bluuebunny/crossref_metadata_2025 huggingface dataset:
Abstract 178: Predicting Employee Health and Cost: Application of Machine Learning on Employee Health Claims Data, Insights, and Possibilities
Authors: Saxena Anshul , Das Sankalp , Rubens Muni , Salami Joseph , Bhatt Chintan , Tian Tian , McGranaghan Peter , Gidel Louis , Veledar Emir
Published: April 2019

Background:
Self-insured employers, which are majority in US, face an increasing financial burden as health care costs have increased relative to savings. By applying machine learning (ML) techniques, we may decrease unnecessary hospitalizations by identifying low risk patients, who are on the path to become high risk, and eventually high cost. In this complex simulation, we applied and compared several ML techniques on data gathered from medical insurance claims.

Methods:
The analysis was limited to employees and their spouses only. We identified about 8000 employees and spouses who were covered by employer sponsored health insurance plan between 2011 and 2016. De-identified data were used to predict high cost claimants. High cost was defined as annual spending of over $10,000 in medical claims. We utilized and compared methods namely: bootstrapped random forest, gradient boosted tree, neural networks, Naïve Bayes classifier, and logistic regression with LASSO. Variables in the model included yearly inpatient and ER visits, CCS categories, CPT codes, and demographic information.

Results:
The mean (SD) age of eligible population was 49.7 (11.0) years. Approximately, the employee to spouse ratio each year was 3.5:1. Results show that bootstrapped random forest performed better than other techniques (AUROC: 80.33%). All other algorithms also performed extremely well. Future progress of these models will be oriented towards including employee health fair data and also strategies to avoid over fitting.

Conclusion:
Healthcare is way behind other industries in successfully using ML to improve processes and disrupt one industry that is admittedly in need of positive disruption. Models allowing successful preventive actions will both increase revenue and lower costs. Application of modern ML techniques can identify high cost claimants earlier. Our approach is one of the first applications of significant recent advances in ML algorithms and computer technologies, opening new opportunities for the future analysis of high healthcare costs.
Abstract 181: Application of the 2018 Cholesterol Guideline for Secondary Prevention in Very High-risk Patients with Atherosclerotic Cardiovascular Disease in the United States
Authors: Shin Doosup , Bohra Chandrashekar , Kongpakpaisarn Kullatham
Published: April 2019

Background:
As opposed to the prior cholesterol guideline which did not include low-density lipoprotein cholesterol (LDL-C) goal for secondary prevention, the 2018 American Heart Association (AHA)/American College of Cardiology (ACC) guideline recommends to lower LDL-C levels below 70 mg/dL in patients with atherosclerotic cardiovascular disease (ASCVD) who are at very high-risk. In this cross-sectional study, we investigated an epidemiological impact of the 2018 guideline with the new LDL-C goal for secondary prevention in the U.S.

Methods:
From the National Health and Nutrition Examination Survey (NHANES) 2005-2014, we identified very high-risk patients with clinical ASCVD who had completed a fasting blood test including LDL-C. ASCVD included self-reported coronary heart disease, angina, myocardial infarction, and any stroke. As per the guideline, very high-risk was defined as presence of multiple (≥ 2) high-risk conditions, such as old age (≥ 65 years), diabetes, hypertension, chronic kidney disease, current smoking, and history of heart failure. We estimated patients who were taking prescribed cholesterol medications and whose LDL-C level was ≥ 70 mg/dL despite the lipid-lowering therapy. Sampling weights were used in all statistical analyses to account for complex sampling design and nonresponse of the NHANES. Data are presented as weighted prevalence (%) and 95 % confidence interval (CI) or mean ± standard deviation.

Results:
Among 1093 nationally representative patients with clinical ASCVD, we finally included 978 patients who were at very-high risk (86.3 % [95 % CI, 83.1-88.9]). Their mean LDL-C level was 100.3 ± 2.8 mg/dL, and 77.5 % (95 % CI, 74.0-80.6) had LDL-C levels ≥ 70 mg/dL. Among patients who answered the survey question regarding lipid-lowering therapy (n =574), 91.3 % (95 % CI, 88.3-93.6) were taking prescribed cholesterol medications. Mean LDL-C level of those who were taking the cholesterol medications was 98.2 ± 14.0 mg/dL, and 68.9 % (95 % CI, 64.0-73.5) of them had LDL-C levels ≥ 70 mg/dL despite the lipid-lowering therapy. When this result was extrapolated to the entire U.S. population using the sampling weights, 6.1 million ASCVD patients at very high-risk who were on lipid-lowering therapy had LDL-C levels above the goal. Although we used data from 2005 to 2014 to include more patients, results were not so much different when the analyses were restricted to the most recent survey cycle (2013-2014).

Conclusion:
In the U.S., more than eight out of ten patients with clinical ASCVD were at very high-risk. Although majority of those very high-risk patients with ASCVD were receiving lipid-lowering therapy, about two thirds of them still had LDL-C levels above the goal recommended by the new cholesterol guideline. Therefore, more attention should be made for secondary prevention after ASCVD in very high-risk patients.
Abstract 189: Why Digital Health Trials Can Fail: Lessons Learned from the SmartGUIDE Randomized Trial of Virtual Cardiac Rehabilitation
Authors: Olivier Christoph , Middleton Stephanie , Purington Natasha , Shashidhar Sumana , Mahaffey Kenneth W , Turakhia Mintu
Published: April 2019

Background:
We performed a trial to evaluate the efficacy of a blended intervention with a custom-designed smartphone app combined with health coaching to improve medication adherence and cardiovascular risk factors. The trial failed to achieve objectives due to a number of factors. Here, we describe findings from a root cause analysis and lessons learned.

Methods:
SmartGUIDE was an open-label, single center trial of patients with coronary artery disease who were prescribed a statin and/or P
2
Y
12
antagonist that randomized 1:1 to either usual care or the use of a mobile app paired with personal virtual coaching. The primary outcome for medication adherence was the proportion of days covered (PDC). The planned sample size was 132 for 80% power. However, the study was terminated early with only 63 patients randomized and data of 26 patients ultimately available for analysis. We performed a root cause analysis using the 5-Whys technique to evaluate processes from study development to closure.

Results:
Figure 1 shows an Ishikawa cause-and-effect diagram for root cause analysis of trial failure. Root causes included aspects of intervention design, study design, enrollment, and retention. Many factors were related to the startup partner’s product design, inexperience in health care, and insufficient funding.

Conclusion:
We identified important and preventable factors leading to failure to achieve the trial objectives. These factors may be common across digital health trials and may explain prior observations that many such trials are never completed. Careful vetting of technology partners and more pragmatic study designs may be important lessons.
Abstract 2: Association of Ambient Air PM
2.5
Concentration with Health Status in Patients Following Myocardial Infarction
Authors: Malik Ali O , Peri-Okonny Poghni , Hejjaji Vittal , Jones Philip G , Chan Paul , Spertus John
Published: April 2019

Background:
Despite extensive demonstration of a strong association between fine particulate matter (PM
2.5,
< 2.5 μm in diameter) and development/progression of coronary artery disease (CAD), no study has examined the association of PM
2.5
with patients’ health status. We leveraged data from 2 prospective multicenter myocardial infarction (MI) registries, TRIUMPH and PREMIER, to assess the relationship between ambient air PM
2.5
concentration and health status in patients 1 year after MI.

Methods:
TRIUMPH and PREMIER enrolled patients presenting with MI among 31 U.S. hospitals between 2003 and 2008. One year after their MI, we assessed patients’ CAD-related health status using the Seattle Angina Questionnaire (SAQ), general physical functioning using the Short Form-12 Physical Component Summary (SF-12 PCS), and dyspnea symptoms using a 4-level ordinal item based on the Rose Dyspnea Scale. We estimated patients’ exposure to ambient air PM
2.5
using publicly available data from EPA’s Fused Air Quality Surface Using Downscaling tool, which integrates monitoring station data and atmospheric models to predict daily air pollution parameters at the census tract level. We calculated patients’ average daily PM
2.5
exposure over the year prior to their assessment, and evaluated the association with health status using generalized additive models with smoothing splines, adjusting for age, sex, race, socioeconomic status and date of enrollment.

Results:
A total of 3,913 patients (age 61±21, 33% female, 25% non-Caucasian) completed 1-year health status assessments and were linkable to air quality data. The median 12-month average PM
2.5
exposure per patient was 12.1 μg/m
3
(range = 4.3-20.5). In adjusted analyses, higher PM
2.5
concentration was associated in a modest, linear fashion with worse health status for all measures (lower SAQ scores, lower SF-12 PCS scores, higher dyspnea scores).

Conclusion:
Higher ambient air PM
2.5
concentration was modestly associated with worse CAD-related health status, worse physical functioning and greater dyspnea. Impacting patients’ symptoms and quality of life underscores the importance of policy initiatives to limit exposure to PM
2.5
.
Abstract 20: Are We Ready for High(er) Sensitivity Troponin Assays? The Positive Predictive Value of a Contemporary Troponin Assay for Acute Myocardial Infarction
Authors: Levy Andrew E , Ream Karen S , Hammes Andrew , Rudofker Eric , Beck Natalie , Raines Joshua D , Marshall Kimberly J , Nensel Jessica D , Anoff Debra L , Messenger John C , Masoudi Frederick A , Allen Larry A , Ho P. M
Published: April 2019

Background:
Patients with elevated troponin (cTn) in the absence of Acute Myocardial Infarction (AMI) present a diagnostic dilemma: they at high risk of adverse cardiac events, yet there is little evidence on how they should be managed clinically. In recent years, greater sensitivity of cTn assays and less selective use of cTn in clinical practice have led to a greater number of non-AMI patients with elevated troponin. The exact scope of the problem in routine clinical practice is not known, as published estimates of troponin’s positive predictive value (PPV) for AMI range from 15% to 70%. We sought to define troponin’s PPV for AMI at our institution and examine both process and outcome measures for AMI and non-AMI patients.

Methods:
In a retrospective cohort analysis of patients evaluated at the University of Colorado Hospital, we identified 5,903 hospital encounters between January 1, 2017 and October 6, 2018 in which patients were found to have an elevated serum troponin level (>0.04 ng/mL, Siemens Advia Centaur Tnl-Ultra). We used ICD-10 codes, billing diagnosis related groups (DRGs), or inclusion in the NCDR® ACTION Registry® to identify patients with a diagnosis of AMI. Patients not captured by one of these diagnoses were categorized as non-AMI. We then compared AMI and non-AMI patients according to the primary outcome of 100-day mortality. Secondary outcomes and process measures were also examined.

Results:
Out of 5,903 hospital encounters in which elevated cTn was detected, 730 were associated with any diagnosis of AMI (PPV 12.4%). The PPV was lower for individual AMI diagnosis groups. The primary outcome of 100-day mortality was observed in 10.3% of AMI patients versus 20.5% of non-AMI patients (p < 0.001). AMI patients also had significantly shorter hospitalizations, higher rates of inpatient echocardiography and higher rates of P2Y12 inhibitor therapy (Table 1).

Conclusion:
The PPV of a contemporary troponin assay for AMI was 12.4% in routine clinical practice at a tertiary care academic hospital. Concordant with prior studies, non-AMI patients had worse outcomes. With high-sensitivity troponin (hsTn) assays poised to increase the prevalence of non-AMI troponin elevation, guidance is needed regarding the appropriate use of hsTn as well as the evaluation and treatment of non-ACS patients.
Abstract 21: Comparison of Methods to Identify Atrial Fibrillation Patients from the Electronic Medical Record
Authors: Mukherjee Rebeka , Jones Aubrey E , Hackett Ian , Llyod-Jones Donald M , Springer Jennifer , Steinberg Benjamin A , Shah Rashmee U
Published: April 2019

Background:
Healthcare systems lack a validated method to identify specific patient populations for quality improvement and research. We compared the performance of 5 different methods, for identifying patients with atrial fibrillation (AF) from the electronic medical record (EMR). We also evaluated the impact of each method on apparent oral anticoagulant (OAC) treatment rates.

Methods:
We identified 22000 patients with at least one billing code for AF from 2010 to 2017. A training subset (n=786) was classified as having clinical AF present or absent by 2 clinicians, with a third for adjudication (reference standard). We constructed 5 different models to predict the presence of AF: regression based on a published Medicare model, regression based on a published Kaiser model, 2 regression models using additional EMR data, and a natural language processing (NLP) model. For NLP, the code scans each note for target AF-specific terms and negating modifiers (“e.g. no history of AF”). Patients with at least one positive reference for AF were classified as having AF. We calculated the total number of patients that would be included based on each model and resulting OAC treatment rates using on prescription orders.

Results:
The NLP method had the highest accuracy, sensitivity and NPV. The Kaiser model had the highest PPV at the expense of sensitivity. Among all 22000 patients, the Kaiser model resulted in the fewest patients (11512, 52%) and the NLP model resulted in the most patients classified as AF present (18212, 83%). OAC treatment rates varied from 50% to 62%, depending on which model was used for patient selection.

Conclusions:
Some of the most widely-used administrative data algorithms to identify AF patients appear to have limitations, which may be improved upon using NLP. Our findings have implications for EMR-based quality improvement and research. In future revisions, we will incorporate the CHA2DS2-VASc score and examine variations in apparent OAC treatment rates.
Abstract 220: Understanding Recovery in Patients Following Acute Myocardial Infarction
Authors: Dreyer Rachel P , Pavlo Anthony , Hersey Denise , Horne Anna , Dunn Robert , Krumholz Harlan M , Davidson Larry
Published: April 2019

Background:
Acute myocardial infarction (AMI) poses a traumatic experience that is a life-challenging event for patients. Yet, there is a lack of descriptive accounts of patients’ experiences post AMI and little is known about how people go about the challenge of recovering from such an event, either in terms of their physical health or in terms of their overall well-being on a day-to-day basis. Thus, recovery in regards to cardiac disease has been primarily understood in a narrow medical sense – as secondary prevention focuses largely on outcomes important to clinicians. We conducted a meta-synthesis review of the available data on personal accounts of recovery following AMI, using a modified narrative synthesis approach.

Methods and Results:
We searched for relevant papers that were descriptive, qualitative accounts of patients’ experiences following AMI across four electronic databases in April 2016. Our research group established keywords that conveyed personal ‘recovery’ and established inclusion/exclusion criteria
(Figure)
. The search strategy identified a total of 489 articles (excluding duplicates/other irrelevant articles). After a review of titles and abstracts and reading each article twice in full text, and cross-referencing articles by each of the authors, this resulted in 17 final studies and 224 participants (48% women) aged between 23-90 years. All participants provided a first-person account of an AMI within the prior 3 days to 25-year time frame, with the majority of studies conducted outside the US (N=16). Four major themes emerged from the synthesis and characterized patients’ experiences including: (1) uncertain second chances; (2) moving forward and threat; (3) what can I do now? and (4) social components – each consisting of various sub-themes. The overarching theme of the analysis was the concern “Is my heart healing?”

Conclusions:
Our findings suggest that patients move through various non-linear stages and processes of recovery post AMI – from their acute post-hospital phase through to long-term maintenance of their illness. This data will provide the basis for the development of a new conceptual framework of personal recovery for cardiology in future qualitative studies, thereby providing a novel structure around which research and clinical efforts can be oriented.
Abstract 222: Comparison of Noninvasive Testing to No Testing in Stable Angina: A Secondary Analysis of the PROMISE Trial
Authors: Gallo Robert J , Brown David L
Published: April 2019

Background:
Several randomized controlled trials (RCT) of noninvasive testing strategies in stable angina have compared coronary computed tomographic angiography (CCTA) to functional testing, with mixed results. No RCT has included clinical evaluation alone as a control group to determine if noninvasive testing improves outcomes.

Methods:
Using data from the Prospective Multicenter Imaging Study for Evaluation of Chest Pain (PROMISE) trial of 10003 patients presenting with symptoms suspicious for CAD, patients that received either CCTA or functional testing were compared to those that received neither test. The primary outcome was the composite of death, MI, procedural complications (stroke, major bleeding, renal failure, or anaphylaxis), or hospitalization for unstable angina.

Results:
No testing was performed in 402 patients (246 from functional testing group and 156 from CCTA group) whereas 9601 underwent either CCTA or functional testing. The patients that did not undergo testing were significantly more likely to be female (60.9% vs 52.3%), younger (59.4 vs 60.3 years), a minority (30.7% vs 22.3%), have a family history of premature CAD (36.8% vs 31.9%), and live a sedentary lifestyle (56.5% vs 48.4%). There were not significant differences with regards to BMI, hypertension, diabetes, dyslipidemia, smoking history, number of CAD risk factors, or type of angina (typical, atypical, or nonanginal). The time from randomization to death or last known follow-up was shorter in patients who did not undergo testing (451 vs 791 days). No testing was not associated with an increased hazard of the primary outcome (11 vs 304; HR 0.66, 95% Confidence Interval 0.36-1.20; P=0.171), nor death from any cause (11 vs 138; HR 1.10, 95% CI 0.57-2.03; P=0.769). There was a decreased hazard of the composite of death or nonfatal myocardial infarction (11 vs 205; HR 0.40, 95% CI 0.22-0.74; P=0.003) in patients who did not undergo testing.

Conclusion:
This analysis raises doubt as to whether noninvasive testing improves outcomes when used in the evaluation of patients with stable chest pain. It is possible that noninvasive testing provides little incremental benefit in guiding management decisions over modern prognostic models based only on clinical evaluation. Given the potential to reduce costs and radiation exposure, further research to develop improved prognostic clinical models, with noninvasive testing reserved for identified high risk patients most likely to benefit from more intensive management, is warranted.
Abstract 224: Recent Trends in Coronary Artery Disease Quality Performance and Implications for Clinical Practice in the Era of Alternative Payment Models
Authors: Gilstrap Lauren , Aaron Micah , Wild Robert , Beaulieu Nancy , Chernew Michael , Landrum Mary Beth
Published: April 2019

Background:
In light of recent shifts away from fee-for-service and toward alternative payment models (APM), national trends in quality performance for common cardiac conditions, such as CAD, are important for identifying areas for quality improvement and also for determining physician/health system reimbursement.

Methods:
Using Medicare data from 2010-2013, we created a cohort of patients with CAD using a combination of chronic condition warehouse (CCW) flags, ICD-9 and CPT codes. We the determined national performance trends for the 2011 ACC/AHA CAD performance measures. For drug use metrics, we used 80% of days covered after the index event as the threshold.

Results:
From 2010-2013, performance trends for testing (annual LDL) and post-MI metrics (beta blocker use, P2Y12 use and cardiac rehab) were flat (p=ns). Among patients with CAD and another comorbidity such as heart failure or diabetes, neurohormonal therapy use increased (p<0.001,
Figure 1
).

Conclusion:
The rate of neurohormonal therapy use in patients with CAD and another comorbidity improved while testing and post-MI performance in patients with CAD alone changed little. The reasons for this and may relate to an increased emphasis on complex, costly patients in APMs. Whether these trends impact longer-term patient outcomes should be explored.
Abstract 237: An Analytic Approach to and Creation of a Data Visualization Platform for National Cardiovascular Surveillance Data
Authors: Poudel Remy , Shay Chris , Stitzel Kim , Khan Yosef
Published: April 2019

Background:
Data visualization (DV) is a creative method to depict data and convey the underlying meaning of analytical results. Effective and interactive DV includes integrating, unifying, and standardizing data from different sources, allowing users to directly modify the style and outcome of the results to create visual access of big data in simple ingestible pieces. Such freedom to users brings substantial enhancements in productivity and practicality of complex surveillance data. Well-designed interactive DV also increases value gained from data and analytics. The American Heart Association’s (AHA) Center for Heath Metrics and Evaluation (CHME) has developed resources and tools for DV of surveillance data. The purpose of this study is to illustrate the processes of data analyses and visualizations of national surveillance data.

Methods:
This study introduces the different surveillance data, describes the concept, analytic approaches, and demonstrates the visualization platforms. The National Health and Nutrition Examination Survey (NHANES) data from 1999 - 2016 and Compressed Mortality data from the CDC (Centers for Disease Control and Prevention) WONDER, which included mortality by 15 underlying causes of cardiovascular diseases using ICD-10 from 1999 to 2017, were used to apply DV methods and tools for programming and creating interactive dashboards depicting trends in key cardiovascular health indices. Data were extracted, cleaned, and merged using SAS 9.4. Final estimates were calculated using SUDAAN 11 for weighting, imputing, and analyzing. Tableau 18.2 was used to share an interactive visualization of the analyzed data.

Results:
The CHME DV resources offer a variety of options to help parse and understand the significance of key health indicator data by placing it in a visual context. Using the NHANES and CDC Wonder datasets we developed interactive dashboards and a visualization platform depicting trends over time in cardiovascular metrics such as AHA’s Life’s Simple 7 indices of Blood Pressure, Blood Sugar, Cholesterol, Physical Activity, Diet, Body Weight, and Smoking. Trends in cardiovascular disease mortality in the United States were also developed. A visualization platform has been imbedded into the AHA CHME website for a wide variety of stakeholders, such as consumers and researchers, to view dashboards and customize products based on their specific needs.

Conclusion:
Patterns, trends, and associations that may otherwise go undetected or overlooked can be clearly exposed and communicated with an effective DV approach. DV tools comprise an array of resources from simple infographics to interactive dashboards. More interactive visualizations using other data sources, innovative methodologies and tools are needed and being developed by the CHME to present data more interactively enabling users to further understand and leverage data and trends.
Abstract 238: Healthcare Utilization Among Adults with Non-Fatal Cardiovascular Diseases in the United States
Authors: Poudel Remy , Khan Yosef , Shay Chris , Stitzel Kim
Published: April 2019

Background:
Cardiovascular diseases (CVD) are ranked as the top health problem in the US. Approximately 1 of 3 deaths results from CVD, which is more than cancer, chronic lower respiratory diseases, and accidents combined. Stroke, an important CVD itself, is a foremost cause of serious and long-term disability accounting for more than 50% percent of hospitalized patients for neurological diseases. Access to an efficient healthcare facility can contribute positively to overall population health by effective prognosis and timely care of CVD. This study aimed to evaluate the patterns of healthcare utilization by adults with non-fatal CVD in the United States of America.

Method:
Cross-sectional survey data from the National Health and Nutrition Examination Surveys (NHANES) 2015-2016 were used for this study. Using highly stratified, multistage probability designs, participants aged 20 years and more within the NHANES 2015-2016 cycle were included in this study. Pregnant women and participants who did not answer questions regarding CVD status were excluded. Participants diagnosed with congestive heart failure or coronary heart disease or angina/angina pectoris, or heart attack, or stroke were defined as having non-fatal CVD. Adults participants utilizing healthcare were those who visited the clinic, doctor’s office, or hospital outpatient department for treatment. Using SAS 9.4 and SUDAAN 11, appropriate age-standardization and tests were performed to assess the difference among races and other demographic variables in relation to healthcare utilization.

Results:
Of 5621 adults (representing 230.8 million (M) adults), 634 adults representing 18.5 million had self-reported CVD. Age adjusted prevalence of CVD by race/ethnicity was significantly different (p<0.05). African American adults had the highest prevalence of 2.8 M of 26.2 M (10.7%) followed by White adults at 10.8 M of 147.8 M (7.3%), Hispanic adults with 2.6 M of 35.1 M (7.4%). Asian adults had the least prevalence at 0.6 M of 13.4 M (4.5%). Among adults who had non-fatal CVD, healthcare utilization was also found to be significantly different (p<0.05). White adults were on the top of healthcare utilization at 9.7 M of 10.8 M (89.8%) followed by Asian adults at 0.5 M of 0.6 M (83.3%). African American adults ranked third at 2.1 M of 2.8 M (75.0%) and Hispanic adults were on the bottom the list at 1.1 M of 2.6 M (42.3%). Healthcare utilization amongst females was 6.5 M of 7.7 M (84.4%), compared to males at 6.5 M of 10.8 M (61.90%). Prevalence and utilization along education and income poverty ratio (IPR) were also examined.

Conclusion:
There are high discrepancies in CVD prevalence and healthcare utilization by race/ethnicity and other demographic variables. Further studies are required to assess the underlying causes of such discrepancies.
Abstract 245: Association Between Levels of Caregiver Engagement and Rates of Self-reported Healthcare Utilization After Stroke: A Mixed Methods Study
Authors: Liu Chelsea , Marino Victoria R , Sheehan Orla C , Huang Jin , Roth David L , Haley William E
Published: April 2019

Background:
Caregivers (CGs) are critical in helping stroke survivors retain function and receive adequate healthcare. We aimed to identify the activities that CGs are engaged in over the course of stroke care and explore the association between levels of CG engagement and rates of post-stroke formal healthcare utilization for care recipients (CRs).

Methods:
We qualitatively analyzed 9-month post-stroke interviews with CRs (n=118; 57.6) and CGs (n=87; 42.4%) about their experience before, during and following the stroke from the Caring for Adults Recovering from the Effects of Stroke (CARES)study. A codebook with data-driven codes was developed using an inductive iterative process and codes were assigned by two coders with 94.9% agreement. We classified CRs as having either engaged or non-engaged CGs based on whether they indicated CG involvement in any part of their stroke care in the interviews. Using Fisher’s exact test, we compared differences between groups in rates of self-reported visits to medical doctors, physical therapists, hearing or speech language pathologists (SLP), home health nurses/aids/personal attendants and mental health professionals, as well as differences in the number of emergency room (ER) visits and hospitalizations.

Results:
Interviewees most often described CG involvement as driving the CR to the hospital or calling 911 (n=48; 23.4%), requesting additional inpatient care (n=20; 9.8%), coordinating medical appointments (n=43; 21.0%) and monitoring recovery (n=23; 11.2%). CRs with engaged CGs had worse functioning (Stroke Impact Scale [median]=630.0 vs. 689.2; P=0.045), higher rates of audiology or SLP visits (33.3% vs. 22.6%; P=0.033) and more ER visits (mean 2 visits vs.1 visit; P=0.0032) compared to those with non-engaged CGs (Table 1).

Conclusions:
CRs with engaged CGs had higher rates of healthcare utilization compared to those with non-engaged CGs, suggesting that access to services may be facilitated by the CG. Higher rates of utilization may also be driven by more severe strokes and greater need for healthcare services in this group. Future research should focus on stroke severity as a potential modifier in the relationship between CG engagement and healthcare utilization.
Abstract 247: Reducing Costs and Intensive Care Unit Length of Stay in Patients with Intracerebral Hemorrhage
Authors: MacLellan Adam , Kronfeld Kassi , Ang Hans , Murray Nick , Fox Elizabeth , Wilson Alicia , Hirsch Karen , Threlkeld Zachary
Published: April 2019

Background/Objectives:
Intravenous (IV) antihypertensive infusions are often used acutely in patients with intracerebral hemorrhage (ICH). However, they are often administered inconsistently with current guidelines, and are associated with increased length of stay (LOS) and nosocomial morbidity. They are also more expensive than oral formulations and typically necessitate care in the intensive care unit (ICU), further increasing cost and morbidity. We examined the use of antihypertensive infusions in ICH patients in our institution, characterized their impact on cost and LOS, and developed an intervention grounded in quality improvement methodology to reduce the cost and LOS of ICH patients.

Methods:
Patients were included if they had been admitted to our comprehensive stroke center with an ICD-10 diagnosis of non-traumatic ICH from September 2017-2018. LOS and cost data were obtained from Vizient. IV antihypertensive use was extracted via retrospective chart review. We also collected data on quality metrics guided by focus group discussions with key stakeholders.

Results:
Eighty-one ICH patients over a 12-month period were reviewed. Fifty-nine patients (73%) received IV antihypertensive infusions. In patients who received infusions versus those who did not, mean total LOS (8.4 vs. 4.1 days, p=0.001) and total direct cost of stay ($44115 vs. $17249, p<0.001) were increased. Mean duration of infusion was 51 hours; 54% of patients received <2 IV antihypertensive boluses during the infusion to facilitate weaning, and 40% of patients were not started on oral antihypertensives on their first day with enteral access. Documented blood pressure goals were discordant with medication order parameters in 59% of patients. Root cause analysis (Figure 1) led to implementation of interventions focused on key drivers of consistent communication, concordant documentation, adequate medication availability, and consistent enteral option utilization.

Conclusions:
ICH patients requiring antihypertensive infusions had significantly higher direct costs and increased LOS. Root-cause analysis identified targets to improve quality of ICH care, leading to the development of formal interventions to facilitate transition from IV to oral antihypertensives. Post-intervention data will be available April 2019.
Abstract 25: Abnormalities within Normal Sinus Rhythm
Authors: Giri Sadip , Jefferies John L , Thomas Fridtjof , Davis Robert L , Akbilgic Oguz
Published: April 2019

Trained cardiologists detect atrial fibrillation by visual interpretation of certain segments of electrocardiogram (EKG) lines known as the QRS complex. Similarly, available EKG-software also evaluates anomalies in the signals from the EKG-leads that produce the traces/lines in order to flag for atrial fibrillation. We and others have previously shown that machine learning methods successfully identify patients with paroxysmal atrial fibrillation (PAF) based on their EKG during normal sinus rhythm. In this work, we go beyond the earlier black-box approaches and identify specific patterns in the QRS complex of a normal sinus rhythm that are associated with atrial fibrillation. We implemented frequent pattern mining on discretized waveform raw EKG data to determine patterns that are specific to patients with PAF based on 1-minute Lead 1 EKG recordings sampled at 128 Hz from 25 patients with PAF and 50 healthy subjects from a Physionet data repository. We discretized the down-sampled (16 Hz) EKG traces with seven symbols corresponding to various degrees of local variability within the traces and selected from the existing unique 1,306 4-symbol patterns the 850 patterns occurring at least 5 times (to mitigate sparsity related problems). The resulting 75x850 pattern frequency matrix represented the frequency of each pattern within each of the 75 patients and PAF subjects were distinguished based on a binary Least Absolute Shrinkage and Selection Operator regression with 5-fold cross validation that selected 50 of the patterns (AUC = 0.95; 95% C.I. 0.88-1.00; 94% specificity, 88% sensitivity). These 50 patterns are candidates to elicit the “fingerprint” of PAF within normal sinus rhythm: e.g., one of the selected patterns in Figure 1 was observed in 76% of PAF patients while it was present only in 30% of healthy patients. Our study is a proof of concept that machine learning and artificial intelligence techniques are not restricted to black-box approaches and can be used to derive interpretable insights that could lead to novel biomarkers associated with certain health conditions.
Abstract 251: Thrombolysis, DAPT, and Anticoagulation Administration in the Region Around Amarillo, Texas, in Patients with ST Elevation Myocardial Infarction From 2013-2016
Authors: McMaster Kelly S , Wang Grace C , McBrayer Michael A , Chatterjee Totini S , Patel Manish , Urban Steven , Brabham David G
Published: April 2019

While primary PCI is the gold standard of STEMI treatment, STEMI patients presenting to rural facilities without PCI capabilities are unlikely to undergo timely intervention. The outcomes of the CLARITY-TIMI and COMMIT/CCS-2 trials, among others, support treating these patients with the “triad” of thrombolysis, DAPT, and anticoagulation prior to transfer for higher-level care. Amarillo, Texas, serves the population of the Panhandle region of West Texas, and has primary PCI capabilities. We reviewed the use of the therapy triad in the rural facilities surrounding Amarillo prior to transfer for primary PCI. The medical records of 219 patients transferred from 21 outlying facilities to the two hospitals in Amarillo for STEMI from 2013 to 2016 were retrospectively evaluated. Patients were sorted into one of five predefined groups: (1) appropriate therapy triad delivered to STEMI patient, (2) thrombolysis not given to STEMI patient, (3) thrombolysis given to patient who did not have STEMI, (4) thrombolysis given to STEMI patient without DAPT and/or anticoagulation, and (5) appropriate failure to deliver thrombolysis in STEMI patient with contraindications. Out of the 219 STEMI patients transferred to Amarillo, 154 received inappropriate thrombolysis, DAPT, anticoagulation, or a combination thereof. In 44% (96 of 219) of patients, DAPT and/or anticoagulation was not given with thrombolysis. Errors included failure to deliver thrombolysis in patients without clear contraindications (24%; 52 of 219), and administering thrombolysis to patients who had not suffered STEMI (3%; 6 of 219). While evidence suggests thrombolysis, DAPT and anticoagulation prior to transfer for primary PCI can be beneficial, in the Panhandle region of West Texas, only 14% (31 of 219) of STEMI patients received the appropriate therapy triad prior to transfer to Amarillo.
Abstract 257: Adherence and Persistence to Direct Oral Anticoagulants in Patients with Atrial Fibrillation
Authors: Ozaki Aya F , Choi Austin S , Le Quan T , Ko Dennis T , Jackevicius Cynthia A
Published: April 2019

Background:
Direct oral anticoagulants (DOACs) are increasing in popularity for stroke prevention in atrial fibrillation (AF) due to ease of use. However, varying levels of DOAC adherence and persistence has been reported, which may result in poor clinical outcomes. We undertook a systematic review to characterize the real-world observational evidence for DOAC adherence and persistence.

Methods:
We searched MEDLINE, EMBASE, and CINAHL from inception to June 2018. We included studies that reported DOAC adherence and/or persistence rates in patients with AF. Two investigators independently screened abstracts and full text articles for inclusion, abstracted data from eligible studies, and assessed risk of bias using the Newcastle-Ottawa Scale. Prevalence rates of nonadherence were pooled using a DerSimonian and Laird random-effects model. Heterogeneity was assessed using I
2
.

Results:
We identified 48 studies including 576,360 patients in 40 countries. Seventeen studies (35%) reported proportion of days covered (PDC) as the adherence measure, 5 (10%) reported medication possession ratio (MPR), 36 (75%) reported persistence, and 6 (13%) evaluated clinical outcomes associated with DOAC adherence/persistence. Seventeen studies (35%) evaluated DOACs as a class, 11 (23%) evaluated apixaban, 35 (73%) evaluated dabigatran, and 28 (58%) evaluated rivaroxaban. The overall pooled proportion of patients who had good adherence (PDC/MPR≥80%) at all times was 66% (CI: 63%-70%) and at 1 year was 69% (CI: 63%-74%). There was wide variation in persistence definitions used in individual studies, with permissible gaps varying from 14 to 365 days. The overall pooled persistence rate was 71% (CI: 69%-74%) at all times and was 63% (CI: 58%-68%) at 1 year. Heterogeneity was high for all analyses. In subgroup analyses by agent, the pooled 1-year persistence rate was 72% (CI: 60%-83%) for apixaban, 54% (CI: 46%-62%) for dabigatran, and 67% (CI: 59%-75%) for rivaroxaban. Similar patterns were found for pooled adherence rates by agent.

Conclusions:
We found that DOAC adherence and persistence is low among patients with AF, with 1 in 3 patients not taking a DOAC after initiation. Clinicians need to be aware of the low real-world adherence and persistence rates when using DOACs in AF. High heterogeneity warrants further subgroup analyses.
Abstract 264: Rapid Deployment Aortic Valve Replacement is Associated with Lower Mortality and Hospital Utilization: Initial Findings from a Retrospective Multi-Institutional Hospital Database
Authors: Reifenberger Matt , Irish William , Moore Matt
Published: April 2019

Purpose:
Advances in valve technology for surgical aortic valve replacement (SAVR) include rapid deployment valves which may reduce cross-clamp time, important to reduce complications especially when concomitant procedures need to be performed. This study uses real world hospital data to compare conventional surgical aortic valves to rapid deployment surgical aortic valves in patients undergoing primary SAVR procedures.

Methods:
Eligible patients undergoing a primary SAVR procedure were captured in the Premier Hospital database between August 2016 and September 2017. Inpatient mortality, length of stay, OR time and hospital costs were compared for patients having conventional SAVR versus rapid deployment using multivariable modeling. Each model adjusted for baseline demographics, comorbid conditions (as measured by the Elixhauser Comorbidity Index), hospital characteristics and whether the procedure was isolated SAVR versus concomitant SAVR.

Results:
A total of 10,811 patients underwent a SAVR procedure with conventional (10,333) or a rapid deployment (478) valve type. Concomitant procedures occurred in 47% of conventional SAVR and 44% of rapid deployment patients. Differences between conventional SAVR and rapid deployment in age (67.3 versus 69.6 years old on average, respectively) and Elixhauser Comorbidity Score (5.7 versus 5.8 on average, respectively) were present. After multivariable modeling, patients undergoing conventional SAVR had 2.13 (P=.054) higher odds of mortality than rapid deployment. Least square means estimates for operating room time (423 vs 381 minutes; P<.0001) and length of stay (10.5 vs 9.7 days; P=0.0022) were significantly higher for conventional SAVR compared to rapid deployment. There were no statistical differences found in hospital costs between the two cohorts.

Conclusions:
SAVR continues to be the criterion standard for treatment of aortic stenosis and initial ‘real world’ experience with rapid deployment valves, was associated with better outcomes and lower hospital utilization without significantly increasing hospital costs.
Abstract 265: Improving Compliance with Get with the Guidelines-Resuscitation Metrics by Standardizing Code Note Documentation
Authors: Moss Benjamin , Richards Katherine , Davignon Seanna , Mankidy Babith , Morgan Christopher K
Published: April 2019

Background:
Accurate documentation of in-hospital cardiac arrest is essential to patient care. Recently, the American Heart Association created Get with the Guidelines-Resuscitation (GWTG-R) to facilitate the capture, analysis, and reporting of cardiac arrest data. GWTG-R criteria include witnessed events, confirmation of airway, two minute time-to-shock for ventricular tachycardia or fibrillation, and five minute time-to-first dose of epinephrine for pulseless electrical activity. The goal of our study was to increase adherence to GWTG-R measures by improving the quality of documentation of cardiac arrests by resident code leaders through the development and implementation of a standardized code note template.

Methods:
A retrospective chart review was performed on resident-authored code notes before and after implementation of a standardized code note. An electronic medical record template was developed which highlighted GWTG-R metrics. This note was made available to all resident physician code leaders. Reviewed notes were assessed for documentation of GWTG-R metrics and clinical reasoning.

Results:
Twenty-two notes were reviewed prior to and seventeen notes after implementation of a standardized code note template. 14/17 (82%) of post-implementation notes used the standardized template. All GWTG-R categories and clinical reasoning documentation trended toward improvement. Documentation of time-to-epinephrine significantly increased from 5.9% to 91.7% (Figure 1).

Conclusions:
Development of a standardized code note template improved documentation of GWTG-R quality measures for in-hospital cardiac arrest. Hospital compliance with guidelines improved following this intervention, qualifying for GWTG-R Bronze for three consecutive months of compliance. Expansion of the standardized code note for widespread use by providers throughout the hospital is expected to continue to improve code documentation and hospital compliance with GWTG-R.
Abstract 28: Electronic Medical Record-Based Appointment Order Improves Cardiology Follow Up After Discharge
Authors: Telukuntla Kartik , Sobol Tim , Huded Chetan , Abdallah Mouin , Kravitz Kathleen , Hulseman Michael , Barzilai Benico , Starling Randall , Svensson Lars , Khot Umesh
Published: April 2019

Background:
Outpatient follow up after hospital discharge improves transitions in care, reduces readmissions, and may improve mortality. However, there is no convincing evidence that electronic medical record (EMR)-based tools increase rates of post-discharge follow up. We implemented an EMR-based appointment scheduling order to secure cardiology follow up appointments at the time of hospital discharge and measured the impact on outpatient follow up rates.

Methods:
We performed an analysis of 39,211 discharges from the cardiology services within an academic center between 2012 and 2017. In the pre-implementation era, scheduling of follow up was at the discretion of clinical team (2012-2013). The EMR-based order ensured patients had scheduled follow up at time of discharge (2014-2017). We monitored follow up rates for six years and compared pre- (2012-2013, n=12,853) and post-implementation (2014-2017, n=26,358) era rates of follow up using chi square analysis. Multivariate logistic regression analysis was used to adjust for patient demographics and payor status.

Results:
The average age of our patients was 69.3 ± 14.7 and 60.7% were male. A majority of our patients were white (75.7%) and on Medicare (62.8%). The average rate of follow up within 90 days in the pre-implementation era was 56.7% (7,286 of 12,854) and increased to 67.6% (17,816 of 26,357, P < 0.0001) in the post-implementation era. Annualized rates of 90 day follow up increased from 57% in 2012 to 70% in 2017 (2012: 57% (3,598 of 6,349), 2013: 57% (3,688 of 6,505), 2014: 66% (4,267 of 6,445), 2015: 67% (4,441 of 6,650), 2016: 68% (4,465 of 6,642), 2017: 70% (4,643 of 6,620, P<0.0001). Use of the EMR-based follow up order was independently associated with increased outpatient follow-up within 90 days after adjusting for patient demographics and payor status (OR 3.28, 95% CI 3.103 to 3.467, P<0.0001).

Conclusion:
An EMR-based order for follow up appointment scheduling is successful in improving cardiovascular medicine follow up independent of patient demographics or payor status. This benefit was sustained over four years post-implementation. EMR-based tools are an attractive solution to improve patient follow-up after hospital discharge.