Evangelos Hytopoulos

Circulating Angiopoietins-1 and -2, Angiopoietin Receptor Tie-2 and Vascular Endothelial Growth Factor-A as Biomarkers of Acute Myocardial Infarction: a Prospective Nested Case-Control Study

BackgroundAngiogenesis is up-regulated in myocardial ischemia. However, limited data exist assessing the value of circulating angiogenic biomarkers in predicting future incidence of acute myocardial infarction (AMI). Our aim was to examine the association between circulating levels of markers of angiogenesis with risk of incident acute myocardial infarction (AMI) in men and women.MethodsWe performed a case-control study (nested within a large cohort of persons receiving care within Kaiser Permanente of Northern California) including 695 AMI cases and 690 controls individually matched on age, gender and race/ethnicity.ResultsMedian [inter-quartile range] serum concentrations of vascular endothelial growth factor-A (VEGF-A; 260 [252] vs. 235 [224] pg/mL; p = 0.01) and angiopoietin-2 (Ang-2; 1.18 [0.66] vs. 1.05 [0.58] ng/mL; p < 0.0001) were significantly higher in AMI cases than in controls. By contrast, endothelium-specific receptor tyrosine kinase (Tie-2; 14.2 [3.7] vs. 14.0 [3.1] ng/mL; p = 0.07) and angiopoietin-1 levels (Ang-1; 33.1 [13.6] vs. 32.5 [12.7] ng/mL; p = 0.52) did not differ significantly by case-control status. After adjustment for educational attainment, hypertension, diabetes, smoking, alcohol consumption, body mass index, LDL-C, HDL-C, triglycerides and C-reactive protein, each increment of 1 unit of Ang-2 as a Z score was associated with 1.17-fold (95 percent confidence interval, 1.02 to 1.35) increased odds of AMI, and the upper quartile of Ang-2, relative to the lowest quartile, was associated with 1.63-fold (95 percent confidence interval, 1.09 to 2.45) increased odds of AMI.ConclusionsOur data support a role of Ang-2 as a biomarker of incident AMI independent of traditional risk factors.

Coronary risk assessment among intermediate risk patients using a clinical and biomarker based algorithm developed and validated in two population cohorts

Abstract Background: Many coronary heart disease (CHD) events occur in individuals classified as intermediate risk by commonly used assessment tools. Over half the individuals presenting with a severe cardiac event, such as myocardial infarction (MI), have at most one risk factor as included in the widely used Framingham risk assessment. Individuals classified as intermediate risk, who are actually at high risk, may not receive guideline recommended treatments. A clinically useful method for accurately predicting 5-year CHD risk among intermediate risk patients remains an unmet medical need. Objective: This study sought to develop a CHD Risk Assessment (CHDRA) model that improves 5-year risk stratification among intermediate risk individuals. Methods: Assay panels for biomarkers associated with atherosclerosis biology (inflammation, angiogenesis, apoptosis, chemotaxis, etc.) were optimized for measuring baseline serum samples from 1084 initially CHD-free Marshfield Clinic Personalized Medicine Research Project (PMRP) individuals. A multivariable Cox regression model was fit using the most powerful risk predictors within the clinical and protein variables identified by repeated cross-validation. The resulting CHDRA algorithm was validated in a Multiple-Ethnic Study of Atherosclerosis (MESA) case-cohort sample. Results: A CHDRA algorithm of age, sex, diabetes, and family history of MI, combined with serum levels of seven biomarkers (CTACK, Eotaxin, Fas Ligand, HGF, IL-16, MCP-3, and sFas) yielded a clinical net reclassification index of 42.7% (p < 0.001) for MESA patients with a recalibrated Framingham 5-year intermediate risk level. Across all patients, the model predicted acute coronary events (hazard ratio = 2.17, p < 0.001), and remained an independent predictor after Framingham risk factor adjustments. Limitations: These include the slightly different event definition with the MESA samples and inability to include PMRP fatal CHD events. Conclusions: A novel risk score of serum protein levels plus clinical risk factors, developed and validated in independent cohorts, demonstrated clinical utility for assessing the true risk of CHD events in intermediate risk patients. Improved accuracy in cardiovascular risk classification could lead to improved preventive care and fewer deaths.

Clinical Utility of a Novel Coronary Heart Disease Risk-Assessment Test to Further Classify Intermediate-Risk Patients

Current coronary heart disease (CHD) risk assessments inadequately assess intermediate‐risk patients, leaving many undertreated and vulnerable to heart attacks. A novel CHD risk‐assessment (CHDRA) tool was developed for intermediate‐risk stratification using biomarkers and established risk factors to significantly improve CHD risk discrimination.

Cost effectiveness analysis of a next generation risk assessment score for cardiovascular disease

Abstract Objectives: The goal of this study is to determine the cost-effectiveness of MIRISK VP, a next generation coronary heart disease risk assessment score, in correctly reclassifying and appropriately treating asymptomatic, intermediate risk patients. Study design: A Markov model was employed with simulated subjects based on the Multi-Ethnic Study of Atherosclerosis (MESA). This study evaluated three treatment strategies: (i) practice at MESA enrollment, (ii) current guidelines, and (iii) MIRISK VP in MESA. Methods: The model assessed patient healthcare costs and outcomes, expressed in terms of life years and quality-adjusted life years (QALYs), over the lifetime of the cohort from the provider and payer perspective. A total of 50,000 hypothetical individuals were used in the model. A sensitivity analysis was conducted (based on the various input parameters) for the entire cohort and also for individuals aged 65 and older. Results: Guiding treatment with MIRISK VP leads to the highest net monetary benefits when compared to the ‘Practice at MESA Enrollment’ or to the ‘Current Guidelines’ strategies. MIRISK VP resulted in a lower mortality rate from any CHD event and a modest increase in QALY of 0.12–0.17 years compared to the other two approaches. Limitations: This study has limitations of not comparing performance against strategies other than the FRS, the results are simulated as with all models, the model does not incorporate indirect healthcare costs, and the impact of patient or physician behaviors on outcomes were not taken into account. Conclusions: MIRISK VP has the potential to improve patient outcomes compared to the alternative strategies. It is marginally more costly than both the ‘Practice at MESA Enrollment’ and the ‘Current Guidelines’ strategies, but it provides increased effectiveness, which leads to positive net monetary benefits over either strategy.

CB1-05: Development of Reusable Logic for Calculating Statin Exposure-Time from Electronic Medical Record Notes and Orders

Background/Aims HMG-CoA inhibitor (statin) use is a common treatment for elevated cholesterol. Statins are effective in reducing hepatic cholesterol synthesis in prospective studies. Examination of EHR data for estimating statin exposure-time is difficult because prescriptions are in unstructured notes and clinicians prescribe combinations of ‘pill splitting’, while frequently adjusting dosage to keep cholesterol levels under control. Here, we present logic for estimating statin exposure-time based on EHR data. Methods The EHR of individuals studied in the Electronic Medical Records and Genomics (eMERGE) study were interrogated for evidence of exposure to statin medications. Exposure was identified via natural language processing (used between 1998–2007) and extraction from the medications orders system (beginning in 2004). Pill-splitting was identified in text notes: (‘1/2’, ’0.5’, ’one-half’, ‘half of’, ‘1 1/2’, ‘1.5’, ‘one and one half’, ‘bid’, ‘two qd’, ‘2 q.d.’, ‘twice daily’, ‘1/4’, ‘0.25’, ‘one quarter’). Where conflicts occurred between NLP and medication orders, order data took precedence, both for dosage and drug identification. Manual validation of electronic charts was performed to determine gold-standard dosage and exposure dates. Because statin data are frequently difficult to interpret, even in medical charts, the study team used source documents though they may contradict notes. Results The subset of the eMERGE population (N=4,427) were identified with an indication of statin exposure (51%, 2,540/4,427). Average age 65 at the first exposure. Both exposed and non-exposed populations had lengthy follow-up periods: (Avg 27 vs. 28 yr, Std. Dev 6.1 vs. 6.9 yr, Min 1 vs. 3yr, Max 33 vs. 33 yr).Where statin exposure was detected, the average patient received 2.1 (max=7) different statins, over 8 years (Avg 8.1 yr, Std. Dev 4.9 yr, Min .002 yr, Max 16.8 yr). Statin-exposed population was slightly more comorbid based on the Charlson comorbidity index (scores: 0.72 vs. 0.55). Vitals and lab results were comparable between the two groups as well: BMI (30 vs. 29), LDL (113 vs. 114), Triglycerides (80 vs. 79). Discussion Combining the medications data obtained via NLP with the medications orders resulted in a substantial improvement in estimation of statin exposure-time, when compared to the raw EHR source data.

Robust automatic feature extraction for protein microarrays

In this paper, we present a robust methodology for image registration, segmentation, and feature extraction for protein microarrays. Originally designed for application to an Agilent microarray platform, the algorithms used are easily adapted to other platforms. Linear and nonlinear filtering techniques are used to identify protein signals on the array. After signal identification, expression values for each protein are then derived. Emphasis is placed on robustness of feature identification and low computational complexity.