MeSH terms).

METHODS

We downloaded 1000 recent top 20 most similar articles related to the Respiratory Care journal from the PubMed library, plotted the clusters of related journals using social network analysis (SNA), and compared the MeSH terms in differences in an odds ratio unit using the forest plot relevant to Respiratory Care and the most similar journals. Q statistic and I-square (I2) index were used to evaluate the difference in the proportion of events.

RESULTS

This study found that (1) the journals related to Respiratory Care are easily presented on Google Maps; (2) 10 journal clusters were identified using SNA; (3) the top 3 MeSH terms are methods, therapy, and physiopathology, and (4) the odds ratios of MeSH terms between journals associated with the Respiratory Care showing different from Int J Chron Obstruct Pulmori Dis and similar to Curr Opin Endocrinol Diabetes Obes within heterogeneity with I2 = 70.5% (P < 0.001) and 0% (P = 0.803), respectively.

CONCLUSIONS

SNA and forest plots provide deep insight into the relationships between journals in MeSH terms. The results of this research can provide readers with a concept diagram that can be used for future submissions to a given journal.

The Role of National Registries in Improving Quality of Care and Outcomes for Cardiovascular Disease

Cardiovascular registries play an integral role in providing real-world data on a number of cardiovascular conditions and allowing measurement of quality metrics across a large cohort of patients. Over the past 35 years, the number of cardiovascular registries has skyrocketed, and their use will only continue to grow as data on novel procedures and devices will need to be collected and analyzed. The American College of Cardiology and Society of Thoracic Surgeons Transcatheter Valve Therapy Registry is just one example of a modern registry that plays a crucial role in collecting data on patients undergoing transcatheter valvular procedures. Through public reporting registries, data can be shared on a hospital and provider level for many quality performance measures. There remains much work to be done on allowing automated data extraction from the electronic medical record directly into registries. No matter how sophisticated and complete a registry is, it can never overcome the problem of treatment selection bias that is inherent in observational data. This review discusses the growth, benefits, and limitations of national registries and their role in developing evidence for best clinical practice, measuring outcomes, providing feedback to clinicians, and improving quality of care.

Big Data Applications in Medical Field: A Literature Review

Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.

A New Therapeutic Framework for Atrial Fibrillation Drug Development

Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia and cause of significant morbidity and mortality. Its increasing prevalence in aging societies constitutes a growing challenge to global healthcare systems. Despite substantial unmet needs in AF prevention and treatment, drug developments hitherto have been challenging, and the current pharmaceutical pipeline is nearly empty. In this review, we argue that current drugs for AF are inadequate because of an oversimplified system for patient classification and the development of drugs that do not interdict underlying disease mechanisms. We posit that an improved understanding of AF molecular pathophysiology related to the continuous identification of novel disease-modifying drug targets and an increased appreciation of patient heterogeneity provide a new framework to personalize AF drug development. Together with recent innovations in diagnostics, remote rhythm monitoring, and big data capabilities, we anticipate that adoption of a new framework for patient subsegmentation based on pathophysiological, genetic, and molecular subsets will improve success rates of clinical trials and advance drugs that reduce the individual patient and public health burden of AF.