Original Article
Use of Multiple Biomarkers to Improve the Prediction of Death from Cardiovascular Causes
N Engl J Med 2008; 358:2107-2116May 15, 2008
- Abstract
Background
The incremental usefulness of adding multiple biomarkers from different disease pathways for predicting the risk of death from cardiovascular causes has not, to our knowledge, been evaluated among the elderly.
Methods
We used data from the Uppsala Longitudinal Study of Adult Men (ULSAM), a community-based cohort of elderly men, to investigate whether a combination of biomarkers that reflect myocardial cell damage, left ventricular dysfunction, renal failure, and inflammation (troponin I, N-terminal pro–brain natriuretic peptide, cystatin C, and C-reactive protein, respectively) improved the risk stratification of a person beyond an assessment that was based on the established risk factors for cardiovascular disease (age, systolic blood pressure, use or nonuse of antihypertensive treatment, total cholesterol, high-density lipoprotein cholesterol, use or nonuse of lipid-lowering treatment, presence or absence of diabetes, smoking status, and body-mass index).
Results
During follow-up (median, 10.0 years), 315 of the 1135 participants in our study (mean age, 71 years at baseline) died; 136 deaths were the result of cardiovascular disease. In Cox proportional-hazards models adjusted for established risk factors, all of the biomarkers significantly predicted the risk of death from cardiovascular causes. The C statistic increased significantly when the four biomarkers were incorporated into a model with established risk factors, both in the whole cohort (C statistic with biomarkers vs. without biomarkers, 0.766 vs. 0.664; P<0.001) and in the group of 661 participants who did not have cardiovascular disease at baseline (0.748 vs. 0.688, P=0.03). The improvement in risk assessment remained strong when it was estimated by other statistical measures of model discrimination, calibration, and global fit.
Conclusions
Our data suggest that in elderly men with or without prevalent cardiovascular disease, the simultaneous addition of several biomarkers of cardiovascular and renal abnormalities substantially improves the risk stratification for death from cardiovascular causes beyond that of a model that is based only on established risk factors.
Media in This Article
Table 1
Baseline Characteristics of the Study Cohort.Table 2
Hazard Ratios for Death from All Causes and from Cardiovascular Causes, According to Biomarker Levels.Article Activity
- Article
The assessment of cardiovascular risk with the use of established risk factors does not fully explain the development of cardiovascular disease in the community.1-3 Even though several new biomarkers have been reported to be associated with increased relative risks of cardiovascular events independently of established risk factors, these biomarkers have not been shown to contribute substantially to the risk stratification of the individual patient as evaluated by the area under the receiver-operating-characteristic curve (C statistic).1-3 It has been suggested that the evaluation of whether a risk marker more accurately stratifies people into higher- or lower-risk categories (reclassification) is a valuable supplement to the C-statistic analyses.4-6 An enhanced risk assessment would be of great clinical value if it could more accurately identify people who are at increased risk for cardiovascular disease and who could then be targeted for preventive measures.
Established risk factors do not directly reflect myocardial cell damage, left ventricular dysfunction, renal failure, and inflammation — clinical conditions that have been shown to be associated with an increased risk of cardiovascular disease and death.7-10 We hypothesized that the addition of a combination of biomarkers from these pathophysiological pathways — a multimarker approach — could add substantial prognostic information with respect to the risk of death from cardiovascular causes. Accordingly, we investigated whether the incorporation of troponin I, N-terminal pro–brain natriuretic peptide, cystatin C, and high-sensitivity C-reactive protein in a model with established risk factors improved the prediction of death from cardiovascular causes in a community-based cohort of elderly men.
Methods
Study Population
We used data from the Uppsala Longitudinal Study of Adult Men (ULSAM), which was initiated in 1970. All 50-year-old men who were born between 1920 and 1924 and were living in Uppsala, Sweden, were invited to participate in a health survey that focused on identifying cardiovascular risk factors (described in detail at www.pubcare.uu.se/ULSAM). The present analyses are based on the third examination-cycle of the ULSAM cohort, when participants were approximately 71 years of age (1991 to 1995). Of the 1221 participants, 1135 had valid measurements of troponin I, N-terminal pro–brain natriuretic peptide, cystatin C, high-sensitivity C-reactive protein, and all covariates that we examined. We also examined a subgroup of 661 men who did not have prevalent cardiovascular disease at baseline. For this subgroup, the following exclusion criteria were used: prior myocardial infarction or angina pectoris, as noted in the medical history; Q or QS waves or left bundle-branch block (Minnesota codes 1.1 to 1.3 and 7.1, respectively) on the baseline electrocardiogram; a history of any cardiovascular disease, as noted in the Swedish Hospital Discharge Register (International Classification of Diseases, 10th revision [ICD-10] codes I00 to I99); or current treatment with nitroglycerin or cardiac glycosides. All participants gave written informed consent, and the ethics committee of Uppsala University approved the study.
Baseline Examinations
Venous blood samples were drawn at baseline and were stored at –70°C for a mean (±SD) of 11±2 years until analysis.10 Biomarker determinations were performed as previously described in detail.10-15 In short, plasma troponin I was measured with the AccuTnI assay (Beckman Coulter).10 Plasma N-terminal pro–brain natriuretic peptide was determined with a sandwich immunoassay on an Elecsys 2010 analyzer (Roche Diagnostics).12 Cystatin C and high-sensitivity C-reactive protein were assayed with the use of latex-enhanced reagents (Siemens), on a BN ProSpec analyzer (Siemens).11,13 Fasting plasma glucose and serum cholesterol levels were measured by routine laboratory analysis.14,15 Weight, height, body-mass index, electrocardiogram, and systolic and diastolic blood pressures (measured with the participant in a supine position) were assessed under standardized conditions.14,15 Diabetes mellitus was defined as a fasting plasma glucose level of 7.0 mmol per liter (126 mg per deciliter) or more or the use of oral hypoglycemic agents or insulin. Information with respect to smoking status (current smoker vs. nonsmoker) was obtained from a questionnaire.
Outcomes and Follow-up
Death from all causes and death from cardiovascular disease (ICD-10 codes I00 to I99) were identified with the use of the Swedish Cause of Death Register. The median follow-up was 10.0 years (range, 0.9 to 12.4).
Statistical Analyses
Logarithmic transformation was performed to achieve a normal distribution for skewed variables (troponin I, N-terminal pro−brain natriuretic peptide, cystatin C, and C-reactive protein). The relations of biomarkers to death from all causes and from cardiovascular causes were investigated with the use of Cox proportional-hazards regression in two sets of models: crude models and models adjusted for established risk factors for cardiovascular disease (age at baseline, systolic blood pressure, use or nonuse of antihypertensive treatment, total cholesterol, high-density lipoprotein cholesterol, use or nonuse of lipid-lowering treatment, presence or absence of diabetes, smoking status, and body-mass index).
In these models, we evaluated the effects of the biomarkers according to three criteria: a 1-SD increase in the biomarker levels (continuous model); biomarker levels that were above or below cutoff points suggested in the literature (troponin I, >0.021 μg per liter10; N-terminal pro–brain natriuretic peptide, ≥386 ng per liter16; cystatin C, ≥1.29 mg per liter8; and C-reactive protein, >3 mg per liter17); and biomarker levels that were above or below cutoff points identified to achieve optimal discrimination as evaluated by the integrated discrimination improvement measure5 (troponin I, >0.035 μg per liter; N-terminal pro–brain natriuretic peptide, >309 ng per liter; cystatin C, >1.50 mg per liter; and C-reactive protein, ≥4.60 mg per liter).
Proportional-hazards assumptions were confirmed by Schoenfeld's tests. Estimates of the C statistic for the Cox regression models were calculated according to the method of Pencina et al.18 Differences in C statistics (with 95% confidence intervals) after the addition of the biomarkers to a model with established risk factors were estimated with the method described by Antolini et al.19 We also investigated whether the addition of different combinations of two or three of the biomarkers improved the discrimination of the model.
The increased discriminative value of the biomarkers was further examined with the method described by Pencina et al.5 This method is based on the difference between two models in the individual estimated probability that a case subject will be categorized as a case subject. An increased probability that case subjects will be categorized as case subjects and a decreased probability that control subjects will be categorized as case subjects imply better prediction ability, whereas the opposite implies worse prediction ability. There are two versions of the measure. The first version (net reclassification improvement) requires that there exist a priori meaningful risk categories (we have used 0 to 5%, 6 to 20%, and >20% for the risk of death from cardiovascular causes). In net reclassification improvement, only those changes in estimated prediction probabilities that imply a change from one category to another are considered. The second version (integrated discrimination improvement) considers the change in the estimated prediction probabilities as a continuous variable.
To assess the calibration of the Cox models, we used the Grønnesby and Borgan calibration test,20 which compares the number of events that are observed with those that are expected on the basis of estimation from the models, within five risk-score groups. We also performed likelihood-ratio tests to evaluate whether the global model fit improved after the addition of the biomarkers.
All analyses were also performed among participants who did not have prevalent cardiovascular disease at baseline.
We examined effect modification by testing the statistical significance of the interaction among all of the biomarkers. None of the interaction terms reached statistical significance (P>0.08 for all tests).
P values of less than 0.05 from two-sided tests were considered to indicate statistical significance. The statistical software packages STATA (version 10.0) (StataCorp) and SAS (version 9.1 for Windows) (SAS Institute) were used.
Results
Baseline characteristics of the entire sample and of the participants who did not have cardiovascular disease at baseline are shown in Table 1Table 1
Baseline Characteristics of the Study Cohort.. The mean age at baseline was 71.0 years (range, 69.4 to 73.6). During the follow-up period (median, 10.0 years; range, 0.9 to 12.4), 315 participants died (rate, 3.0 per 100 person-years at risk); 136 deaths were from cardiovascular disease (rate, 1.3 per 100 person-years at risk). In the subgroup of participants who did not have prevalent cardiovascular disease at baseline, 149 died (rate, 2.4 per 100 person-years at risk); 54 deaths were from cardiovascular disease (rate, 0.9 per 100 person-years at risk). No subjects were lost to follow-up.Cox Regression
All of the biomarkers (troponin I, N-terminal pro−brain natriuretic peptide, cystatin C, and C-reactive protein) predicted death from cardiovascular causes and from all causes in crude and multivariable models in the whole cohort and in the group of participants who did not have cardiovascular disease at baseline (Table 2Table 2
Hazard Ratios for Death from All Causes and from Cardiovascular Causes, According to Biomarker Levels.). The crude estimates of hazard ratios, C statistics, and calibration chi-square values for the individual established risk factors and for the biomarkers for the prediction of death from cardiovascular causes and from all causes are shown in Table 1 of the Supplementary Appendix (available with the full text of this article at www.nejm.org).Hazard ratios for different combinations of elevated biomarkers in the whole cohort are shown in Table 2 of the Supplementary Appendix. Participants who had elevated levels of any two of the biomarkers had a risk of death from cardiovascular causes that was increased by a factor of more than 3 (P<0.001 for all comparisons). The risk was increased by a factor of more than 7 among participants with elevated levels of any three of the biomarkers, and by more than 16 among participants with elevated levels of all four biomarkers (P<0.001 for all comparisons).
Discrimination
In the whole cohort, as well as in the group of participants who did not have cardiovascular disease at baseline, the C statistic increased significantly for the prediction of death from cardiovascular causes when all the biomarkers we measured were incorporated into a model with the established risk factors (Table 3Table 3
C Statistic for Cox Regression Models Predicting Death from Cardiovascular Causes and from All Causes in the Whole Sample and in the Subsample without Cardiovascular Disease at Baseline.). This was also the case when we used the cutoff points suggested in the literature (C statistic for the whole cohort, 0.756; P<0.001; for participants without cardiovascular disease at baseline, 0.742; P=0.05) and when we used the cutoff points that were identified to achieve optimal discrimination (C statistic for the whole cohort, 0.755; P<0.001; for participants without cardiovascular disease at baseline, 0.751; P=0.02). The separate addition of troponin I and N-terminal pro–brain natriuretic peptide to the model with established risk factors also significantly improved the C statistic for predicting death from cardiovascular causes in the whole cohort, but not in the group of participants who did not have cardiovascular disease at baseline. The C statistic for the prediction of death from all causes followed a pattern that was similar to that for the prediction of death from cardiovascular causes, but with weaker associations (Table 3).In the group of participants without cardiovascular disease at baseline, the C-statistic estimate for the addition of any combination of two biomarkers to the established risk factors was highest for the combination of N-terminal pro–brain natriuretic peptide and troponin I (0.737; P=0.06) (Table 3 of the Supplementary Appendix). The C-statistic estimate for the addition of any combination of three biomarkers was highest for the combination of N-terminal pro–brain natriuretic peptide, troponin I, and C-reactive protein (0.748; P<0.03) (Table 3 of the Supplementary Appendix).
The replacement of body-mass index with waist-to-hip ratio in the model with established risk factors did not alter the results (data not shown).
Reclassification for participants who died from cardiovascular causes and for those who did not die in the subgroup of participants who did not have cardiovascular disease at baseline is summarized in Table 4Table 4
Reclassification of Participants without Cardiovascular Disease at Baseline Who Died from Cardiovascular Causes or Who Did Not Die.. For 16 participants who died from cardiovascular causes, reclassification was more accurate when the model with all of the biomarkers was used, and for 7 participants, it became less accurate. Among the subjects who did not die, 115 were reclassified in a lower risk category and 60 were reclassified in a higher risk category. The net improvement in reclassification was estimated at 0.26 (P=0.005) after the addition of all of the biomarkers, and the integrated discrimination improvement was estimated as 0.074 (P=0.002). The estimates of integrated discrimination improvement suggest that the separate addition of troponin I, N-terminal pro–brain natriuretic peptide, and C-reactive protein or the addition of any combination of two or three biomarkers to the model with established risk factors improved the discriminatory property of the model for the prediction of risk among participants without cardiovascular disease at baseline (P<0.05 for all comparisons) (Table 3 of the Supplementary Appendix).Calibration
The P values for the Grønnesby and Borgan statistics indicated good calibration for the model with and without all four biomarkers, both in the whole cohort and in the group of participants who did not have cardiovascular disease at baseline (P>0.11 for all comparisons).
Global Model Fit
Models that included all four biomarkers showed better global fit than models with only the established risk factors, as evaluated by likelihood-ratio tests (P<0.001).
Discussion
In this community-based sample of elderly men, the incorporation of a combination of biomarkers that reflect myocardial cell damage, ventricular function, renal function, and inflammation to a model with established risk factors improved the risk stratification for death from cardiovascular causes, as evidenced by a substantial increase in the C statistic. The results were consistent when we applied the model to participants who did not have apparent cardiovascular disease at baseline and when we used either the cutoff points for the biomarkers that have been suggested in the literature or those that were identified in the present study in order to achieve optimal discrimination. The improvement in risk assessment remained strong when it was estimated by means of statistical measures that evaluate model discrimination, model calibration, and global model fit.
In previous community-based studies that have evaluated a multimarker approach to the prediction of cardiovascular events,1,3,21 the addition of multiple biomarkers to a model with established risk factors resulted in minor increases in the C statistic that did not reach statistical significance. The discrepancy between the results of the present study and those of previous studies might be explained, in part, by two key differences.
First, the participants in the present study were, on average, more than 10 years older than participants in previous studies. The fact that we evaluated the addition of the biomarkers in an elderly cohort may be of particular importance because the relative risk that is associated with the established risk factors has been shown to diminish with advancing age.22-24 This observation is supported by the fact that the C statistic for the established risk factors in the present study appears to be lower than that in previous studies of cohorts with younger participants.1-3,21 It is possible that the established risk factors would have performed better in a sample of younger people who had a lower absolute risk and a lower prevalence of subclinical cardiovascular damage.
Second, the predictive ability of the combination of N-terminal pro−brain natriuretic peptide, troponin I, cystatin C, and C-reactive protein was not evaluated in previous studies. These biomarkers have been suggested to be closely associated with cardiovascular and renal damage.25-28 A recent study showed that 20% of 70-year-old Swedish men and women who did not have apparent cardiovascular disease had myocardial scarring, possibly as a result of previous, unrecognized myocardial infarctions.29 Thus, one reason that the addition of these biomarkers contributed to the prediction of death from cardiovascular causes in people who did not have apparent cardiovascular disease may be that they better identify those who have structural and functional abnormalities in the cardiovascular system that have not yet led to overt cardiovascular disease.
In our group of subjects without cardiovascular disease at baseline, none of the biomarkers significantly increased the C statistic when they were added separately to a model with established risk (Table 3), a finding that is consistent with the results of previous studies.1-3 However, the combination of all of the biomarkers increased the C statistic. This finding supports the rationale of adding multiple biomarkers that reflect different disease pathways in assessing the risk of death from cardiovascular causes.
The C statistic is the most commonly used method of determining model discrimination — how well the model can discriminate between persons in whom the disease of interest will develop and those who will remain free of the disease. In the present study, we also evaluated a recently described measure of model discrimination (integrated discrimination improvement),5 which appears to be a more sensitive test of improvement in model discrimination than the C-statistic analyses (Table 3 of the Supplementary Appendix). Our findings suggest that it may be worthwhile to reevaluate previous studies in which biomarkers were discarded as potentially important risk factors because of a nonsignificant increase in the C statistic.
The sole reliance on the C statistic for the evaluation of biomarkers as risk predictors has been questioned, because very large independent associations of a new marker with the outcome are required to result in a significant increase in the C statistic.4-6,30 Consequently, the significant increments in the C statistic in the present study indicate that our multimarker approach represents a substantial improvement in the performance of the model. The consistency of results across all models, subgroups, and statistical methods of determining model improvement provides further support for the validity of our findings.
Because we examined only men of the same age with a similar ethnic background, the generalizability of our findings to women or to other age and ethnic groups is unknown. Further studies are needed to determine whether the combination of biomarkers improves the risk stratification in women, in younger persons, and in other ethnic groups.
All of the biomarkers were analyzed in samples that had been frozen for more than 10 years. Consequently, there is a risk that the absolute levels of the biomarkers could have been affected by having been taken from frozen, rather than fresh, samples. Ideally, future studies should validate the suggested optimal cutoff points for the biomarkers in fresh samples.
Whereas it has been shown that reducing the levels of established risk factors decreases the risk of cardiovascular disease in the general population, there is currently little evidence that reducing the levels of the biomarkers will reduce the risk. Thus, our data should not be construed as implying a direct benefit of a reduction in the biomarkers. We selected biomarkers that have previously been shown to be promising predictors of cardiovascular events in the general population7-10; it is possible that other biomarkers that were not tested would have provided additional prognostic information.
Our data suggest that the approach of simultaneously adding several biomarkers of cardiovascular disease to the model that includes the established risk factors substantially improves the risk stratification for death from cardiovascular causes among elderly men, both those with and those without prevalent cardiovascular disease. If these results are validated, the incorporation of these factors in clinical practice for the prediction of death from cardiovascular causes could be accomplished quickly, since the measurement of these biomarkers is already well established for diagnostic use.
Supported by grants from the Swedish Research Council (2006-6555), Swedish Heart–Lung Foundation, Erik, Karin, och Gösta Selander Foundation, Loo och Hans Osterman Foundation, Ernfors Foundation, Thuring Foundation, Stiftelsen Sigurd och Elsa Goljes Minne, Uppsala University. The reagents and the instrument for the troponin I assay were supplied by Beckman Coulter. The reagents and the instrument for the N-terminal pro−brain natriuretic peptide assay were supplied by Roche Diagnostics, Basel, Switzerland. The funding sources did not play any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript.
Dr. Venge reports receiving lecture fees from Abbott Diagnostics, Beckman Coulter, and Siemens, and grant support from Abbott Diagnostics, Beckman Coulter, Roche Diagnostics, and Radiometer. No other potential conflict of interest relevant to this article was reported.
Source Information
From the Department of Public Health and Caring Sciences/Geriatrics (B.Z., L.B., E.I., S.B., J.Ä.), Uppsala Clinical Research Center (L.B.), and the Department of Medical Sciences (J.S., A.L., P.V.), Uppsala University, Uppsala, Sweden.
Address reprint requests to Dr. Ärnlöv at the Department of Public Health and Caring Sciences/Geriatrics, Uppsala Science Park, SE-75185 Uppsala, Sweden, or at johan.arnlov@pubcare.uu.se.
- References
References
1
Wang TJ ,Gona P ,Larson MG , et al. Multiple biomarkers for the prediction of first major cardiovascular events and death. N Engl J Med 2006;355:2631-2639
Full Text | Web of Science | Medline2
Cook NR ,Buring JE ,Ridker PM . The effect of including C-reactive protein in cardiovascular risk prediction models for women. Ann Intern Med 2006;145:21-29
Web of Science | Medline3
Olsen MH ,Hansen TW ,Christensen MK , et al. N-terminal pro-brain natriuretic peptide, but not high sensitivity C-reactive protein, improves cardiovascular risk prediction in the general population. Eur Heart J 2007;28:1374-1381
CrossRef | Web of Science | Medline4
Cook NR . Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115:928-935
CrossRef | Web of Science | Medline5
Pencina MJ ,D'Agostino RB Sr ,D'Agostino RB Jr ,Vasan RS . Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27:157-172
CrossRef | Web of Science | Medline6
Pepe MS ,Janes H ,Longton G ,Leisenring W ,Newcomb P . Limitations of the odds ratio in gauging the performance of a diagnostic, prognostic, or screening marker. Am J Epidemiol 2004;159:882-890
CrossRef | Web of Science | Medline7
Ridker PM ,Hennekens CH ,Buring JE ,Rifai N . C-reactive protein and other markers of inflammation in the prediction of cardiovascular disease in women. N Engl J Med 2000;342:836-843
Full Text | Web of Science | Medline8
Shlipak MG ,Sarnak MJ ,Katz R , et al. Cystatin C and the risk of death and cardiovascular events among elderly persons. N Engl J Med 2005;352:2049-2060
Full Text | Web of Science | Medline9
Wang TJ ,Larson MG ,Levy D , et al. Plasma natriuretic peptide levels and the risk of cardiovascular events and death. N Engl J Med 2004;350:655-663
Full Text | Web of Science | Medline10
Zethelius B ,Johnston N ,Venge P . Troponin I as a predictor of coronary heart disease and mortality in 70-year-old men: a community-based cohort study. Circulation 2006;113:1071-1078
CrossRef | Web of Science | Medline11
Helmersson J ,Vessby B ,Larsson A ,Basu S . Association of type 2 diabetes with cyclooxygenase-mediated inflammation and oxidative stress in an elderly population. Circulation 2004;109:1729-1734
CrossRef | Web of Science | Medline12
James SK ,Lindahl B ,Siegbahn A , et al. N-terminal pro-brain natriuretic peptide and other risk markers for the separate prediction of mortality and subsequent myocardial infarction in patients with unstable coronary artery disease: a Global Utilization of Strategies To Open occluded arteries (GUSTO)-IV substudy. Circulation 2003;108:275-281
CrossRef | Web of Science | Medline13
Larsson A ,Helmersson J ,Hansson LO ,Basu S . Increased serum cystatin C is associated with increased mortality in elderly men. Scand J Clin Lab Invest 2005;65:301-305
CrossRef | Web of Science | Medline14
Byberg L ,McKeigue PM ,Zethelius B ,Lithell HO . Birth weight and the insulin resistance syndrome: association of low birth weight with truncal obesity and raised plasminogen activator inhibitor-1 but not with abdominal obesity or plasma lipid disturbances. Diabetologia 2000;43:54-60
CrossRef | Web of Science | Medline15
Zethelius B ,Hales CN ,Lithell HO ,Berne C . Insulin resistance, impaired early insulin response, and insulin propeptides as predictors of the development of type 2 diabetes: a population-based, 7-year follow-up study in 70-year-old men. Diabetes Care 2004;27:1433-1438
CrossRef | Web of Science | Medline16
Emberson JR ,Ng LL ,Armitage J , et al. N-terminal pro-B-type natriuretic peptide, vascular disease risk, and cholesterol reduction among 20,536 patients in the MRC/BHF heart protection study. J Am Coll Cardiol 2007;49:311-319
CrossRef | Web of Science | Medline17
Ridker PM . C-reactive protein and the prediction of cardiovascular events among those at intermediate risk: moving an inflammatory hypothesis toward consensus. J Am Coll Cardiol 2007;49:2129-2138
CrossRef | Web of Science | Medline18
Pencina MJ ,D'Agostino RB . Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation. Stat Med 2004;23:2109-2123
CrossRef | Web of Science | Medline19
Antolini L ,Nam B-H ,D'Agostino RB . Inference on correlated discrimination measures in survival analysis: a nonparametric approach. Comm Statist Theory Methods 2004;33:2117-2135
CrossRef | Web of Science20
Gronnesby JK ,Borgan O . A method for checking regression models in survival analysis based on the risk score. Lifetime Data Anal 1996;2:315-328
CrossRef | Medline21
Ridker PM ,Buring JE ,Rifai N ,Cook NR . Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. JAMA 2007;297:611-619[Erratum, JAMA 2007;297:1433.]
CrossRef | Web of Science | Medline22
Moller CS ,Zethelius B ,Sundstrom J ,Lind L . Impact of follow-up time and re-measurement of the electrocardiogram and conventional cardiovascular risk factors on their predictive value for myocardial infarction. J Intern Med 2006;260:22-30
CrossRef | Web of Science | Medline23
Kannel WB . Coronary heart disease risk factors in the elderly. Am J Geriatr Cardiol 2002;11:101-107
CrossRef | Medline24
Kannel WB ,D'Agostino RB . The importance of cardiovascular risk factors in the elderly. Am J Geriatr Cardiol 1995;4:10-23
Medline25
Groenning BA ,Raymond I ,Hildebrandt PR , et al. Diagnostic and prognostic evaluation of left ventricular systolic heart failure by plasma N-terminal pro-brain natriuretic peptide concentrations in a large sample of the general population. Heart 2004;90:297-303
CrossRef | Web of Science | Medline26
Wasen E ,Isoaho R ,Mattila K ,Vahlberg T ,Kivela SL ,Irjala K . Estimation of glomerular filtration rate in the elderly: a comparison of creatinine-based formulae with serum cystatin C. J Intern Med 2004;256:70-78
CrossRef | Web of Science | Medline27
Lucia P ,Coppola A ,Manetti LL , et al. Cardiac troponin I in acute coronary ischemic syndromes: epidemiological and clinical correlates. Int J Cardiol 2001;77:215-222
CrossRef | Web of Science | Medline28
Wohlin M ,Helmersson J ,Sundstrom J , et al. Both cyclooxygenase- and cytokine-mediated inflammation are associated with carotid intima-media thickness. Cytokine 2007;38:130-136
CrossRef | Web of Science | Medline29
Barbier CE ,Bjerner T ,Johansson L ,Lind L ,Ahlstrom H . Myocardial scars more frequent than expected: magnetic resonance imaging detects potential risk group. J Am Coll Cardiol 2006;48:765-771
CrossRef | Web of Science | Medline30
Ware JH . The limitations of risk factors as prognostic tools. N Engl J Med 2006;355:2615-2617
Full Text | Web of Science | Medline
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Michael Böhm, Adriaan A. Voors, Jean-Marie Ketelslegers, Stephan H. Schirmer, Eva Turgonyi, Peter Bramlage, Faiez Zannad. (2011) Biomarkers: optimizing treatment guidance in heart failure. Clinical Research in Cardiology 100:11, 973-981
CrossRef17
Miguel-Ángel Torralba-Cabeza, Susana Olivera, Derralynn A. Hughes, Gregory M. Pastores, Ramón Nuviala Mateo, Juan-Ignacio Pérez-Calvo. (2011) Cystatin C and NT-proBNP as prognostic biomarkers in Fabry disease. Molecular Genetics and Metabolism 104:3, 301-307
CrossRef18
Joachim R. Ehrlich, Magdalena Kaluzny, Stefan Baumann, Ralf Lehmann, Stefan H. Hohnloser. (2011) Biomarkers of structural remodelling and endothelial dysfunction for prediction of cardiovascular events or death in patients with atrial fibrillation. Clinical Research in Cardiology 100:11, 1029-1036
CrossRef19
UT Kadam, A Holmberg, M Blagojevic, PM Nilsson, K Åkesson. (2011) Risk factors for cardiovascular disease and future osteoarthritis-related arthroplasty: a population-based cohort study in men and women from Malmö, Sweden. Scandinavian Journal of Rheumatology 40:6, 478-485
CrossRef20
Jiang Zhang, Moltu J. Guy, Holly S. Norman, Yi-Chen Chen, Qingge Xu, Xintong Dong, Huseyin Guner, Sijian Wang, Takushi Kohmoto, Ken H. Young, Richard L. Moss, Ying Ge. (2011) Top-Down Quantitative Proteomics Identified Phosphorylation of Cardiac Troponin I as a Candidate Biomarker for Chronic Heart Failure. Journal of Proteome Research 10:9, 4054-4065
CrossRef21
Sarah A. Bovenberg, Boudewijn Klop, Arash Alipour, Sergio Martinez-Hervas, Andrew Westzaan, Gert-Jan M. van de Geijn, Hans W. J. Janssen, Tjin Njo, Erwin Birnie, Rob van Mechelen, Arie P. Rietveld, Jan Willem F. Elte, Manuel Castro Cabezas. (2011) Erythrocyte-associated apolipoprotein B and its relationship with clinical and subclinical atherosclerosis. European Journal of Clinical Investigationno-no
CrossRef22
S.W. Jung, M.D. Kittleson. (2011) The Effect of Atenolol on NT-proBNP and Troponin in Asymptomatic Cats with Severe Left Ventricular Hypertrophy because of Hypertrophic Cardiomyopathy: A Pilot Study. Journal of Veterinary Internal Medicine 25:5, 1044-1049
CrossRef23
P. Ray, Y. Manach, B. Riou, T. T. Houle. (2011) Évaluation statistique d’un biomarqueur. Annales françaises de médecine d'urgence 1:5, 355-372
CrossRef24
E. Nerpin, E. Ingelsson, U. Riserus, J. Sundstrom, A. Larsson, E. Jobs, M. Jobs, S. Hallan, B. Zethelius, L. Berglund, S. Basu, J. Arnlov. (2011) The combined contribution of albuminuria and glomerular filtration rate to the prediction of cardiovascular mortality in elderly men. Nephrology Dialysis Transplantation 26:9, 2820-2827
CrossRef25
Claire Keogh, Emma Wallace, Kirsty K. O’Brien, Paul J. Murphy, Conor Teljeur, Brid McGrath, Susan M. Smith, Niall Doherty, Borislav D. Dimitrov, Tom Fahey. (2011) Optimized retrieval of primary care clinical prediction rules from MEDLINE to establish a web-based register. Journal of Clinical Epidemiology 64:8, 848-860
CrossRef26
Lori B Daniels. (2011) Combining multiple biomarkers for cardiovascular risk assessment: more is usually better – up to a point. Bioanalysis 3:15, 1679-1682
CrossRef27
I. Tzoulaki, G. Liberopoulos, J. P. A. Ioannidis. (2011) Use of reclassification for assessment of improved prediction: an empirical evaluation. International Journal of Epidemiology 40:4, 1094-1105
CrossRef28
Ilaria Spoletini, Cristiana Vitale, Giuseppe MC Rosano. (2011) Biomarkers for predicting postmenopausal coronary heart disease. Biomarkers in Medicine 5:4, 485-495
CrossRef29
Sean R. Wilson, Fay Y. Lin, James K. Min. (2011) Role of Coronary Artery Calcium Score and Coronary CT Angiography in the Diagnosis and Risk Stratification of Individuals with Suspected Coronary Artery Disease. Current Cardiology Reports 13:4, 271-279
CrossRef30
Rajoo Dhangana, Timothy P. Murphy, Joseph R. Coll, Sun H. Ahn, Abdul M. Zafar, Fathima F. Qadeer, William R. Hiatt. (2011) Prevalence of Abnormal Ankle-Brachial Index Among Individuals With Low or Intermediate Framingham Risk Scores. Journal of Vascular and Interventional Radiology 22:8, 1077-1082
CrossRef31
B. Buijsse, R. K. Simmons, S. J. Griffin, M. B. Schulze. (2011) Risk Assessment Tools for Identifying Individuals at Risk of Developing Type 2 Diabetes. Epidemiologic Reviews 33:1, 46-62
CrossRef32
C Herder, M Karakas, W Koenig. (2011) Biomarkers for the Prediction of Type 2 Diabetes and Cardiovascular Disease. Clinical Pharmacology & Therapeutics 90:1, 52-66
CrossRef33
Ranae L. Larsen, Charles E. Canter, David C. Naftel, Margaret Tressler, David N. Rosenthal, Elizabeth D. Blume, William T. Mahle, Delphine Yung, William R. Morrow, E. John Orav, James D. Wilkinson, Jeffrey A. Towbin, Stephen E. Lipshultz. (2011) The impact of heart failure severity at time of listing for cardiac transplantation on survival in pediatric cardiomyopathy. The Journal of Heart and Lung Transplantation 30:7, 755-760
CrossRef34
Adriana Unić, Dunja Rogić, Gordana Rajsman. (2011) Copeptin - is There a Role for Another Cardiac Biomarker?. Journal of Medical Biochemistry 30:3, 224-229
CrossRef35
Allegra Battistoni, Speranza Rubattu, Massimo Volpe. (2011) Circulating biomarkers with preventive, diagnostic and prognostic implications in cardiovascular diseases. International Journal of Cardiology
CrossRef36
Norberto A. Guzman, Terry M. Phillips. (2011) Immunoaffinity capillary electrophoresis: A new versatile tool for determining protein biomarkers in inflammatory processes. ELECTROPHORESISn/a-n/a
CrossRef37
Christian Delles, Javier Diez, Anna F. Dominiczak. (2011) Urinary proteomics in cardiovascular disease: Achievements, limits and hopes. PROTEOMICS - Clinical Applications 5:5-6, 222-232
CrossRef38
S. Goya Wannamethee, Paul Welsh, Gordon D. Lowe, Vilmundur Gudnason, Emanuele Di Angelantonio, Lucy Lennon, Ann Rumley, Peter H. Whincup, Naveed Sattar. (2011) N-Terminal Pro-Brain Natriuretic Peptide Is a More Useful Predictor of Cardiovascular Disease Risk Than C-Reactive Protein in Older Men With and Without Pre-Existing Cardiovascular Disease. Journal of the American College of Cardiology 58:1, 56-64
CrossRef39
Michael E. Sughrue, Jay Choi, Martin J. Rutkowski, Derick Aranda, Ari J. Kane, Igor J. Barani, Andrew T. Parsa. (2011) Clinical features and post-surgical outcome of patients with astroblastoma. Journal of Clinical Neuroscience 18:6, 750-754
CrossRef40
Lee Ingle, Sean Carroll, Emmanuel Stamatakis, Mark Hamer. (2011) Benefit of adding lifestyle-related risk factors for prediction of cardiovascular death among cardiac patients. International Journal of Cardiology
CrossRef41
D. Schmidt, A. Kwetkat, M. Gogol. (2011) Chronische Inflammation und Biomarker. Zeitschrift für Gerontologie und Geriatrie 44:3, 153-157
CrossRef42
Cyrus Chargari, Krassen M. Kirov, Marc A. Bollet, Nicolas Magné, Lionel Védrine, Serge Cremades, Philippe Beuzeboc, Alain Fourquet, Youlia M. Kirova. (2011) Cardiac toxicity in breast cancer patients: From a fractional point of view to a global assessment. Cancer Treatment Reviews 37:4, 321-330
CrossRef43
Cristiano Fava, Martina Montagnana, Gian Cesare Guidi, Olle Melander. (2011) From circulating biomarkers to genomics and imaging in the prediction of cardiovascular events in the general population. Annals of Medicine1-15
CrossRef44
Rajoo Dhangana, Timothy P. Murphy, Michael J. Pencina, Abdul M. Zafar. (2011) Prevalence of low ankle-brachial index, elevated plasma fibrinogen and CRP across Framingham risk categories: Data from the National Health and Nutrition Examination Survey (NHANES) 1999–2004. Atherosclerosis 216:1, 174-179
CrossRef45
Carolyn S. Calfee, Lorraine B. Ware, David V. Glidden, Mark D. Eisner, Polly E. Parsons, B. Taylor Thompson, Michael A. Matthay. (2011) Use of risk reclassification with multiple biomarkers improves mortality prediction in acute lung injury. Critical Care Medicine 39:4, 711-717
CrossRef46
Maria Hughes, Frank Kee, Veikko Salomaa. (2011) Clinical Utility of Multiple Biomarker Panels for Cardiovascular Disease Risk Prediction. Current Cardiovascular Risk Reports 5:2, 165-173
CrossRef47
Fabrice Larrazet, Thierry Folliguet, François Laborde, Jean Bachet, Christian Hervé. (2011) Mortality meetings: ethical considerations and adherence to evidence-based practice from 6 years of experience in a mixed cardiac department. European Journal of Cardio-Thoracic Surgery
CrossRef48
Jonas Hallén, Lene Madsen, Søren Ladefoged, Morten W. Fagerland, Victor L. Serebruany, Stefan Agewall, Dan Atar. (2011) Incremental value of a combination of cardiac troponin T, N-terminal pro-brain natriuretic peptide and C-reactive protein for prediction of mortality in end-stage renal disease. Scandinavian Journal of Urology and Nephrology 45:2, 151-158
CrossRef49
Eva Gimeno, Blanca Sánchez-González, Alberto Álvarez-Larrán, Carmen Pedro, Eugenia Abella, Josep Comín, Silvia Saumell, Francesc García-Pallarols, Miquel Gómez, Carles Besses, Antonio Salar. (2011) Intermediate dose of nonpegylated liposomal doxorubicin combination (R-CMyOP) as first line chemotherapy for frail elderly patients with aggressive lymphoma. Leukemia Research 35:3, 358-362
CrossRef50
Anders Grubb, Jonas Björk, Ulf Nyman, Joanna Pollak, Johan Bengzon, Gustav Östner, Veronica Lindström. (2011) Cystatin C, a marker for successful aging and glomerular filtration rate, is not influenced by inflammation. Scandinavian Journal of Clinical & Laboratory Investigation1-5
CrossRef51
Xiu-Quan Shi, Xiao-Bo Yang, Zhong-Xu Wang, Li Qiu, Tang-Chun Wu, Zeng-Zhen Wang. (2011) A novel use of neural network model to determine the effects of multibiomarker on early health damage among Chinese steel workers. Environmental Toxicology 26:1, 1-9
CrossRef52
Marina J. Harutyunyan, Anders B. Mathiasen, Per Winkel, Jens P. Gøtze, Jørgen Fischer Hansen, Per Hildebrandt, Gorm Boje Jensen, Jørgen Hilden, Christian M. Jespersen, Erik Kjøller, Hans J. Kolmos, Christian Gluud, Jens Kastrup. (2011) High-sensitivity C-reactive protein and N-terminal pro-B-type natriuretic peptide in patients with stable coronary artery disease: a prognostic study within the CLARICOR Trial. Scandinavian Journal of Clinical & Laboratory Investigation 71:1, 52-62
CrossRef53
Jong Pil Park, Moon Ki Park, Jong Won Yun. (2011) Proteomic biomarkers for diagnosis in acute myocardial infarction. Biomarkers 16:1, 1-11
CrossRef54
Robin Haring, You-Shan Feng, Jörn Moock, Henry Völzke, Marcus Dörr, Matthias Nauck, Henri Wallaschofski, Thomas Kohlmann. (2011) Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best. BMC Medical Research Methodology 11:1, 103
CrossRef55
Benoit J. Arsenault, Philip Barter, David A. DeMicco, Weihang Bao, Gregory M. Preston, John C. LaRosa, Scott M. Grundy, Prakash Deedwania, Heiner Greten, Nanette K. Wenger, James Shepherd, David D. Waters, John J.P. Kastelein. (2011) Prediction of Cardiovascular Events in Statin-Treated Stable Coronary Patients by Lipid and Nonlipid Biomarkers. Journal of the American College of Cardiology 57:1, 63-69
CrossRef56
H. Wang, S. Chavali, R. Mobini, A. Muraro, F. Barbon, D. Boldrin, N. Åberg, M. Benson. (2011) A pathway-based approach to find novel markers of local glucocorticoid treatment in intermittent allergic rhinitis. Allergy 66:1, 132-140
CrossRef57
Hans-Erik Johansson, Arvo Haenni, Björn Zethelius. (2011) Changes in Erythrocyte Sedimentation Rate, White Blood Cell Count, Liver Enzymes, and Magnesium after Gastric Bypass Surgery. Journal of Obesity 2011, 1-6
CrossRef58
E. Qu
net, D. Huynh, L. Tr
CrossRef59
Alex Chang, Holly Kramer. (2011) Should eGFR and Albuminuria Be Added to the Framingham Risk Score? Chronic Kidney Disease and Cardiovascular Disease Risk Prediction. Nephron Clinical Practice 119:2, c171-c178
CrossRef60
Husam M. Abdel-Qadir, Shaan Chugh, Douglas S. Lee. (2011) Improving Prognosis Estimation in Patients with Heart Failure and the Cardiorenal Syndrome. International Journal of Nephrology 2011, 1-11
CrossRef61
E. Quénet, D. Huynh, L. Trémelot, J. Marty. (2011) Exploraciones cardíacas preoperatorias en cirugía no cardíaca. EMC - Anestesia-Reanimación 37:2, 1-9
CrossRef62
Peter Damman, Marcel A.M. Beijk, Wichert J. Kuijt, Niels J.W. Verouden, Nan van Geloven, José P.S. Henriques, Jan Baan, Marije M. Vis, Martijn Meuwissen, Jan P. van Straalen, Johan Fischer, Karel T. Koch, Jan J. Piek, Jan G.P. Tijssen, Robbert J. de Winter. (2011) Multiple Biomarkers at Admission Significantly Improve the Prediction of Mortality in Patients Undergoing Primary Percutaneous Coronary Intervention for Acute ST-Segment Elevation Myocardial Infarction. Journal of the American College of Cardiology 57:1, 29-36
CrossRef63
Panniyammakal Jeemon, Dorairaj Prabhakaran, Lakhmy Ramakrishnan, Ruby Gupta, F Ahmed, KR Thankappan, CC Kartha, Vivek Chaturvedi, KS Reddy, . (2011) Association of high sensitive C-reactive protein (hsCRP) with established cardiovascular risk factors in the Indian population. Nutrition & Metabolism 8:1, 19
CrossRef64
Yvan Devaux, Melanie Bousquenaud, Sophie Rodius, Pierre-Yves Marie, Fatiha Maskali, Lu Zhang, Francisco Azuaje, Daniel R Wagner. (2011) Transforming growth factor β receptor 1 is a new candidate prognostic biomarker after acute myocardial infarction. BMC Medical Genomics 4:1, 83
CrossRef65
Anders Grubb. 2011. Cystatin C as a Biomarker in Kidney Disease. , 291-312.
CrossRef66
L. B. Daniels, A. S. Maisel. (2010) Multiple marker approach to risk stratification in patients with stable coronary artery disease: to have or have not. European Heart Journal 31:24, 2980-2983
CrossRef67
R. B. Schnabel, A. Schulz, C. M. Messow, E. Lubos, P. S. Wild, T. Zeller, C. R. Sinning, H. J. Rupprecht, C. Bickel, D. Peetz, F. Cambien, T. Kempf, K. C. Wollert, E. J. Benjamin, K. J. Lackner, T. F. Munzel, L. Tiret, R. S. Vasan, S. Blankenberg. (2010) Multiple marker approach to risk stratification in patients with stable coronary artery disease. European Heart Journal 31:24, 3024-3031
CrossRef68
Nicolas Magné, Cyrus Chargari, Dhara MacDermed, Rosa Conforti, Lionel Védrine, Jean-Philippe Spano, David Khayat. (2010) Tomorrow's targeted therapies in breast cancer patients: What is the risk for increased radiation-induced cardiac toxicity?. Critical Reviews in Oncology/Hematology 76:3, 186-195
CrossRef69
Philip Greenland, Joseph S. Alpert, George A. Beller, Emelia J. Benjamin, Matthew J. Budoff, Zahi A. Fayad, Elyse Foster, Mark A. Hlatky, John McB. Hodgson, Frederick G. Kushner, Michael S. Lauer, Leslee J. Shaw, Sidney C. Smith, Allen J. Taylor, William S. Weintraub, Nanette K. Wenger. (2010) 2010 ACCF/AHA Guideline for Assessment of Cardiovascular Risk in Asymptomatic Adults. Journal of the American College of Cardiology 56:25, e50-e103
CrossRef70
I. A. Scott, P. B. Greenberg. (2010) Cautionary tales in the interpretation of studies of tools for predicting risk and prognosis. Internal Medicine Journal 40:12, 803-812
CrossRef71
Karl J. Lackner. (2010) Biomarkers of cardiovascular risk – matters of prediction and precision. Clinical Chemistry and Laboratory Medicine 48:12, 1683-1684
CrossRef72
THS Dent. (2010) Predicting the risk of coronary heart disease. II: The role of novel molecular biomarkers and genetics in estimating risk, and the future of risk prediction. Atherosclerosis 213:2, 352-362
CrossRef73
Martin J. Landray, Jonathan R. Emberson, Lisa Blackwell, Tanaji Dasgupta, Rosita Zakeri, Matthew D. Morgan, Charlie J. Ferro, Susan Vickery, Puja Ayrton, Devaki Nair, R. Neil Dalton, Edmund J. Lamb, Colin Baigent, Jonathan N. Townend, David C. Wheeler. (2010) Prediction of ESRD and Death Among People With CKD: The Chronic Renal Impairment in Birmingham (CRIB) Prospective Cohort Study. American Journal of Kidney Diseases 56:6, 1082-1094
CrossRef74
Per Venge, Claes Öhberg, Mats Flodin, Bertil Lindahl. (2010) Early and late outcome prediction of death in the emergency room setting by point-of-care and laboratory assays of cardiac troponin I. American Heart Journal 160:5, 835-841
CrossRef75
Ingar Holme, Are H. Aastveit, Niklas Hammar, Ingmar Jungner, Göran Walldius. (2010) Inflammatory markers, lipoprotein components and risk of major cardiovascular events in 65,005 men and women in the Apolipoprotein MOrtality RISk study (AMORIS). Atherosclerosis 213:1, 299-305
CrossRef76
E. Hagstrom, E. Ingelsson, J. Sundstrom, P. Hellman, T. E. Larsson, L. Berglund, H. Melhus, C. Held, K. Michaelsson, L. Lind, J. Arnlov. (2010) Plasma parathyroid hormone and risk of congestive heart failure in the community. European Journal of Heart Failure 12:11, 1186-1192
CrossRef77
Raimund Erbel, Stefan Möhlenkamp, Susanne Moebus, Axel Schmermund, Nils Lehmann, Andreas Stang, Nico Dragano, Dietrich Grönemeyer, Rainer Seibel, Hagen Kälsch, Martina Bröcker-Preuss, Klaus Mann, Johannes Siegrist, Karl-Heinz Jöckel. (2010) Coronary Risk Stratification, Discrimination, and Reclassification Improvement Based on Quantification of Subclinical Coronary Atherosclerosis. Journal of the American College of Cardiology 56:17, 1397-1406
CrossRef78
Marie Therese Cooney, Helen C. Cooney, Alexandra Dudina, Ian M. Graham. (2010) Assessment of Cardiovascular Risk. Current Hypertension Reports 12:5, 384-393
CrossRef79
Lori B. Daniels, Paul Clopton, Navaid Iqbal, Kimberly Tran, Alan S. Maisel. (2010) Association of ST2 levels with cardiac structure and function and mortality in outpatients. American Heart Journal 160:4, 721-728
CrossRef80
Anthony S. McLean, Stephen J. Huang. 2010. Biomarkers of Cardiac Injury. , 119-155.
CrossRef81
Chris J Packard. (2010) Optimizing lipid-lowering therapy in the prevention of coronary heart disease. Expert Review of Clinical Pharmacology 3:5, 649-661
CrossRef82
Mira Katan, Felix Fluri, Philipp Schuetz, Nils G. Morgenthaler, Christian Zweifel, Roland Bingisser, Ludwig Kappos, Andreas Steck, Stefan T. Engelter, Beat Müller, Mirjam Christ-Crain. (2010) Midregional Pro-Atrial Natriuretic Peptide and Outcome in Patients With Acute Ischemic Stroke. Journal of the American College of Cardiology 56:13, 1045-1053
CrossRef83
(2010) Congress of Clinical Chemistry and Laboratory Medicine 7th Annual Conference of the German Society for Clinical Chemistry and Laboratory Medicine (DGKL) Mannheim, Germany, 29th September – 02nd October, 2010. Clinical Chemistry and Laboratory Medicine 48:9, A79-A155
CrossRef84
Sotirios Tsimikas, Ziad Mallat, Philippa J. Talmud, John J.P. Kastelein, Nicholas J. Wareham, Manjinder S. Sandhu, Elizabeth R. Miller, Joelle Benessiano, Alain Tedgui, Joseph L. Witztum, Kay-Tee Khaw, S. Matthijs Boekholdt. (2010) Oxidation-Specific Biomarkers, Lipoprotein(a), and Risk of Fatal and Nonfatal Coronary Events. Journal of the American College of Cardiology 56:12, 946-955
CrossRef85
Nathan D. Wong. (2010) Should We Focus on Novel Risk Markers and Screening Tests to Better Predict and Prevent Cardiovascular Disease? Or Are We Putting the Cart Before the Horse?. Preventive Cardiology 13:4, 149-151
CrossRef86
Christopher J. Bates, Mohammed A. Mansoor, Kristina D. Pentieva, Mark Hamer, Gita D. Mishra. (2010) Biochemical risk indices, including plasma homocysteine, that prospectively predict mortality in older British people: the National Diet and Nutrition Survey of People Aged 65 Years and Over. British Journal of Nutrition 104:06, 893-899
CrossRef87
Chung-Feng Huang, Ming-Yen Hsieh, Jeng-Fu Yang, Wu-Cheng Chen, Ming-Lun Yeh, Ching-I Huang, Chia-Yen Dai, Ming-Lung Yu, Zu-Yau Lin, Shinn-Chern Chen, Wan-Long Chuang, Jee-Fu Huang. (2010) Serum hs-CRP was correlated with treatment response to pegylated interferon and ribavirin combination therapy in chronic hepatitis C patients. Hepatology International 4:3, 621-627
CrossRef88
T. Omland. (2010) New features of troponin testing in different clinical settings. Journal of Internal Medicine 268:3, 207-217
CrossRef89
Lars-Olof Hansson, Anders Grubb, Anders Lidén, Mats Flodin, Annacarin Berggren, Joris Delanghe, Veronique Stove, Hilmar Luthe, Karl-Heinz Rhode, Claus Beck, Ingrid Domke. (2010) Performance evaluation of a turbidimetric cystatin C assay on different high-throughput platforms. Scandinavian Journal of Clinical & Laboratory Investigation 70:5, 347-353
CrossRef90
B. J. Wolf, E. G. Hill, E. H. Slate. (2010) Logic Forest: an ensemble classifier for discovering logical combinations of binary markers. Bioinformatics 26:17, 2183-2189
CrossRef91
Jonathan R. Enriquez, James A. De Lemos. (2010) Should We Focus on Novel Risk Markers and Screening Tests to Better Predict and Prevent Cardiovascular Disease?. Preventive Cardiology 13:4, 152-159
CrossRef92
Yvan Devaux, Francisco Azuaje, Mélanie Vausort, Céline Yvorra, Daniel R. Wagner. (2010) Integrated protein network and microarray analysis to identify potential biomarkers after myocardial infarction. Functional & Integrative Genomics 10:3, 329-337
CrossRef93
Camilla Noelle Rathcke, Ilan Raymond, Caroline Kistorp, Per Hildebrandt, Jens Faber, Henrik Vestergaard. (2010) Low grade inflammation as measured by levels of YKL-40: Association with an increased overall and cardiovascular mortality rate in an elderly population. International Journal of Cardiology 143:1, 35-42
CrossRef94
R. Mihaescu, M. van Zitteren, M. van Hoek, E. J. G. Sijbrands, A. G. Uitterlinden, J. C. M. Witteman, A. Hofman, M. G. M. Hunink, C. M. van Duijn, A. C. J. W. Janssens. (2010) Improvement of Risk Prediction by Genomic Profiling: Reclassification Measures Versus the Area Under the Receiver Operating Characteristic Curve. American Journal of Epidemiology 172:3, 353-361
CrossRef95
Gerard Devlin. (2010) Women and elderly: subgroups under-represented in clinical trials. Current Opinion in Cardiology 25:4, 335-339
CrossRef96
Tamsin M. Lindstrom, William H. Robinson. (2010) Biomarkers for rheumatoid arthritis: Making it personal. Scandinavian Journal of Clinical & Laboratory Investigation 70:s242, 79-84
CrossRef97
Christopher T Johansen, Matthew B Lanktree, Robert A Hegele. (2010) Translating genomic analyses into improved management of coronary artery disease. Future Cardiology 6:4, 507-521
CrossRef98
Wolfgang Koenig. (2010) Integrating biomarkers: The new frontier?. Scandinavian Journal of Clinical & Laboratory Investigation 70:s242, 117-123
CrossRef99
Michael H. Criqui, Lindsey A. Ho, Julie O. Denenberg, Paul M. Ridker, Christina L. Wassel, Mary M. McDermott. (2010) Biomarkers in peripheral arterial disease patients and near- and longer-term mortality. Journal of Vascular Surgery 52:1, 85-90
CrossRef100
Iftikhar J. Kullo, Leslie T. Cooper. (2010) Early identification of cardiovascular risk using genomics and proteomics. Nature Reviews Cardiology 7:6, 309-317
CrossRef101
Geoffrey de Couto, Maral Ouzounian, Peter P. Liu. (2010) Early detection of myocardial dysfunction and heart failure. Nature Reviews Cardiology 7:6, 334-344
CrossRef102
Gina Biasillo, Milena Leo, Roberta Della Bona, Luigi Marzio Biasucci. (2010) Inflammatory biomarkers and coronary heart disease: from bench to bedside and back. Internal and Emergency Medicine 5:3, 225-233
CrossRef103
D. Gruson, S. Bodovitz. (2010) Rapid emergence of multimarker strategies in laboratory medicine. Biomarkers 15:4, 289-296
CrossRef104
Jennifer E. Ho, Daniel Levy. (2010) B-Type Natriuretic Peptide Testing in the General Population. Journal of the American College of Cardiology 55:19, 2148-2149
CrossRef105
Thomas J. Wang. (2010) Multiple Biomarkers for Predicting Cardiovascular Events. Journal of the American College of Cardiology 55:19, 2092-2095
CrossRef106
Thomas M. Tadros, Joseph M. Massaro, Guido A. Rosito, Udo Hoffmann, Ramachandran S. Vasan, Martin G. Larson, John F. Keaney Jr, Izabella Lipinska, James B. Meigs, Sekar Kathiresan, Christopher J. O'Donnell, Caroline S. Fox, Emelia J. Benjamin. (2010) Pericardial Fat Volume Correlates With Inflammatory Markers: The Framingham Heart Study. Obesity 18:5, 1039-1045
CrossRef107
G. Tripepi, K. J. Jager, F. W. Dekker, C. Zoccali. (2010) Statistical methods for the assessment of prognostic biomarkers(part II): calibration and re-classification. Nephrology Dialysis Transplantation 25:5, 1402-1405
CrossRef108
Hyeon Chang Kim, Philip Greenland, Jacques E. Rossouw, JoAnn E. Manson, Barbara B. Cochrane, Norman L. Lasser, Marian C. Limacher, Donald M. Lloyd-Jones, Karen L. Margolis, Jennifer G. Robinson. (2010) Multimarker Prediction of Coronary Heart Disease Risk. Journal of the American College of Cardiology 55:19, 2080-2091
CrossRef109
Fabrizio Tomai, Alessandro Petrolini, Leonardo De Luca, Francesco Nudi, Gaetano Lanza, Corrado Vassanelli, Flavio Ribichini. (2010) Rationale and design of the Randomized comparison of XiEnce V and Multilink VisioN coronary stents in the sAme muLtivessel patient with chronic kiDnEy disease (RENAL-DES) study. Journal of Cardiovascular Medicine 11:4, 310-317
CrossRef110
Patrick Ray, Yannick Le Manach, Bruno Riou, Tim T. Houle. (2010) Statistical Evaluation of a Biomarker. Anesthesiology 112:4, 1023-1040
CrossRef111
Samar Basu. (2010) Fatty acid oxidation and isoprostanes: Oxidative strain and oxidative stress. Prostaglandins, Leukotrienes and Essential Fatty Acids 82:4-6, 219-225
CrossRef112
Alberto Morales Salinas, Antonio Coca. (2010) Obesidad, actividad física y riesgo cardiovascular: clasificación ergoantropométrica, variables farmacológicas, biomarcadores y «paradoja del obeso». Medicina Clínica 134:11, 492-498
CrossRef113
N. Rodondi, P. Marques-Vidal, J. Butler, K. Sutton-Tyrrell, J. Cornuz, S. Satterfield, T. Harris, D. C. Bauer, L. Ferrucci, E. Vittinghoff, A. B. Newman, . (2010) Markers of Atherosclerosis and Inflammation for Prediction of Coronary Heart Disease in Older Adults. American Journal of Epidemiology 171:5, 540-549
CrossRef114
Benoit J. Arsenault, Jamal S. Rana, Isabelle Lemieux, Jean-Pierre Després, Nicholas J. Wareham, John J.P. Kastelein, S. Matthijs Boekholdt, Kay-Tee Khaw. (2010) Physical activity, the Framingham risk score and risk of coronary heart disease in men and women of the EPIC-Norfolk study. Atherosclerosis 209:1, 261-265
CrossRef115
Lori B. Daniels. (2010) Natriuretic Peptides and Assessment of Cardiovascular Disease Risk in Asymptomatic Persons. Current Cardiovascular Risk Reports 4:2, 120-127
CrossRef116
Naveed Sattar, Paul Welsh, Nadeem Sarwar, John Danesh, Emanuele Di Angelantonio, Vilmundur Gudnason, George Davey Smith, Shah Ebrahim, Debbie A. Lawlor. (2010) NT-proBNP is associated with coronary heart disease risk in healthy older women but fails to enhance prediction beyond established risk factors: Results from the British Women's Heart and Health Study. Atherosclerosis 209:1, 295-299
CrossRef117
Simona Ferraro, Giuseppe Marano, Angelo S. Bongo, Patrizia Boracchi, Elia M. Biganzoli. (2010) From multimarker approach to multiplex assays in acute coronary syndromes: What are we searching for?. Acute Cardiac Care 12:1, 18-24
CrossRef118
Matthew G. Daly, Christopher M. Frampton, Richard W. Troughton. (2010) Improving Risk Stratification for Heart Failure. Journal of the American College of Cardiology 55:5, 451-453
CrossRef119
Christopher R. deFilippi, Robert H. Christenson, John S. Gottdiener, Willem J. Kop, Stephen L. Seliger. (2010) Dynamic Cardiovascular Risk Assessment in Elderly People. Journal of the American College of Cardiology 55:5, 441-450
CrossRef120
R. Paniagua, M.-d.-J. Ventura, M. Avila-Diaz, H. Hinojosa-Heredia, A. Mendez-Duran, A. Cueto-Manzano, A. Cisneros, A. Ramos, C. Madonia-Juseino, F. Belio-Caro, F. Garcia-Contreras, P. Trinidad-Ramos, R. Vazquez, B. Ilabaca, G. Alcantara, D. Amato. (2010) NT-proBNP, fluid volume overload and dialysis modality are independent predictors of mortality in ESRD patients. Nephrology Dialysis Transplantation 25:2, 551-557
CrossRef121
Shepard D. Weiner, LeRoy E. Rabbani. (2010) Secondary prevention strategies for coronary heart disease. Journal of Thrombosis and Thrombolysis 29:1, 8-24
CrossRef122
J Blacher, A Evans, D Arveiler, P Amouyel, J Ferrières, A Bingham, J Yarnell, B Haas, M Montaye, J-B Ruidavets, P Ducimetière. (2010) Residual cardiovascular risk in treated hypertension and hyperlipidaemia: the PRIME Study. Journal of Human Hypertension 24:1, 19-26
CrossRef123
Pramod K. Dash, Jing Zhao, Georgene Hergenroeder, Anthony N. Moore. (2010) Biomarkers for the diagnosis, prognosis, and evaluation of treatment efficacy for traumatic brain injury. Neurotherapeutics 7:1, 100-114
CrossRef124
Noreen Goldman, Jennifer B. Dowd. (2009) Considering the Inclusion of Metabolic and Cardiovascular Markers in the Panel Study of Income Dynamics. Biodemography and Social Biology 55:2, 140-158
CrossRef125
Vicente Bertomeu Martínez, J. Ramón González-Juanatey. (2009) Markers of Inflammation and Cardiovascular Disease. American Journal Cardiovascular Drugs 9:Supplement 1, 3-7
CrossRef126
Mahir Karakas, Wolfgang Koenig. (2009) CRP in Cardiovascular Disease. Herz 34:8, 607-613
CrossRef127
Rangeen Rafik Hamad, Anders Larsson, John Pernow, Katarina Bremme, Maria J Eriksson. (2009) Assessment of left ventricular structure and function in preeclampsia by echocardiography and cardiovascular biomarkers. Journal of Hypertension 27:11, 2257-2264
CrossRef128
Rigas Kalaitzidis, George Bakris. (2009) Pathogenesis and Treatment of Microalbuminuria in Patients With Diabetes: The Road Ahead. The Journal of Clinical Hypertension 11:11, 636-643
CrossRef129
C. M. Matter, M. Stuber, M. Nahrendorf. (2009) Imaging of the unstable plaque: how far have we got?. European Heart Journal 30:21, 2566-2574
CrossRef130
Ulla Petersson, Carl Johan Östgren, Lars Brudin, Peter M. Nilsson. (2009) A consultation-based method is equal to SCORE and an extensive laboratory-based method in predicting risk of future cardiovascular disease. European Journal of Cardiovascular Prevention & Rehabilitation 16:5, 536-540
CrossRef131
M.T. Cooney, A. Dudina, D. De Bacquer, L. Wilhelmsen, S. Sans, A. Menotti, G. De Backer, P. Jousilahti, U. Keil, T. Thomsen, P. Whincup, I.M. Graham. (2009) HDL cholesterol protects against cardiovascular disease in both genders, at all ages and at all levels of risk. Atherosclerosis 206:2, 611-616
CrossRef132
Roberto Latini, Serge Masson. (2009) Biomarkers of Myocyte Injury in Heart Failure. Heart Failure Clinics 5:4, 529-536
CrossRef133
Eugene Braunwald. (2009) Preface. Heart Failure Clinics 5:4, xiii-xiv
CrossRef134
Leslee J. Shaw, Raffaelle Bugiardini, C. Noel Bairey Merz. (2009) Women and Ischemic Heart Disease. Journal of the American College of Cardiology 54:17, 1561-1575
CrossRef135
Marie Therese Cooney, Alexandra L. Dudina, Ian M. Graham. (2009) Value and Limitations of Existing Scores for the Assessment of Cardiovascular Risk. Journal of the American College of Cardiology 54:14, 1209-1227
CrossRef136
Per Venge, Nina Johnston, Bertil Lindahl, Stefan James. (2009) Normal Plasma Levels of Cardiac Troponin I Measured by the High-Sensitivity Cardiac Troponin I Access Prototype Assay and the Impact on the Diagnosis of Myocardial Ischemia. Journal of the American College of Cardiology 54:13, 1165-1172
CrossRef137
José Ignacio Bernardino De La Serna, José Ramón Arribas López. (2009) Controversias y futuro del abordaje de la enfermedad cardiovascular en pacientes con VIH. Enfermedades Infecciosas y Microbiología Clínica 27, 48-53
CrossRef138
Eduardo Delgado Parada, Francisco Manuel Suárez García, Juan Carlos Miñana Climent, Alfonso Medina García, Virginia López Gaona, Solange Gutiérrez Vara, Juan José Solano Jaurrieta. (2009) Insuficiencia cardíaca y NT-proBNP. Respuesta de los autores. Revista Española de Geriatría y Gerontología 44:5, 291-292
CrossRef139
Alan S. Maisel. (2009) Cardiovascular and Renal Surrogate Markers in the Clinical Management of Hypertension. Cardiovascular Drugs and Therapy 23:4, 317-326
CrossRef140
Toshimitsu Nozaki, Seigo Sugiyama, Hidenobu Koga, Koichi Sugamura, Keisuke Ohba, Yasushi Matsuzawa, Hitoshi Sumida, Kunihiko Matsui, Hideaki Jinnouchi, Hisao Ogawa. (2009) Significance of a Multiple Biomarkers Strategy Including Endothelial Dysfunction to Improve Risk Stratification for Cardiovascular Events in Patients at High Risk for Coronary Heart Disease. Journal of the American College of Cardiology 54:7, 601-608
CrossRef141
Lu Qi. (2009) Mendelian randomization in nutritional epidemiology. Nutrition Reviews 67:8, 439-450
CrossRef142
Mark Hamer, Yoichi Chida, Emmanuel Stamatakis. (2009) Utility of C-Reactive Protein for Cardiovascular Risk Stratification Across Three Age Groups in Subjects Without Existing Cardiovascular Diseases. The American Journal of Cardiology 104:4, 538-542
CrossRef143
Kai M. Eggers, Bo Lagerqvist, Per Venge, Lars Wallentin, Bertil Lindahl. (2009) Prognostic Value of Biomarkers During and After Non–ST-Segment Elevation Acute Coronary Syndrome. Journal of the American College of Cardiology 54:4, 357-364
CrossRef144
Juliana Caballero Güeto, Miguel Ángel Ulecia Martínez, Emilio González Cocina, Manuel Lagares Carballo. (2009) Estrategias adecuadas en la enfermedad cardiovascular. Los pacientes de alto riesgo. Medicina Clínica 133:7, 261-271
CrossRef145
Johan Sundström. (2009) Troponin and heart failure: an early warning system worth listening to?. Future Cardiology 5:4, 321-324
CrossRef146
F. Azuaje, Y. Devaux, D. Wagner. (2009) Computational biology for cardiovascular biomarker discovery. Briefings in Bioinformatics 10:4, 367-377
CrossRef147
Elliot M. Friedman, David R. Williams, Burton H. Singer, Carol D. Ryff. (2009) Chronic discrimination predicts higher circulating levels of E-selectin in a national sample: The MIDUS study. Brain, Behavior, and Immunity 23:5, 684-692
CrossRef148
Jorge R. Kizer, Daniel G. Krauser, Richard J. Rodeheffer, John C. Burnett, Peter M. Okin, Mary J. Roman, Jason G. Umans, Lyle G. Best, Elisa T. Lee, Richard B. Devereux. (2009) Prognostic Value of Multiple Biomarkers in American Indians Free of Clinically Overt Cardiovascular Disease (from the Strong Heart Study). The American Journal of Cardiology 104:2, 247-253
CrossRef149
Christopher R. deFilippi, Stephen L. Seliger. (2009) Biomarkers for Prognostication After Acute Coronary Syndromes. Journal of the American College of Cardiology 54:4, 365-367
CrossRef150
K. Rask, E. O'Malley, B. Druss. (2009) Impact of socioeconomic, behavioral and clinical risk factors on mortality. Journal of Public Health 31:2, 231-238
CrossRef151
Thomas Sehestedt, Jørgen Jeppesen, Tine W Hansen, Susanne Rasmussen, Kristian Wachtell, Hans Ibsen, Christian Torp-Pedersen, Michael H Olsen. (2009) Which markers of subclinical organ damage to measure in individuals with high normal blood pressure?. Journal of Hypertension 27:6, 1165-1171
CrossRef152
Paul O. Collinson. (2009) We don't need another hero — Is there a role for ischemia biomarkers in patients with chest pain?. Clinica Chimica Acta 404:2, 87-88
CrossRef153
Kornél Simon. (2009) Civilizációs stressz, cardiovascularis kockázat, evidence based medicine, guideline-ok. Orvosi Hetilap 150:19, 895-902
CrossRef154
J. Kastrup, J. S. Johansen, P. Winkel, J. F. Hansen, P. Hildebrandt, G. B. Jensen, C. M. Jespersen, E. Kjoller, H. J. Kolmos, I. Lind, H. Nielsen, C. Gluud, . (2009) High serum YKL-40 concentration is associated with cardiovascular and all-cause mortality in patients with stable coronary artery disease. European Heart Journal 30:9, 1066-1072
CrossRef155
Magdalena Madero, Mark J Sarnak. (2009) Association of cystatin C with adverse outcomes. Current Opinion in Nephrology and Hypertension 18:3, 258-263
CrossRef156
Luis Vigil, Manuel Lopez, Emilia Condés, Manuel Varela, Dulce Lorence, Rafael Garcia-Carretero, Julian Ruiz. (2009) Cystatin C is associated with the metabolic syndrome and other cardiovascular risk factors in a hypertensive population. Journal of the American Society of Hypertension 3:3, 201-209
CrossRef157
Mark A. Hlatky, Paul A. Heidenreich. (2009) The Year in Epidemiology, Health Services Research, and Outcomes Research. Journal of the American College of Cardiology 53:16, 1459-1466
CrossRef158
Daniel Bolliger, Manfred D. Seeberger, Giovanna A. L. Lurati Buse, Peter Christen, Brian Rupinski, Lorenz Gürke, Miodrag Filipovic. (2009) A Preliminary Report on the Prognostic Significance of Preoperative Brain Natriuretic Peptide and Postoperative Cardiac Troponin in Patients Undergoing Major Vascular Surgery. Anesthesia & Analgesia 108:4, 1069-1075
CrossRef159
Javier Sanz, Pedro R. Moreno, Valentin Fuster. (2009) The Year in Atherothrombosis. Journal of the American College of Cardiology 53:15, 1326-1337
CrossRef160
Stephan J. L. Bakker, Ron T. Gansevoort, Dick Zeeuw. (2009) Albuminuria: What can we expect from the determination of nonimmunoreactive albumin?. Current Hypertension Reports 11:2, 111-117
CrossRef161
Sola Aoun Bahous, Jacques Blacher, Michel E. Safar. (2009) Aortic stiffness, kidney disease, and renal transplantation. Current Hypertension Reports 11:2, 98-103
CrossRef162
Lori B. Daniels. (2009) Multiple biomarker assessment in primary prevention of cardiovascular disease. Current Cardiovascular Risk Reports 3:2, 131-136
CrossRef163
Stacy Loeb, H. Ballentine Carter. (2009) Re: Long-Term Prediction of Prostate Cancer: Prostate-Specific Antigen (PSA) Velocity Is Predictive but Does Not Improve the Predictive Accuracy of a Single PSA Measurement 15 Years or More Before Cancer Diagnosis in a Large, Representative, Unscreened Population. European Urology 55:2, 523-524
CrossRef164
N. Goldman, D. A. Glei, Y.-H. Lin, M. Weinstein. (2009) Improving Mortality Prediction Using Biosocial Surveys. American Journal of Epidemiology 169:6, 769-779
CrossRef165
Todd M. Brown, Vera Bittner. (2009) Biomarkers of atherosclerosis: Clinical applications. Current Cardiovascular Risk Reports 3:1, 23-30
CrossRef166
Ciro Casanova Macario, Juan Pablo de Torres Tajes, Elizabeth Córdoba Lanus. (2009) Enfermedad pulmonar obstructiva crónica: ¿qué hay más allá del índice BODE?. Archivos de Bronconeumología 45, 35-39
CrossRef167
Paul Welsh, Chris J Packard, Naveed Sattar. (2008) Novel antecedent plasma biomarkers of cardiovascular disease: improved evaluation methods and comparator benchmarks raise the bar. Current Opinion in Lipidology 19:6, 563-571
CrossRef168
Todd M. Brown, Vera Bittner. (2008) Biomarkers of atherosclerosis: Clinical applications. Current Cardiology Reports 10:6, 497-504
CrossRef169
Rahul Kakkar, Richard T. Lee. (2008) The IL-33/ST2 pathway: therapeutic target and novel biomarker. Nature Reviews Drug Discovery 7:10, 827-840
CrossRef170
(2008) PC-FACS. Journal of Palliative Medicine 11:8, 1166-1168
CrossRef171
Tobias Kurth, Robert J. Glynn. (2008) Predicting Outcomes in CKD. American Journal of Kidney Diseases 52:4, 635-637
CrossRef172
Eileen M. Crimmins, Sarinnapha Vasunilashorn, Jung Ki Kim, Aaron Hagedorn, Yasuhiko Saito. (2008) A Comparison of Biological Risk Factors in Two Populations: The United States and Japan. Population and Development Review 34:3, 457-482
CrossRef173
A. Viljoen. (2008) Cardiovascular risk estimation - making sense of the numbers. International Journal of Clinical Practice 62:9, 1300-1303
CrossRef174
Robert Elkeles. (2008) Computed tomography imaging, coronary calcium and atherosclerosis. Expert Review of Cardiovascular Therapy 6:8, 1083-1093
CrossRef175
Robert P. Giugliano, Eugene Braunwald. (2008) The Year in Non–ST-Segment Elevation Acute Coronary Syndrome. Journal of the American College of Cardiology 52:13, 1095-1103
CrossRef176
(2008) Multiple Biomarkers and Cardiovascular Risk. New England Journal of Medicine 359:7, 760-761
Full Text177
P. Devarajan. (2008) Neutrophil gelatinase-associated lipocalin--an emerging troponin for kidney injury. Nephrology Dialysis Transplantation 23:12, 3737-3743
CrossRef178
Braunwald, Eugene, . (2008) Biomarkers in Heart Failure. New England Journal of Medicine 358:20, 2148-2159
Full Text179
de Lemos, James A., Lloyd-Jones, Donald M., . (2008) Multiple Biomarker Panels for Cardiovascular Risk Assessment. New England Journal of Medicine 358:20, 2172-2174
Full Text180
Seung-Hwan Lee, Kang-Woo Lee, Eun Sook Kim, Ye Ree Park, Hun-Sung Kim, Shin-Ae Park, Mi Ja Kang, Yu-Bai Ahn, Kun-Ho Yoon, Bong-Yun Cha, Ho-Young Son, Hyuk-Sang Kwon. (2008) Cystatin C is a Valuable Marker for Predicting Future Cardiovascular Diseases in Type 2 Diabetic Patients. Korean Diabetes Journal 32:6, 488
CrossRef181
Louis S. Constine. (2008) Cured of Hodgkin lymphoma, but suffering a broken heart. Leukemia & Lymphoma 49:8, 1433-1435
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