Point of Care Clinical Risk Score to Improve the Negative Diagnostic Utility of an Agatston Score of Zero: Averting the Need for Coronary Computed Tomography Angiography

Chest Pain Evaluation: Diagnostic Testing

Chest pain/discomfort (CP) is a common symptom and can be a diagnostic dilemma for many clinicians. The misdiagnosis of an acute or progressive chronic cardiac etiology may carry a significant risk of morbidity and mortality. This review summarizes the different options and modalities for establishing the diagnosis and severity of coronary artery disease. An effective test selection algorithm should be individually tailored to each patient to maximize diagnostic accuracy in a timely fashion, determine short- and long-term prognosis, and permit implementation of evidence-based treatments in a cost-effective manner. Through collaboration, a decision algorithm was developed (www.chowmd.ca/cadtesting) that could be adopted widely into clinical practice.

Role of Deep Learning in Computed Tomography

Computed tomography has played an instrumental role in the understanding of the pathophysiology of atherosclerosis in coronary artery disease. It enables visualization of the plaque obstruction and vessel stenosis in a comprehensive manner. As technology for computed tomography is constantly evolving, coronary applications and possibilities are constantly expanding. This influx of information can overwhelm a physician's ability to interpret information in this era of big data. Machine learning is a revolutionary approach that can help provide limitless pathways in patient management. Within these machine algorithms, deep learning has tremendous potential and can revolutionize computed tomography and cardiovascular imaging. In this review article, we highlight the role of deep learning in various aspects of computed tomography.

Diagnostic value of clinical risk scores for predicting normal stress myocardial perfusion imaging in subjects without coronary artery calcium

BACKGROUND

We evaluated if risk scores commonly used to predict the absence of significant stenosis at coronary computed tomography (CT) angiography are useful to predict a normal stress myocardial perfusion imaging (MPI) study.

METHODS

Our cohort included a total of 1422 consecutive patients with zero coronary artery calcium score (ZCS) who underwent 82Rb PET/CT for evaluation of suspected coronary artery disease (CAD). Predictive models were constructed as reported by Genders et al. and Alshahrani et al., and the probability of abnormal summed stress score (SSS) and of reduced myocardial perfusion reserve (MPR) based on these risk scores was assessed.

RESULTS

In the overall population, the prevalence of abnormal SSS was 0.10 and the prevalence of reduced MPR was 0.17 (both P < .001).The observed frequencies of abnormal SSS and reduced MPR vs the probabilities predicted by the Genders and Alshahrani models were above the diagonal identity line, highlighting an underestimation of the observed occurrence by these models. The areas under the receiver operating characteristic curve of the Genders and Alshahrani models indicated lack of discriminative ability for predicting abnormal SSS (0.547 and 0.527) and reduced MPR (0.509 and 0.538). The Hosmer-Lemeshow test revealed that both models underestimated the observed occurrence of abnormal SSS and reduced MPR.

CONCLUSIONS

Available models were unable to identify among patients with ZCS those with a low probability of a normal stress MPI study. Thus, an optimal approach to rule out from MPI patients without detectable coronary calcium still needs to be improved.

Should We Quantify Valvular Calcifications on Cardiac CT in Patients with Infective Endocarditis?

BACKGROUND

Evaluate the impact of valvular calcifications measured on cardiac computed tomography (CCT) in patients with infective endocarditis (IE).

METHODS

Seventy patients with native IE (36 aortic IE, 31 mitral IE, 3 bivalvular IE) were included and explored with CCT between January 2016 and April 2018. Mitral and aortic valvular calcium score (VCS) were measured on unenhanced calcium scoring images, and correlated with clinical, surgical data, and 1-year death rate.

RESULTS

VCS of patients with mitral IE and no peripheral embolism was higher than those with peripheral embolism (868 (25-1725) vs. 6 (0-95), p < 0.05). Patients with high calcified mitral IE (mitral VCS > 100; n = 15) had a lower rate of surgery (40.0% vs.78.9%; p = 0.03) and a higher 1-year-death risk (53.3% vs. 10.5%, p = 0.04; OR = 8.5 (2.75-16.40) than patients with low mitral VCS (n = 19). Patients with aortic IE and high aortic calcifications (aortic VCS > 100; n = 18) present more frequently atypical bacteria on blood cultures (33.3% vs. 4.8%; p = 0.03) than patients with low aortic VCS (n = 21).

CONCLUSION

The amount of valvular calcifications on CT was associated with embolism risk, rate of surgery and 1-year risk of death in patients with mitral IE, and germ's type in aortic IE raising the question of their systematic quantification in native IE.

Use of cardiovascular imaging in risk restratification of the diabetic patient

PURPOSE OF REVIEW

Diabetes mellitus is no longer considered a cardiovascular disease (CVD) risk equivalent, but the optimal methods of risk stratification are a matter of debate. The coronary calcium score (CCS) is a measure of the burden of atherosclerosis and is widely used for CVD risk stratification in the general population. We review recently published data to describe the role of the CCS in people with diabetes mellitus.

RECENT FINDINGS

People with diabetes mellitus have 10-year event rates for CVD and CVD mortality that are considered high, at a much lower level of CCS than the general population. Different categories of CCS are pertinent to men and women with diabetes mellitus. CCS may be particularly useful in clinical settings when CVD risk is known to be increased but difficult to quantify, for example peri-menopausal women, young persons with diabetes, type 1 diabetic individuals and others. With modern techniques, the radiation dose of a CSS has fallen to levels wherein screening and surveillance could be considered.

SUMMARY

The CCS is able to quantify CVD risk in people with diabetes mellitus when there is clinical uncertainty and identifies those with very high event rates. Future research should aim to identify effective risk reduction strategies in this important group.