Independent Correlates of Glycemic Control among Adults with Diabetes in South Africa.

BACKGROUND: Globally, the prevalence of diabetes is increasing, especially in low- and middle-income countries (LMICs), including those in the sub-Saharan African region. However, the independent socioeconomic correlates of glycemic control as measured by hemoglobin A1C have yet to be identified. Therefore, the aim of this analysis was to understand the independent correlates of glycemic control in South Africa. METHODS: Data from the 2016 South Africa Demographic and Health Survey on adults with diabetes were used for this analysis. The dependent variable, glycemic control, was defined using hemoglobin A1c (HbA1c). Independent variables included: age, gender, ethnicity, marital status, region, urban/rural residence, ability to read, education, insurance, wealth, occupation, and employment in the last year. Analysis of variance was used to test for differences in mean HbA1c for each category of all independent variables, and a fully adjusted linear regression model was used to identify independent correlates of glycemic control (HbA1c). RESULTS: Among the 772 people included in this analysis, there were significant differences in mean HbA1c by age (p < 0.001), ethnicity (p < 0.001), place of residence (p = 0.024), wealth index (p = 0.001), and employment in the last year (p = 0.008). Independent correlates of HbA1c included age, ethnicity, and wealth index. CONCLUSIONS: This study used data from a large diverse population with a high prevalence of diabetes in sub-Saharan Africa and provides new evidence on the correlates of glycemic control and potential targets for interventions designed to lower HbA1c and improve diabetes-related health outcomes of adults in South Africa.

Advancing the care of individuals with cancer through innovation & technology: Proceedings from the cardiology oncology innovation summit 2020 and 2021.

As cancer therapies increase in effectiveness and patients' life expectancies improve, balancing oncologic efficacy while reducing acute and long-term cardiovascular toxicities has become of paramount importance. To address this pressing need, the Cardiology Oncology Innovation Network (COIN) was formed to bring together domain experts with the overarching goal of collaboratively investigating, applying, and educating widely on various forms of innovation to improve the quality of life and cardiovascular healthcare of patients undergoing and surviving cancer therapies. The COIN mission pillars of innovation, collaboration, and education have been implemented with cross-collaboration among academic institutions, private and public establishments, and industry and technology companies. In this report, we summarize proceedings from the first two annual COIN summits (inaugural in 2020 and subsequent in 2021) including educational sessions on technological innovations for establishing best practices and aligning resources. Herein, we highlight emerging areas for innovation and defining unmet needs to further improve the outcome for cancer patients and survivors of all ages. Additionally, we provide actionable suggestions for advancing innovation, collaboration, and education in cardio-oncology in the digital era.

Simplified rules-based tool to facilitate the application of up-to-date management recommendations in cardio-oncology.

BACKGROUND: Millions of cancer survivors are at risk of cardiovascular diseases, a leading cause of morbidity and mortality. Tools to potentially facilitate implementation of cardiology guidelines, consensus recommendations, and scientific statements to prevent atherosclerotic cardiovascular disease (ASCVD) and other cardiovascular diseases are limited. Thus, inadequate utilization of cardiovascular medications and imaging is widespread, including significantly lower rates of statin use among cancer survivors for whom statin therapy is indicated. METHODS: In this methodological study, we leveraged published guidelines documents to create a rules-based tool to include guidelines, expert consensus, and medical society scientific statements relevant to point of care cardiovascular disease prevention in the cardiovascular care of cancer survivors. Any overlap, redundancy, or ambiguous recommendations were identified and eliminated across all converted sources of knowledge. The integrity of the tool was assessed with use case examples and review of subsequent care suggestions. RESULTS: An initial selection of 10 guidelines, expert consensus, and medical society scientific statements was made for this study. Then 7 were kept owing to overlap and revisions in society recommendations over recent years. Extensive formulae were employed to translate the recommendations of 7 selected guidelines into rules and proposed action measures. Patient suitability and care suggestions were assessed for several use case examples. CONCLUSION: A simple rules-based application was designed to provide a potential format to deliver critical cardiovascular disease best-practice prevention recommendations at the point of care for cancer survivors. A version of this tool may potentially facilitate implementing these guidelines across clinics, payers, and health systems for preventing cardiovascular diseases in cancer survivors. TRIAL REGISTRATION: ClinicalTrials.Gov Identifier: NCT05377320.

Patient similarity and other artificial intelligence machine learning algorithms in clinical decision aid for shared decision-making in the Prevention of Cardiovascular Toxicity (PACT): a feasibility trial design.

BACKGROUND: The many improvements in cancer therapies have led to an increased number of survivors, which comes with a greater risk of consequent/subsequent cardiovascular disease. Identifying effective management strategies that can mitigate this risk of cardiovascular complications is vital. Therefore, developing computer-driven and personalized clinical decision aid interventions that can provide early detection of patients at risk, stratify that risk, and recommend specific cardio-oncology management guidelines and expert consensus recommendations is critically important. OBJECTIVES: To assess the feasibility, acceptability, and utility of the use of an artificial intelligence (AI)-powered clinical decision aid tool in shared decision making between the cancer survivor patient and the cardiologist regarding prevention of cardiovascular disease. DESIGN: This is a single-center, double-arm, open-label, randomized interventional feasibility study. Our cardio-oncology cohort of > 4000 individuals from our Clinical Research Data Warehouse will be queried to identify at least 200 adult cancer survivors who meet the eligibility criteria. Study participants will be randomized into either the Clinical Decision Aid Group (where patients will use the clinical decision aid in addition to current practice) or the Control Group (current practice). The primary endpoint of this study is to assess for each patient encounter whether cardiovascular medications and imaging pursued were consistent with current medical society recommendations. Additionally, the perceptions of using the clinical decision tool will be evaluated based on patient and physician feedback through surveys and focus groups. This trial will determine whether a clinical decision aid tool improves cancer survivors' medication use and imaging surveillance recommendations aligned with current medical guidelines. TRIAL REGISTRATION: ClinicalTrials.Gov Identifier: NCT05377320.

Team principles for successful interdisciplinary research teams.

Interdisciplinary research teams can be extremely beneficial when addressing difficult clinical problems. The incorporation of conceptual and methodological strategies from a variety of research disciplines and health professions yields transformative results. In this setting, the long-term goal of team science is to improve patient care, with emphasis on population health outcomes. However, team principles necessary for effective research teams are rarely taught in health professional schools. To form successful interdisciplinary research teams in cardio-oncology and beyond, guiding principles and organizational recommendations are necessary. Cardiovascular disease results in annual direct costs of $220 billion (about $680 per person in the US) and is the leading cause of death for cancer survivors, including adult survivors of childhood cancers. Optimizing cardio-oncology research in interdisciplinary research teams has the potential to aid in the investigation of strategies for saving hundreds of thousands of lives each year in the United States and mitigating the annual cost of cardiovascular disease. Despite published reports on experiences developing research teams across organizations, specialties and settings, there is no single journal article that compiles principles for cardiology or cardio-oncology research teams. In this review, recurring threads linked to working as a team, as well as optimal methods, advantages, and problems that arise when managing teams are described in the context of career development and research. The worth and hurdles of a team approach, based on practical lessons learned from establishing our multidisciplinary research team and information gleaned from relevant specialties in the development of a successful team are presented.

Pan-cancer analysis of co-occurring mutations in RAD52 and the BRCA1-BRCA2-PALB2 axis in human cancers.

In human cells homologous recombination (HR) is critical for repair of DNA double strand breaks (DSBs) and rescue of stalled or collapsed replication forks. HR is facilitated by RAD51 which is loaded onto DNA by either BRCA2-BRCA1-PALB2 or RAD52. In human culture cells, double-knockdowns of RAD52 and genes in the BRCA1-BRCA2-PALB2 axis are lethal. Mutations in BRCA2, BRCA1 or PALB2 significantly impairs error free HR as RAD51 loading relies on RAD52 which is not as proficient as BRCA2-BRCA1-PALB2. RAD52 also facilitates Single Strand Annealing (SSA) that produces intra-chromosomal deletions. Some RAD52 mutations that affect the SSA function or decrease RAD52 association with DNA can suppress certain BRCA2 associated phenotypes in breast cancers. In this report we did a pan-cancer analysis using data reported on the Catalogue of Somatic Mutations in Cancers (COSMIC) to identify double mutants between RAD52 and BRCA1, BRCA2 or PALB2 that occur in cancer cells. We find that co-occurring mutations are likely in certain cancer tissues but not others. However, all mutations occur in a heterozygous state. Further, using computational and machine learning tools we identified only a handful of pathogenic or driver mutations predicted to significantly affect the function of the proteins. This supports previous findings that co-inactivation of RAD52 with any members of the BRCA2-BRCA1-PALB2 axis is lethal. Molecular modeling also revealed that pathogenic RAD52 mutations co-occurring with mutations in BRCA2-BRCA1-PALB2 axis are either expected to attenuate its SSA function or its interaction with DNA. This study extends previous breast cancer findings to other cancer types and shows that co-occurring mutations likely destabilize HR by similar mechanisms as in breast cancers.

Artificial intelligence and imaging: Opportunities in cardio-oncology.

Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.

Artificial intelligence opportunities in cardio-oncology: Overview with spotlight on electrocardiography.

Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.

Establishing an interdisciplinary research team for cardio-oncology artificial intelligence informatics precision and health equity.

Study objective: A multi-institutional interdisciplinary team was created to develop a research group focused on leveraging artificial intelligence and informatics for cardio-oncology patients. Cardio-oncology is an emerging medical field dedicated to prevention, screening, and management of adverse cardiovascular effects of cancer/ cancer therapies. Cardiovascular disease is a leading cause of death in cancer survivors. Cardiovascular risk in these patients is higher than in the general population. However, prediction and prevention of adverse cardiovascular events in individuals with a history of cancer/cancer treatment is challenging. Thus, establishing an interdisciplinary team to create cardiovascular risk stratification clinical decision aids for integration into electronic health records for oncology patients was considered crucial. Design/setting/participants: Core team members from the Medical College of Wisconsin (MCW), University of Wisconsin-Milwaukee (UWM), and Milwaukee School of Engineering (MSOE), and additional members from Cleveland Clinic, Mayo Clinic, and other institutions have joined forces to apply high-performance computing in cardio-oncology. Results: The team is comprised of clinicians and researchers from relevant complementary and synergistic fields relevant to this work. The team has built an epidemiological cohort of ~5000 cancer survivors that will serve as a database for interdisciplinary multi-institutional artificial intelligence projects. Conclusion: Lessons learned from establishing this team, as well as initial findings from the epidemiology cohort, are presented. Barriers have been broken down to form a multi-institutional interdisciplinary team for health informatics research in cardio-oncology. A database of cancer survivors has been created collaboratively by the team and provides initial insight into cardiovascular outcomes and comorbidities in this population.

Connected Health Innovation Research Program (C.H.I.R.P.): A bridge for digital health and wellness in cardiology and oncology.

Study objective: Cancer and heart disease are leading causes of mortality, and cardio-oncology is emerging as a new field addressing the cardiovascular toxicities related to cancer and cancer therapy. Interdisciplinary research platforms that incorporate digital health to optimize cardiovascular health and wellness in cancer survivors are therefore needed as we advance in the digital era. Our goal was to develop the Connected Health Innovation Research Program (C.H.I.R.P.) to serve as a foundation for future integration and assessments of adoption and clinical efficacy of digital health tools for cardiovascular health and wellness in the general population and in oncology patients. Design/setting/participants: Partner companies were identified through the American Medical Association innovation platform, as well as LinkedIn and direct contact by our team. Company leaders met with our team to discuss features of their technology or software. Non-disclosure agreements were signed and data were discussed and obtained for descriptive or statistical analysis. Results: A suite of companies with technologies focused on wellness, biometrics tracking, audio companions, oxygen saturation, weight trends, sleep patterns, heart rate variability, electrocardiogram patterns, blood pressure patterns, real-time metabolism tracking, instructional video modules, or integration of these technologies into electronic health records was collated. We formed an interdisciplinary research team and established an academia-industry collaborative foundation for connecting patients with wellness digital health technologies. Conclusions: A suite of software and device technologies accessible to the cardiology and oncology population has been established and will facilitate retrospective, prospective, and case research studies assessing adoption and clinical efficacy of digital health tools in cardiology/oncology.

Trends in cardiology and oncology artificial intelligence publications.

Study objective: To determine whether there has been growth in publications on the use of artificial intelligence in cardiology and oncology, we assessed historical trends in publications related to artificial intelligence applications in cardiology and oncology, which are the two fields studying the leading causes of death worldwide. Upward trends in publications may indicate increasing interest in the use of artificial intelligence in these crucial fields. Design/setting: To evaluate evidence of increasing publications on the use of artificial intelligence in cardiology and oncology, historical trends in related publications on PubMed (the biomedical repository most frequently used by clinicians and scientists in these fields) were reviewed. Results: Findings indicated that research output related to artificial intelligence (and its subcategories) generally increased over time, particularly in the last five years. With some initial degree of vacillation in publication trends, a slight qualitative inflection was noted in approximately 2015, in general publications and especially for oncology and cardiology, with subsequent consistent exponential growth. Publications predominantly focused on "machine learning" (n = 20,301), which contributed to the majority of the accelerated growth in the field, compared to "artificial intelligence" (n = 4535), "natural language processing" (n = 2608), and "deep learning" (n = 4459). Conclusion: Trends in the general biomedical literature and particularly in cardiology and oncology indicated exponential growth over time. Further exponential growth is expected in future years, as awareness and cross-disciplinary collaboration and education increase. Publications specifically on machine learning will likely continue to lead the way.

The pursuit of health equity in digital transformation, health informatics, and the cardiovascular learning healthcare system.

African Americans have a higher rate of cardiovascular morbidity and mortality and a lower rate of specialty consultation and treatment than Caucasians. These disparities also exist in the care and treatment of chemotherapy-related cardiovascular complications. African Americans suffer from cardiotoxicity at a higher rate than Caucasians and are underrepresented in clinical trials aimed at preventing cardiovascular injury associated with cancer therapies. To eliminate racial and ethnic disparities in the prevention of cardiotoxicity, an interdisciplinary and innovative approach will be required. Diverse forms of digital transformation leveraging health informatics have the potential to contribute to health equity if they are implemented carefully and thoughtfully in collaboration with minority communities. A learning healthcare system can serve as a model for developing, deploying, and disseminating interventions to minimize health inequities and maximize beneficial impact.

Secondary Resistant Mutations to Small Molecule Inhibitors in Cancer Cells.

Secondary resistant mutations in cancer cells arise in response to certain small molecule inhibitors. These mutations inevitably cause recurrence and often progression to a more aggressive form. Resistant mutations may manifest in various forms. For example, some mutations decrease or abrogate the affinity of the drug for the protein. Others restore the function of the enzyme even in the presence of the inhibitor. In some cases, resistance is acquired through activation of a parallel pathway which bypasses the function of the drug targeted pathway. The Catalogue of Somatic Mutations in Cancer (COSMIC) produced a compendium of resistant mutations to small molecule inhibitors reported in the literature. Here, we build on these data and provide a comprehensive review of resistant mutations in cancers. We also discuss mechanistic parallels of resistance.

Frequent homozygous deletions of the CDKN2A locus in somatic cancer tissues.

Here we present and describe data on homozygous deletions (HD) of human CDKN2 A and neighboring regions on the p arm of Chromosome 9 from cancer genome sequences deposited on the online Catalogue of Somatic Mutations in Cancer (COSMIC) database. Although CDKN2 A HDs have been previously described in many cancers, this is a pan-cancer report of these aberrations with the aim to map the distribution of the breakpoints. We find that HDs of this locus have a median range of 1,255,650bps. When the deletion breakpoints were mapped on both the telomere and centromere proximal sides of CDKN2A, most of the telomere proximal breakpoints concentrate to a narrow region of the chromosome which includes the gene MTAP.. The centromere proximal breakpoints of the deletions are distributed over a wider chromosomal region. Furthermore, gene expression analysis shows that the deletions that include the CDKN2A region also include the MTAP region and this observation is tissue independent. We propose a model that may explain the origin of the telomere proximal CDKN2A breakpoints Finally, we find that HD distributions for at least three other loci, RB1, SMAD4 and PTEN are also not random.