Citations

Abstract Clinical biomarker research is growing at a fast pace, particularly in the cardiovascular field, due to the demanding requirement to provide personalized precision medicine. The lack of a distinct molecular signature for each cardiovascular derangement results in a one-size-fits-all diagnostic and therapeutic approach, which may partially explain suboptimal outcomes in heterogeneous cardiovascular diseases (e.g., heart failure with preserved ejection fraction). A multidimensional approach using different biomarkers is quickly evolving, but it is necessary to consider pre-analytical variables, those to which a biological sample is subject before being analyzed, namely sample collection, handling, processing, and storage. Pre-analytical errors can induce systematic bias and imprecision, which may compromise research results, and are easy to avoid with an adequate study design. Academic clinicians and investigators must be aware of the basic considerations for biospecimen management and essential pre-analytical recommendations as lynchpin for biological material to provide efficient and valid data.

Background: Patients are surviving longer after myocardial infarction (MI), but little is known about the occurrence and predictors of subsequent hospitalizations. Methods: We identified all Olmsted Count residents with incident MI from 1987-2008 and evaluated Olmsted County hospitalizations through 2009. ICD-9 codes were used to determine the primary reason for hospitalization. To account for repeated events, Andersen-Gill models were used to examine the predictors of hospitalization post-MI. Patients were censored at death or last follow-up. Results: A total of 2617 patients (mean 67 years, 41% female, 32% with ST-elevation MI) were diagnosed with incident MI from 1987-2007 and survived MI hospitalization. Over a mean follow-up of 7.1 years, 10116 hospitalizations occurred equating to a median of 3 (range 0 to 43) per person. Only 37.5% (n=3793) of hospitalizations were due to cardiovascular causes, and of these, most were due to ischemic heart disease (n= 1865, 49.2%) and heart failure (n= 733, 19.3%). The proportion of non-cardiovascular hospitalizations increased over time and was higher in women than men, but did not differ by ST-segment status. Several factors were associated with the risk of hospitalization after adjusting for year of diagnosis and sex (Figure). Biomarker levels were not predictors of hospitalization risk. Conclusions: Two-thirds of hospitalizations among incident MI survivors in the community are for non-cardiovascular reasons, and this proportion has increased. Comorbidities are important predictors of recurrent hospitalizations. Therapies focused solely on MI management may be insufficient to prevent the majority of future admissions.

Background: Hospital readmissions are considered to be a quality of care indicator, yet little is known about their occurrence and predictors after myocardial infarction (MI) in the community. Methods: We identified all Olmsted County residents with incident MI from 1987-2007 and evaluated 30-day rehospitalizations. The reason for readmission was categorized based on ICD-9 code. Safety endpoints of revascularization (percutaneous coronary intervention [PCI] or coronary artery bypass grafting [CABG]) or fibrinolysis included stroke, acute kidney injury (rise in creatinine post-procedure of >0.5 mg/dL), access site complication (PCI), and gastrointestinal or other bleeding. Cox proportional hazard regression models were used to examine the predictors of readmission. Results: A total of 2617 patients (mean age 67 years, 41% female) had incident MI (32% with ST-elevation) from 1987-2007 and survived their initial hospitalization. A total of 583 readmissions occurred within 30 days among 512 (19.6%) patients. Approximately half (n=286, 49.1%) of readmissions were due to cardiovascular causes, and of these, most were due to ischemic heart disease (n=175, 61.2%) or heart failure (n=56, 19.6%). The proportion of non-cardiovascular rehospitalizations increased over time, from 42.2% from 1993-97 to 59.4% from 2003-07 (p for trend=0.01). A minority of those readmitted for cardiovascular reasons required repeat angiogram (n=110, 38.5%) or revascularization (n=88, 50 PCI, 38 CABG). After adjustment for confounders, key factors were associated with increased rehospitalization risk ( Figure ). Following PCI (n=1230), a stroke or bleeding event (adjusted HR 1.69, 95% CI 1.09-2.61, p=0.02) and acute kidney injury (HR 2.50, 95% CI 1.61-3.87, p<0.01) were associated with markedly increased readmission risk. Conclusions: Half of readmissions within 30 days of MI are for non-cardiovascular reasons, and this proportion has increased. Comorbidity and complications of revascularization are key predictors of readmission.

Introduction: Left ventricular longitudinal strain (LV-LS) 1) predicts mortality in patients with aortic stenosis (AS) and 2) is highly correlated to type-B natriuretic peptide (BNP) values. The BNP ratio (measured BNP/maximal expected BNP value specific for age and sex) is a powerful independent predictor of death in patients with AS. Hypothesis: we hypothesize that BNP activation (i.e. BNP ratio >1) affects the association between LV-LS and mortality in patients with asymptomatic AS and preserved LV ejection fraction (EF ≥50%). Methods: 315 patients (age 74±12 years, 56% men and mean aortic valve area = 1.02±0.15cm2) underwent simultaneous Doppler echocardiographic and BNP measurements. LV-LS was calculated as the average of 12 LV segments from apical 2- and 4-chamber views using Velocity Vector Imaging. Results: Mean LV-LS was -16.8±3.2%, LV EF 66±7%, median BNP level 121 (interquartile 48-320) pg/ml. 58% of patients had BNP activation. Better LV-LS was associated with lower log BNPratio (regression coefficient 0.10, p<0.001). After a median follow-up of 6.5 yrs (interquartile: 3.6-8.2), 119 deaths occurred. After adjustment for age, sex, Charlson score index, hemoglobin level, aortic valve replacement (as a time dependent variable), LV-LS and log BNPratio were separately associated with increased risk for death (all p<0.01). Further adjustment for predictors of mortality, LV-LS and log-BNP ratio remained associated with increased risk for death (hazard ratio HR [95%CI]: 1.09 [1.03-1.15]; p=0.003 and 1.82 [1.52-2.19]; p<0.0001 respectively). In patients without BNP activation (i.e. normal BNP), LV-LS was associated with mortality (HR: 1.22 [1.04-1.43]; p=0.01) while it was not in patients with BNP activation (p=0.22). Conclusions: In patients with asymptomatic AS, without clinically obvious myocardial impairment (i.e. normal LVEF), a notable proportion of patients present with myocardial alterations detected by an elevated BNPratio or reduced LV-LS. These signs of myocardial alterations were predictive of mortality after diagnosis. Thus both BNP and LV-LS should be assessed in the clinical setting to provide complementary information on prognosis in patients with asymptomatic AS and preserved LV EF.

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: Copeptin has demonstrated a role in early rule out for acute myocardial infarction in combination with a negative troponin. However, the value of copeptin in patients with a positive troponin is not established. Hypothesis: The addition of a baseline copeptin at chest pain presentation will improve risk stratification in patients with mild troponin elevation. Methods: The multi-center CHOPIN trial enrolled 2071 acute chest pain patients. All subjects with ST segment elevations were excluded from this analysis. Of the remaining patients, 124 had a mildly elevated troponin (defined by < 2x URL) and were included in the study cohort. Baseline cTnI and copeptin levels were drawn on presentation, and another cTnI at 2 hours. Copeptin ≤14 pmol/l and troponin deltas ≤10% from baseline were considered positive. Two independent blinded cardiologists adjudicated AMI diagnosis. The value of a baseline copeptin in this cohort was assessed using AMI incidence, odds ratio, sensitivity, and negative predictive value (NPV). Results: Of the 124 patients in the study cohort, 73 (59%) had an elevated copeptin and an associated AMI incidence of 25% (18 AMIs). The remaining 51 patients (41%) were copeptin negative and diagnosed with 3 AMIs, an incidence of 5.9% (p = 0.006, figure 1). A positive copeptin increased the likelihood of AMI diagnosis with an odds ratio of 5.3 (95% CI: 1.5-19.2). The sensitivity and NPV for AMI were 86% and 94%, both of which were higher than the delta troponin (table 1). When an initial copeptin was combined with the delta troponin the sensitivity and NPV for AMI improved to 100%. Conclusion: Use of copeptin in chest pain patients with mild troponin elevation provides further risk stratification. The combination of copeptin with a delta troponin may aid in earlier AMI rule out in this subset of patients. This approach could be especially relevant as the increasing use of high sensitivity troponin assays leads to greater rates of mild troponin elevations.

Introduction: Levels of type-B natriuretic peptide (BNP) adjusted for age and sex are a powerful predictor of mortality after the diagnosis of aortic stenosis (AS). Objective: To assess whether BNP activation (compared to normal values) affects mortality after aortic valve replacement (AVR). Methods: In 494 patients with AS, plasma level of BNP was measured pre-operatively at the time of Doppler-Echocardiography. BNP was normalized as BNPratio (BNP measured/BNP maximum normal value for age and sex) to evaluate the impact of BNP activation on post-operative outcomes. Results: Mean age was73±11 years and 300 (61%) patients were male. Mean aortic valve area was 0.82±0.20cm2, mean gradient 51±16mmHg, and mean Left Ventricular Ejection Fraction 59±15%. Incidence of hypertension was 65%, diabetes 27% and chronic kidney disease 22%. Median of BNP was 211 (interquartile: 87-508) and BNPratio 2.2 (1.0-5.2). After adjustment age, gender, body surface area, Charlson score index, presence of symptoms, creatinine, hemoglobin, systolic blood pressure, indexed aortic valve area and left ventricular ejection fraction, the BNPratio (log transformed) was a predictor of long-term mortality (p<0.0001) with an hazard ratio 1.34 (1.14-1.60). Moreover, stratification by BNPratio showed increasing long-term post-operative mortality with BNPratios>2 (Figure). Conclusions: BNP activation defined using BNPratios (normalized for age and sex) is a powerful independent predictor of long-term mortality after aortic valve replacement for AS. Furthermore, patients with a BNPratio>2 before aortic valve replacement incur excess mortality during follow-up in relation to the increasing BNPratio. These data support the use of the BNPratio to guide the timing of AVR.

Background: Contemporary data on survival after incident acute coronary syndrome (ACS), including both myocardial infarction (MI) and unstable angina (UA), are limited. Objective: To describe survival after incident ACS, to determine if it differs by ACS type (MI or UA) and to determine whether it has improved over time. Methods: Olmsted County, MN residents hospitalized between 1/1/2005-12/31/2010 were screened for incident ACS. ACS was defined as either MI validated by standard epidemiological criteria or UA validated by the Braunwald classification. Patients were followed for death from any cause. Cox proportional hazards regression was used to determine whether survival differed by ACS type, while adjusting for year of diagnosis, age, sex and comorbidities. Results: Among 1,160 incident ACS cases (mean±SD age 66.9±14.8, 60% male), 35% were UA and 65% were MI. After a mean (SD) follow up of 3.7 (2.1) years, 274 deaths occurred. The 3-year Kaplan-Meier survival estimate for MI was 79.6% (95% CI: 76.7%-82.6%) and for UA was 84.9% (95% CI: 81.3%-88.6%) (log-rank p=0.011). The association of ACS type with survival differed by age (p=0.056). After adjustment for year of diagnosis, sex and comorbidities, no difference in survival was observed between ACS types among those aged <60 (HR for MI vs. UA: 0.64, 95% 0.29-1.42). By contrast, among patients aged 60-79, those with an MI had 2 times the risk of death compared to those with UA (HR: 2.04, 95% CI: 1.24-3.37). Patients aged 80 or older who had an MI had a 40% increased risk of death compared to patients of the same age who had UA (HR: 1.42, 95% CI: 1.02-1.98). There was no difference in survival over time (HR for 2010 vs. 2005: 0.91, 95% CI: 0.61-1.36). Conclusions: Survival did not differ between UA and MI patients younger than 60, however among patients 60 or older, survival was worse among those with an MI. Survival after ACS did not change over the study period.