PREDICTORS OF NON-ADHERENCE TO PRIMARY CARE FOLLOW-UP AFTER DIAGNOSIS OF ASYMPTOMATIC HYPERTENSION IN THE EMERGENCY DEPARTMENT

Background: One strategy to decrease uncontrolled hypertension is to increase follow-up with primary care after diagnosis of asymptomatic hypertension in the Emergency Department (ED). To improve such interventions, this study identified risk factors of nonadherence among individuals 18-60 years old with a diagnosis of asymptomatic hypertension in the ED and access to care. Methods: Data was obtained from the IBM® MarketScan® Commercial Database between January 2012 and September 2015. Rates of non-adherence to follow-up was determined for individuals discharged from the ED with a primary diagnosis of essential hypertension. Multivariate logistic regression was used to calculate adjusted odds ratios. Demographic and structural variables were evaluated to determine their relationship with non-adherence to follow-up. Results: Two-thirds of the study population did not adhere to follow-up within 30 days. Risk factors for non-adherence included no history of recent visit with primary care (OR=1.87; 95% CI=1.81-1.93) and multiple prior ED visits (OR=1.65; 95% CI=1.57-1.73). Protective characteristics included history of filling an anti-hypertensive prescription in last year (OR=0.42; 95% CI=0.40-0.43); or history of filling a 30-day anti-hypertensive prescription on day of index event (OR=0.83; 95% CI=0.80-0.87). Conclusion: Individuals who have not visited primary care or who are at the ED for the third time in 12 months are more likely to be non-adherent to follow-up. History of filling a 30-day anti-hypertensive prescription within one day of index event or in prior 12 months is associated with increased adherence to follow-up and should be further explored as a strategy for encouraging follow-up in this population

Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation

Reinforcement learning (RL) has helped improve decision-making in several applications. However, applying traditional RL is challenging in some applications, such as rehabilitation of people with a spinal cord injury (SCI). Among other factors, using RL in this domain is difficult because there are many possible treatments (i.e., large action space) and few patients (i.e., limited training data). Treatments for SCIs have natural groupings, so we propose two approaches to grouping treatments so that an RL agent can learn effectively from limited data. One relies on domain knowledge of SCI rehabilitation and the other learns similarities among treatments using an embedding technique. We then use Fitted Q Iteration to train an agent that learns optimal treatments. Through a simulation study designed to reflect the properties of SCI rehabilitation, we find that both methods can help improve the treatment decisions of physiotherapists, but the approach based on domain knowledge offers better performance. Our findings provide a "proof of concept" that RL can be used to help improve the treatment of those with an SCI and indicates that continued efforts to gather data and apply RL to this domain are worthwhile.Comment: 31 pages, 7 figure

Investigations into the Modification of DNA by Doxorubicin Analogs

Doxorubicin (DOX) is an anthracycline chemotherapeutic that has seen widespread use to treat numerous cancer types. Its mechanism of action is still unclear, but is thought to include the intercalation of DNA, halting transcription and inducing apoptosis. Although DOX has shown strong antitumor activity, its usage is limited due to a dose-dependent onset of cumulative and irreversible life-threatening cardiac damage. Consequently, the harmful side effects necessitate the need for the production of new, less harmful anthracycline chemotherapeutics with greater effectiveness for the treatment of cancer. Three analogs of DOX (P-DOX, GPX-150 and GPX-160) have been synthesized and determined to have antitumor activity against multiple cancer cell lines. This study seeks to investigate the mechanism by which these analogs display their activity, specifically probing for DNA modification. Each compound was tested for and found to have greater DNA-modifying abilities than DOX by the alkaline COMET, DNA gel electrophoresis, and the K-SDS DNA-protein crosslinking assays. These and related experimental results will be presented

The Registry of Senior Australians outcome monitoring system: quality and safety indicators for residential aged care.

ObjectivesTo introduce the Registry of Senior Australians (ROSA) Outcome Monitoring System, which can monitor the quality and safety of care provided to individuals accessing residential aged care. Development and examination of 12 quality and safety indicators of care and their 2016 prevalence estimates are presented.DesignRetrospective.Setting2690 national and 254 South Australian (SA) aged care facilities.Participants208 355 unique residents nationally and 18 956 in SA.Main outcome measuresRisk-adjusted prevalence of high sedative load, antipsychotic use, chronic opioid use, antibiotic use, premature mortality, falls, fractures, medication-related adverse events, weight loss/malnutrition, delirium and/or dementia hospitalisations, emergency department presentations, and pressure injuries.ResultsFive indicators were estimated nationally; antibiotic use (67.5%, 95% confidence interval (CI): 67.3-67.7%) had the highest prevalence, followed by high sedative load (48.1%, 95% CI: 47.9-48.3%), chronic opioid use (26.8%, 95% CI: 26.6-26.9%), antipsychotic use (23.5%, 95% CI: 23.4-23.7%) and premature mortality (0.6%, 95% CI: 0.6-0.7%). Seven indicators were estimated in SA; emergency department presentations (19.1%, 95% CI: 18.3-20.0%) had the highest prevalence, followed by falls (10.1%, 95% CI: 9.7-10.4%), fractures (4.8%, 95% CI: 4.6-5.1%), pressure injuries (2.9%, 95% CI: 2.7-3.1%), delirium and/or dementia related hospitalisations (2.3%, 95% CI: 2.1-2.6%), weight loss/malnutrition (0.7%, 95% CI: 0.6-0.8%) and medication-related events (0.6%, 95% CI: 0.5-0.7%).ConclusionsTwelve quality and safety indicators were developed to monitor aged care provided to older Australians based on the synthesis of existing literature and expert advisory input. These indicators rely on existing data within the aged care and healthcare sectors, therefore creating a pragmatic tool to examine quality and unwarranted care variation

Air Pollution, Urgent Asthma Medical Visits and the Modifying Effect of Neighborhood Asthma Prevalence

Background: Social and environmental stressors, may modify associations between environmental pollutants and asthma symptoms. We examined if neighborhood asthma prevalence (higher: HAPN vs. lower: LAPN), a surrogate for underlying risk factors for asthma, modified the relationship between pollutants and urgent asthma visits. Methods: Through zip code, home addresses were linked to New York City Community Air Survey’s land use regression model for street-level, annual average nitrogen dioxide (NO2), particulate matter (PM2.5), elemental carbon (EC); summer average ozone (O3); winter average sulfur dioxide (SO2) concentrations. Poisson regression models were fit to estimate the association (prevalence ratio, PR) between pollutant exposures and seeking urgent asthma care. Results: All pollutants, except O3 were higher in HAPN than LAPN (P0.05). Conclusions: Relationships between modeled street-level pollutants and urgent asthma were stronger in LAPN compared to HAPN. Social stressors that may be more prevalent in HAPN than LAPN, could play a greater role in asthma exacerbations in HAPN versus pollutant exposure alone

Transcription factor MITF and remodeller BRG1 define chromatin organisation at regulatory elements in melanoma cells

Microphthalmia-associated transcription factor (MITF) is the master regulator of the melanocyte lineage. To understand how MITF regulates transcription, we used tandem affinity purification and mass spectrometry to define a comprehensive MITF interactome identifying novel cofactors involved in transcription, DNA replication and repair, and chromatin organisation. We show that MITF interacts with a PBAF chromatin remodelling complex comprising BRG1 and CHD7. BRG1 is essential for melanoma cell proliferation in vitro and for normal melanocyte development in vivo. MITF and SOX10 actively recruit BRG1 to a set of MITF-associated regulatory elements (MAREs) at active enhancers. Combinations of MITF, SOX10, TFAP2A, and YY1 bind between two BRG1-occupied nucleosomes thus defining both a signature of transcription factors essential for the melanocyte lineage and a specific chromatin organisation of the regulatory elements they occupy. BRG1 also regulates the dynamics of MITF genomic occupancy. MITF-BRG1 interplay thus plays an essential role in transcription regulation in melanoma

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages