Berger, Peter B.

Abstract Funding Acknowledgements Type of funding sources: Private hospital(s). Main funding source(s): Mayo Clinic Department of Laboratory Medicine and Pathology. Background Elevated lipoprotein(a) [Lp(a)] is associated with increased risk for atherosclerotic cardiovascular disease (ASCVD). Treatment of patients with elevated Lp(a) usually includes marked reduction in low density lipoprotein cholesterol (LDL-C). However, there are uncertainties about whether all increases in Lp(a) are equivalent in there risk relationship. Purpose The aim of this investigation was to risk stratify patients with elevated Lp(a) mass [Lp(a)-M] based on Lp(a) cholesterol (Lp(a)-C) measurements. We assessed coronary artery disease (CAD) by coronary angiogram and major adverse cardiac events (MACE) in a cohort of patients with elevated Lp(a) mass (≥30mg/dL) over a 15 year followup. Methods Electrophoretic and immunologic assays were used to measure Lp(a)-C and Lp(a)-M, respectively. Univariate and multivariate logistic regression analysis was employed to test for association between Lp(a) and CAD in a cohort patients with elevated Lp(a)-M (≥30mg/dL). The relationship between Lp(a) and time to major cardiac events was evaluated using cox regression analysis. Receiver operator curve (ROC) analyses were used to identify optimal cutoff values for Lp(a)-M and C for predicting CAD in our model. Results We analyzed 179 patients with elevated Lp(a)-M. Cutoff values of 3.3mg/dL for Lp(a)-C and 42.1mg/dL for Lp(a)-M optimally predicted CAD in an ROC analysis. Of these patients, 84 were found to have Lp(a)-C < 3.3mg/dL (low Lp(a)-C), while 95 patients had Lp(a)-C levels ≥ 3.3mg/dL (high Lp(a)-C). High Lp(a)-C was associated with CAD identified on coronary angiography in univariate (OR = 2.17, 95% CI = 1.18 – 4.00, p = 0.013) and multivariate (OR = 3.36, 95% CI = 1.64 – 6.89, p < 0.001) models controlling for traditional risk factors. In univariate analyses high Lp(a)-C was also associated with MACE (HR = 2.38, 95% CI = 1.19 – 4.75, p = 0.01) while Lp(a)-M ≥ 42.1mg/dL (high Lp(a)-M) was not (HR = 1.68, 95% CI = 0.82 – 3.44, p = 0.15) (Table 1, Figure 1). Lp(a)-C remained significantly correlated with MACE after adjusting for traditional risk factors. When included in a model together, Lp(a)-C maintained its significance while Lp(a)-M failed to predict MACE, indicating that Lp(a)-C is a stronger predictor of MACE than Lp(a)-M. Conclusion This study demonstrates that patients with elevated Lp(a)-M and high Lp(a)-C are approximately 2.5 times more likely to encounter MACE over time when compared to patients with elevated Lp(a)-M and low Lp(a)-C. Our investigation suggests that Lp(a)-C can be used to risk stratify patients with elevated Lp(a)-M and identify those at higher risk.

Introduction: Elevated plasma levels of lipoprotein(a) [Lp(a)] are associated with increased risk for coronary artery disease (CAD), but the strength of the association and whether it is independent of other risk factors remains controversial. Differences in prospective studies may be partially explained by variability in methods used to measure Lp(a). Methods: We utilized two different analytical methods (electrophoretic and immunologic) to measure Lp(a) and determine the predictive value of Llp(a) in assessing angiographic coronary artery disease (CAD) and association with major adverse events in 500 patients. Results: In univariate analyses, median Lp(a) cholesterol and Lp(a) mass were significantly associated with angiographic CAD. In a multivariable model, Lp(a) cholesterol remained a significant correlate of CAD (OR 1.61, 95% CI 1.13-2.31, P = 0.009) while Lp(a) mass was not (OR 1.18, 95% CI 0.95-1.47, P = 0.14). Additionally, on multivariable analysis, the presence of a detectable amount of Lp(a) cholesterol (> or =2.5 mg/dL) was more strongly correlated with CAD than HDL cholesterol < 40 mg/dL, and, along withLp(a) cholesterol, was strongly correlated with major adverse events (OR 2.08 95% CI 1.22-3.56, P = 0.007 and OR 1.22, 95% CI 1.05-1.42, P = 0.012, respectively). Conclusions: Lp(a) cholesterol measured electrophoretically is independently correlated with angiographic CAD and presence of major adverse events, and may be used as an alternative or supplement to Lp(a) mass analysis.

Introduction: Patient understanding of available therapies for atrial fibrillation (AF) is foundational to shared medical decision making and long term medication adherence. Yet, there is a paucity of data regarding the extent to which patients newly diagnosed with AF in routine community practice understand their options. Hypotheses: 1) Understanding of warfarin, novel oral anticoagulants (NOAC), rhythm control therapy, cardioversion and radio frequency ablation changes little from baseline to 6 months and 2) treatment rates at 6 months are associated with patient understanding of therapies at baseline. Methods: We analyzed survey data from SATELLITE, a substudy of new-onset AF patients enrolled at 56 US sites participating in the ORBIT-AF registry. Patients were surveyed at the baseline and 6 month follow up clinic visit using Likert scales. Agreement between time points was assessed with the McNemar test, and the relationship between understanding and treatment was assessed only for the subset not on treatment at baseline. Results: Of 1000 patients enrolled in SATELLITE, 506 had 6-month survey data (data collection is continuing). Among these, the median age was 69.0 years (IQR 63.0 - 76.0) and 93.7% (474 of 506) were white. There was evidence of improvement in the self-reported understanding of warfarin and NOACs from baseline to 6 months, but not for rhythm control, ablation or cardioversion. The proportion reporting high understanding improved significantly for warfarin (p<.0001) and NOACs (p<.0001) from 47% (223 of 474) and 51% (245 of 481) at baseline to 60% (284 of 474) and 69% (332 of 481) at 6 months respectively (Figure 1). Patients with high understanding of the benefits of ablation (p=0.0005) and options for ablation (p=0.0093) at baseline were more likely to have this therapy at the 6 month follow up (N=590), but improved understanding was not associated with increased use of warfarin/NOACs (N=83) or rhythm control (N=444). Conclusions: Patients with new-onset AF had improved self-reported understanding of some treatment options over the first 6-months from diagnosis; however, factors other than patient understanding may influence AF treatments received at 6 months. Patient understanding of AF treatments remains suboptimal at 6 months, and our results suggest a need for ongoing patient education.

Objective— Ceramides are sphingolipids involved with cellular signaling. Synthesis of ceramides occurs in all tissues. Ceramides accumulate within tissues and the blood plasma during metabolic dysfunction, dyslipidemia, and inflammation. Elevations of ceramides are predictive of cardiovascular mortality. We sought to verify the utility of plasma concentrations of 4 ceramides: N-palmitoyl-sphingosine [Cer(16:0)], N-stearoyl-sphingosine [Cer(18:0)], N-nervonoyl-sphingosine [Cer(24:1)], and N-lignoceroyl-sphingosine [Cer(24:0)] in predicting major adverse cardiovascular events in a diverse patient population referred for coronary angiography. Approach and Results— Plasma ceramides were measured in 495 participants before nonurgent coronary angiography. Coronary artery disease, defined as >50% stenosis in ≥1 coronary artery, was identified 265 (54%) cases. Ceramides were not significantly associated with coronary artery disease. Patients were followed for a combined primary end point of myocardial infarction, percutaneous intervention, coronary artery bypass, stroke, or death within 4 years. Ceramides were significantly predictive of outcomes after adjusting for age, sex, body mass index, hypertension, smoking, LDL (low-density lipoprotein) cholesterol, HDL (high-density lipoprotein) cholesterol, triglycerides, serum glucose, and family history of coronary artery disease. The fully adjusted per SD hazard ratios (95% confidence interval) were 1.50 (1.16–1.93) for Cer(16:0), 1.42 (1.11–1.83) for Cer(18:0), 1.43 (1.08–1.89) for Cer(24:1), and 1.58 (1.22–2.04) for the ceramide risk score. Conclusions— Elevated plasma concentrations of ceramides are independently associated with major adverse cardiovascular events in patients with and without coronary artery disease.

Background: Hospitals’ risk-standardized mortality rates and outlier status (significantly higher/lower rates) are reported by the Centers for Medicare and Medicaid Services (CMS) for acute myocardial infarction (AMI) patients using Medicare claims data. New York now has AMI claims data with blood pressure and heart rate added. Objective: The objective of this study was to see whether the appended database yields different hospital assessments than standard claims data. Methods: New York State clinically appended claims data for AMI were used to create 2 different risk models based on CMS methods: 1 with and 1 without the added clinical data. Model discrimination was compared, and differences between the models in hospital outlier status and tertile status were examined. Results: Mean arterial pressure and heart rate were both significant predictors of mortality in the clinically appended model. The C statistic for the model with the clinical variables added was significantly higher (0.803 vs. 0.773, P<0.001). The model without clinical variables identified 10 low outliers and all of them were percutaneous coronary intervention hospitals. When clinical variables were included in the model, only 6 of those 10 hospitals were low outliers, but there were 2 new low outliers. The model without clinical variables had only 3 high outliers, and the model with clinical variables included identified 2 new high outliers. Conclusion: Appending even a small number of clinical data elements to administrative data resulted in a difference in the assessment of hospital mortality outliers for AMI. The strategy of adding limited but important clinical data elements to administrative datasets should be considered when evaluating hospital quality for procedures and other medical conditions.

Background— A simple risk score to predict long-term mortality after percutaneous coronary intervention (PCI) using preprocedural risk factors is currently not available. In this study, we created one by simplifying the results of a Cox proportional hazards model. Methods and Results— A total of 11 897 patients who underwent PCI from October through December 2003 in New York State were randomly divided into derivation and validation samples. Patients’ vital statuses were tracked using the National Death Index through the end of 2008. A Cox proportional hazards model was fit to predict death after PCI using the derivation sample, and a simplified risk score was created. The Cox model identified 12 separate risk factors for mortality including older age, extreme body mass indexes, multivessel disease, a lower ejection fraction, unstable hemodynamic state or shock, several comorbidities (cerebrovascular disease, peripheral vascular disease, congestive heart failure, chronic obstructive pulmonary disease, diabetes mellitus, and renal failure), and a history of coronary artery bypass graft surgery. The C statistics of this model when applied to the validation sample were 0.787, 0.785, and 0.773 for risks of death within 1, 3, and 5 years after PCI, respectively. In addition, the point-based risk score demonstrated good agreement between patients’ observed and predicted risks of death. Conclusions— A simple risk score created from a more complicated Cox proportional hazards model can be used to accurately predict a patient’s risk of long-term mortality after PCI.