National Trends in Transfer of Patients with Primary Intracerebral Hemorrhage: An Analysis of 12-Year Nationwide Data.

OBJECTIVES: The guidelines of the American Hospital Association encourage transferring intracerebral hemorrhage patients from community hospitals to centers with stroke expertise. However, research on the differences in outcomes between transferred intracerebral hemorrhage hospitalizations and directly admitted hospitalizations have been largely limited to small single-center studies. In this study, we explored the national trends in transferred intracerebral hemorrhage hospitalizations, as well as evaluated the differences, in terms of demographic characteristics, co-morbidity, resource utilization, and outcomes, between transferred intracerebral hemorrhage hospitalizations and directly admitted hospitalizations. MATERIALS AND METHODS: From the National Inpatient Sample (2004 - 2016), we assessed the linear trends in the proportion of interhospital transfers for intracerebral hemorrhage hospitalizations. We constructed a series of multivariate logistic regression models to explore the association of transfer status with inpatient mortality and discharge disposition, controlling for demographic, clinical, and hospital characteristics. We used survey design variables to report nationally weighted estimates. RESULTS: Among 786,999 hospitalizations, 137,340 (17.5%, 95% CI: 16.4-18.6) were transferred. Overall, interhospital transfers for intracerebral hemorrhage has been increasing over the 12-year period of this study. Patients in transferred hospitalizations were younger, more likely to be white, and more likely to have private insurance. Transferred hospitalizations were associated with significantly lower adjusted odds of inpatient mortality, compared to directly admitted hospitalizations. CONCLUSIONS: As the US healthcare system continues shifting towards value-based care, evidence on the short- and long-term outcomes of transfer of intracerebral hemorrhage patients will inform optimal management of intracerebral hemorrhage patients.

Rapid Response to Drive COVID-19 Research in a Learning Health Care System: Rationale and Design of the Houston Methodist COVID-19 Surveillance and Outcomes Registry (CURATOR).

BACKGROUND: The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic. OBJECTIVE: We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization. METHODS: Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository-the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context. RESULTS: The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources. CONCLUSIONS: A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.

Is the worst of the COVID-19 global pandemic yet to come? Application of financial mathematics as candidate predictive tools.

The Elliott Wave principle is a time-honored, oft-used method for predicting variations in the financial markets. It is based on the notion that human emotions drive financial decisions. In the fight against the COVID-19 global pandemic, human emotions are similarly decisive, for instance in that they determine one's willingness to be vaccinated, and/or to follow preventive measures including the personal wearing of masks, the application of social distancing protocols, and frequent handwashing. On this basis, we postulated that the Elliott Wave Principle may similarly be used to predict the future evolution of the COVID-19 pandemic. We demonstrated that this method reproduces the data pattern for various countries and the world (daily new cases). Potential scenarios were then extrapolated, from the best-case corresponding to a rapid, full vaccination of the population, to the utterly disastrous case of slow vaccination, and poor adherence to preventive protocols.

Sociodemographic Disparities in Influenza Vaccination Among Adults With Atherosclerotic Cardiovascular Disease in the United States.

Importance: Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of death and disability in the US and worldwide. Influenza vaccination has shown to decrease overall morbidity, mortality, severity of infection, and hospital readmissions among these individuals. However, national estimates of influenza vaccination among individuals with ASCVD in the US are not well studied. Objective: To evaluate the prevalence of and sociodemographic disparities in influenza vaccination among a nationally representative sample of individuals with ASCVD. Design, Setting, and Participants: Pooled Medical Expenditure Panel Survey data from 2008 to 2016 were used and included adults 40 years or older with ASCVD. Participants' ASCVD status was ascertained via self-report and/or International Classification of Diseases, Ninth Revision diagnosis of coronary heart disease, peripheral artery disease, and/or cerebrovascular disease. Analysis began April 2020. Main Outcomes and Measures: Prevalence and characteristics of adults with ASCVD who lacked influenza vaccination during the past year. Covariates including age, sex, race/ethnicity, family income, insurance status, education level, and usual source of care were assessed. Results: Of 131 881 adults, 19 793 (15.7%) had ASCVD, corresponding to 22.8 million US adults annually. A total of 7028 adults with ASCVD (32.7%), representing 7.4 million adults, lacked influenza vaccination. The highest odds of lacking vaccination were observed among individuals aged 40 to 64 years (odds ratio [OR], 2.32; 95% CI, 2.06-2.62), without a usual source of care (OR, 2.00; 95% CI, 1.71-2.33), without insurance (OR, 2.05; 95% CI, 1.63-2.58), with a lower education level (OR, 1. 25; 95% CI, 1.12-1.40), with a lower income level (OR, 1.14; 95% CI, 1.01-1.27), and of non-Hispanic Black race/ethnicity (OR, 1.24, 95% CI, 1.10-1.41). A stepwise increase was found in the prevalence and odds of lacking influenza vaccination among individuals with increase in high-risk characteristics. Overall, 1171 individuals (59.7%; 95% CI, 55.8%-63.5%) with 4 or more high-risk characteristics and ASCVD (representing 732 524 US adults annually) reported lack of influenza vaccination (OR, 6.06; 95% CI, 4.88-7.53). Conclusion and Relevance: Despite current recommendations, a large proportion of US adults with established ASCVD lack influenza vaccination, with several sociodemographic subgroups having greater risk. Focused public health initiatives are needed to increase access to influenza vaccinations for high-risk and underserved populations.