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Original Contribution |

Common Carotid Intima-Media Thickness Measurements in Cardiovascular Risk Prediction:  A Meta-analysis FREE

Hester M. Den Ruijter, PhD; Sanne A. E. Peters, MSc; Todd J. Anderson, MD; Annie R. Britton, PhD; Jacqueline M. Dekker, PhD; Marinus J. Eijkemans, PhD; Gunnar Engström, MD, PhD; Gregory W. Evans, MA; Jacqueline de Graaf, MD, PhD; Diederick E. Grobbee, MD, PhD; Bo Hedblad, MD, PhD; Albert Hofman, MD, PhD; Suzanne Holewijn, PhD; Ai Ikeda, PhD; Maryam Kavousi, MD, MSc; Kazuo Kitagawa, MD; Akihiko Kitamura, MD, PhD; Hendrik Koffijberg, PhD; Eva M. Lonn, MD; Matthias W. Lorenz, MD; Ellisiv B. Mathiesen, MD; Giel Nijpels, MD, PhD; Shuhei Okazaki, MD; Daniel H. O’Leary, MD; Joseph F. Polak, MD; Jackie F. Price, MD; Christine Robertson, MBChB; Christopher M. Rembold, MD; Maria Rosvall, MD, PhD; Tatjana Rundek, MD, PhD; Jukka T. Salonen, MD, PhD; Matthias Sitzer, MD; Coen D. A. Stehouwer, MD, PhD; Jacqueline C. Witteman, PhD; Karel G. Moons, PhD; Michiel L. Bots, MD, PhD
[+] Author Affiliations

Author Affiliations: Julius Center for Health Sciences and Primary Care (Drs Den Ruijter, Peters, Eijkemans, Grobbee, Koffijberg, Moons, and Bots) and Department of Experimental Cardiology (Dr Den Ruijter), University Medical Center Utrecht, Utrecht, the Netherlands; Department of Cardiac Sciences and Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, Alberta, Canada (Dr Anderson); Department of Epidemiology and Public Health, University College London, London, United Kingdom (Dr Britton); Institute for Health and Care Research, VU Medical Center, Amsterdam, the Netherlands (Drs Dekker and Nijpels); Department of Clinical Sciences in Malmö, Lund University, Skåne University Hospital, Malmö, Sweden (Drs Engström, Hedblad, and Rosvall); Department of Biostatistical Sciences and Neurology, Wake Forest School of Medicine, Winston-Salem, North Carolina (Mr Evans); Department of General Internal Medicine, Division of Vascular Medicine, Nijmegen University Medical Centre, the Netherlands (Drs de Graaf and Holewijn); University of Malaya Medical Center, Kuala Lumpur, Malaysia (Dr Grobbee); Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands (Drs Hofman, Kavousi, and Witteman); Osaka Medical Center for Health Science and Promotion, Osaka, Japan (Drs Ikeda and Kitamura); Stroke Center, Department of Neurology, Osaka University Graduate School of Medicine, Osaka (Drs Kitagawa and Okazaki); Department of Medicine, Division of Cardiology and Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada (Dr Lonn); Department of Neurology, University Hospital, Goethe-University, Frankfurt am Main, Germany (Drs Lorenz and Sitzer); Brain and Circulation Research Group, Institute of Clinical Medicine, University of Tromsø, Tromsø, Norway (Dr Mathiesen); Department of Radiology, Tufts Medical Center, Boston, Massachusetts (Drs O’Leary and Polak); Centre for Population Health Sciences, University of Edinburgh, Edinburgh, United Kingdom (Drs Price and Robertson); Cardiology Division, Department of Internal Medicine, University of Virginia, Charlottesville (Dr Rembold); Department of Neurology, Miller School of Medicine, University of Miami, Miami, Florida (Dr Rundek); MAS-Metabolic Analytical Services Oy, Helsinki, Finland (Dr Salonen); Department of Neurology Klinikum Herford, Herford, Germany (Drs Sitzer); and Department of Internal Medicine and Cardiovascular Research Institute Maastricht, Maastricht University Medical Center, Maastricht, the Netherlands (Dr Stehouwer).


JAMA. 2012;308(8):796-803. doi:10.1001/jama.2012.9630.
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Published online

Context The evidence that measurement of the common carotid intima-media thickness (CIMT) improves the risk scores in prediction of the absolute risk of cardiovascular events is inconsistent.

Objective To determine whether common CIMT has added value in 10-year risk prediction of first-time myocardial infarctions or strokes, above that of the Framingham Risk Score.

Data Sources Relevant studies were identified through literature searches of databases (PubMed from 1950 to June 2012 and EMBASE from 1980 to June 2012) and expert opinion.

Study Selection Studies were included if participants were drawn from the general population, common CIMT was measured at baseline, and individuals were followed up for first-time myocardial infarction or stroke.

Data Extraction Individual data were combined into 1 data set and an individual participant data meta-analysis was performed on individuals without existing cardiovascular disease.

Results We included 14 population-based cohorts contributing data for 45 828 individuals. During a median follow-up of 11 years, 4007 first-time myocardial infarctions or strokes occurred. We first refitted the risk factors of the Framingham Risk Score and then extended the model with common CIMT measurements to estimate the absolute 10-year risks to develop a first-time myocardial infarction or stroke in both models. The C statistic of both models was similar (0.757; 95% CI, 0.749-0.764; and 0.759; 95% CI, 0.752-0.766). The net reclassification improvement with the addition of common CIMT was small (0.8%; 95% CI, 0.1%-1.6%). In those at intermediate risk, the net reclassification improvement was 3.6% in all individuals (95% CI, 2.7%-4.6%) and no differences between men and women.

Conclusion The addition of common CIMT measurements to the Framingham Risk Score was associated with small improvement in 10-year risk prediction of first-time myocardial infarction or stroke, but this improvement is unlikely to be of clinical importance.

Figures in this Article

Cardiovascular disease is among the leading causes of morbidity and mortality worldwide. Preventive treatment of high-risk asymptomatic individuals depends on accurate prediction of a person's risk to develop a cardiovascular event. Currently, cardiovascular risk prediction in asymptomatic individuals is based on the level of cardiovascular risk factors incorporated in scoring equations.1 Several scores are available, with the Framingham Risk Score among the most widely used.1,2 These risk equations perform reasonably well, yet there remains considerable overlap in estimated risk between those who are affected by a cardiovascular event and those who are not.3 Improvement in cardiovascular risk prediction is needed and may be established by including a measure of preclinical atherosclerosis in the risk prediction algorithms4 because atherosclerosis underlies the occurrence of cardiovascular events, develops over decades, and has a prolonged asymptomatic phase during which it is possible to modify the course of the disease.5

Measurement of carotid intima-media thickness (CIMT) has been proposed to be added to cardiovascular risk factors to improve individual risk assessment.6,7 So far, individual studies reported on the added value of CIMT measurements in cardiovascular risk prediction, but the evidence is not consistent across studies.814 Furthermore, guidelines differ in their recommendations for using CIMT measurements in primary prevention and which patients to consider, ranging from measurement in all individuals15 to measurement in only those at intermediate risk.4 Therefore, solid and valid evidence on this issue is needed. The USE Intima-Media Thickness (USE-IMT) collaboration is a global meta-analysis project using individual participant data from prospective cohort studies to determine the added value of the CIMT to current risk prediction models in asymptomatic individuals at risk for cardiovascular disease.

The USE-IMT project is an ongoing meta-analysis of individual participant data. Eligible cohorts are identified through literature searches of databases and through expert suggestion (the current analysis used PubMed from 1950 to June 2012 and EMBASE from 1980 to June 2012 using the search query published elsewhere16). A flowchart of the search (performed on June 19, 2012) and the inclusion in USE-IMT is displayed in eFigure 1. At present, 17 cohorts participate in USE-IMT of which 14 cohorts are included in this analysis. One cohort was excluded because only maximal common CIMT values were measured.17 The individual information from 2 other cohorts was not available yet.18,19 The cohorts were required to have available baseline data on age, sex, cigarette smoking status, antihypertensive medication use, blood pressure, cholesterol fractions, CIMT measurements, history of cardiovascular disease and diabetes mellitus, and follow-up information on occurrence of cardiovascular events. Individual data from cohorts were collected and harmonized for the statistical analyses using SPSS version 17 (SPSS).

Study Population

Of the 63 514 individuals included in USE-IMT, we selected 45 828 individuals to whom the cardiovascular risk scores like Framingham Risk Score apply (aged 45-75 years, systolic blood pressure <180 mm Hg, total cholesterol <300 mg/dL; no symptomatic cardiovascular disease at baseline). Using these criteria, the number of excluded individuals was 6154 because of age, 2977 for total cholesterol level, 1757 for systolic pressure, and 7740 for previous cardiovascular disease (not mutually exclusive). Incomplete data on common CIMT, cardiovascular risk factors, and (time to) events resulted in 2.2% missing data points, which were imputed using single imputation for each cohort separately (using the Multivariate Imputation by Chained Equations package of R). Predictors in our imputation model included all variables in our database including the outcome of interest, as recommended previously.20 For a sensitivity analysis, we also performed a complete case analysis.

Common CIMT and Outcome Measure

Per cohort, we averaged all available common CIMT measurements (from the number of angles; from either the far wall, near wall, or both; and from one or both sides of the neck). This choice was based on the observation that the magnitude of the relation between common CIMT and cardiovascular events risk do not differ greatly across various measures.21 All CIMT values were used in the analysis, including values larger than1 mm, which are suggestive of plaque. To account for differences in absolute CIMT levels across cohorts because of differences in methodology, we also calculated cohort-specific z scores, which were created by subtracting the individual CIMT values from the cohort mean CIMT. This value was then divided by the cohort CIMT standard deviation. First-time myocardial infarction and first-time stroke were included as a combined end point. These included both fatal and nonfatal events.

Statistical Analysis

The original variables of the 10-year Framingham Risk Score2 (age, sex, cigarette smoking status, blood pressure, antihypertensive medication use, total cholesterol level, high-density lipoprotein cholesterol level, and presence of diabetes mellitus) were first refit using multivariable Cox proportional-hazards model. This baseline model was then extended by a log-transformed common CIMT variable. Both models included cohort as a random effect using the frailty model. Heterogeneity in CIMT and events across cohorts was tested with a likelihood ratio test for interaction between cohort and CIMT in the Cox proportional-hazards model. In addition, we also tested for heterogeneity of the hazards ratios across cohorts using a random effects meta-analysis.

The improvement of addition of mean common CIMT to the baseline model was tested with the Wald test and the likelihood ratio test. The predictive performance of both models was assessed by comparing the predicted vs the 10-year observed risk, based on the Kaplan-Meier estimate (eFigure 2). The discriminative value of both models was expressed with Harrell C index.22 The 10-year absolute risk to develop a myocardial infarction or stroke was calculated and was used to classify individuals into risk categories of less than 5% (low risk), 5% to less than 20% (intermediate risk), or 20% or greater (high risk) according to the risk classification of the Framingham Heart Study.12 The net reclassification improvement was calculated and quantifies the percentage of correct movement across categories for those with and without events. Correct movement is upward classification by a new marker in those with events and downward classification for those without events. Our risk prediction model was based on time-to-event data, which contain not only events and nonevents but also individuals who discontinue prematurely. Therefore, the number of individuals reclassified due to a change in risk category was then described using the net reclassification improvement taking survival time into account.23 The corresponding 95% confidence intervals were obtained with bootstrapping.

We also calculated the net reclassification improvement for 4 risk categories: less than 5% (low risk), 5% to less than 10% (low to intermediate risk), 10% to less than 20% (intermediate to high risk), and 20% or greater (high risk) because the 4-level risk category approach is still widely used outside the United States. In addition, we assessed improvement without cutoff by risk categories using the integrated discrimination improvement, which can be seen as equal to differences in discrimination slopes.24 The relative integrated discrimination improvement was calculated by dividing the integrated discrimination improvement by the discrimination of the baseline model (based on the predicted probabilities in those with events and those without events).24

Finally, the net reclassification improvement and the (relative) integrated discrimination improvement were assessed separately in men and women. This sex-specific analysis was performed as the relation between CIMT and first-time myocardial infarction or stroke was different for men and women (interaction term in Cox proportional hazards model, P = .017). In addition, we specifically addressed individuals classified in the intermediate-risk groups (according to the baseline model and defined as a 10-year absolute risk of 5% to 20%). All analyses were performed in the statistical environment R (version 2.10.0). We did not validate our model with CIMT measurements because the aim was not to create and validate a new prediction rule, but to assess the actual improvement in risk prediction. All statistical testing was 2-sided and a P < .05 was considered statistically significant.

Baseline characteristics of the cohorts are presented in Table 1. The majority of the studied population was white. Mean (SD) common CIMT in USE-IMT was 0.73 (0.16) mm. Mean CIMT increased with age in every cohort (eTable 1). The median (SD) follow-up in USE-IMT was 11 (3.7) years, during which 4007 first-time myocardial infarctions or first-time strokes occurred (Table 2).

Table Graphic Jump LocationTable 1. Baseline Characteristics of the Cohorts in USE-IMTa
Table Graphic Jump LocationTable 2. Baseline Risk and Follow-up Characteristics of the Cohorts in USE-IMTa
Common CIMT and First-Time Myocardial Infarction or Stroke

The risk factors included in the Framingham Risk Score and increased common CIMT were all related to first-time myocardial infarction or stroke (eTable 2), and there was no evidence for heterogeneity in the relation between CIMT and outcome between studies (likelihood ratio rest for interaction, P = .18). Adjusted common CIMT was positively related to myocardial infarction and stroke with a hazard ratio per 0.1-mm difference of common CIMT of 1.12 (95% CI, 1.09-1.14) for women and 1.08 (95% CI, 1.05-1.11) for men. The hazard ratio per 0.1-mm difference of common CIMT was 1.08 (95% CI, 1.05-1.10) for myocardial infarction and 1.12 (95% CI, 1.10-1.15) for stroke. The study-specific hazard ratios for mean common CIMT and first-time myocardial infarction or stroke are displayed in Figure 1. Based on a random-effects meta-analysis on the study-specific hazard ratios, there was no evidence for heterogeneity in CIMT and outcome between studies (Q test of heterogeneity P value, 0.24; I2, 12.30%).

Place holder to copy figure label and caption
Figure 1. Relation of Common Carotid Intima-Media Thickness With First-Time Myocardial Infarction or Stroke Across Studies
Graphic Jump Location

Study-specific hazard ratios (HRs) and the pooled hazard ratio based on a random-effects meta-analysis. Error bars indicate 95% CI; data marker sizes indicate the sample sizes of the cohorts.
aHazard ratios are per 0.1-mm increase in common carotid intima-media thickness.

Calibration and Discrimination

The addition of mean common CIMT improved the baseline model (Wald test and likelihood ratio test, both P < .001). For both models, the 10-year predicted risk was closely in agreement with the 10-year cardiovascular disease risk as estimated with Kaplan-Meier (eFigure 2). Harrell C index for the baseline model was 0.757 (95% CI, 0.749-0.764) and 0.759 (95% CI, 0.752-0.766) with addition of common CIMT.

Net Reclassification

Figure 2A shows the distribution of the number of individuals without and with events across risk categories based on the Framingham Risk Score and the distribution of individuals after the addition of the common CIMT. More than 90% of the individuals remained in the same risk category. The numbers of individuals shifting downward or upward without and with events were similar.

Place holder to copy figure label and caption
Figure 2. Reclassification With CIMT Added to Framingham Risk Score
Graphic Jump Location

A, Individuals without and with events classified according to their 10-year absolute risk to develop a myocardial infarction or stroke predicted with the Framingham Risk Score variables or classified according to their 10-year absolute risk to develop a first-time myocardial infarction or stroke predicted with the Framingham Risk Score and a common carotid intima-media thickness (CIMT) measurement. B, Observed Kaplan-Meier absolute risk estimates for all individuals (with and without events). The observed risk in reclassified individuals is significantly different from the observed risk of the individuals in the gray cells.

Figure 2B shows the observed risks of all the individuals in the categories. The observed risks of the individuals that remained in the same risk categories corresponded well to their allocated risk categories. Individuals reclassified to a higher risk category indeed had a significantly higher observed risk compared than those not reclassified. Also, individuals reclassified to a lower risk category indeed had a lower observed risk than those not reclassified. Yet the confidence intervals indicate some overlap in observed risk in categories of those reclassified.

The net reclassification improvement indicated that the added value of mean common CIMT was 0.8% (95% CI, 0.1%-1.6%) with no differences between men and women (Table 3). The sex-specific reclassification tables are displayed in eFigure 3 and eFigure 4. The integrated discrimination improvement was 0.0024 (Table 3). The discrimination of the baseline model based on the predicted probabilities in those with and without events was 0.067. Thus, the relative integrated discrimination improvement was 3.6% and similar in men and women (Table 3).

Table Graphic Jump LocationTable 3. Summary of the Indices of Added Value in the Total USE-IMT Cohort and in the Intermediate-Risk Categories, by Sex

Of the individuals at intermediate risk, 88% remained in the same risk category after addition of CIMT to the Framingham Risk Score (Figure 2A). The reclassification was slightly more favorable than in the whole population with more individuals without events reclassified to a lower risk category and more individuals with events reclassified to a higher risk category.

Individuals classified to a higher risk category by CIMT had an observed risk above 20% and those classified to a lower risk category by CIMT had an observed risk less than 5%. The net reclassification improvement for the intermediate-risk group was 3.6% (95% CI, 2.7%-4.6%) with no differences between men and women (Table 3). The relative integrated discrimination improvement indicated that the improvement in the prediction model was 3.6% (Table 3).

The net reclassification improvements in all individuals for myocardial infarction and stroke separately were 0.6% and 1.0%, respectively. When 4 risk categories were applied (<5%, 5%-<10%, 10%-<20%, ≥20%), the net reclassification improvement in the overall population was 1.2% (95% CI, 0.1%-2.2%) with no differences between men and women (eFigure 5). In individuals at intermediate risk, the net reclassification improvement was 4.6% (95% CI, 3.1%-6.1%) with no differences between men (3.9%; 95% CI, 2.3%-5.9%) and women (5.5%; 95% CI, 3.0%-6.9%). Results from the complete case analysis and from the analysis with the cohort-specific z scores were similar to the results presented here.

In this meta-analysis based on participant data of 45 828 individuals from 14 cohort studies worldwide, the added value of common CIMT measurements to the Framingham Risk Score in the general population was small (0.8% were correctly reclassified). In individuals at intermediate risk, the added value was 3.2% in men and 3.9% in women. Our results suggest that common CIMT measurements should not routinely be performed in the general population because the overall added value is small and unlikely to be of clinical importance.

Recently, conflicting results have been published on the added value of CIMT measurements in cardiovascular risk prediction. These differences may be attributed to differences across studies in CIMT measurement (eg, carotid segments [common, bifurcation, internal], including or excluding carotid plaques), individuals' characteristics, cutoff values for risk categories, number of events (small numbers, especially in those that are shifting risk categories), and end-point definition. Within USE-IMT, we were able to summarize the majority of the existing evidence using uniform definitions of common CIMT, study population, risk categories, and cardiovascular events. We used only data on common CIMT and included only individuals to whom the risk scores apply. Also, as fatal and nonfatal myocardial infarction and stroke compose the majority of the cardiovascular events, we used these outcomes, which were available in all cohorts in USE-IMT. We used state-of-the-art statistical methods such as the net reclassification improvement, which incorporates time to event by Kaplan-Meier estimates rather than only distinguishing between events and nonevents. In addition, because the populations in USE-IMT may be very different from that in Framingham,38 we refitted the cardiovascular risk factors and also fitted the common CIMT measurements, which may be the most straightforward method to assess the added value of common CIMT measurements. Finally, to evaluate the robustness of our results, we also performed a complete-case analysis and used cohort-specific z scores of CIMT. These results were not different from our main analysis. Our results indicate no improvement in risk stratification through common CIMT measurements for the general population, neither for men nor for women.

We based our analysis on measurements of the mean common CIMT. We restricted to common CIMT measurements because they were available in all studies, they are generally feasible to use in routine clinical practice, and their use has been recommended.15,39 Measurements of CIMT obtained from other carotid segments and the inclusion of a separate measure of carotid plaque may be important in risk prediction. Recently, the Framingham investigators showed that the maximal CIMT of the internal carotid artery has added value in risk prediction whereas the common CIMT of the mean common carotid artery did not.12

Our results are very similar to those of the Framingham cohort, a study that was not included in this meta-analysis. A recent meta-analysis suggested that carotid plaque was better than CIMT in predicting coronary events.40 In several cohorts included in that meta-analysis, plaque was defined based on a certain arbitrary CIMT cutoff, and results were not presented for different definitions of plaque. In addition, others found the opposite for risk of stroke.41 The ARIC investigators reported that plaque information, in addition to CIMT, resulted in a net reclassification improvement of 9.9% in the overall population.11 In our study, we included all the reported CIMT values, even the thicker CIMT values suggestive of plaque. However, we did not separate plaque analysis, because separate information on plaque presence or absence was not available in USE-IMT. Furthermore, the reproducibility of plaque assessment is far less than that of CIMT (κ for plaques, 0.60-0.70, vs intraclass correlation coefficients for CIMT, 0.90-0.95).42,43 The added value of CIMT measurements from other sites than the common carotid segment (eg, maximal CIMT) obtainable by carotid ultrasound is yet to be determined.

Our results suggest that common CIMT measurements should not routinely be performed in the general population, as the overall added value may be too limited to result in health benefits. In individuals classified as being at intermediate risk by the Framingham Risk Score, information on the common CIMT measurements showed a slightly higher yield (net reclassification improvement of 3.2% in men and 3.9% in women). Yet, as described by Cook and Paynter,44 the net reclassification improvement for useless markers may not be zero in the intermediate-risk group, and one should be cautious in overinterpreting the net reclassification improvement in the intermediate-risk group. Therefore, the added value of mean common CIMT in 10-year risk prediction for cardiovascular disease, even in the intermediate-risk category, is most likely too small to result in health benefit. However, as the interest in risk prediction is currently shifting from a 10-year risk to lifetime risk, the added value of a CIMT measurement and its cost-effectiveness using a horizon of 20 to 30 years may be worthwhile to explore.

Our study has several limitations. The cohorts included in USE-IMT showed variation in statin use because they were studied across different decades. Yet there was no heterogeneity in the relation between common CIMT measurements and cardiovascular events, suggesting that differences in statin use did not affect the relationship between CIMT and events. There are differences in the adjudication of events across studies. Although we do not think that these differences are related to CIMT measurement (so nondifferential misclassification), we included hard end points such as myocardial infarction and stroke as these were least likely to be affected. It is well established that ethnicity is an important determinant of CIMT.45 Because most individuals in USE-IMT were derived from a white population, our findings on the added value of CIMT in risk prediction may not necessarily apply to other ethnicities.

In conclusion, the added value of common CIMT in 10-year risk prediction of cardiovascular events, in addition to the Framingham Risk Score, was small and unlikely to be of clinical importance.

Corresponding Author: Hester M. Den Ruijter, PhD, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands (hruijte2@umcutrecht.nl).

Author Contributions: Dr Den Ruijter had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Den Ruijter, Peters, de Graaf, Grobbee, Hofman, Koffijberg, Lonn, O’Leary, Rembold, Rosvall, Rundek, Salonen, Sitzer, Moons, Bots.

Acquisition of data: Den Ruijter, Peters, Anderson, Britton, Dekker, Engström, Evans, de Graaf, Grobbee, Hedblad, Holewijn, Ikeda, Kavousi, Kitagawa, Kitamura, Koffijberg, Lonn, Lorenz, Mathiesen, Nijpels, Okazaki, O’Leary, Polak, Price, Robertson, Rembold, Rosvall, Salonen, Sitzer, Stehouwer, Moons, Bots.

Analysis and interpretation of data: Den Ruijter, Peters, Eijkemans, Grobbee, Koffijberg, Lonn, Price, Rosvall, Rundek, Salonen, Sitzer, Stehouwer, Moons, Bots.

Drafting of the manuscript: Den Ruijter, Peters, Britton, Grobbee, Kavousi, Koffijberg, Rosvall, Salonen, Sitzer, Moons, Bots.

Critical revision of the manuscript for important intellectual content: Peters, Anderson, Dekker, Eijkemans, Engström, Evans, de Graaf, Grobbee, Hedblad, Hofman, Holewijn, Ikeda, Kavousi, Kitagawa, Kitamura, Koffijberg, Lonn, Lorenz, Mathiesen, Nijpels, Okazaki, O’Leary, Polak, Price, Robertson, Rembold, Rosvall, Rundek, Salonen, Sitzer, Stehouwer, Witteman, Moons, Bots.

Statistical analysis: Den Ruijter, Peters, Eijkemans, Grobbee, Koffijberg, Rosvall, Salonen, Sitzer, Moons, Bots.

Obtained funding: Peters, Anderson, Grobbee, Hofman, Koffijberg, O’Leary, Polak, Rosvall, Salonen, Sitzer, Stehouwer, Moons, Bots.

Administrative, technical, or material support: Den Ruijter, Peters, Anderson, Britton, Engström, Evans, Grobbee, Hedblad, Holewijn, Ikeda, Kavousi, Kitamura, Koffijberg, Mathiesen, Okazaki, O’Leary, Polak, Rembold, Rosvall, Rundek, Salonen, Sitzer, Moons.

Study supervision: Den Ruijter, Peters, de Graaf, Grobbee, Hofman, Koffijberg, Lorenz, Polak, Rosvall, Rundek, Salonen, Sitzer, Stehouwer, Moons, Bots.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Dr de Graaf reported having received a Dutch Heart Foundation grant to perform the NBS2 study (Nijmegen Biomedical Study). Dr Engström reported being employed as a senior epidemiologist by AstraZeneca R&D. Dr Kitagawa reported being employed by Osaka University Hospital; having received a grant from the Ministry of Education, Culture, Sports, and Technology of Japan; and having received lecture fees from sanofi-aventis. Dr Lonn reported having been a consultant for Merck and Hoffman Laroche; having provided expert testimony for Merck; having received grants from AstraZeneca, sanofi-aventis, Novartis, and GlaxoSmithKline; and having received lecture fees from Merck and Novartis. Dr Mathiesen reported having received a grant from the North Norwegian Health Authorities. Dr O’Leary reported owning stock in Medpace. Dr Polak reported having received a grant from the National Heart, Lung, and Blood Institute. Dr Price reported having received a grant from the British Heart Foundation. Dr Rundek reported having received grants from the National Institutes of Health. Dr Salonen reported having received a grant from the University of Eastern Finland for the funding of the Kuopio Ischaemic Heart Disease Risk Factor study. Dr Grobbee and Dr Bots reported having been a consultant for and having received grants and lecture fees from AstraZeneca. No other disclosures were reported.

Funding/Support: This project is supported by a grant from the Netherlands Organisation for Health Research and Development (ZonMw 200320003).

Role of the Sponsor: The funding source had no role in the design and conduct of the study; in the collection, analysis, and interpretation of the data; or in the preparation, review, or approval of the manuscript.

Additional Contributions: We thank Thomas Debray, MSc (Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht), for his help in performing the literature searches. He did not receive compensation for the contribution.

This article was corrected for errors on September 25, 2013.

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Elias-Smale SE, Kavousi M, Verwoert GC,  et al.  Common carotid intima-media thickness in cardiovascular risk stratification of older people: the Rotterdam Study.  Eur J Prev Cardiol. 2012;19(4):698-705
PubMed   |  Link to Article
Lorenz MW, Schaefer C, Steinmetz H, Sitzer M. Is carotid intima media thickness useful for individual prediction of cardiovascular risk? ten-year results from the Carotid Atherosclerosis Progression Study (CAPS).  Eur Heart J. 2010;31(16):2041-2048
PubMed   |  Link to Article
Nambi V, Chambless L, Folsom AR,  et al.  Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk in Communities) study.  J Am Coll Cardiol. 2010;55(15):1600-1607
PubMed   |  Link to Article
Polak JF, Pencina MJ, Pencina KM, O’Donnell CJ, Wolf PA, D’Agostino RB Sr. Carotid-wall intima-media thickness and cardiovascular events.  N Engl J Med. 2011;365(3):213-221
PubMed   |  Link to Article
Salonen JT, Salonen R. Ultrasonographically assessed carotid morphology and the risk of coronary heart disease.  Arterioscler Thromb. 1991;11(5):1245-1249
PubMed   |  Link to Article
Xie W, Wu Y, Wang W,  et al.  A longitudinal study of carotid plaque and risk of ischemic cardiovascular disease in the Chinese population.  J Am Soc Echocardiogr. 2011;24(7):729-737
PubMed   |  Link to Article
Naghavi M, Falk E, Hecht HS,  et al; SHAPE Task Force.  From vulnerable plaque to vulnerable patient: part III, executive summary of the Screening for Heart Attack Prevention and Education (SHAPE) Task Force report.  Am J Cardiol. 2006;98(2A):2H-15H
PubMed   |  Link to Article
Plantinga Y, Dogan S, Grobbee DE, Bots ML. Carotid intima-media thickness measurement in cardiovascular screening programmes.  Eur J Cardiovasc Prev Rehabil. 2009;16(6):639-644
PubMed   |  Link to Article
Kitamura A, Iso H, Imano H,  et al.  Carotid intima-media thickness and plaque characteristics as a risk factor for stroke in Japanese elderly men.  Stroke. 2004;35(12):2788-2794
PubMed   |  Link to Article
Singh-Manoux A, Britton A, Kivimaki M, Guéguen A, Halcox J, Marmot M. Socioeconomic status moderates the association between carotid intima-media thickness and cognition in midlife: evidence from the Whitehall II study.  Atherosclerosis. 2008;197(2):541-548
PubMed   |  Link to Article
Allan PL, Mowbray PI, Lee AJ, Fowkes FG. Relationship between carotid intima-media thickness and symptomatic and asymptomatic peripheral arterial disease: the Edinburgh Artery Study.  Stroke. 1997;28(2):348-353
PubMed   |  Link to Article
Moons KGM, Donders RART, Stijnen T, Harrell FE Jr. Using the outcome for imputation of missing predictor values was preferred.  J Clin Epidemiol. 2006;59(10):1092-1101
PubMed   |  Link to Article
Bots ML, de Jong PTVM, Hofman A, Grobbee DE. Left, right, near or far wall common carotid intima-media thickness measurements: associations with cardiovascular disease and lower extremity arterial atherosclerosis.  J Clin Epidemiol. 1997;50(7):801-807
PubMed   |  Link to Article
Harrell FE Jr, Lee KL, Califf RM, Pryor DB, Rosati RA. Regression modelling strategies for improved prognostic prediction.  Stat Med. 1984;3(2):143-152
PubMed   |  Link to Article
Pencina MJ, D’Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.  Stat Med. 2011;30(1):11-21
PubMed   |  Link to Article
Pencina MJ, D’Agostino RB, Vasan RS. Statistical methods for assessment of added usefulness of new biomarkers.  Clin Chem Lab Med. 2010;48(12):1703-1711
PubMed   |  Link to Article
Li R, Duncan BB, Metcalf PA,  et al; Atherosclerosis Risk in Communities (ARIC) Study Investigators.  B-mode-detected carotid artery plaque in a general population.  Stroke. 1994;25(12):2377-2383
PubMed   |  Link to Article
Lorenz MW, von Kegler S, Steinmetz H, Markus HS, Sitzer M. Carotid intima-media thickening indicates a higher vascular risk across a wide age range: prospective data from the Carotid Atherosclerosis Progression Study (CAPS).  Stroke. 2006;37(1):87-92
PubMed   |  Link to Article
Ali YS, Rembold KE, Weaver B,  et al.  Prediction of major adverse cardiovascular events by age-normalized carotid intimal medial thickness.  Atherosclerosis. 2006;187(1):186-190
PubMed   |  Link to Article
Cao JJ, Arnold AM, Manolio TA,  et al.  Association of carotid artery intima-media thickness, plaques, and C-reactive protein with future cardiovascular disease and all-cause mortality: the Cardiovascular Health Study.  Circulation. 2007;116(1):32-38
PubMed   |  Link to Article
Henry RMA, Kostense PJ, Spijkerman AMW,  et al; Hoorn Study.  Arterial stiffness increases with deteriorating glucose tolerance status: the Hoorn Study.  Circulation. 2003;107(16):2089-2095
PubMed   |  Link to Article
Salonen R, Salonen JT. Determinants of carotid intima-media thickness: a population-based ultrasonography study in eastern Finnish men.  J Intern Med. 1991;229(3):225-231
PubMed   |  Link to Article
Rosvall M, Östergren PO, Hedblad B, Isacsson SO, Janzon L, Berglund G. Occupational status, educational level, and the prevalence of carotid atherosclerosis in a general population sample of middle-aged Swedish men and women: results from the Malmö Diet and Cancer Study.  Am J Epidemiol. 2000;152(4):334-346
PubMed   |  Link to Article
Mora S, Szklo M, Otvos JD,  et al.  LDL particle subclasses, LDL particle size, and carotid atherosclerosis in the Multi-Ethnic Study of Atherosclerosis (MESA).  Atherosclerosis. 2007;192(1):211-217
PubMed   |  Link to Article
Holewijn S, den Heijer M, Swinkels DW, Stalenhoef AFH, de Graaf J. The metabolic syndrome and its traits as risk factors for subclinical atherosclerosis.  J Clin Endocrinol Metab. 2009;94(8):2893-2899
PubMed   |  Link to Article
Prabhakaran S, Singh R, Zhou X, Ramas R, Sacco RL, Rundek T. Presence of calcified carotid plaque predicts vascular events: the Northern Manhattan Study.  Atherosclerosis. 2007;195(1):e197-e201
PubMed   |  Link to Article
Kitagawa K, Hougaku H, Yamagami H,  et al; OSACA2 Study Group.  Carotid intima-media thickness and risk of cardiovascular events in high-risk patients: results of the Osaka Follow-Up Study for Carotid Atherosclerosis 2.  Cerebrovasc Dis. 2007;24(1):35-42
PubMed   |  Link to Article
Hofman A, van Duijn CM, Franco OH,  et al.  The Rotterdam Study: 2012 objectives and design update.  Eur J Epidemiol. 2011;26(8):657-686
PubMed   |  Link to Article
Stensland-Bugge E, Bønaa KH, Joakimsen O, Njølstad I. Sex differences in the relationship of risk factors to subclinical carotid atherosclerosis measured 15 years later: the Tromsø study.  Stroke. 2000;31(3):574-581
PubMed   |  Link to Article
D’Agostino RB Sr, Grundy S, Sullivan LM, Wilson P.CHD Risk Prediction Group.  Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation.  JAMA. 2001;286(2):180-187
PubMed   |  Link to Article
Stein JH, Korcarz CE, Hurst RT,  et al; American Society of Echocardiography Carotid Intima-Media Thickness Task Force; Endorsed by the Society for Vascular Medicine.  Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force.  J Am Soc Echocardiogr. 2008;21(2):93-111
PubMed   |  Link to Article
Inaba Y, Chen JA, Bergmann SR. Carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: a meta-analysis.  Atherosclerosis. 2012;220(1):128-133
PubMed   |  Link to Article
Hollander M, Hak AE, Koudstaal PJ,  et al.  Comparison between measures of atherosclerosis and risk of stroke: the Rotterdam Study.  Stroke. 2003;34(10):2367-2372
PubMed   |  Link to Article
Peters SA, Dogan S, Meijer R,  et al.  The use of plaque score measurements to assess changes in atherosclerotic plaque burden induced by lipid-lowering therapy over time: the METEOR study.  J Atheroscler Thromb. 2011;18(9):784-795
PubMed   |  Link to Article
Bots ML, Hofman A, De Jong PTVM, Grobbee DE. Common carotid intima-media thickness as an indicator of atherosclerosis at other sites of the carotid artery: the Rotterdam Study.  Ann Epidemiol. 1996;6(2):147-153
PubMed   |  Link to Article
Cook NR, Paynter NP. Performance of reclassification statistics in comparing risk prediction models.  Biom J. 2011;53(2):237-258
PubMed   |  Link to Article
Whincup PH, Nightingale CM, Owen CG,  et al.  Ethnic differences in carotid intima-media thickness between UK children of black African-Caribbean and white European origin.  Stroke. 2012;43(7):1747-1754
PubMed   |  Link to Article

Figures

Place holder to copy figure label and caption
Figure 1. Relation of Common Carotid Intima-Media Thickness With First-Time Myocardial Infarction or Stroke Across Studies
Graphic Jump Location

Study-specific hazard ratios (HRs) and the pooled hazard ratio based on a random-effects meta-analysis. Error bars indicate 95% CI; data marker sizes indicate the sample sizes of the cohorts.
aHazard ratios are per 0.1-mm increase in common carotid intima-media thickness.

Place holder to copy figure label and caption
Figure 2. Reclassification With CIMT Added to Framingham Risk Score
Graphic Jump Location

A, Individuals without and with events classified according to their 10-year absolute risk to develop a myocardial infarction or stroke predicted with the Framingham Risk Score variables or classified according to their 10-year absolute risk to develop a first-time myocardial infarction or stroke predicted with the Framingham Risk Score and a common carotid intima-media thickness (CIMT) measurement. B, Observed Kaplan-Meier absolute risk estimates for all individuals (with and without events). The observed risk in reclassified individuals is significantly different from the observed risk of the individuals in the gray cells.

Tables

Table Graphic Jump LocationTable 1. Baseline Characteristics of the Cohorts in USE-IMTa
Table Graphic Jump LocationTable 2. Baseline Risk and Follow-up Characteristics of the Cohorts in USE-IMTa
Table Graphic Jump LocationTable 3. Summary of the Indices of Added Value in the Total USE-IMT Cohort and in the Intermediate-Risk Categories, by Sex

References

Berger JS, Jordan CO, Lloyd-Jones D, Blumenthal RS. Screening for cardiovascular risk in asymptomatic patients.  J Am Coll Cardiol. 2010;55(12):1169-1177
PubMed   |  Link to Article
D’Agostino RB Sr, Vasan RS, Pencina MJ,  et al.  General cardiovascular risk profile for use in primary care: the Framingham Heart Study.  Circulation. 2008;117(6):743-753
PubMed   |  Link to Article
Akosah KO, Schaper A, Cogbill C, Schoenfeld P. Preventing myocardial infarction in the young adult in the first place: how do the National Cholesterol Education Panel III guidelines perform?  J Am Coll Cardiol. 2003;41(9):1475-1479
PubMed   |  Link to Article
Greenland P, Alpert JS, Beller GA,  et al.  2010 ACCF/AHA guideline for assessment of cardiovascular risk in asymptomatic adults: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines.  Circulation. 2010;112(25):2748-2764
PubMed
Libby P, Theroux P. Pathophysiology of coronary artery disease.  Circulation. 2005;111(25):3481-3488
PubMed   |  Link to Article
Lorenz MW, Markus HS, Bots ML, Rosvall M, Sitzer M. Prediction of clinical cardiovascular events with carotid intima-media thickness: a systematic review and meta-analysis.  Circulation. 2007;115(4):459-467
PubMed   |  Link to Article
Society of Atherosclerosis Imaging and Prevention Developed in collaboration with the International Atherosclerosis Society.  Appropriate use criteria for carotid intima media thickness testing.  Atherosclerosis. 2011;214(1):43-46
PubMed   |  Link to Article
Anderson TJ, Charbonneau F, Title LM,  et al.  Microvascular function predicts cardiovascular events in primary prevention: long-term results from the Firefighters and Their Endothelium (FATE) study.  Circulation. 2011;123(2):163-169
PubMed   |  Link to Article
Elias-Smale SE, Kavousi M, Verwoert GC,  et al.  Common carotid intima-media thickness in cardiovascular risk stratification of older people: the Rotterdam Study.  Eur J Prev Cardiol. 2012;19(4):698-705
PubMed   |  Link to Article
Lorenz MW, Schaefer C, Steinmetz H, Sitzer M. Is carotid intima media thickness useful for individual prediction of cardiovascular risk? ten-year results from the Carotid Atherosclerosis Progression Study (CAPS).  Eur Heart J. 2010;31(16):2041-2048
PubMed   |  Link to Article
Nambi V, Chambless L, Folsom AR,  et al.  Carotid intima-media thickness and presence or absence of plaque improves prediction of coronary heart disease risk: the ARIC (Atherosclerosis Risk in Communities) study.  J Am Coll Cardiol. 2010;55(15):1600-1607
PubMed   |  Link to Article
Polak JF, Pencina MJ, Pencina KM, O’Donnell CJ, Wolf PA, D’Agostino RB Sr. Carotid-wall intima-media thickness and cardiovascular events.  N Engl J Med. 2011;365(3):213-221
PubMed   |  Link to Article
Salonen JT, Salonen R. Ultrasonographically assessed carotid morphology and the risk of coronary heart disease.  Arterioscler Thromb. 1991;11(5):1245-1249
PubMed   |  Link to Article
Xie W, Wu Y, Wang W,  et al.  A longitudinal study of carotid plaque and risk of ischemic cardiovascular disease in the Chinese population.  J Am Soc Echocardiogr. 2011;24(7):729-737
PubMed   |  Link to Article
Naghavi M, Falk E, Hecht HS,  et al; SHAPE Task Force.  From vulnerable plaque to vulnerable patient: part III, executive summary of the Screening for Heart Attack Prevention and Education (SHAPE) Task Force report.  Am J Cardiol. 2006;98(2A):2H-15H
PubMed   |  Link to Article
Plantinga Y, Dogan S, Grobbee DE, Bots ML. Carotid intima-media thickness measurement in cardiovascular screening programmes.  Eur J Cardiovasc Prev Rehabil. 2009;16(6):639-644
PubMed   |  Link to Article
Kitamura A, Iso H, Imano H,  et al.  Carotid intima-media thickness and plaque characteristics as a risk factor for stroke in Japanese elderly men.  Stroke. 2004;35(12):2788-2794
PubMed   |  Link to Article
Singh-Manoux A, Britton A, Kivimaki M, Guéguen A, Halcox J, Marmot M. Socioeconomic status moderates the association between carotid intima-media thickness and cognition in midlife: evidence from the Whitehall II study.  Atherosclerosis. 2008;197(2):541-548
PubMed   |  Link to Article
Allan PL, Mowbray PI, Lee AJ, Fowkes FG. Relationship between carotid intima-media thickness and symptomatic and asymptomatic peripheral arterial disease: the Edinburgh Artery Study.  Stroke. 1997;28(2):348-353
PubMed   |  Link to Article
Moons KGM, Donders RART, Stijnen T, Harrell FE Jr. Using the outcome for imputation of missing predictor values was preferred.  J Clin Epidemiol. 2006;59(10):1092-1101
PubMed   |  Link to Article
Bots ML, de Jong PTVM, Hofman A, Grobbee DE. Left, right, near or far wall common carotid intima-media thickness measurements: associations with cardiovascular disease and lower extremity arterial atherosclerosis.  J Clin Epidemiol. 1997;50(7):801-807
PubMed   |  Link to Article
Harrell FE Jr, Lee KL, Califf RM, Pryor DB, Rosati RA. Regression modelling strategies for improved prognostic prediction.  Stat Med. 1984;3(2):143-152
PubMed   |  Link to Article
Pencina MJ, D’Agostino RB Sr, Steyerberg EW. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.  Stat Med. 2011;30(1):11-21
PubMed   |  Link to Article
Pencina MJ, D’Agostino RB, Vasan RS. Statistical methods for assessment of added usefulness of new biomarkers.  Clin Chem Lab Med. 2010;48(12):1703-1711
PubMed   |  Link to Article
Li R, Duncan BB, Metcalf PA,  et al; Atherosclerosis Risk in Communities (ARIC) Study Investigators.  B-mode-detected carotid artery plaque in a general population.  Stroke. 1994;25(12):2377-2383
PubMed   |  Link to Article
Lorenz MW, von Kegler S, Steinmetz H, Markus HS, Sitzer M. Carotid intima-media thickening indicates a higher vascular risk across a wide age range: prospective data from the Carotid Atherosclerosis Progression Study (CAPS).  Stroke. 2006;37(1):87-92
PubMed   |  Link to Article
Ali YS, Rembold KE, Weaver B,  et al.  Prediction of major adverse cardiovascular events by age-normalized carotid intimal medial thickness.  Atherosclerosis. 2006;187(1):186-190
PubMed   |  Link to Article
Cao JJ, Arnold AM, Manolio TA,  et al.  Association of carotid artery intima-media thickness, plaques, and C-reactive protein with future cardiovascular disease and all-cause mortality: the Cardiovascular Health Study.  Circulation. 2007;116(1):32-38
PubMed   |  Link to Article
Henry RMA, Kostense PJ, Spijkerman AMW,  et al; Hoorn Study.  Arterial stiffness increases with deteriorating glucose tolerance status: the Hoorn Study.  Circulation. 2003;107(16):2089-2095
PubMed   |  Link to Article
Salonen R, Salonen JT. Determinants of carotid intima-media thickness: a population-based ultrasonography study in eastern Finnish men.  J Intern Med. 1991;229(3):225-231
PubMed   |  Link to Article
Rosvall M, Östergren PO, Hedblad B, Isacsson SO, Janzon L, Berglund G. Occupational status, educational level, and the prevalence of carotid atherosclerosis in a general population sample of middle-aged Swedish men and women: results from the Malmö Diet and Cancer Study.  Am J Epidemiol. 2000;152(4):334-346
PubMed   |  Link to Article
Mora S, Szklo M, Otvos JD,  et al.  LDL particle subclasses, LDL particle size, and carotid atherosclerosis in the Multi-Ethnic Study of Atherosclerosis (MESA).  Atherosclerosis. 2007;192(1):211-217
PubMed   |  Link to Article
Holewijn S, den Heijer M, Swinkels DW, Stalenhoef AFH, de Graaf J. The metabolic syndrome and its traits as risk factors for subclinical atherosclerosis.  J Clin Endocrinol Metab. 2009;94(8):2893-2899
PubMed   |  Link to Article
Prabhakaran S, Singh R, Zhou X, Ramas R, Sacco RL, Rundek T. Presence of calcified carotid plaque predicts vascular events: the Northern Manhattan Study.  Atherosclerosis. 2007;195(1):e197-e201
PubMed   |  Link to Article
Kitagawa K, Hougaku H, Yamagami H,  et al; OSACA2 Study Group.  Carotid intima-media thickness and risk of cardiovascular events in high-risk patients: results of the Osaka Follow-Up Study for Carotid Atherosclerosis 2.  Cerebrovasc Dis. 2007;24(1):35-42
PubMed   |  Link to Article
Hofman A, van Duijn CM, Franco OH,  et al.  The Rotterdam Study: 2012 objectives and design update.  Eur J Epidemiol. 2011;26(8):657-686
PubMed   |  Link to Article
Stensland-Bugge E, Bønaa KH, Joakimsen O, Njølstad I. Sex differences in the relationship of risk factors to subclinical carotid atherosclerosis measured 15 years later: the Tromsø study.  Stroke. 2000;31(3):574-581
PubMed   |  Link to Article
D’Agostino RB Sr, Grundy S, Sullivan LM, Wilson P.CHD Risk Prediction Group.  Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation.  JAMA. 2001;286(2):180-187
PubMed   |  Link to Article
Stein JH, Korcarz CE, Hurst RT,  et al; American Society of Echocardiography Carotid Intima-Media Thickness Task Force; Endorsed by the Society for Vascular Medicine.  Use of carotid ultrasound to identify subclinical vascular disease and evaluate cardiovascular disease risk: a consensus statement from the American Society of Echocardiography Carotid Intima-Media Thickness Task Force.  J Am Soc Echocardiogr. 2008;21(2):93-111
PubMed   |  Link to Article
Inaba Y, Chen JA, Bergmann SR. Carotid plaque, compared with carotid intima-media thickness, more accurately predicts coronary artery disease events: a meta-analysis.  Atherosclerosis. 2012;220(1):128-133
PubMed   |  Link to Article
Hollander M, Hak AE, Koudstaal PJ,  et al.  Comparison between measures of atherosclerosis and risk of stroke: the Rotterdam Study.  Stroke. 2003;34(10):2367-2372
PubMed   |  Link to Article
Peters SA, Dogan S, Meijer R,  et al.  The use of plaque score measurements to assess changes in atherosclerotic plaque burden induced by lipid-lowering therapy over time: the METEOR study.  J Atheroscler Thromb. 2011;18(9):784-795
PubMed   |  Link to Article
Bots ML, Hofman A, De Jong PTVM, Grobbee DE. Common carotid intima-media thickness as an indicator of atherosclerosis at other sites of the carotid artery: the Rotterdam Study.  Ann Epidemiol. 1996;6(2):147-153
PubMed   |  Link to Article
Cook NR, Paynter NP. Performance of reclassification statistics in comparing risk prediction models.  Biom J. 2011;53(2):237-258
PubMed   |  Link to Article
Whincup PH, Nightingale CM, Owen CG,  et al.  Ethnic differences in carotid intima-media thickness between UK children of black African-Caribbean and white European origin.  Stroke. 2012;43(7):1747-1754
PubMed   |  Link to Article
December 19, 2012
James H. Stein, MD
JAMA. 2012;308(23):2455-2456. doi:10.1001/jama.2012.65884.
December 19, 2012
Hester M. Den Ruijter, PhD; Michiel L. Bots, MD, PhD
JAMA. 2012;308(23):2455-2456. doi:10.1001/jama.2012.65890.
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