Performance of risk models to predict mortality risk for patients with heart failure: evaluation in an integrated health system

BACKGROUND

Referral of patients with heart failure (HF) who are at high mortality risk for specialist evaluation is recommended. Yet, most tools for identifying such patients are difficult to implement in electronic health record (EHR) systems.

OBJECTIVE

To assess the performance and ease of implementation of Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a machine-learning model that uses structured data that is readily available in the EHR, and compare it with two commonly used risk scores: the Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score.

DESIGN

Retrospective, cohort study.

PARTICIPANTS

Data from 6764 adults with HF were abstracted from EHRs at a large integrated health system from 1/1/10 to 12/31/19.

MAIN MEASURES

One-year survival from time of first cardiology or primary care visit was estimated using MARKER-HF, SHFM, and MAGGIC. Discrimination was measured by the area under the receiver operating curve (AUC). Calibration was assessed graphically.

KEY RESULTS

Compared to MARKER-HF, both SHFM and MAGGIC required a considerably larger amount of data engineering and imputation to generate risk score estimates. MARKER-HF, SHFM, and MAGGIC exhibited similar discriminations with AUCs of 0.70 (0.69-0.73), 0.71 (0.69-0.72), and 0.71 (95% CI 0.70-0.73), respectively. All three scores showed good calibration across the full risk spectrum.

CONCLUSIONS

These findings suggest that MARKER-HF, which uses readily available clinical and lab measurements in the EHR and required less imputation and data engineering than SHFM and MAGGIC, is an easier tool to identify high-risk patients in ambulatory clinics who could benefit from referral to a HF specialist.

Multidisciplinary Center Care for Long COVID Syndrome - a Retrospective Cohort Study

BACKGROUND

Persistent multi-organ symptoms after COVID-19 have been termed "long COVID" or "post-acute sequelae of SARS-CoV-2 infection" (PASC). The complexity of these clinical manifestations posed challenges early in the pandemic as different ambulatory models formed out of necessity to manage the influx of patients. Little is known about the characteristics and outcomes of patients seeking care at multidisciplinary post-COVID centers.

METHODS

We performed a retrospective cohort study of patients evaluated at our multidisciplinary Comprehensive COVID-19 Center (CCC) in Chicago, IL, between May 2020 and February 2022. We analyzed specialty clinic utilization and clinical test results according to severity of acute COVID-19.

RESULTS

We evaluated 1802 patients a median of 8 months from acute COVID-19 onset, including 350 post-hospitalization and 1452 non-hospitalized patients. Patients were seen in 2361 initial visits in 12 specialty clinics, with 1151 (48.8%) in neurology, 591 (25%) in pulmonology, and 284 (12%) in cardiology. Among patients tested, 742/878(85%) reported decreased quality of life, 284/553(51%) had cognitive impairment, 195/434(44.9%) had alteration of lung function, 249/299(83.3%) had abnormal CT chest scans, and 14/116(12.1%) had elevated heart rate on rhythm monitoring. Frequency of cognitive impairment and pulmonary dysfunction was associated with severity of acute COVID-19. Non-hospitalized patients with positive SARS-CoV-2 testing had similar findings than those with negative or no test results.

CONCLUSIONS

The CCC experience shows common utilization of multiple specialists by long COVID patients, who harbor frequent neurologic, pulmonary, and cardiologic abnormalities. Differences in post-hospitalization and non-hospitalized groups suggest distinct pathogenic mechanisms of long COVID in these populations.

Clinical Trial Participation and COVID-19: a Descriptive Analysis from the American Heart Association's Get With The Guidelines Registry

As COVID-19 cases begin to decrease in the USA, learning from the pandemic experience will provide insights regarding disparities of care delivery. We sought to determine if specific populations hospitalized with COVID-19 are equally likely to be enrolled in clinical trials. We examined patients hospitalized with COVID-19 at centers participating in the American Heart Association's COVID-19 CVD Registry. The primary outcome was odds of enrollment in a clinical trial, according to sex, race, and ethnicity. Among 14,397 adults hospitalized with COVID-19, 9.5% (n = 1,377) were enrolled in a clinical trial. The proportion of enrolled patients was the lowest for Black patients (8%); in multivariable analysis, female and Black patients were less likely to be enrolled in a clinical trial related to COVID-19 compared to men and other racial groups, respectively. Determination of specific reasons for the disparities in trial participation related to COVID-19 in these populations should be further investigated.

Comparison of Sodium-Glucose Cotransporter-2 Inhibitor and Glucagon-Like Peptide-1 Receptor Agonist Prescribing in Patients With Diabetes Mellitus With and Without Cardiovascular Disease

Sodium-glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP1-RAs) reduce cardiovascular events and mortality in patients with type 2 diabetes mellitus (T2DM). We sought to describe trends in prescribing for SGLT2is and GLP1-RAs in diverse care settings, including (1) the outpatient clinics of a midwestern integrated health system and (2) small- and medium-sized community-based primary care practices and health centers in 3 midwestern states. We included adults with T2DM and ≥1 outpatient clinic visit. The outcomes of interest were annual active prescription rates for SGLT2is and GLP1-RAs (separately). In the integrated health system, 22,672 patients met the case definition of T2DM. From 2013 to 2019, the overall prescription rate for SGLT2is increased from 1% to 15% (absolute difference [AD] 14%, 95% confidence interval [CI] 13% to 15%, p <0.01). The GLP1-RA prescription rate was stable at 10% (AD 0%, 95% CI -1% to 1%, p = 0.9). In community-based primary care practices, 43,340 patients met the case definition of T2DM. From 2013 to 2017, the SGLT2i prescription rate increased from 3% to 7% (AD 4%, 95% CI 3% to 6%, p <0.01), whereas the GLP1-RA prescription rate was stable at 2% to 3% (AD 1%, 95% CI -1 to 1%, p = 0.40). In a fully adjusted regression model, non-Hispanic Black patients had lower odds of SGLT2i or GLP1-RA prescription (odds ratio 0.56, 95% CI 0.34 to 0.89, p = 0.016). In conclusion, the increase in prescription rates was greater for SGLT2is than for GLP1-RAs in patients with T2DM in a large integrated medical center and community primary care practices. Overall, prescription rates for eligible patients were low, and racial disparities were observed.

Augmented Intelligence to Identify Patients With Advanced Heart Failure in an Integrated Health System

BACKGROUND

Timely referral for specialist evaluation in patients with advanced heart failure (HF) is a Class 1 recommendation. However, the transition from stage C HF to advanced or stage D HF often goes undetected in routine care, resulting in delayed referral and higher mortality rates.

OBJECTIVES

The authors sought to develop an augmented intelligence-enabled workflow using machine learning to identify patients with stage D HF and streamline referral.

METHODS

We extracted data on HF patients with encounters from January 1, 2007, to November 30, 2020, from a HF registry within a regional, integrated health system. We created an ensemble machine learning model to predict stage C or stage D HF and integrated the results within the electronic health record.

RESULTS

In a retrospective data set of 14,846 patients, the model had a good positive predictive value (60%) and low sensitivity (25%) for identifying stage D HF in a 100-person, physician-reviewed, holdout test set. During prospective implementation of the workflow from April 1, 2021, to February 15, 2022, 416 patients were reviewed by a clinical coordinator, with agreement between the model and the coordinator in 50.3% of stage D predictions. Twenty-four patients have been scheduled for evaluation in a HF clinic, 4 patients started an evaluation for advanced therapies, and 1 patient received a left ventricular assist device.

CONCLUSIONS

An augmented intelligence-enabled workflow was integrated into clinical operations to identify patients with advanced HF. Endeavors such as this require a multidisciplinary team with experience in design thinking, informatics, quality improvement, operations, and health information technology, as well as dedicated resources to monitor and improve performance over time.

The Cardiovascular Quality Improvement and Care Innovation Consortium: Inception of a Multicenter Collaborative to Improve Cardiovascular Care

Despite decades of improvement in the quality and outcomes of cardiovascular care, significant gaps remain. Existing quality improvement strategies are often limited in scope to specific clinical conditions and episodic care. Health services and outcomes research is essential to inform gaps in care but rarely results in the development and implementation of care delivery solutions. Although individual health systems are engaged in projects to improve the quality of care delivery, these efforts often lack a robust study design or implementation evaluation that can inform generalizability and further dissemination. Aligning the work of health care systems and health services and outcomes researchers could serve as a strategy to overcome persisting gaps in cardiovascular quality and outcomes. We describe the inception of the Cardiovascular Quality Improvement and Care Innovation Consortium that seeks to rapidly improve cardiovascular care by (1) developing, implementing, and evaluating multicenter quality improvement projects using innovative care designs; (2) serving as a resource for quality improvement and care innovation partners; and (3) establishing a presence within existing quality improvement and care innovation structures. Success of the collaborative will be defined by projects that result in changes to care delivery with demonstrable impacts on the quality and outcomes of care across multiple health systems. Furthermore, insights gained from implementation of these projects across sites in Cardiovascular Quality Improvement and Care Innovation Consortium will inform and promote broad dissemination for greater impact.

Development of a Portable Tool to Identify Patients With Atrial Fibrillation Using Clinical Notes From the Electronic Medical Record

BACKGROUND

The electronic medical record contains a wealth of information buried in free text. We created a natural language processing algorithm to identify patients with atrial fibrillation (AF) using text alone.

METHODS AND RESULTS

We created 3 data sets from patients with at least one AF billing code from 2010 to 2017: a training set (n=886), an internal validation set from site no. 1 (n=285), and an external validation set from site no. 2 (n=276). A team of clinicians reviewed and adjudicated patients as AF present or absent, which served as the reference standard. We trained 54 algorithms to classify each patient, varying the model, number of features, number of stop words, and the method used to create the feature set. The algorithm with the highest F-score (the harmonic mean of sensitivity and positive predictive value) in the training set was applied to the validation sets. F-scores and area under the receiver operating characteristic curves were compared between site no. 1 and site no. 2 using bootstrapping. Adjudicated AF prevalence was 75.1% at site no. 1 and 86.2% at site no. 2. Among 54 algorithms, the best performing model was logistic regression, using 1000 features, 100 stop words, and term frequency-inverse document frequency method to create the feature set, with sensitivity 92.8%, specificity 93.9%, and an area under the receiver operating characteristic curve of 0.93 in the training set. The performance at site no. 1 was sensitivity 92.5%, specificity 88.7%, with an area under the receiver operating characteristic curve of 0.91. The performance at site no. 2 was sensitivity 89.5%, specificity 71.1%, with an area under the receiver operating characteristic curve of 0.80. The F-score was lower at site no. 2 compared with site no. 1 (92.5% [SD, 1.1%] versus 94.2% [SD, 1.1%]; P<0.001).

CONCLUSIONS

We developed a natural language processing algorithm to identify patients with AF using text alone, with >90% F-score at 2 separate sites. This approach allows better use of the clinical narrative and creates an opportunity for precise, high-throughput cohort identification.

Approaching Process Improvement

Process improvement begins with the process view: understanding patient care from the patient's point of view. Organizations must also clearly articulate for themselves how they define operational excellence so that the tradeoffs taken in process improvement can be clearly made. Constructing a process map allows application of powerful analytical tools, such as Little's law, which in turn uncovers targets for process improvement from the patient's point of view. Often tradeoffs among process performance metrics, such as quality, cost, time, personalization, and innovation, must be made when deciding upon improvements to be made in certain processes.

Transitioning Patients with Heart Failure to Outpatient Care

The transition from hospitalization to outpatient care is a vulnerable time for patients with heart failure. This requires specific focus on the transitional care period. Here the authors propose a framework to guide process improvement in the transitional care period. The authors extend this framework by (1) examining the role new technology might play in transitional care, and (2) offering practical advice for teams building transitional care programs.

Dapagliflozin Effects on Biomarkers, Symptoms, and Functional Status in Patients With Heart Failure With Reduced Ejection Fraction: The DEFINE-HF Trial

BACKGROUND

Outcome trials in patients with type 2 diabetes mellitus have demonstrated reduced hospitalizations for heart failure (HF) with sodium-glucose co-transporter-2 inhibitors. However, few of these patients had HF, and those that did were not well-characterized. Thus, the effects of sodium-glucose co-transporter-2 inhibitors in patients with established HF with reduced ejection fraction, including those with and without type 2 diabetes mellitus, remain unknown.

METHODS

DEFINE-HF (Dapagliflozin Effects on Biomarkers, Symptoms and Functional Status in Patients with HF with Reduced Ejection Fraction) was an investigator-initiated, multi-center, randomized controlled trial of HF patients with left ventricular ejection fraction ≤40%, New York Heart Association (NYHA) class II-III, estimated glomerular filtration rate ≥30 mL/min/1.73m², and elevated natriuretic peptides. In total, 263 patients were randomized to dapagliflozin 10 mg daily or placebo for 12 weeks. Dual primary outcomes were (1) mean NT-proBNP (N-terminal pro b-type natriuretic peptide) and (2) proportion of patients with ≥5-point increase in HF disease-specific health status on the Kansas City Cardiomyopathy Questionnaire overall summary score, or a ≥20% decrease in NT-proBNP.

RESULTS

Patient characteristics reflected stable, chronic HF with reduced ejection fraction with high use of optimal medical therapy. There was no significant difference in average 6- and 12-week adjusted NT-proBNP with dapagliflozin versus placebo (1133 pg/dL (95% CI 1036-1238) vs 1191 pg/dL (95% CI 1089-1304), P=0.43). For the second dual-primary outcome of a meaningful improvement in Kansas City Cardiomyopathy Questionnaire overall summary score or NT-proBNP, 61.5% of dapagliflozin-treated patients met this end point versus 50.4% with placebo (adjusted OR 1.8, 95% CI 1.03-3.06, nominal P=0.039). This was attributable to both higher proportions of patients with ≥5-point improvement in Kansas City Cardiomyopathy Questionnaire overall summary score (42.9 vs 32.5%, adjusted OR 1.73, 95% CI 0.98-3.05), and ≥20% reduction in NT-proBNP (44.0 vs 29.4%, adjusted OR 1.9, 95% CI 1.1-3.3) by 12 weeks. Results were consistent among patients with or without type 2 diabetes mellitus, and other prespecified subgroups (all P values for interaction=NS).

CONCLUSIONS

In patients with heart failure and reduced ejection fraction, use of dapagliflozin over 12 weeks did not affect mean NT-proBNP but increased the proportion of patients experiencing clinically meaningful improvements in HF-related health status or natriuretic peptides. Benefits of dapagliflozin on clinically meaningful HF measures appear to extend to patients without type 2 diabetes mellitus.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov. Unique identifier: NCT02653482.

Risk Adjusting Health Care Provider Collaboration Networks

OBJECTIVES

The quality of hospital discharge care and patient factors (health and sociodemographic) impact the rates of unplanned readmissions. This study aims to measure the effects of controlling for the patient factors when using readmission rates to quantify the weighted edges between health care providers in a collaboration network. This improved understanding may inform strategies to reduce hospital readmissions, and facilitate quality-improvement initiatives.

METHODS

We extracted 4 years of patient, provider, and activity data related to cardiology discharge workflow. A Weibull model was developed to predict the risk of unplanned 30-day readmission. A provider-patient bipartite network was used to connect providers by shared patient encounters. We built collaboration networks and calculated the Shared Positive Outcome Ratio (SPOR) to quantify the relationship between providers by the relative rate of patient outcomes, using both risk-adjusted readmission rates and unadjusted readmission rates. The effect of risk adjustment on the calculation of the SPOR metric was quantified using a permutation test and descriptive statistics.

RESULTS

Comparing the collaboration networks consisting of 2,359 provider pairs, we found that SPOR values with risk-adjusted outcomes are significantly different than unadjusted readmission as an outcome measure (p-value = 0.025). The two networks classified the same provider pairs as high-scoring 51.5% of the time, and the same low scoring provider pairs 85.6% of the time. The observed differences in patient demographics and disease characteristics between high-scoring and low-scoring provider pairs were reduced by applying the risk-adjusted model. The risk-adjusted model reduced the average variation across each individual's SPOR scored provider connections.

CONCLUSIONS

Risk adjusting unplanned readmission in a collaboration network has an effect on SPOR-weighted edges, especially on classifying high-scoring SPOR provider pairs. The risk-adjusted model reduces the variance of providers' connections and balances shared patient characteristics between low- and high-scoring provider pairs. This indicates that the risk-adjusted SPOR edges better measure the impact of collaboration on readmissions by accounting for patients' risk of readmission.

Electrocardiographic Findings in National Basketball Association Athletes

Importance

While it is known that long-term intensive athletic training is associated with cardiac structural changes that can be reflected on surface electrocardiograms (ECGs), there is a paucity of sport-specific ECG data. This study seeks to clarify the applicability of existing athlete ECG interpretation criteria to elite basketball players, an athlete group shown to develop significant athletic cardiac remodeling.

Objective

To generate normative ECG data for National Basketball Association (NBA) athletes and to assess the accuracy of athlete ECG interpretation criteria in this population.

Design, Setting, and Participants

The NBA has partnered with Columbia University Medical Center to annually perform a review of policy-mandated annual preseason ECGs and stress echocardiograms for all players and predraft participants. This observational study includes the preseason ECG examinations of NBA athletes who participated in the 2013-2014 and 2014-2015 seasons, plus all participants in the 2014 and 2015 NBA predraft combines. Examinations were performed from July 2013 to May 2015. Data analysis was performed between December 2015 and March 2017.

Exposures

Active roster or draft status in the NBA and routine preseason ECGs and echocardiograms.

Main Outcomes and Measures

Baseline quantitative ECG variables were measured and ECG data qualitatively analyzed using 3 existing, athlete-specific interpretation criteria: Seattle (2012), refined (2014), and international (2017). Abnormal ECG findings were compared with matched echocardiographic data.

Results

Of 519 male athletes, 409 (78.8%) were African American, 96 (18.5%) were white, and the remaining 14 (2.7%) were of other races/ethnicities; 115 were predraft combine participants, and the remaining 404 were on active rosters of NBA teams. The mean (SD) age was 24.8 (4.3) years. Physiologic, training-related changes were present in 462 (89.0%) athletes in the study. Under Seattle criteria, 131 (25.2%) had abnormal findings, compared with 108 (20.8%) and 81 (15.6%) under refined and international criteria, respectively. Increased age and increased left ventricular relative wall thickness (RWT) on echocardiogram were highly associated with abnormal ECG classifications; 17 of 186 athletes (9.1%) in the youngest age group (age 18-22 years) had abnormal ECGs compared with 36 of the 159 athletes (22.6%) in the oldest age group (age 27-39 years) (odds ratio, 2.9; 95% CI, 1.6-5.4; P < .001). Abnormal T-wave inversions (TWI) were present in 32 athletes (6.2%), and this was associated with smaller left ventricular cavity size and increased RWT. One of the 172 athletes (0.6%) in the lowest RWT group (range, 0.24-0.35) had TWIs compared with 24 of the 163 athletes (14.7%) in the highest RWT group (range, 0.41-0.57) (odds ratio, 29.5; 95% CI, 3.9-221.0; P < .001).

Conclusions and Relevance

Despite the improved specificity of the international recommendations over previous athlete-specific ECG criteria, abnormal ECG classification rates remain high in NBA athletes. The development of left ventricular concentric remodeling appears to have a significant influence on the prevalence of abnormal ECG classification and repolarization abnormalities in this athlete group.

A Targeted, Differential Top-Down Proteomic Methodology for Comparison of ApoA-I Proteoforms in Individuals with High and Low HDL Efflux Capacity

Top-down proteomics (TDP) allows precise determination/characterization of the different proteoforms derived from the expression of a single gene. In this study, we targeted apolipoprotein A-I (ApoA-I), a mediator of high-density-lipoprotein cholesterol efflux (HDL-E), which is inversely associated with coronary heart disease risk. Absolute ApoA-I concentration and allelic variation only partially explain interindividual HDL-E variation. Therefore, we hypothesize that differences in HDL-E are associated with the abundances of different ApoA-I proteoforms. Here, we present a targeted TDP methodology to characterize ApoA-I proteoforms in serum samples and compare their abundances between individuals. We characterized 18 ApoA-I proteoforms using selected-ion monitoring coupled to electron-transfer dissociation mass spectrometry. We then compared the abundances of these proteoforms between two groups of four participants, representing the individuals with highest and lowest HDL-E values within the Chicago Healthy Aging Study ( n = 420). Six proteoforms showed significantly ( p < 0.0005) higher intensity in high HDL-E individuals: canonical ApoA-I [fold difference (fd) = 1.17], carboxymethylated ApoA-I (fd = 1.24) and, with highest difference, four fatty acylated forms: palmitoylated (fd = 2.16), oleoylated (fd = 2.08), arachidonoylated (fd = 2.31) and one bearing two modifications: palmitoylation and truncation (fd = 2.13). These results demonstrate translational potential for targeted TDP in revealing, with high sensitivity, associations between interindividual proteoform variation and physiological differences underlying disease risk.

Abstract 5: Optimizing Emergency Room Throughput for Cardiac Telemetry Patients: A Queuing Theory Approach

Background: The capacity of telemetry monitoring in hospitalized patients is constrained due to limited equipment and personnel. Electing for telemetry monitoring may pose a bottleneck for patients admitted through the Emergency Department (ED). This can result in increased length of stays in the ED, a parameter that is correlated with decreased quality of care due to crowding and delay in treatment. Queuing theory is the mathematical study of waiting times. We opted to use queuing theory to analyze system performance before and after an expansion of telemetry capacity.

Objective: To retrospectively analyze the impact of telemetry capacity on ED wait times using enterprise data warehouse (EDW) data and queuing theory.

Methods: Unused telemetry boxes were used as a proxy metric for available cardiac telemetry monitoring. The number of telemetry boxes in use at a large urban academic medical center was determined by daily manual count. The number of telemetry admissions, average length of telemetry use, and length of stay in the ED were obtained from EDW data. Using queuing theory, system parameters including expected wait time and percent of patients who will wait were calculated for scenarios to simulate before and after the expansion of telemetry capacity. These modeled system behaviors were compared to observed data from before and after the intervention to understand the utility of the mathematical model.

Results: Over the study period of 365 days, a total of 13,515 inpatient non-critical care hospital encounters had orders for telemetry monitoring. Prior to the increase in telemetry capacity, there were 144 telemetry boxes, which were utilized at 96.7% of capacity. Using these parameters, the model predicts a 5.8-hour average wait time and a 42.1% chance that a patient will have to wait for a telemetry box. Increasing telemetry capacity modestly by 10 boxes (144 to154 boxes) results in a dramatic system improvement, reducing utilization to 88.6%, average wait time for a telemetry bed to 34 minutes, and percent of patients waiting for a telemetry slot to 9.4%. These predicted results compared well to the observed data. After the intervention, there was a decrease in utilization to 86.9%, and a statistically significant reduction in time waiting in the ED for an inpatient bed by 1.24 (p<0.05) hours per telemetry patient, representing a total savings of ED boarding time of 11.2 bed-days per week.

Conclusions: Queuing theory allows for modeling of healthcare system behavior to predict needed clinical capacity for achieving system goals. Our simplified model demonstrates that a modest increase in capacity can drive significant system improvements. Utilizing queuing theory and understanding the behavior of the healthcare system can improve the predicted impact of interventions on cost-effectiveness and quality of care.

Abstract 251: Gender Equity in Rates of Guideline-Directed Medical Therapy Prescription at Discharge for Hospitalized Heart Failure Patients

Background: Heart failure (HF) is an increasingly prevalent condition with significant morbidity and mortality. Guideline-directed medical therapy (GDMT) reduces morbidity and mortality in heart failure with reduced ejection fraction, but continues to be under-prescribed. There is a paucity of data on the impact of patient gender on the quality of HF care, although it has previously been shown that women with other cardiovascular conditions, such as coronary artery disease, receive less intensive treatment. We sought to determine whether there is a difference in rates of prescription of GDMT for patients hospitalized with heart failure by gender with the hypothesis that rates of GDMT prescription in women would be lower than that in men.

Methods: Over a six month study window, we identified 246 patients discharged from an urban academic medical center with a primary discharge diagnosis of systolic heart failure. Systematic chart review was performed to identify whether patients were discharged with the following medications: angiotensin converting enzyme inhibitor (ACE-I), angiotensin receptor blocker (ARB), beta blocker (BB), mineralocorticoid receptor antagonist (MRA), hydralazine and a nitrate, and neprilysin inhibitor/ARB. Statistical analysis was performed to evaluate for an association between rates of GDMT prescription and gender. Regression analysis was then performed to adjust for age, weight, serum potassium, and 1/(discharge creatinine).

Results: At the time of discharge, there was no statistically significant difference in the rate of GDMT prescription for females compared to males in either the unadjusted or adjusted models. ACE-I or ARB prescription rates were 59% in females and 50% in males (p=0.11); BB prescription rates were 89% in females and 84% in males (p=0.27); MRA prescription rates were 43% in females and 52% in males (p=0.19). Prescription rates of combination therapy of ACE-I or ARB with BB and MRA were 30% in females and 31% in males (p=0.78). There was a significant difference in 30 day all-cause unadjusted readmission rates in females (22%) compared to males (37%) (p<0.05).

Conclusions: In this sample of hospitalized heart failure patients, no difference was found in rates of GDMT prescription for women as compared to men. Readmission rates were significantly higher for men than women, consistent with national data. Further investigation will determine the extent to which equity in prescription rates translates into improved outcomes for women hospitalized with heart failure.

High-Density Lipoprotein Subfractions and Cholesterol Efflux Capacity Are Not Affected by Supervised Exercise but Are Associated with Baseline Interleukin-6 in Patients with Peripheral Artery Disease

OBJECTIVE

To quantify the association between high-density lipoprotein (HDL) subfractions, efflux capacity, and inflammatory markers at baseline and the effect of supervised exercise on these HDL parameters in patients with peripheral artery disease (PAD).

METHODS

The study to improve leg circulation (SILC) was a randomized trial of supervised treadmill exercise, leg resistance training, or control in individuals with PAD. In a post hoc cross-sectional analysis, we quantified the associations between baseline HDL subfraction concentrations (HDL2 and HDL3), HDL-C efflux capacity, and inflammatory markers [C-reactive protein (CRP) and interleukin-6 (IL-6)]. We then examined the effect of supervised exercise on changes in these lipoprotein parameters and inflammatory markers in 88 patients from SILC.

RESULTS

Baseline HDL-C efflux capacity was associated with baseline concentrations of HDL2 (β = 0.008, p = 0.0106), HDL3 (β = 0.013, p < 0.0001), and IL-6 (β = -0.019, p = 0.03). Baseline HDL3 concentration was inversely associated with IL-6 concentration (β = -0.99, p = 0.008). Compared to control, changes in HDL2, HDL3, normalized HDL-C efflux capacity, CRP, or IL-6 were not significantly different at 6 months following the structured exercise intervention.

CONCLUSION

HDL efflux and HDL3 were inversely associated with IL-6 in PAD patients. Structured exercise was not associated with changes in HDL subfractions, HDL-C efflux capacity, CRP, and IL-6 in PAD patients. Our preliminary findings support the theory that inflammation may adversely affect HDL structure and function; however, further studies are needed to evaluate these findings.

Abstract 077: Hospitalized Heart Failure Epidemiology: Active Surveillance to Enhance Inpatient Cardiology Consultation Rates

Background: Co-management of hospitalized heart failure (HHF) patients by hospitalists and cardiologists may enhance care quality. Aided by an enterprise data warehouse (EDW) screen for HHF patients, we implemented a house surveillance model of heart failure consultation, whereby cardiology consultation and multidisciplinary heart failure intervention was offered for patients meeting criteria.

Objective: To analyze the impact of house surveillance for HHF on cardiology consultation rates for patients coding into heart failure diagnosis-related groups (DRGs) on medicine units.

Methods: An EDW screen for HHF was deployed, and services with HHF patients were offered cardiology consultation. The intervention was deployed 7/2015; chart review for patients 6 months pre- and post-intervention was conducted to ascertain consultation rate.

Results: There were 386 patient discharges from a non-cardiac service with a heart failure DRG. In the six months prior to intervention, 40% of patients had cardiology consultation. This figure rose to 69% post-intervention, a highly statistically significant result.

Conclusions: EDW-enabled active surveillance for HHF increases cardiology consultation rate, allowing for multidisciplinary intervention, co-management, and potentially improved outcomes for HHF patients.

Leveraging electronic health record documentation for Failure Mode and Effects Analysis team identification

OBJECTIVE

Using Failure Mode and Effects Analysis (FMEA) as an example quality improvement approach, our objective was to evaluate whether secondary use of orders, forms, and notes recorded by the electronic health record (EHR) during daily practice can enhance the accuracy of process maps used to guide improvement. We examined discrepancies between expected and observed activities and individuals involved in a high-risk process and devised diagnostic measures for understanding discrepancies that may be used to inform quality improvement planning.

METHODS

Inpatient cardiology unit staff developed a process map of discharge from the unit. We matched activities and providers identified on the process map to EHR data. Using four diagnostic measures, we analyzed discrepancies between expectation and observation.

RESULTS

EHR data showed that 35% of activities were completed by unexpected providers, including providers from 12 categories not identified as part of the discharge workflow. The EHR also revealed sub-components of process activities not identified on the process map. Additional information from the EHR was used to revise the process map and show differences between expectation and observation.

CONCLUSION

Findings suggest EHR data may reveal gaps in process maps used for quality improvement and identify characteristics about workflow activities that can identify perspectives for inclusion in an FMEA. Organizations with access to EHR data may be able to leverage clinical documentation to enhance process maps used for quality improvement. While focused on FMEA protocols, findings from this study may be applicable to other quality activities that require process maps.

HDL efflux capacity, HDL particle size, and high-risk carotid atherosclerosis in a cohort of asymptomatic older adults: the Chicago Healthy Aging Study

HDL efflux capacity and HDL particle size are associated with atherosclerotic CVD (ASCVD) events in middle-aged individuals; however, it is unclear whether these associations are present in older adults. We sampled 402 Chicago Healthy Aging Study participants who underwent a dedicated carotid MRI assessment for lipid-rich necrotic core (LRNC) plaque. We measured HDL particle size, HDL particle number, and LDL particle number with NMR spectroscopy, as well as HDL efflux capacity. We quantified the associations between HDL particle size and HDL efflux using adjusted linear regression models. We quantified associations between the presence of LRNC and HDL and LDL particle number, HDL particle size, and HDL efflux capacity using adjusted logistic regression models. HDL efflux capacity was directly associated with large (β = 0.037, P < 0.001) and medium (β = 0.0065, P = 0.002) HDL particle concentration and inversely associated with small (β = -0.0049, P = 0.018) HDL particle concentration in multivariable adjusted models. HDL efflux capacity and HDL particle number were inversely associated with prevalent LRNC plaque in unadjusted models (odds ratio: 0.5; 95% confidence interval: 0.26, 0.96), but not after multivariable adjustment. HDL particle size was not associated with prevalent LRNC. HDL particle size was significantly associated with HDL efflux capacity, suggesting that differences in HDL efflux capacity may be due to structural differences in HDL particles. Future research is needed to determine whether HDL efflux is a marker of ASCVD risk in older populations.

Characterizing Teamwork in Cardiovascular Care Outcomes: A Network Analytics Approach

BACKGROUND

The nature of teamwork in healthcare is complex and interdisciplinary, and provider collaboration based on shared patient encounters is crucial to its success. Characterizing the intensity of working relationships with risk-adjusted patient outcomes supplies insight into provider interactions in a hospital environment.

METHODS AND RESULTS

We extracted 4 years of patient, provider, and activity data for encounters in an inpatient cardiology unit from Northwestern Medicine's Enterprise Data Warehouse. We then created a provider-patient network to identify healthcare providers who jointly participated in patient encounters and calculated satisfaction rates for provider-provider pairs. We demonstrated the application of a novel parameter, the shared positive outcome ratio, a measure that assesses the strength of a patient-sharing relationship between 2 providers based on risk-adjusted encounter outcomes. We compared an observed collaboration network of 334 providers and 3453 relationships to 1000 networks with shared positive outcome ratio scores based on randomized outcomes and found 188 collaborative relationships between pairs of providers that showed significantly higher than expected patient satisfaction ratings. A group of 22 providers performed exceptionally in terms of patient satisfaction. Our results indicate high variability in collaboration scores across the network and highlight our ability to identify relationships with both higher and lower than expected scores across a set of shared patient encounters.

CONCLUSIONS

Satisfaction rates seem to vary across different teams of providers. Team collaboration can be quantified using a composite measure of collaboration across provider pairs. Tracking provider pair outcomes over a sufficient set of shared encounters may inform quality improvement strategies such as optimizing team staffing, identifying characteristics and practices of high-performing teams, developing evidence-based team guidelines, and redesigning inpatient care processes.

Abstract 215: Leveraging Electronic Health Record Documentation for Failure Mode and Effects Analysis Team Identification on an Inpatient Cardiology Unit

Objectives: Failure Mode and Effects Analysis (FMEA) is a frequently-used approach for prospective risk assessment and quality improvement in healthcare, particularly for high-risk care processes such as hospital discharge planning. Our goal was to evaluate whether secondary use of metadata collected by the electronic health record (EHR) during daily practice can inform assembly of a comprehensive FMEA team by showing: 1) discrepancies between expected and observed process activities and individuals involved, and 2) the presence of individuals who may be appropriate to include in an FMEA based on their variable familiarity with a process.

Methods: We extracted discharge planning data for an inpatient cardiology unit from the Enterprise Data Warehouse (EDW) and compared it to a hand-drawn map (HDM) indicating clinicians’ understanding of discharge activities and providers expected to complete each activity. We assessed the presence of providers highly experienced in the process, the diversity of involved disciplines, and the accuracy of the HDM compared to observation from EDW data.

Findings: Over 500 providers completed nearly 35,000 discharge-related activities across 18 activity types over 2,000 encounters. Experience was skewed such that 90% (510 of 569) of providers completed between 0 and 99 activities while the remaining 10% (59 of 569) performed up to 1,200 activities. Frequent performers completed similar activities to their peers, but did so as many as 12 times more frequently than average for their discipline. Expectation of who performed an activity closely matched observation for 11 discipline-specific activities, such as case management assessment. However, providers from up to 10 different disciplines performed the remaining 7 activities, such as scheduling a follow-up visit or ordering a therapy consult. Overall, 35% (12,183 of 34,939) of activities were performed by an unexpected provider.

Conclusions: Analyzing metadata from EHRs is a novel method to inform FMEA of high-risk processes. This study provides a framework for assessing process activities and the providers involved. In the discharge planning process, there appears to be significant discrepancy between clinicians’ understanding and the actual discharge process and team, which suggests the presence of providers who could be overlooked during typical FMEA team construction. This methodology can empirically enrich the FMEA team and highlight quality improvement target areas.

Abstract 161: Heart Failure Care Transitions: A Queuing Theory Approach to Match Variable Hospital Discharge Rate With Outpatient Clinic Capacity

Background: Heart failure (HF) readmissions remain a major driver of cost and health care utilization. Timely follow-up of patients post-discharge represents an evidence-based intervention proven to reduce readmission rates. A previously unexplored characteristic of hospital discharges is variability in discharge caseload. This variability thwarts the timeliness of follow-up, negates the benefit of transition care planning and may lead to a higher risk of HF readmissions. Queuing theory is the mathematical study of waiting times. We opted to use queuing theory to determine if caseload can be determined more precisely in a manner that sufficiently accommodates HF discharge variability.

Objective: To analyze the impact of hospital discharge rate variability on outpatient clinic capacity needs using HF hospitalization discharge data and operations management approaches.

Methods: Higher risk hospitalizations requiring active transitional care heart failure management were detected using an enterprise data warehouse-supported process over the study period. Queuing theory approaches were used to model the impact of HF discharge clinic capacity on wait time to an appointment. Discharge clinic was modeled as a single 7-day follow-up appointment, with an acceptable scheduling window of 5 to 9 days post-discharge.

Results: During the study period of 100 days, 566 HF discharges were made, for a median of 5.66 discharges daily, or 39.6 discharges weekly. The distribution of daily discharges was skewed rightward (mode = 3, range = 0 to 18, standard deviation = 3.3, coefficient of variation = 0.58). Current clinic design: Providing one discharge slot for every hospital discharge (100% utilization) leads to an average wait of 18.3 days prior to an appointment, with only 31.9% of appointments scheduled within 7 days, and 38.9% of appointments scheduled within 9 days. Clinic re-design (queuing theory): Providing five extra discharge appointment slots per week (88% utilization or 13.6% excess capacity) reduces the expected waiting period to 1.1 days, with 99.8% of patients seen within 7 days, and virtually all patients seen within 9 days of discharge.

Conclusions: Deployment of queuing theory allows for a more precise quantification of needed clinical capacity to accomplish appropriate HF follow-up with a reasonable degree of certainty. Our simplified model demonstrates that variability in hospital discharge rates leads to excessive clinic wait times in the absence of a modest capacity buffer and consequently exposes patients to a higher risk of HF readmission. We show using single center HF discharge data that a 10-15% increase in capacity is needed to ensure an adequate follow-up service level. Ongoing process of care work will demonstrate if optimization of clinic load yields a significant reduction in HF readmissions.

Abstract 136: Complexity and Collaboration in Discharge Planning for Inpatient Cardiology

Background: Multidisciplinary, collaborative care improves outcomes and reduces costs but may unintentionally increase the operational overhead to manage the complexity of increasingly sophisticated teamwork.

Objective: To quantify complexity of a standardized discharge planning process by closely examining team continuity, composition, and experience for both explicitly defined, deliberately organized teams and implicitly defined, organically assembled teams.

Methods: We examined discharge planning team membership for inpatient cardiology encounters with a length of stay (LOS) greater than 48 hours for a three-year period from 2012-2014 in a large academic medical center. By constructing a co-affiliation graph from transactional EHR data, we determined the frequency of team occurrence and size. We then calculated the shared experience of teammates using a pairwise similarity metric and identified modularity, groups of healthcare personnel with dense connections, within the graph by applying a community detection algorithm.

Results: We identified 52,254 transactions for 3,213 encounters with an average LOS of 8.7 days for the time period. The standard discharge planning team accounted for 41,101 (79%) transactions, consisted of 7 team member types, and comprised 709 individual providers performing 36 activities. We identified 569 additional discharge planning team members, consisting of 22 additional provider types. When constrained to only explicitly defined teams, 404 unique teams with an average size of 5.9 members occurred with a mean of 8 (min=1, max=118) shared encounters. When unconstrained, organically assembled teams with no explicit definition occurred 3,209 times (mean=1; min=1, max=3) with an average size of 7.3 members. Single-encounters accounted for 21,107 (62%) of all provider pairs. However, over 50% of all discharges had at least 1 pair sharing over 99 encounters (median=4 pairs). Community detection found 9 modules (range: 12-275 members) among pairs with more than 5 shared encounters.

Conclusions: We have shown by deconstructing digital interactions via the EHR, a core group of providers, defined by role and activity, anchor most discharge planning teams. However, the EHR can also identify identify the 20% of teams with constantly recombining membership due to situational care; this can impose overhead when targeting team-wide process improvements, communication strategies, or educational initiatives.

Abstract 15: Quantifying Teamwork at Hospital Discharge for Readmissions Reduction: A Network Analytics Approach

Background: The vulnerable period for hospital readmission begins upon hospital discharge, a complex, interdisciplinary process that necessitates close teamwork for accurate execution of the discharge plan. Thus, better understanding of the quality of teamwork throughout the discharge process may inform strategies to reduce hospital readmissions rates. Novel methods using a network analytics approach to quantify teamwork may better characterize this critical clinical process, facilitate quality improvement (QI), and become an important tool in learning healthcare systems.

Methods: We extracted three years of patient, provider, and activity data related to discharge planning for an inpatient cardiology unit from Northwestern Medicine’s Enterprise Data Warehouse. We then created a provider-patient network to identify providers who shared patients and calculated readmissions rates for provider pairs. Using these data, we calculated a novel parameter, the Shared Positive Outcome Ratio (SPOR), an objective composite measure that quantifies the concentration of positive outcomes over a set of shared patients. To identify significant low-readmission and high-readmission collaborative relationships, we compared the observed network to 1000 sample networks containing randomized readmission values.

Results: We identified 133,927 actions distributed among 38 discharge activity types performed during 13,720 patient encounters. The collaborative network was composed of 1,542 providers including 503 nurses, 432 residents, 207 physicians, 111 physical and occupational therapists, 59 medical students, 32 dieticians, and other medical and administrative staff. The average encounter involved 4 providers performing 10 discharge-related actions. After pruning the network to include only provider pairs with 6 or more shared patients, we found that 6% of collaborative interactions had a significantly low SPOR, indicating lower than expected readmission rates. Conversely, 12% of collaborative interactions had a significantly high SPOR, indicating higher than expected readmission rates. We identified 21 providers who had a low SPOR for a significant percentage of their collaborations, indicating potential top performers in the teamwork domain.

Conclusions: Readmission rates appear to vary across different teams of providers. Team collaboration can be quantified using a composite measure of collaboration across provider pairs. Tracking provider pair outcomes over a sufficient set of shared encounters may inform various QI strategies, such as optimizing team staffing, identifying high-performing teams who can share their best practices, and redesigning discharge care processes. Ongoing work on this model is focused on accurately risk-adjusting outcomes, which will increase the robustness of this method.

Abstract 152: Enterprise Data Warehouse-Supported Early Identification of Acute Decompensated Heart Failure Admissions for Efficient and Multidisciplinary Transitional Care Team Interventions

Background: Multidisciplinary transitional care teams represent a model for reducing heart failure readmissions. Within this context, early identification of patients hospitalized with acute decompensated heart failure (ADHF) permits meaningful transitional care plan development. Improving the efficiency of early identification of the higher risk ADHF patient represents an area not well studied in hospitalized heart failure (HF).

Objective: To validate the sensitivity and specificity of an enterprise data warehouse (EDW)-based strategy for early identification of patients with ADHF.

Methods: An EDW query was constructed to identify patients with ADHF based on clinical and diagnosis-related parameters, including BNP level and administration of intravenous diuretics. The EDW query was run daily; expert clinicians verified the diagnosis of ADHF based on comprehensive chart review. This classification was used to determine specificity of the query for ADHF. We computed the sensitivity of the EDW-based approach by matching query results to heart failure diagnosis related group (DRG) data and primary discharge diagnosis data from separate hospital systems.

Results: During the study period of 70 days, a total of 2354 charts were screened (33.6 charts per day). A total of 410 patients were identified by chart review as having heart failure requiring active management, for a specificity of 17.4%. Sensitivity was computed using both heart failure DRG data and primary discharge diagnosis data. Of the 114 patients discharged with a heart failure DRG (291, 292, or 293), all 114 were detected a priori by the admission EDW screen, for a sensitivity of 100%. A similar analysis conducted using HF principal diagnoses, which includes cardiac surgery-related admissions, yielded a sensitivity of 97.2%.

Conclusions: EDW-based screening of patients based on simple clinical parameters early in the hospitalization is highly sensitive for detection of ADHF hospitalizations, but specificity is low. Brief chart review by expert clinicians is rapid, and identifies a specific cohort of patients that can be targeted for multidisciplinary HF transitional care. A better delineation of risk has broad outpatient workflow implications. Ongoing process improvements will demonstrate if early identification of at-risk patients yields significant reduction in HF readmissions.

High-Density Lipoproteins for Therapeutic Delivery Systems

High-density lipoproteins (HDL) are a class of natural nanostructures found in the blood and are composed of lipids, proteins, and nucleic acids (e.g. microRNA). Their size, which appears to be well-suited for both tissue penetration/retention as well as payload delivery, long circulation half-life, avoidance of endosomal sequestration, and potential low toxicity are all excellent properties to model in a drug delivery vehicle. In this review, we consider high-density lipoproteins for therapeutic delivery systems. First we discuss the structure and function of natural HDL, describing in detail its biogenesis and transformation from immature, discoidal forms, to more mature, spherical forms. Next we consider features of HDL making them suitable vehicles for drug delivery. We then describe the use of natural HDL, discoidal HDL analogs, and spherical HDL analogs to deliver various classes of drugs, including small molecules, lipids, and oligonucleotides. We briefly consider the notion that the drug delivery vehicles themselves are therapeutic, constituting entities that exhibit "theralivery." Finally, we discuss challenges and future directions in the field.

Reducing protein oxidation in low-flow electrospray enables deeper investigation of proteoforms by top down proteomics

Enabling the implementation of top down proteomic techniques within clinical workflows requires a dramatic increase in sensitivity. It has been previously demonstrated that electrospray ionization (ESI) becomes more efficient with decreasing volumetric flow rates at the emitter. Therefore, narrow inner diameter (I.D.) columns used in front-end chromatographic separations yield increased sensitivity. However, the smaller cross-sectional area of a narrow I.D. column places a larger fraction of the eluent in fluid communication with the electrode within the high voltage union that facilitates electrospray ionization (ESI), leading to increased oxidation of solution-phase proteins. Oxidation of proteins alters their chemical state of the protein, complicates data analysis, and reduces the depth of proteome coverage attained in a typical top-down proteomics experiment. Excessive protein oxidation results in poor deconvolution and exact mass calculations from MS1 spectra, interferes with peak isolation for MS/MS fragmentation, and effectively reduces sensitivity by splitting ion current. All of these factors deteriorate top down mass spectral data quality, an effect that becomes more pronounced as column diameter decreases. Artificial protein oxidation can also mislead investigations of in vivo protein oxidation. All of these effects are accentuated in comparison to bottom up proteomics due to the increased probability of having oxidizable residues within a particular species with increasing mass. Herein, we describe a configuration (which we term "Low Protein Oxidation (LPOx)") for proteomics experiments created by re-arranging liquid chromatography (LC) plumbing and present its application to artificial protein oxidation and show a marked improvement in detection sensitivity. Using a standard mixture of five intact proteins, we demonstrate that the LPOx configuration reduces protein oxidation up to 90% using 50 μm I.D. columns when compared to a conventional LC plumbing configuration with 50 μm I.D. column. As a proof-of-concept study, at least 11 distinct proteoforms of serum Apolipoprotein A1 were detected with the LPOx configuration. This innovative LC configuration can be applied to the top down identification and characterization of proteoforms obscured by abundant artificial protein oxidation at low flowrates, all while using reduced amounts of valuable protein samples.

Abstract 631: Lipid Composition of Gold Nanoparticle-Templated High-Density Lipoprotein Analogs Dictates Cholesterol Efflux Function

Background: Reverse cholesterol transport, the process by which cholesterol is effluxed from cells to high-density lipoproteins (HDL) and is delivered to the liver for clearance, is a promising pathway to augment for treatment of atherosclerosis. Though structure-function relationships for nascent, discoidal HDL and cholesterol efflux have been well studied, how the lipid composition of spherical HDL species - which varies in pathophysiological conditions - impacts their ability to mediate cholesterol efflux has not been investigated.

Methods and Results: Spherical gold nanoparticles (5 nm) were used to synthesize spherical HDL analogs (HDL-NP) by adding ApoAI protein, and various lipids. With this strategy a panel of HDL-NP varying in lipid content was generated. HDL-NP designs tested include: dipalmitylphosphatidylcholine (DPPC, saturated fatty acid), dioleoylphosphatidylcholine (DOPC, unsaturated fatty acid), sphingomyelin, lysophosphatidylcholine (LPC), and mixtures thereof. All of these species are found in natural HDL. After characterizing protein and lipid stoichiometry of the purified HDL-NP, these HDL-NP designs were tested in the cellular reverse cholesterol transport assay using J774 mouse macrophages. These studies demonstrate that all HDL-NP designs mediate more efflux than equimolar amounts of ApoAI protein control, and further demonstrate that HDL-NP designs incorporating unsaturated phospholipid (DOPC), sphingomyelin, and LPC - each of which can increase disorder in the lipid membrane and thus give rise to opportunity for cholesterol to intercalate and bind - enhance cholesterol efflux compared to saturated phospholipid (DPPC) design.

Conclusion: In summary, these results demonstrate that lipid content of HDL-NP - analogs of spherical HDL - dictates cholesterol efflux function, a finding which sheds light on the functional importance of lipid content variation seen in mature, spherical HDL species.

Cardiotoxicity of doxorubicin is mediated through mitochondrial iron accumulation

Doxorubicin is an effective anticancer drug with known cardiotoxic side effects. It has been hypothesized that doxorubicin-dependent cardiotoxicity occurs through ROS production and possibly cellular iron accumulation. Here, we found that cardiotoxicity develops through the preferential accumulation of iron inside the mitochondria following doxorubicin treatment. In isolated cardiomyocytes, doxorubicin became concentrated in the mitochondria and increased both mitochondrial iron and cellular ROS levels. Overexpression of ABCB8, a mitochondrial protein that facilitates iron export, in vitro and in the hearts of transgenic mice decreased mitochondrial iron and cellular ROS and protected against doxorubicin-induced cardiomyopathy. Dexrazoxane, a drug that attenuates doxorubicin-induced cardiotoxicity, decreased mitochondrial iron levels and reversed doxorubicin-induced cardiac damage. Finally, hearts from patients with doxorubicin-induced cardiomyopathy had markedly higher mitochondrial iron levels than hearts from patients with other types of cardiomyopathies or normal cardiac function. These results suggest that the cardiotoxic effects of doxorubicin develop from mitochondrial iron accumulation and that reducing mitochondrial iron levels protects against doxorubicin-induced cardiomyopathy.

Templated high density lipoprotein nanoparticles as potential therapies and for molecular delivery

High density lipoproteins (HDLs) are dynamic natural nanoparticles best known for their role in cholesterol transport and the inverse correlation that exists between blood HDL levels and the risk of developing coronary heart disease. In addition, enhanced HDL-cholesterol uptake has been demonstrated in several human cancers. As such, the use of HDL as a therapeutic and as a vehicle for systemic delivery of drugs and as imaging agents is increasingly important. HDLs exist on a continuum from the secreted HDL-scaffolding protein, apolipoprotein A-1 (Apo A1), to complex, spherical "mature" HDLs. Aspects of HDL particles including their size, shape, and surface chemical composition are being recognized as critical to their diverse biological functions. Here we review HDL biology; strategies for synthesizing HDLs; data supporting the clinical use and benefit of directly administered HDL; a rationale for developing synthetic methods for spherical, mature HDLs; and, the potential to employ HDLs as therapies, imaging agents, and drug delivery vehicles. Importantly, methods that utilize nanoparticle templates to control synthetic HDL size, shape, and surface chemistry are highlighted.

MicroRNA-210 decreases heme levels by targeting ferrochelatase in cardiomyocytes

Background

MicroRNA‐210 (miR‐210) increases in hypoxia and regulates mitochondrial respiration through modulation of iron‐sulfur cluster assembly proteins (ISCU1/2), a protein that is involved in Fe/S cluster synthesis. However, it is not known how miR‐210 affects cellular iron levels or production of heme, another iron containing molecule that is also needed for cellular and mitochondrial function.

Methods and Results

To screen for micro‐ribonucleic acids (miRNAs) regulated by iron, we performed a miRNA gene array in neonatal rat cardiomyocytes treated with iron chelators. Levels of miR‐210 are significantly increased with iron chelation, however, this response was mediated entirely through the hypoxia‐inducible factor (HIF) pathway. Furthermore, miR‐210 reduced cellular heme levels and the activity of mitochondrial and cytosolic heme‐containing proteins by modulating ferrochelatase (FECH), the last enzyme in heme biosynthesis. Mutation of the 2 miR‐210 binding sites in the 3′ untranslated region (UTR) of FECH reversed the miR‐210 response, while mutation of either binding site in isolation did not exert any effects. Changes mediated by miR‐210 in heme and FECH were independent of ISCU, as overexpression of an ISCU construct lacking the 3′ UTR does not alter miR‐210 regulation of heme and FECH. Finally, FECH levels increased in hypoxia, and this effect was not reversed by miR‐210 knockdown, suggesting that the effects of miR‐210 on heme are restricted to normoxic conditions, and that the pathway is overriden in hypoxia.

Conclusions

Our results identify a role for miR‐210 in the regulation of heme production by targeting and inhibiting FECH under normoxic conditions.

microRNA-210 is upregulated in hypoxic cardiomyocytes through Akt- and p53-dependent pathways and exerts cytoprotective effects

microRNA-210 (miR-210) is upregulated in hypoxia, but its function in cardiomyocytes and its regulation in response to hypoxia are not well characterized. The purpose of this study was to identify upstream regulators of miR-210, as well as to characterize miR-210's function in cardiomyocytes. We first showed miR-210 is upregulated through both hypoxia-inducible factor (HIF)-dependent and -independent pathways, since aryl hydrocarbon nuclear translocator (ARNT) knockout mouse embryonic fibroblasts (MEF), lacking intact HIF signaling, still displayed increased miR-210 levels in hypoxia. To determine the mechanism for HIF-independent regulation of miR-210, we focused on p53 and protein kinase B (Akt). Overexpression of p53 in wild-type MEFs induced miR-210, whereas p53 overexpression in ARNT knockout MEFs did not, suggesting p53 regulates miR-210 in a HIF-dependent mechanism. Akt inhibition reduced miR-210 induction by hypoxia, whereas Akt overexpression increased miR-210 levels in both wild-type and ARNT knockout MEFs, indicating Akt regulation of miR-210 is HIF-independent. We then studied the effects of miR-210 in cardiomyocytes. Overexpression of miR-210 reduced cell death in response to oxidative stress and reduced reactive oxygen species (ROS) production both at baseline and after treatment with antimycin A. Furthermore, downregulation of miR-210 increased ROS after hypoxia-reoxygenation. To determine a mechanism for the cytoprotective effects of miR-210, we focused on the predicted target, apoptosis-inducing factor, mitochondrion-associated 3 (AIFM3), known to induce cell death. Although miR-210 reduced AIFM3 levels, overexpression of AIFM3 in the presence of miR-210 overexpression did not reduce cellular viability either at baseline or after hydrogen peroxide treatment, suggesting AIFM3 does not mediate miR-210's cytoprotective effects. Furthermore, HIF-3α, a negative regulator of HIF signaling, is targeted by miR-210, but miR-210 does not modulate HIF activity. In conclusion, we demonstrate a novel role for p53 and Akt in regulating miR-210 and demonstrate that, in cardiomyocytes, miR-210 exerts cytoprotective effects, potentially by reducing mitochondrial ROS production.

Biomimetic high density lipoprotein nanoparticles for nucleic acid delivery

We report a gold nanoparticle-templated high density lipoprotein (HDL AuNP) platform for gene therapy that combines lipid-based nucleic acid transfection strategies with HDL biomimicry. For proof-of-concept, HDL AuNPs are shown to adsorb antisense cholesterylated DNA. The conjugates are internalized by human cells, can be tracked within cells using transmission electron microscopy, and regulate target gene expression. Overall, the ability to directly image the AuNP core within cells, the chemical tailorability of the HDL AuNP platform, and the potential for cell-specific targeting afforded by HDL biomimicry make this platform appealing for nucleic acid delivery.

Nanotechnology for synthetic high-density lipoproteins

Atherosclerosis is the disease mechanism responsible for coronary heart disease (CHD), the leading cause of death worldwide. One strategy to combat atherosclerosis is to increase the amount of circulating high-density lipoproteins (HDL), which transport cholesterol from peripheral tissues to the liver for excretion. The process, known as reverse cholesterol transport, is thought to be one of the main reasons for the significant inverse correlation observed between HDL blood levels and the development of CHD. This article highlights the most common strategies for treating atherosclerosis using HDL. We further detail potential treatment opportunities that utilize nanotechnology to increase the amount of HDL in circulation. The synthesis of biomimetic HDL nanostructures that replicate the chemical and physical properties of natural HDL provides novel materials for investigating the structure-function relationships of HDL and for potential new therapeutics to combat CHD.

Identification of Hydrophobic Interactions between Proteins and Lipids: Free Fatty Acids Activate Phospholipase C δ1 via Allosterism

Lipids are well recognized ligands that bind to proteins in a specific manner and regulate their function. Most attention has been placed on the headgroup of phospholipids, and little is known about the role of the acyl chains in mediating any effects of lipids on proteins. In this report, free fatty acids (FFA) were found to bind and activate phospholipase C delta1(PLC delta1). The unsaturated FFA arachidonic acid (AA) and oleic acid were able to stimulate PLC delta1 up to 30-fold in a dose-dependent manner. The saturated FFA stearic acid and palmitic acid were less efficacious than unsaturated FFA, activating the enzyme up to 8-fold. The mechanism of activation appears to be due to a change in K(m) for substrate; 50 microM arachidonate reduced the K(m) for the soluble PLC substrate diC(4)PI from 1.7 +/- 0.6 mM to 0.24 +/- 0.04 mM (7-fold reduction). V(max) was not significantly altered. PLC delta1 bound to sucrose-loaded vesicles that contained AA in a concentration-dependent manner. A fragment of PLC delta1 that encompasses the EF-hand domain also bound to micelles containing AA using nondenaturing PAGE. This same fragment also inhibited AA activation of PLC delta1 in a competition assay. These results suggest that the function of the EF-hand domain of PLC delta1 is to bind lipid and to allosterically regulate catalysis. These results also suggest that esterified and nonesterified fatty acids can bind to and regulate protein function, identifying a functional role for hydrophobic interactions between lipids and proteins.