The effect of socioeconomic factors on patient outcomes in cardiac surgery.

OBJECTIVES: Healthcare delivery is heterogenous; the reasons for this are numerous and complex. Patient-specific factors including geography, income, insurance status, age, and gender have been shown to bias surgical outcomes. Utilizing a prospectively collected all-payer database, we aim to evaluate the influence of socioeconomic factors on mortality and length of stay (LOS) after common cardiac surgical procedures. METHODS: We utilized the National Inpatient Sample, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality for the year 2019. We included patients undergoing coronary artery bypass grafting (CABG), aortic valve replacement (AVR), transcatheter aortic valve replacement (TAVR), and combined AVR/CABG using the 10th revision of the International Classification of Diseases procedure codes. AVR and CABG were combined into a separate cohort as this was felt to represent a different pathology than isolated valvular or coronary arterial disease. Baseline demographics were summarized. Multivariable regression was performed within each procedure group to model the odds of in-hospital mortality and hospital LOS with age, sex, insurance, zip-code median household income, and location as predictors. RESULTS: Baseline patient characteristics including gender, income, geography, and payer status were similar between CABG, AVR, and AVR/CABG. TAVR patients had a higher proportion of female sex and Medicare as the primary payer, with an overall greater age. Multivariable Cox proportional hazards regression found that higher income was strongly associated with decreased LOS following AVR and CABG, and moderately associated in TAVR and AVR/CABG. Private insurance was associated with a decreased LOS in patients undergoing CABG, AVR, TAVR, and AVR/CABG. Female sex and increased age were associated with increased odds of mortality in TAVR, CABG, and AVR/CABG. Private insurance was associated with a decreased odds of mortality in patients undergoing AVR. CONCLUSIONS: These findings reveal significant disparities in patient outcomes after routine cardiac operations that are associated with socioeconomic status. Patients who did not have private insurance or had lower incomes were found to be at risk for increased LOS. Women were at a higher risk of mortality for several operations, a finding which has been previously described elsewhere. Private insurance conveyed a decreased odds of mortality in patients undergoing AVR. This data set serves to highlight differences in healthcare outcomes based on a variety of socioeconomic, geographic, and other inherent factors. Additional research is needed to identify the mechanisms behind these disparities with the goal of providing equitable care to all patients.

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.

Modeling Precision Cardio-Oncology: Using Human-Induced Pluripotent Stem Cells for Risk Stratification and Prevention.

PURPOSE OF REVIEW: Cardiovascular toxicity is a leading cause of mortality among cancer survivors and has become increasingly prevalent due to improved cancer survival rates. In this review, we synthesize evidence illustrating how common cancer therapeutic agents, such as anthracyclines, human epidermal growth factors receptors (HER2) monoclonal antibodies, and tyrosine kinase inhibitors (TKIs), have been evaluated in cardiomyocytes (CMs) derived from human-induced pluripotent stem cells (hiPSCs) to understand the underlying mechanisms of cardiovascular toxicity. We place this in the context of precision cardio-oncology, an emerging concept for personalizing the prevention and management of cardiovascular toxicities from cancer therapies, accounting for each individual patient's unique factors. We outline steps that will need to be addressed by multidisciplinary teams of cardiologists and oncologists in partnership with regulators to implement future applications of hiPSCs in precision cardio-oncology. RECENT FINDINGS: Current prevention of cardiovascular toxicity involves routine screenings and management of modifiable risk factors for cancer patients, as well as the initiation of cardioprotective medications. Despite recent advancements in precision cardio-oncology, knowledge gaps remain and limit our ability to appropriately predict with precision which patients will develop cardiovascular toxicity. Investigations using patient-specific CMs facilitate pharmacological discovery, mechanistic toxicity studies, and the identification of cardioprotective pathways. Studies with hiPSCs demonstrate that patients with comorbidities have more frequent adverse responses, compared to their counterparts without cardiac disease. Further studies utilizing hiPSC modeling should be considered, to evaluate the impact and mitigation of known cardiovascular risk factors, including blood pressure, body mass index (BMI), smoking status, diabetes, and physical activity in their role in cardiovascular toxicity after cancer therapy. Future real-world applications will depend on understanding the current use of hiPSC modeling in order for oncologists and cardiologists together to inform their potential to improve our clinical collaborative practice in cardio-oncology. When applying such in vitro characterization, it is hypothesized that a safety score can be assigned to each individual to determine who has a greater probability of developing cardiovascular toxicity. Using hiPSCs to create personalized models and ultimately evaluate the cardiovascular toxicity of individuals' treatments may one day lead to more patient-specific treatment plans in precision cardio-oncology while reducing cardiovascular disease (CVD) morbidity and mortality.