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 coronavirus disease 2019 (COVID-19) have been termed "long COVID" or "post-acute sequelae of SARS-CoV-2 infection." 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 in Chicago, Ill, 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 the 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 computed tomography 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 findings similar to those with negative or no test results. CONCLUSIONS: The experience at our multidisciplinary comprehensive COVID-19 center 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.

Racial and Socioeconomic Differences in Heart Failure Hospitalizations and Telemedicine Follow-up During the COVID-19 Pandemic: Retrospective Cohort Study.

BACKGROUND: Low rates of heart failure (HF) hospitalizations were observed during the 2020 peak of the COVID-19 pandemic. Additionally, posthospitalization follow-up transitioned to a predominantly telemedicine model. It is unknown whether the shift to telemedicine impacted disparities in posthospitalization follow-up or HF readmissions. OBJECTIVE: The aim of this paper is to determine whether the shift to telemedicine impacted racial and ethnic as well as socioeconomic disparities in acute decompensated heart failure (ADHF) follow-up and HF readmissions. We additionally sought to investigate the impact of the COVID-19 pandemic on the severity of ADHF hospitalizations. METHODS: This was a retrospective cohort study of HF admissions across 8 participating hospitals during the initial peak of the COVID-19 pandemic (March 15 to June 1, 2020), compared to the same time frame in 2019. Patients were stratified by race, ethnicity, and median neighborhood income. Hospital and intensive care unit (ICU) admission rates, inpatient mortality, 7-day follow-up, and 30-day readmissions were assessed. RESULTS: From March 15, 2019, to June 1, 2020, there were 1162 hospitalizations for ADHF included in the study. There were significantly fewer admissions for ADHF in 2020, compared with 2019 (442 vs 720; P<.001). Patients in 2020 had higher rates of ICU admission, compared with 2019 (15.8% vs 11.1%; P=.02). This trend was seen across all subgroups and was significant for patients from the highest income quartile (17.89% vs 10.99%; P=.02). While there was a trend toward higher inpatient mortality in 2020 versus 2019 (4.3% vs 2.8%; P=.17), no difference was seen among different racial and socioeconomic groups. Telemedicine comprised 81.6% of 7-day follow-up in 2020, with improvement in 7-day follow-up rates (40.5% vs 29.6%; P<.001). Inequities in 7-day follow-up for patients from non-Hispanic Black racial backgrounds compared to those from non-Hispanic White backgrounds decreased during the pandemic. Additionally, those with telemedicine follow-up were less likely to be readmitted in 30 days when compared to no follow-up (13.8% vs 22.4%; P=.03). CONCLUSIONS: There were no major differences in HF ICU admissions or inpatient mortality 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 rates for those who had telemedicine follow-up.

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.

Repeated cross-sectional analysis of hydroxychloroquine deimplementation in the AHA COVID-19 CVD Registry.

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.

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.

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.

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.

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 (ß = 0.008, p = 0.0106), HDL3 (ß = 0.013, p < 0.0001), and IL-6 (ß = -0.019, p = 0.03). Baseline HDL3 concentration was inversely associated with IL-6 concentration (ß = -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.