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Problem definition: We study dynamic stochastic appointment scheduling when delaying appointments increases the risk of incurring costly failures, such as readmissions in healthcare or engine failures in preventative maintenance. When near-term base appointment capacity is full, the scheduler faces a trade-off between delaying an appointment at the risk of costly failures versus the additional cost of scheduling the appointment sooner using surge capacity. Academic/practical relevance: Most appointment-scheduling literature in operations focuses on the trade-off between waiting times and utilization. In contrast, we analyze preventative appointment scheduling and its impact on the broader service-supply network when the firm is responsible for service and failure costs. Methodology: We adopt a stochastic dynamic programming (DP) formulation to characterize the optimal scheduling policy and evaluate heuristics. Results: We present sufficient conditions for the optimality of simple policies. When analytical solutions are intractable, we solve the DP numerically and present optimality gaps for several practical policies in a healthcare setting. Managerial implications: Intuitive appointment policies used in practice are robust under moderate capacity utilization, but their optimality gap can quadruple under high load.

BACKGROUND Low rates of heart failure (HF) hospitalizations were observed during the 2020 peak of the COVID-19 pandemic. It is unclear how this affected different racial and socioeconomic groups. It is also unknown whether the shift to telemedicine impacted disparities in follow-up or readmissions. OBJECTIVE To determine the effects of the COVID-19 pandemic on acute decompensated heart failure admissions and post-hospitalization follow-up for different socioeconomic as well as racial and ethnic groups. METHODS HF admissions during the initial peak of the pandemic (March 15–June 1, 2020) were compared to the same timeframe in 2019. Patients were stratified by race, ethnicity, and median neighborhood income. Hospital and ICU admission rates, inpatient mortality, 7-day follow-up, and 30-day readmissions were assessed. RESULTS Patients in 2020 had higher rates of ICU admission compared to 2019 (16.87% vs 10.96%; P=0.008). This trend was seen across all subgroups and was significant for patients from the highest income quartile (17.89% vs 10.99%; P=0.018) and white patients (16.59% vs 9.52%; P=0.007). While there was a trend towards higher inpatient mortality in 2020 versus 2019 (4.60% vs 2.53%; P=0.077), no difference was seen among different racial and socioeconomic groups. Telemedicine comprised 80.69% of 7-day follow-up in 2020, with improvement in 7-day follow-up rates (43.86% vs 32.58%; P CONCLUSIONS There were no major differences in HF admissions for different racial and socioeconomic groups during the COVID-19 pandemic. Inequalities in 7-day follow-up were reduced with the advent of telemedicine and decreased 30-day readmission for those who had follow-up.

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%, p0.01). 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%, p0.01), 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.

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.

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.

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.

There is little data describing trends in the use of hydroxychloroquine for COVID-19 following publication of randomized trials that failed to demonstrate a benefit of this therapy. We identified 13,957 patients admitted for active COVID-19 at 85 U.S. hospitals participating in a national registry between March 1 and August 31, 2020. The overall proportion of patients receiving hydroxychloroquine peaked at 55.2% in March and April and decreased to 4.8% in May and June and 0.8% in July and August. At the hospital-level, median use was 59.4% in March and April (IQR 48.5–71.5%, range 0–100%) and decreased to 0.3% (IQR 0–5.4%, range 0–100%) by May and June and 0% (IQR 0–1.3%, range 0–36.4%) by July and August. The rate and hospital-level uniformity in deimplementation of this ineffective therapy for COVID-19 reflects a rapid response to evolving clinical information and further study may offer strategies to inform deimplementation of ineffective clinical care.

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.

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.

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 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.

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 ( p0.0005) 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.

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.

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/1.73m 2 , 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: NCT 02653482.

Background: As hospitals consolidate into networks, systems grapple with standardization of care delivery. As part of a comprehensive quality improvement program, our health system developed a process bundle comprising 5 interventions for hospitalized heart failure patients. We studied the baseline variation in fidelity of process delivery across our network comprising an academic medical center, tertiary community center, and community hospitals. Methods: Through a series of meetings and literature review, key cardiology, operational, and quality leadership from 5 hospitals developed a bundle of process interventions designed to support optimal, patient-centered care for hospitalized heart failure patients. The 5 bundle interventions were: 1) cardiology consultation, 2) heart failure education, 3) 7-day follow-up appointment with primary care or cardiology printed on discharge instructions, 4) 72-hour post-discharge telephone call placed, and 5) arrived to 7-day follow-up visit. For patients having a principal diagnosis of heart failure, systematic chart review was used to assess metric completion. One-way ANOVA on ranks was used to analyze variation of metric completion among the hospitals, and the Mann-Whitney U test with Bonferroni correction was used to compare hospital pairs. Results: During the 3 month study period, 780 heart failure patients across our network were discharged alive. We noted significant variation in fidelity among interventions, ranging from an 81% (634 of 780) rate of cardiology consultation, to a 36% (210 of 581) rate of 7-day follow-up with primary care or cardiology across the network. All five processes were completed in 12% (52 of 426) of patients, with 51% (217 of 426) of patients receiving 2 or 3 interventions. Furthermore, analysis by one-way ANOVA on ranks demonstrated highly statistically significant variation among the 5 hospitals within each of the 5 process bundle metrics. Pairwise comparisons demonstrated significant differences among pairs of hospitals on these metrics. Conclusions: This study demonstrates that in a large academic health system, significant variation exists in fidelity of care delivery for a 5 component heart failure care bundle. Future work will focus on eliminating barriers to care delivery, and understanding the impact of the process bundle on outcomes including 30-day readmissions, home days, and patient satisfaction. Opportunity exists to share best practices among hospitals to improve system-wide performance and outcomes.

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.

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 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.

This study uses data from a large academic medical center to estimate the accuracy of the criteria used by Medicare to represent the burden of hospitalizations for heart failure.

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 cholesterol 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 cholesterol efflux capacity was associated with baseline concentrations of HDL2 (β=0.008, P=0.0106), HDL3 (β=0.013, P

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.

Purpose Optimal doses of guideline-directed medical therapy (GDMT) are a key component for reducing hospital readmissions in patients with heart failure with reduced ejection fraction (HFrEF). Pharmacist-led medication titration may allow for more rapid up-titration of GDMT in the clinic setting. The objective of this study was to determine the proportion of heart failure patients discharging from the hospital on non-target doses of GDMT. This population represents the group of patients who could benefit from pharmacist-led medication titration. Methods This Institutional Review Board approved, single-center, retrospective, observational chart review study included patients who were at least 18 years old and were discharged with the diagnosis of HFrEF (EF ≤40%) between July 1, 2016 to December 31, 2016. The optimized group consisted of patients at ≥50% or maximum tolerated doses of GDMT (ACEI/ARB/ARNI and BB) at hospital discharge. The potential to optimize group consisted of patients on Results 103 patients were included in the study. At discharge, 37% of patients were on optimal doses of GDMT, whereas in 63% of patients one or both therapies were not optimized at discharge. Rate of hospital readmission at 90 days was numerically lower in patients in the optimized group compared to potential to optimize group at discharge (39% vs. 46%, P= 0.56). Reflecting national data, the number of patients who were discharged on ≥50% of GDMT was small (n= 5). This group of patients had zero readmissions during 90 days. Out of the 65 patients in the potential to optimize group at discharge, only 34% were optimized on GDMT doses by standard of care during the 90-day follow-up period. Patients who were titrated up to ≥50% of GDMT (n=2) during 90 days had zero readmissions. The most common limiting factors for dose titration in the maximum tolerated dose group were hypotension, elevated serum creatinine, and bradycardia. Conclusions In a large academic medical center, the majority of patients leaving the hospital were not optimized on GDMT doses at discharge or during the 90-day follow-up period under routine care. While the 90-day readmission rate between the groups were similar, the sub-analysis showed patients on ≥50% of GDMT (n=7) had no readmissions during the 90 days. A significant opportunity therefore exists to leverage pharmacy expertise to optimize GDMT in this population.

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.

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.

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.

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.

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.

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.

We report a gold nanoparticle-templated high density lipoprotein (HDL AuNP) platform for gene therapy which 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 (TEM), 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.

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.

Exosomes are nanoscale vesicles that mediate intercellular communication. Cellular exosome uptake mechanisms are not well defined partly due to the lack of specific inhibitors of this complex cellular process. Exosome uptake depends on cholesterol-rich membrane microdomains called lipid rafts and can be blocked by non-specific depletion of plasma membrane cholesterol. Scavenger receptor type B-1 (SR-B1), found in lipid rafts, is a receptor for cholesterol-rich high-density lipoproteins (HDL). We hypothesized that a synthetic nanoparticle mimic of HDL (HDL NP) that binds SR-B1 and removes cholesterol through this receptor would inhibit cellular exosome uptake. In cell models, our data show that HDL NPs bind SR-B1, activate cholesterol efflux and attenuate the influx of esterified cholesterol. As a result, HDL NP treatment results in decreased dynamics and clustering of SR-B1 contained in lipid rafts and potently inhibits cellular exosome uptake. Thus, SR-B1 and targeted HDL NPs provide a fundamental advance in studying cholesterol-dependent cellular uptake mechanisms.

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.

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.

Background Micro RNA ‐210 (miR‐210) increases in hypoxia and regulates mitochondrial respiration through modulation of iron‐sulfur cluster assembly proteins ( ISCU 1/2), 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 mi RNA 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.

Mitochondrial iron levels are tightly regulated, as iron is essential for the synthesis of Fe/S clusters and heme in the mitochondria, but high levels can cause oxidative stress. The ATP-binding cassette (ABC) transporter ABCB8 is a mitochondrial inner membrane protein with an unknown function. Here, we show that ABCB8 is involved in mitochondrial iron export and is essential for baseline cardiac function. Induced genetic deletion of ABCB8 in mouse hearts resulted in mitochondrial iron accumulation and cardiomyopathy, as assessed by echocardiography and invasive hemodynamics. Mice with ABCB8 deletion in the heart also displayed mitochondrial damage, and higher levels of reactive oxygen species and cell death. Down-regulation of ABCB8 in vitro resulted in decreased iron export from isolated mitochondria, whereas its overexpression had the opposite effect. Furthermore, ABCB8 is needed for the maturation of the cytosolic Fe/S proteins, as its deletion in vitro and in vivo led to decreased activity of cytosolic, but not mitochondrial, iron–sulfur-containing enzymes. These results indicate that ABCB8 is essential for normal cardiac function, maintenance of mitochondrial iron homeostasis and maturation of cytosolic Fe/S proteins. In summary, this report provides characterization of a protein involved in mitochondrial iron export.