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Surgical mechanical ventricular assistance and cardiac replacement therapies, although life-saving in many heart failure (HF) patients, remain high-risk. Despite this, the difficulty in timely identification of medical therapy nonresponders and the dire consequences of nonresponse have fueled early, less selective surgical referral. Patients who would have ultimately responded to medical therapy are therefore subjected to the risk and life disruption of surgical therapy.The purpose of this study was to develop deep learning models based upon commonly-available electronic health record (EHR) variables to assist clinicians in the timely and accurate identification of HF medical therapy nonresponders.The study cohort consisted of all patients (age 18 to 90 years) admitted to a single tertiary care institution from January 2009 through December 2018, with International Classification of Disease HF diagnostic coding. Ensemble deep learning models employing time-series and densely-connected networks were developed from standard EHR data. The positive class included all observations resulting in severe progression (death from any cause or referral for HF surgical intervention) within 1 year.A total of 79,850 distinct admissions from 52,265 HF patients met observation criteria and contributed350 million EHR datapoints for model training, validation, and testing. A total of 20% of model observations fit positive class criteria. The model C-statistic was 0.91.The demonstrated accuracy of EHR-based deep learning model prediction of 1-year all-cause death or referral for HF surgical therapy supports clinical relevance. EHR-based deep learning models have considerable potential to assist HF clinicians in improving the application of advanced HF surgical therapy in medical therapy nonresponders.

To understand the frequency of social determinants of health (SDOH) diagnosis codes (Z-codes) within the electronic health record (EHR) for patients with prediabetes and diabetes and examine factors influencing the adoption of SDOH documentation in clinical care.EHR data and qualitative interviews with health care providers and stakeholders.An explanatory sequential mixed methods design first examined the use of Z-codes within the EHR and qualitatively examined barriers to documenting SDOH. Data were integrated and interpreted using a joint display. This research was informed by the Framework for Dissemination and Utilization of Research for Health Care Policy and Practice.We queried EHR data for patients with a hemoglobin A1c 5.7 between October 1, 2015 and September 1, 2020 (n = 118,215) to examine the use of Z-codes and demographics and outcomes for patients with and without social needs. Semi-structured interviews were conducted with 23 participants (n = 15 health care providers; n = 7 billing and compliance stakeholders). The interview questions sought to understand how factors at the innovation-, individual-, organizational-, and environmental-level influence SDOH documentation. We used thematic analysis to analyze interview data.Patients with social needs were disproportionately older, female, Black, uninsured, living in low-income and high unemployment neighborhoods, and had a higher number of hospitalizations, obesity, prediabetes, and type 2 diabetes than those without a Z-code. Z-codes were not frequently used in the EHR (1% of patients), and there was an overall lack of congruence between quantitative and qualitative results related to the prevalence of social needs. Providers faced barriers at multiple levels (e.g., individual-level: discomfort discussing social needs; organizational-level: limited time, competing priorities) for documenting SDOH and identified strategies to improve documentation.Providers recognized the impact of SDOH on patient health and had positive perceptions of screening for and documenting social needs. Implementation strategies are needed to improve systematic documentation.

The coronavirus disease 2019 (COVID-19) pandemic has challenged the accuracy and racial biases present in traditional mortality scores. An accurate prognostic model that can be applied to hospitalized patients irrespective of race or COVID-19 status may benefit patient care.This cohort study utilized historical and ongoing electronic health record features to develop and validate a deep-learning model applied on the second day of admission predicting a composite outcome of in-hospital mortality, discharge to hospice, or death within 30 days of admission. Model features included patient demographics, diagnoses, procedures, inpatient medications, laboratory values, vital signs, and substance use history. Conventional performance metrics were assessed, and subgroup analysis was performed based on race, COVID-19 status, and intensive care unit admission.A total of 35,521 patients hospitalized between April 2020 and October 2020 at a single health care system including a tertiary academic referral center and 9 community hospitals.Of 35,521 patients, including 9831 non-White patients and 2020 COVID-19 patients, 2838 (8.0%) met the composite outcome. Patients who experienced the composite outcome were older (73 vs. 61 y old) with similar sex and race distributions between groups. The model achieved an area under the receiver operating characteristic curve of 0.89 (95% confidence interval: 0.88, 0.91) and an average positive predictive value of 0.46 (0.40, 0.52). Model performance did not differ significantly in White (0.89) and non-White (0.90) subgroups or when grouping by COVID-19 status and intensive care unit admission.A deep-learning model using large-volume, structured electronic health record data can effectively predict short-term mortality or hospice outcomes on the second day of admission in the general inpatient population without significant racial bias.

Background Cancer survival rates have been steadily improving in the adolescent and young adult (AYA) population, but survivors are at increased risk for cardiovascular disease (CVD). The cardiotoxic effects of anthracycline therapy have been well studied. However, the cardiovascular toxicity associated with newer therapies, such as the vascular endothelial growth factor (VEGF) inhibitors, is less well understood. Objective This retrospective study of AYA cancer survivors sought to gain insight into their burden of cardiovascular toxicities (CT) following anthracycline and/or VEGF inhibitor therapy. Methods Data were extracted from electronic medical records over a fourteen-year period at a single institution. We utilized Cox proportional hazards regression modeling to examine risk factors for CT within each treatment group. Cumulative incidence was calculated with death as a competing risk. Results Of the 1,165 AYA cancer survivors examined, 32%, 22%, and 34% of patients treated with anthracycline, VEGF inhibitor, or both, developed CT. Hypertension was the most common outcome reported. Males were at increased risk for CT following anthracycline therapy (HR: 1.34, 95% CI 1.04–1.73). The cumulative incidence of CT was highest in patients who received both anthracycline and VEGF inhibitor (50% at ten years of follow up). Conclusions CT was common among AYA cancer survivors who received anthracycline and/or VEGF inhibitor therapy. Male sex was an independent risk factor for CT following anthracycline treatment. Further screening and surveillance are warranted to continue understanding the burden of CVD following VEGF inhibitor therapy.

Background: BREASTChoice is a web-based decision tool about breast reconstruction after mastectomy. The current focused usability assessment was designed to elicit patients’ and clinicians’ perspectives on barriers and facilitators for implementing BREASTChoice into the clinical workflow. Methods: We conducted think-aloud protocols and qualitative interviews with patients and clinicians from two Midwestern medical centers from August 2020 to April 2021. We used the sociotechnical framework to evaluate BREASTChoice’s implementation and sustainability potential according to end-users, human-computer interaction, and contextual factors. Results: Twelve clinicians and ten patients completed interviews. Using the sociotechnical framework, we determined the following. People Using the Tool: Patients and clinicians agreed that BREASTChoice could help patients make more informed decisions about their reconstruction, and prepare better for their first plastic surgery appointment. Workflow and Communications: They felt that BREASTChoice could improve communication and the decision process if the patient could view the tool before the clinical visit. Clinicians suggested the information from BREASTChoice about patients’ risks and preferences be included in the patient’s chart or the clinician electronic health record (EHR) inbox for accessibility during the consultation. Human Computer Interface: Patients and clinicians stated that the tool contains helpful information, does not require much time for the patient to use, and efficiently fills gaps in knowledge. Although patients found the risk profile information helpful, they reported needing time to read and digest it. Conclusion: BREASTChoice was perceived as highly usable by patients and clinicians and has the potential for sustainability. Future research will implement and test the tool after integrating the stakeholder-suggested changes to its delivery process and content. It is critical to conduct usability assessments such as these prior to decision tool implementation in order to improve workflow and risk communication.

ObjectiveExamine whether the relationship between the pooled cohort equations (PCE) predicted 10-year risk for atherosclerotic cardiovascular disease (ASCVD) and absolute risk for ASCVD is modified by socioeconomic status (SES).DesignPopulation-based longitudinal cohort study—Atherosclerosis Risk in Communities (ARIC)—investigating the development of cardiovascular disease across demographic subgroups.SettingFour communities in the USA—Forsyth County, North Carolina, Jackson, Mississippi, suburbs of Minneapolis, Minnesota and Washington County, Maryland.ParticipantsWe identified 9782 ARIC men and women aged 54–73 without ASCVD at study visit 4 (1996–1998).Primary outcome measuresRisk ratio (RR) differences in 10-year incident hospitalisations or death for ASCVD by SES and PCE predicted 10-year ASCVD risk categories to assess for risk modification. SES measures included educational attainment and census-tract neighbourhood deprivation using the Area Deprivation Index. PCE risk categories were 0%–5%, >5%–10%, >10%-15% and >15%. SES as a prognostic factor to estimate ASCVD absolute risk categories was further investigated as an interaction term with the PCE.ResultsASCVD RRs for participants without a high school education (referent college educated) increased at higher PCE estimated risk categories and was consistently >1. Results indicate education is both a risk modifier and delineates populations at higher ASCVD risk independent of PCE. Neighbourhood deprivation did modify association but was less consistent in direction of effect. However, for participants residing in the most deprived neighbourhoods (referent least deprived neighbourhoods) with a PCE estimated risk >10%–15%, risk was significantly elevated (RR 1.65, 95% CI 1.05 to 2.59). Education and neighbourhood deprivation inclusion as an interaction term on the PCE risk score was statistically significant (likelihood ratio p≤0.0001).ConclusionsSES modifies the association between PCE estimated risk and absolute risk of ASCVD. SES added into ASCVD risk prediction models as an interaction term may improve our ability to predict absolute ASCVD risk among socially disadvantaged populations.

BACKGROUND Telehealth has become widely used as a novel way to provide outpatient care during the COVID-19 pandemic, but data about telehealth use in primary care remain limited. Studies in other specialties raise concerns that telehealth may be widening existing health care disparities, requiring further scrutiny of trends in telehealth use. OBJECTIVE Our study aims to further characterize sociodemographic differences in primary care via telehealth compared to in-person office visits before and during the COVID-19 pandemic and determine if these disparities changed throughout 2020. METHODS We conducted a retrospective cohort study in a large US academic center with 46 primary care practices from April-December 2019 to April-December 2020. Data were subdivided into calendar quarters and compared to determine evolving disparities throughout the year. We queried and compared billed outpatient encounters in General Internal Medicine and Family Medicine via binary logic mixed effects regression model and estimated odds ratios (ORs) with 95% CIs. We used sex, race, and ethnicity of the patient attending each encounter as fixed effects. We analyzed socioeconomic status of patients in the institution’s primary county based on the patient’s residence zip code. RESULTS A total of 81,822 encounters in the pre–COVID-19 time frame and 47,994 encounters in the intra–COVID-19 time frame were analyzed; in the intra–COVID-19 time frame, a total of 5322 (11.1%) of encounters were telehealth encounters. Patients living in zip code areas with high utilization rate of supplemental nutrition assistance were less likely to use primary care in the intra–COVID-19 time frame (OR 0.94, 95% CI 0.90-0.98; P=.006). Encounters with the following patients were less likely to be via telehealth compared to in-person office visits: patients who self-identified as Asian (OR 0.74, 95% CI 0.63-0.86) and Nepali (OR 0.37, 95% CI 0.19-0.72), patients insured by Medicare (OR 0.77, 95% CI 0.68-0.88), and patients living in zip code areas with high utilization rate of supplemental nutrition assistance (OR 0.84, 95% CI 0.71-0.99). Many of these disparities persisted throughout the year. Although there was no statistically significant difference in telehealth use for patients insured by Medicaid throughout the whole year, subanalysis of quarter 4 found encounters with patients insured by Medicaid were less likely to be via telehealth (OR 0.73, 95% CI 0.55-0.97; P=.03). CONCLUSIONS Telehealth was not used equally by all patients within primary care throughout the first year of the COVID-19 pandemic, specifically by patients who self-identified as Asian and Nepali, insured by Medicare, and living in zip code areas with low socioeconomic status. As the COVID-19 pandemic and telehealth infrastructure change, it is critical we continue to reassess the use of telehealth. Institutions should continue to monitor disparities in telehealth access and advocate for policy changes that may improve equity.

Public-facing maps of COVID-19 cases, hospital admissions, and deaths are commonly displayed at the state, county, and zip code levels, and low case counts are suppressed to protect confidentiality. Public health authorities are tasked with case identification, contact tracing, and canvasing for educational purposes during a pandemic. Given limited resources, authorities would benefit from the ability to tailor their efforts to a particular neighborhood or congregate living facility.We describe the methods of building a real-time visualization of patients with COVID-19-positive tests, which facilitates timely public health response to the pandemic. We developed an interactive street-level visualization that shows new cases developing over time and resolving after 14 days of infection. Our source data included patient demographics (ie, age, race and ethnicity, and sex), street address of residence, respiratory test results, and date of test.We used colored dots to represent infections. The resulting animation shows where new cases developed in the region and how patterns changed over the course of the pandemic. Users can enlarge specific areas of the map and see street-level detail on residential location of each case and can select from demographic overlays and contour mapping options to see high-level patterns and associations with demographics and chronic disease prevalence as they emerge.Before the development of this tool, local public health departments in our region did not have a means to map cases of disease to the street level and gain real-time insights into the underlying population where hotspots had developed. For privacy reasons, this tool is password-protected and not available to the public. We expect this tool to prove useful to public health departments as they navigate not only COVID-19 pandemic outcomes but also other public health threats, including chronic diseases and communicable disease outbreaks.

Adolescents bear a disproportionate burden of sexually transmitted infections (STIs) and the sequelae of delayed treatment, yet STI screening is infrequently performed in pediatric primary care clinics with many of those at-risk not administered testing. This study aims to understand contextual factors influencing STI screening and testing among adolescents in pediatric primary care.We used the Consolidated Framework for Implementation Research (CFIR) as part of a stepwise approach to facilitate a deep understanding the pediatric primary care environment. We conducted semistructured interviews of physicians, nurses, and patient-parent dyads from 4 pediatric primary care practices in the St. Louis metropolitan area about STI screening practices and common concerns regarding STI screening. Qualitative analysis was conducted using a categorical coding technique informed by the CFIR followed by a thematic coding technique.We interviewed 23 physicians/nurses and 12 patient-parent dyads. Individual-level barriers to STI screening and testing included wide variability in clinicians' practice patterns and their perception of STI risk in the patient population. Structural barriers included a lack of capacity to perform testing in clinic and time constraints during patient visits. Confidentiality issues also created significant barriers to screening and testing on both individual and structural levels. Adopting confidential methods for testing and educating providers on patients' recommendations for STI testing were discussed as ways to potentially improve STI care in pediatric patients.Our use of the CFIR facilitated a systematic approach to identify gaps in STI care for adolescents and identified opportunities to close those gaps. An integrated, systematic approach that enhances patient confidentiality and improves clinicians' knowledge could address gaps in STI care in pediatric primary care settings.

ImportanceGoal-concordant care is an ongoing challenge in hospital settings. Identification of high mortality risk within 30 days may call attention to the need to have serious illness conversations, including the documentation of patient goals of care.ObjectiveTo examine goals of care discussions (GOCDs) in a community hospital setting with patients identified as having a high risk of mortality by a machine learning mortality prediction algorithm.Design, Setting, and ParticipantsThis cohort study took place at community hospitals within 1 health care system. Participants included adult patients with a high risk of 30-day mortality who were admitted to 1 of 4 hospitals between January 2 and July 15, 2021. Patient encounters of inpatients in the intervention hospital where physicians were notified of the computed high risk mortality score were compared with patient encounters of inpatients in 3 community hospitals without the intervention (ie, matched control).InterventionPhysicians of patients with a high risk of mortality within 30 days received notification and were encouraged to arrange for GOCDs.Main Outcomes and MeasuresThe primary outcome was the percentage change of documented GOCDs prior to discharge. Propensity-score matching was completed on a preintervention and postintervention period using age, sex, race, COVID-19 status, and machine learning-predicted mortality risk scores. A difference-in-difference analysis validated the results.ResultsOverall, 537 patients were included in this study with 201 in the preintervention period (94 in the intervention group; 104 in the control group) and 336 patients in the postintervention period. The intervention and control groups included 168 patients per group and were well-balanced in age (mean [SD], 79.3 [9.60] vs 79.6 [9.21] years; standardized mean difference [SMD], 0.03), sex (female, 85 [51%] vs 85 [51%]; SMD, 0), race (White patients, 145 [86%] vs 144 [86%]; SMD 0.006), and Charlson comorbidities (median [range], 8.00 [2.00-15.0] vs 9.00 [2.00 to 19.0]; SMD, 0.34). Patients in the intervention group from preintervention to postintervention period were associated with being 5 times more likely to have documented GOCDs (OR, 5.11 [95% CI, 1.93 to 13.42]; P = .001) by discharge compared with matched controls, and GOCD occurred significantly earlier in the hospitalization in the intervention patients as compared with matched controls (median, 4 [95% CI, 3 to 6] days vs 16 [95% CI, 15 to not applicable] days; P Conclusions and RelevanceIn this cohort study, patients whose physicians had knowledge of high-risk predictions from machine learning mortality algorithms were associated with being 5 times more likely to have documented GOCDs than matched controls. Additional external validation is needed to determine if similar interventions would be helpful at other institutions.

Machine learning algorithms have been widely used to capture the static and temporal patterns within electronic health records (EHRs). While many studies focus on the (primary) prevention of diseases, primordial prevention (preventing the factors that are known to increase the risk of a disease occurring) is still widely under-investigated. In this study, we propose a multi-target regression model leveraging transformers to learn the bidirectional representations of EHR data and predict the future values of 11 major modifiable risk factors of cardiovascular disease (CVD). Inspired by the proven results of pre-training in natural language processing studies, we apply the same principles on EHR data, dividing the training of our model into two phases: pre-training and fine-tuning. We use the fine-tuned transformer model in a “multi-target regression” theme. Following this theme, we combine the 11 disjoint prediction tasks by adding shared and target-specific layers to the model and jointly train the entire model. We evaluate the performance of our proposed method on a large publicly available EHR dataset. Through various experiments, we demonstrate that the proposed method obtains a significant improvement (12.6% MAE on average across all 11 different outputs) over the baselines.

BACKGROUND Computationally derived (“synthetic”) data can enable the creation and analysis of clinical, laboratory, and diagnostic data as if they were the original electronic health record data. Synthetic data can support data sharing to answer critical research questions to address the COVID-19 pandemic. OBJECTIVE We aim to compare the results from analyses of synthetic data to those from original data and assess the strengths and limitations of leveraging computationally derived data for research purposes. METHODS We used the National COVID Cohort Collaborative’s instance of MDClone, a big data platform with data-synthesizing capabilities (MDClone Ltd). We downloaded electronic health record data from 34 National COVID Cohort Collaborative institutional partners and tested three use cases, including (1) exploring the distributions of key features of the COVID-19–positive cohort; (2) training and testing predictive models for assessing the risk of admission among these patients; and (3) determining geospatial and temporal COVID-19–related measures and outcomes, and constructing their epidemic curves. We compared the results from synthetic data to those from original data using traditional statistics, machine learning approaches, and temporal and spatial representations of the data. RESULTS For each use case, the results of the synthetic data analyses successfully mimicked those of the original data such that the distributions of the data were similar and the predictive models demonstrated comparable performance. Although the synthetic and original data yielded overall nearly the same results, there were exceptions that included an odds ratio on either side of the null in multivariable analyses (0.97 vs 1.01) and differences in the magnitude of epidemic curves constructed for zip codes with low population counts. CONCLUSIONS This paper presents the results of each use case and outlines key considerations for the use of synthetic data, examining their role in collaborative research for faster insights.

BACKGROUND The diabetes mellitus cascade of care has been constructed to evaluate diabetes care at a population level by determining the percentage of individuals diagnosed and linked to care as well as their reported glycemic control. OBJECTIVE We sought to adapt the cascade of care to an inpatient-only setting using the electronic health record (EHR) data of 81,633 patients with type 2 diabetes. METHODS In this adaptation, linkage to care was defined as prescription of diabetes medications within 3 months of discharge, and control was defined as hemoglobin A1c (HbA1c) below individual target levels, as these are the most reliably captured items in the inpatient setting. We applied the cascade model to assess differences in demographics and percent loss at each stage of the cascade; we then conducted two-sample chi-square equality of proportions tests for each demographic. Based on findings in the previous literature, we hypothesized that women, Black patients, younger patients ( RESULTS We found that out of 81,633 patients, 28,716 (35.2%) were linked to care via medication prescription. Women and younger patients were slightly less likely to be linked to care than their male and older counterparts, while Black patients (n=19,141, 23.4% of diagnosed population vs n=6741, 23.5% of the linked population) were as proportionately part of the linked population as White patients (n=58,291, 71.4% of diagnosed population vs n=20,402, 71.0% of the linked population). Those living in underserved communities (ie, the Promise Zone) and uninsured patients were slightly overrepresented (n=6789, 8.3% of diagnosed population vs n=2773, 9.7% of the linked population) in the linked population as compared to patients living in wealthier zip codes and those who were insured. Similar patterns were observed among those more likely to reach glycemic control via HbA1c. However, conclusions are limited by the relatively large amount of missing glycemic data. CONCLUSIONS We conclude that inpatient EHR data do not adequately capture the care cascade as defined in the outpatient setting. In particular, missing data in this setting may preclude assessment of glycemic control. Future work should integrate inpatient and outpatient data sources to complete the picture of diabetes care.

BACKGROUND: Non-arthritic hip disease (NAHD) is a clinical condition often accompanied by painful movement. Current literature is lacking regarding how movement abnormalities are evaluated and treated in this population, which may be key to identifying which patients may respond to non-operative versus operative treatment. Combining the expertise of a hip arthroscopist and physical therapist may better inform treatment decisions for persons with NAHD. The primary objective of this study is to identify the extent to which an interdisciplinary evaluation between a physical therapist and surgeon influences treatment decisions of persons presenting to a hip preservation clinic. RATIONALE FOR STUDY DESIGN: A prospective, randomized controlled trial provides the ability to identify cause and effect of this new evaluation type. METHODS: Ninety-six adults with unilateral, NAHD presenting to a hip preservation clinic for initial evaluation will be randomized to receive either a standard evaluation with a surgeon or an interdisciplinary evaluation by a physical therapist and surgeon. Regardless of group, the surgeon conducts a standard-care examination. For participants in the interdisciplinary group, the physical therapist conducts an assessment of 6 postures and movements to identify asymmetrical, abnormal, or painful strategies. Treatment selection(s) and decisional conflict will be compared between groups after the evaluations. DISCUSSION: Persons with NAHD may experience considerable decisional conflict because of prolonged duration of symptoms and minimal evidence to compare operative and non-operative treatment for this population. The findings of this study have the potential to improve patient experience and produce more informed and supported treatment decisions for persons considering surgical treatment for NAHD.

This is a single-center cross-sectional study of consecutive patients who presented to an urban emergency department (ED) with firearm injuries between July 1, 2014 and June 30, 2016. The objective of this study was to describe the characteristics of patients in this cohort and identify those at risk of firearm re-injury to inform future interventions. Patients in this cohort with both initial and recidivistic firearm injures were found to be predominately young, African American men.To describe the characteristics of patients who present to the emergency department (ED) with firearm injuries and identify those at risk of firearm re-injury.This is a single-center cross-sectional study of consecutive patients who presented to the ED with firearm injuries between July 1, 2014 and June 30, 2016. We collected data on patient demographics, history of previous traumatic injury including firearm injury, and whether the firearm injury was self-inflicted. We also evaluated characteristics of patients in this cohort who returned to the ED with firearm re-injury within a one year follow-up period.This study included 1226 unique patients. Our data demonstrate that patients presenting to the ED for firearm injury were predominately young, African American males. Fourteen percent had a history of a previous firearm injury and 20% had a history of other non-firearm assault. Patients who had been shot previously were more likely to be African American, male, uninsured, unemployed, and have a history of other non-accidental trauma. Eight percent of firearm injuries were self-inflicted. All 35 patients who sustained a new firearm injury within 1 year of the index injury were African American males with a median age of 23.Among patients treated in the ED for firearm injuries, young, African American males are disproportionately at risk of firearm injury and re-injury. ED visits for traumatic injury represent an opportunity to provide social work, case work, and counseling-based interventions to help disrupt the cycle of violence in high-risk individuals.

Background: Electronic health records (EHRs) are an increasingly valuable data source for monitoring population health. However, EHR data are rarely shared across health system borders, limiting their utility to researchers and policymakers. The Guideline Advantage™ (TGA) program, a joint initiative by the American Heart Association (AHA), American Cancer Society, and American Diabetes Association, brings together data from EHRs across the country to support disease prevention and management efforts in the outpatient setting. Methods: We analyzed TGA EHR data from >70 clinics comprising 281,837 adult patients from 2010 to 2015. We used the first available measure per patient for each calendar year to characterize trends in the proportion of patients in “ideal”, “intermediate”, and “poor” CVH categories for blood pressure (BP), body mass index (BMI) and smoking. Total cholesterol and fasting glucose values were not reported to TGA. Thus, we used low-density lipoprotein (LDL) and hemoglobin A1c (A1c) treatment guidelines to classify patients into CVH categories for the respective metrics. Results: Patients were an average of 50 years old, and 57.4% were female. Of records with complete data on race, 70.9% of patients were white. Over 6 years of observation, we documented increases in the proportion of patients at ideal levels for BP, smoking, LDL, and A1c, but decreases in the proportion of patients at an ideal level for BMI (Figure). Conclusions: TGA data provide a large-scale perspective of outpatient CVH, yet we acknowledge limitations associated with using EHR data to assess trends in CVH. Specifically, EHR data entry is clinically-driven - BP and BMI values are likely to be updated at each visit for each patient, while smoking status, LDL, and A1c are not. Our analysis lays the groundwork for EHR analyses as these data become less siloed and more accessible to stakeholders. Figure. Trends in CVH from 2010 to 2015: The Guideline Advantage™

Background: Disparities in environmental exposures have been investigated in a multitude of epidemiologic studies. Many of these studies have demonstrated that minorities and poorer individuals were disproportionately exposed to elevated levels of air pollution and other environmental hazards. To date, differential air pollution exposures have not been assessed in a cohort of individuals with compromised respiratory health. We evaluated the distribution of air pollution exposures in such a cohort by area-level socioeconomic factors. Methods: Data from the National Emphysema Treatment Trial (NETT) (1998–2003), which included 1218 subjects ages 39–84, were linked with air pollution data from the U.S. Environmental Protection Agency Air Quality Systems database. The air pollution data included daily values of fine particulate matter (PM2.5) and ozone. ZIP code specific exposures were spatially interpolated with the use of log-normal kriged models. This methodology was employed in order to assign exp...

Background: Heart failure (HF), a leading cause of morbidity and mortality in women, continues to disproportionately affect African-American (AA) women compared to women of other races/ethnicities. In a large cohort of post-menopausal women, we investigated if AA women progress through HF stages and coronary heart disease (CHD) death more rapidly than Caucasian (Cau) or Hispanic/Latina (HL) women. Methods: We assessed the American College of Cardiology/American Heart Association (ACC/AHA) HF stage A upon enrollment into the Women’s Health Initiative (1993-1998) and the progression from Stage A to Stage C, Stage C to CHD death, up to the last date of follow-up (2010). Cox proportional hazard regressions models were used to assess disease progression risk. We adjusted for age, baseline comorbidities, duration of comorbidities, interval development of myocardial infarction, ejection fraction at HF diagnosis, socioeconomic factors, and sex specific variables. Results: At baseline, AA women were younger and had a higher percentage of Stage A HF (76%) than Cau (54%) and HL (55%) women. Overall, AA and HL women had significantly lower risk of progressing from Stage A to Stage C compared to Cau women [AA vs. Cau adjusted Hazard Ratio (HR) 0.77 (95% Confidence Interval (CI) 0.64 -0.93) p = 0.0055; HL vs. Cau HR 0.54 (95% CI 0.40 -0.71) p < 0.0001]. After stratifying by age groups(70 years), AA women had a similar risk of progression from stage A to C compared to Cau women. When compared to Cau women, AA had an insignificant increased risk of progressing from Stage C to CHD death [adjusted HR 1.29 (95% CI 0.72 -2.32) p = 0.3883)]. Conclusions: Cau women were at higher risk for HF progression from Stage A to Stage C compared to HL women, but at similar risk as AA women. AA women had an insignificant but increased risk of progression from Stage C to CHD death compared to Cau women.

Introduction: A growing population of older breast cancer (BC) survivors may be at risk of cardiovascular disease (CVD). Pre-existing CVD risk factors may impact on subsequent development of CVD after BC among postmenopausal women. Objectives: (1) To describe baseline CVD risk factors among postmenopausal women with and without BC prior to diagnosis of BC; and (2) To examine independent associations of BC with individual and composite CVD events (i.e., the first occurrence of coronary heart disease [CHD], angina, coronary revascularization, congestive heart failure, peripheral arterial disease, and stroke) and mortality outcomes (i.e., all-cause, CVD, and CHD death) after BC adjusting for baseline characteristics and CVD risk factors. Methods: This prospective cohort study included 101,916 women in the Women’s Health Initiative (WHI) (including 4,340 women with invasive BC) aged 50-79 years without CVD at baseline (1993-1998) through December 2010. Over a period of 10 years after BC, CVD morbidity and mortality outcomes were identified and age-adjusted rates per 1,000 person-years and hazard ratios (HRs) with 95% confidence intervals (CIs) from Cox models were determined for women who did and did not develop BC. Results: Regardless of BC status, baseline CVD risk factors prior to BC, such as smoking, hypertension, diabetes and hypercholesterolemia, were strong predictors of CVD outcomes in postmenopausal women. Multivariable Cox models revealed that women who developed BC (n = 4,340) had a similar risk of composite CVD (HR = 0.91, 95% CI, 0.82-1.02), but a higher risk of CHD (HR=1.33, 95% CI, 1.12-1.58), compared with women without BC (n = 97,576). Women aged 70-79 and on hormonal therapy at the WHI entry with localized BC during follow-up period had the highest CHD death rate. The results were similar after excluding women with baseline cancer at the WHI study entry. Conclusions: These results emphasize the great need for identification and timely management of pre-existing CVD risk factors for postmenopausal women with BC. CVD accounts for almost as many deaths as BC among women with localized disease, indicating that improved effort to prevent and treat CVD risk factors could improve survival of postmenopausal BC survivors.

Background: Rejection is one of the largest causes of morbidity and mortality early post heart transplant. Patient selection is made to help reduce this risk. We hypothesized that comorbid diseases and socioeconomic status (SES) would lead to earlier readmission post heart transplant. Methods: We retrospectively evaluated 188 patients who underwent orthotopic heart transplant between 7/1999- 11/2013 at our facility. All patients were followed until 9/2014 for rejection hospitalizations and death. Multivariable analysis with adjustment for median household income (MHI), insurance status, age, gender, and race was performed. Results: Medicaid insurance is a significant predictor for time to readmission post heart transplant compared to Non-Medicaid insurance (NMI) [Hazard Ratio (HR) 1.78, p= 0.047]. MHI was not a predictor (HR 1.04, p=0.835). After adjusting for SES, diabetes mellitus (DM) alone remained a significant independent predictor for time to first hospitalization for rejection (HR 3.47, p=0.001). In univariable analysis, race and hypertension approach significance as factors associated with time to first hospitalization respectively (HR 0.057, p=0.09; HR 1.97, p=0.08). SES and comorbidities did not impact time to death post transplant. Conclusions: Medicaid insurance was a significant predictor of time to first hospitalization for rejection, further raised in diabetics. This suggests that immunosuppression medications especially in diabetics require heightened attention.

Background: Readmission rates after placement of left ventricular assist device (LVAD) remain high and are generally attributed to arrhythmia, bleeding, heart failure (HF), infection, and thrombosis. Readmission and mortality risks have not been clearly assessed after adjusting for socioeconomic status (SES). We hypothesized that etiologies unrelated to medication adherence and nutrition would contribute to worse outcomes. Methods: We retrospectively evaluated 101 patients who underwent HeartMate II LVAD between January 2006- December 2010 at our facility. All patients were followed until December 2011 for readmission and death. Multivariable analysis with adjustment for median household income, insurance status, age, gender, and race was performed. Results: The main causes of initial readmission and death respectively were arrhythmia (n=5, 5.4%; n=1, 3%), bleeding (n=13, 14.0%; n=9, 30%), device issue (n=20, 21.5%; n=6, 20%), HF (n=8, 8.6%; n=2, 7%), and infection (n=6, 6.5%; n=0, 0%). After adjusting for SES, bleeding alone remained a significant independent predictor for readmission [Hazard Ratio (HR) 2.57, p=0.003] and mortality (HR 4.73, p Conclusions: Bleeding was associated with significant increased risk of readmission and mortality after LVAD even after adjusting for SES. This suggests that income levels and perhaps education do not provide protection from readmission and mortality after LVAD.

Cardiovascular disease (CVD) and cancer are leading causes of morbidity and mortality. A favorable risk factor and behavior profile, reflected by a metric of cardiovascular health (CV health) recently introduced by the AHA, predict a lower risk of CVD in younger populations, yet has not been well-characterized in older adults. Achieving ideal CV health may also lower cancer risk, since cancer and CVD share common risk factors. We used multivariable Cox regression to estimate hazard ratios (HR) for the association between CV health and incident CVD and cancer, respectively, among Women’s Health Initiative (WHI) participants. We characterized CV health as the number [0 (worst) - 7 (best)] of ideal CV health behaviors and factors at baseline: 1) never smoking, or quit for >1 yr; 2) body mass index 2 ; 3) physical activity of ≥150 min/wk moderate, ≥75 min/wk vigorous, or ≥150 min/wk moderate and vigorous; 4) at least 4 of 5 healthy diet components (≥4.5 cups/day of fruits and vegetables; ≥3.5 oz servings/wk of fish; ≥3 1-oz servings/day of whole grains; Figure 1. HRs and 95% CIs for (a) incident CVD and (b) incident cancer by ideal CV health score: WHI observational study participants

Trajectories of functional status among those with cardiovascular disease A. Kucharska-Newton 1 , R. Foraker 2 , C. Kalbaugh 1 , K. Rose 3 , B.G. Windham 4 , W. Rosamond 1 (1) UNC at Chapel Hill, NC; (2) OSU, Columbus, OH; (3) SRA International, Durham, NC; (4) UM, Jackson, MS Background: We examined functional status trajectories prior to and after hospitalization for non-fatal events (coronary heart disease (CHD), stroke, and heart failure (HF)) among participants of the Atherosclerosis Risk in Communities (ARIC) Study cohort (1993-2007). Methods: At annual follow-up, ARIC participants were asked if they were able (Yes=1/No=0) to walk up and down a flight of stairs, walk half a mile, perform heavy household work, and perform usual activities. A functional status score (range: 0-4) was constructed by summing these responses at each of the five years prior to and following an event. Results: Figure 1 shows that persons with CHD had the highest functional status score (adjusted for gender, race, and age) pre- and post-event, as compared to those with HF or stroke. Functional status post-event was greatest among those with CHD, followed by those with stroke. We observed a significant decrease in functional status following hospitalization for HF with no recovery over five years. Conclusions: Self-reported functional status post CHD and post stroke was either similar to that pre-event or continued to rise. Functional status following HF hospitalization declined and remained depressed over time. Studies are needed to identify treatment strategies that optimize function following CVD events, especially in heart failure patients. Figure 1. Functional status prior to and following a CVD event.

To examine the association of smoking and gender with body image satisfaction, perceived stress, and self-esteem in young adults.Respondents completed a survey consisting of Perceived Stress Scale, Body-Areas Satisfaction Scale, Rosenberg Self-Esteem Scale, and the Positive and Negative Affect Schedule. Current smokers (n = 483) and never smokers (n = 973) are included.Smoking and female gender were independently associated with higher perceived stress (P0.001). Female gender was associated with lower body image satisfaction and lower self-esteem (P0.001). Current smoking was associated with lower self-esteem (P = 0.007).Smoking treatment should include stress management and self-esteem and body image improvement.