Long-term health benefits and costs of measurement of carotid intima–media thickness in prevention of coronary heart disease

Assessment of cardiovascular disease risk: a 2023 update

PURPOSE OF REVIEW

The aim of this study was to highlight the current best practice for atherosclerotic cardiovascular disease (CVD) risk evaluation, including selective use of adjunctive tools for risk stratification [e.g. coronary artery calcium (CAC) scoring] and risk enhancement [e.g. lipoprotein(a) [Lp(a)], polygenic risk scoring (PRS)].

RECENT FINDINGS

New studies have evaluated the efficacy of various risk assessment tools. These studies demonstrate the role of Lp(a) as a risk-enhancing factor ready for more widespread use. CAC is the gold standard method of assessing subclinical atherosclerosis, enabling true risk stratification of patients, and informing net benefit assessment for initiating or titrating lipid-lowering therapy (LLT).

SUMMARY

Lp(a) concentration and CAC scoring, apart from the traditional risk factors, add the most value to the current CVD risk assessment approaches of all available tools, especially in terms of guiding LLT. In addition to new integrative tools such as the MESA CHD Risk Score and Coronary Age calculator, the future of risk assessment may include PRS and more advanced imaging techniques for atherosclerosis burden. Soon, polygenic risk scoring may be used to identify the age at which to begin CAC scoring, with CAC scores guiding preventive strategies.

Feasibility and Applicability of Wireless Handheld Ultrasound Measurement of Carotid Intima-Media Thickness in Patients with Cardiac Symptoms

PURPOSE

Routine screening for carotid intima-media thickness (CIMT) and cardiovascular (CV) disease in asymptomatic patients has been criticized for the high costs and large number of patients required for detecting one patient with coronary artery disease (CAD). In order to overcome the low cost-effectiveness thereof, we investigated the feasibility of an economic wireless handheld ultrasound (WHUS) device for CIMT measurement in symptomatic patients.

MATERIALS AND METHODS

A total of 100 consecutive patients with cardiac symptoms were enrolled. CIMT was measured in all patients. Coronary angiography was performed in 75 patients indicated for the exam.

RESULTS

The mean of maximal CIMT measured from left/right common carotid artery and bulb (max-CIMT) by the WHUS device showed excellent agreement [intraclass correlation coefficient (ICC)=0.960] with a standard ultrasound device and great interobserver repeatability (ICC>0.9 between all observers). Receiver operating characteristic curve analysis showed that the predictive power for CAD was improved when max-CIMT and plaque information (plaque≥2) was added [area under the curve (AUC): 0.838] to the traditional clinical CV risk factors (AUC: 0.769). The cutoff values for CAD prediction with the standard device and the WHUS device were 1.05 mm (AUC: 0.807, sensitivity: 0.78, specificity: 0.53) and 1.10 mm (AUC: 0.725, sensitivity: 0.98, specificity: 0.27), respectively.

CONCLUSION

max-CIMT measured by a WHUS device showed excellent agreement and repeatability, compared with standard ultrasound. Combined max-CIMT and plaque information added predictive power to the traditional clinical CV risk factors in detecting high-risk CAD patients.

An alternative approach identified optimal risk thresholds for treatment indication: an illustration in coronary heart disease

OBJECTIVES

Treatment thresholds based on risk predictions can be optimized by considering various health (economic) outcomes and performing marginal analyses, but this is rarely performed. We demonstrate a general approach to identify treatment thresholds optimizing individual health (economic) outcomes, illustrated for statin treatment based on 10-year coronary heart disease (CHD) risk predicted by the Framingham risk score.

STUDY DESIGN AND SETTING

Creating a health economic model for a risk-based prevention strategy, risk thresholds can be evaluated on several outcomes of interest. Selecting an appropriate threshold range and decrement size for the thresholds and adapting the health economic model accordingly, outcomes can be calculated for each risk threshold. A stepwise, or marginal, comparison of clinical as well as health economic outcomes, that is, comparing outcomes using a specific threshold to outcomes of the former threshold while gradually lowering the threshold, then takes into account the balance between additional numbers of individuals treated and their outcomes (additional health effects and costs). In our illustration, using a Markov model for CHD, we evaluated risk thresholds by gradually lowering thresholds from 20% to 0%.

RESULTS

This approach can be applied to identify optimal risk thresholds on any outcome, such as to limit complications, maximize health outcomes, or optimize cost-effectiveness. In our illustration, keeping the population-level fraction of statin-induced complications <10% resulted in thresholds of T = 6% (men) and T = 2% (women). Lowering the threshold and comparing quality-adjusted life-years (QALYs) after each 1% decrease, QALYs were gained down to T = 1% (men) and T = 0% (women). Also accounting for costs, net health benefits were favorable down to T = 3% (men) and T = 6% (women).

CONCLUSION

Using a stepwise risk-based approach to threshold optimization allows for preventive strategies that optimize outcomes. Presenting this comprehensive overview of outcomes will better inform decision makers when defining a treatment threshold.

Modeling the costs and long-term health benefits of screening the general population for risks of cardiovascular disease: a review of methods used in the literature

BACKGROUND

Strategies for screening and intervening to reduce the risk of cardiovascular disease (CVD) in primary care settings need to be assessed in terms of both their costs and long-term health effects. We undertook a literature review to investigate the methodologies used.

METHODS

In a framework of developing a new health-economic model for evaluating different screening strategies for primary prevention of CVD in Europe (EPIC-CVD project), we identified seven key modeling issues and reviewed papers published between 2000 and 2013 to assess how they were addressed.

RESULTS

We found 13 relevant health-economic modeling studies of screening to prevent CVD in primary care. The models varied in their degree of complexity, with between two and 33 health states. Programmes that screen the whole population by a fixed cut-off (e.g., predicted 10-year CVD risk >20 %) identify predominantly elderly people, who may not be those most likely to benefit from long-term treatment. Uncertainty and model validation were generally poorly addressed. Few studies considered the disutility of taking drugs in otherwise healthy individuals or the budget impact of the programme.

CONCLUSIONS

Model validation, incorporation of parameter uncertainty, and sensitivity analyses for assumptions made are all important components of model building and reporting, and deserve more attention. Complex models may not necessarily give more accurate predictions. Availability of a large enough source dataset to reliably estimate all relevant input parameters is crucial for achieving credible results. Decision criteria should consider budget impact and the medicalization of the population as well as cost-effectiveness thresholds.

Tailoring the implementation of new biomarkers based on their added predictive value in subgroups of individuals

Background

The value of new biomarkers or imaging tests, when added to a prediction model, is currently evaluated using reclassification measures, such as the net reclassification improvement (NRI). However, these measures only provide an estimate of improved reclassification at population level. We present a straightforward approach to characterize subgroups of reclassified individuals in order to tailor implementation of a new prediction model to individuals expected to benefit from it.

Methods

In a large Dutch population cohort (n = 21,992) we classified individuals to low (<5%) and high (≥5%) fatal cardiovascular disease risk by the Framingham risk score (FRS) and reclassified them based on the systematic coronary risk evaluation (SCORE). Subsequently, we characterized the reclassified individuals and, in case of heterogeneity, applied cluster analysis to identify and characterize subgroups. These characterizations were used to select individuals expected to benefit from implementation of SCORE.

Results

Reclassification after applying SCORE in all individuals resulted in an NRI of 5.00% (95% CI [-0.53%; 11.50%]) within the events, 0.06% (95% CI [-0.08%; 0.22%]) within the nonevents, and a total NRI of 0.051 (95% CI [-0.004; 0.116]). Among the correctly downward reclassified individuals cluster analysis identified three subgroups. Using the characterizations of the typically correctly reclassified individuals, implementing SCORE only in individuals expected to benefit (n = 2,707,12.3%) improved the NRI to 5.32% (95% CI [-0.13%; 12.06%]) within the events, 0.24% (95% CI [0.10%; 0.36%]) within the nonevents, and a total NRI of 0.055 (95% CI [0.001; 0.123]). Overall, the risk levels for individuals reclassified by tailored implementation of SCORE were more accurate.

Discussion

In our empirical example the presented approach successfully characterized subgroups of reclassified individuals that could be used to improve reclassification and reduce implementation burden. In particular when newly added biomarkers or imaging tests are costly or burdensome such a tailored implementation strategy may save resources and improve (cost-)effectiveness.

Role of simultaneous carotid ultrasound in patients undergoing stress echocardiography for assessment of chest pain with no previous history of coronary artery disease

BACKGROUND

We prospectively explored prevalence of carotid disease (CD), its independent association with coronary artery disease (CAD) and outcome as well as potential impact on management, in patients undergoing stress echocardiography (SE) for new onset chest pain without known CAD.

METHODS

Accordingly, 591 consecutive patients referred for SE underwent carotid ultrasound. Carotid disease was defined as carotid intima-media thickness (C-IMT) >75th percentile for age and sex and/or presence of plaque.

RESULTS

Myocardial ischemia was demonstrated in only a minority (11%), but there was a high prevalence of CD (70%). Incidence of CD was similar in patients with and without ischemia (76% versus 69%, P = .26). Carotid data led to reclassification of Framingham risk score categories in 65% of patients as well as more than a third of negative SE patients potentially benefitting from primary prevention therapy. Of the 83 patients undergoing coronary arteriography, 59 (71%) demonstrated coronary atherosclerosis (any atheroma) and 33 (40%) CAD. Positive predictive value of SE for CAD was 56%, but presence of carotid plaque improved it to 70%. Although both CD and plaque showed association with CAD and revascularization, after adjustment for conventional risk factors, only carotid plaque maintained significant association (P = .024 and P = .023, respectively).

CONCLUSIONS

There is significantly higher prevalence of CD compared with myocardial ischemia in patients undergoing SE and carotid ultrasound for suspected CAD. This can lead to significant Framingham risk score reclassification with important primary prevention implications. Carotid plaque is superior to clinical assessment for the prediction of CAD and improves positive predictive value of SE for CAD in these patients.

Common carotid intima-media thickness measurements do not improve cardiovascular risk prediction in individuals with elevated blood pressure: the USE-IMT collaboration

Carotid intima-media thickness (CIMT) is a marker of cardiovascular risk. It is unclear whether measurement of mean common CIMT improves 10-year risk prediction of first-time myocardial infarction or stroke in individuals with elevated blood pressure. We performed an analysis among individuals with elevated blood pressure (i.e., a systolic blood pressure ≥140 mm Hg and a diastolic blood pressure ≥ 90 mm Hg) in USE-IMT, a large ongoing individual participant data meta-analysis. We refitted the risk factors of the Framingham Risk Score on asymptomatic individuals (baseline model) and expanded this model with mean common CIMT (CIMT model) measurements. From both models, 10-year risks to develop a myocardial infarction or stroke were estimated. In individuals with elevated blood pressure, we compared discrimination and calibration of the 2 models and calculated the net reclassification improvement (NRI). We included 17 254 individuals with elevated blood pressure from 16 studies. During a median follow-up of 9.9 years, 2014 first-time myocardial infarctions or strokes occurred. The C-statistics of the baseline and CIMT models were similar (0.73). NRI with the addition of mean common CIMT was small and not significant (1.4%; 95% confidence intervals, -1.1 to 3.7). In those at intermediate risk (n=5008, 10-year absolute risk of 10% to 20%), the NRI was 5.6% (95% confidence intervals, 1.6-10.4). There is no added value of measurement of mean common CIMT in individuals with elevated blood pressure for improving cardiovascular risk prediction. For those at intermediate risk, the addition of mean common CIMT to an existing cardiovascular risk score is small but statistically significant.

Relation between the ankle-brachial index and the complexity of coronary artery disease in older patients

Background

In the elderly, the ankle-brachial index (ABI) has greater than 90% sensitivity and specificity for peripheral artery disease identification. A well-known relation exists between peripheral artery disease and the number of diseased coronary vessels. Yet, other anatomical characteristics have important impacts on the type of treatment and prognosis.

Purpose

To determine the relation between ABI and the complexity of coronary artery disease, by different anatomical classifications.

Methods

This study was a prospective analysis of patients ≥65 years old who were undergoing elective coronary angiography for ischemic coronary disease. The ABI was calculated for each leg, as the ratio between the lowest ankle pressure and the highest brachial pressure. The analysis of coronary anatomy was performed by three interventional cardiologists; it included classification of each lesion with >50% diameter stenosis, according to the American Heart Association criteria, and calculation of the SYNTAX score.

Results

The study recruited 204 consecutive patients (median age: 72.5 years). Stable angina was present in 51% of patients. Although only 1% of patients reported peripheral artery disease, 45% exhibited an abnormal ABI. The number of lesions per patient, the number of patients with complex lesions, and the median SYNTAX scores were greater in the group with abnormal ABI. However, among 144 patients with obstructive coronary artery disease, despite abnormal ABI being able to identify a higher rate of patients with B2 or C type lesions (70.9% versus 53.8%; P=0.039), the mean SYNTAX scores (13 versus 9; P=0.14), and the proportion of patients with SYNTAX score >16 (34.2% versus 27.7%; P=0.47), were similar, irrespective of ABI.

Conclusion

In patients ≥65 years old the presence of peripheral artery disease could discriminate a group of patients with greater occurrence of B2 and C type lesions, but similar median SYNTAX score.

Diagnostic and prognostic prediction models

Risk prediction models can be used to estimate the probability of either having (diagnostic model) or developing a particular disease or outcome (prognostic model). In clinical practice, these models are used to inform patients and guide therapeutic management. Examples from the field of venous thrombo-embolism (VTE) include the Wells rule for patients suspected of deep venous thrombosis and pulmonary embolism, and more recently prediction rules to estimate the risk of recurrence after a first episode of unprovoked VTE. In this paper, the three phases that are recommended before a prediction model may be used in daily practice are described: development, validation, and impact assessment. In the development phase, the focus is on model development commonly using a multivariable logistic (diagnostic) or survival (prognostic) regression analysis. The performance of the developed model is expressed by discrimination, calibration and (re-) classification. In the validation phase, the developed model is tested in a new set of patients using these same performance measures. This is important, as model performance is commonly poorer in a new set of patients, e.g. due to case-mix or domain differences. Finally, in the impact phase the ability of a prediction model to actually guide patient management is evaluated. Whereas in the development and validation phase single cohort designs are preferred, this last phase asks for comparative designs, ideally randomized designs; therapeutic management and outcomes after using the prediction model is compared to a control group not using the model (e.g. usual care).