Coronary artery calcium (CAC) is an established predictor of future major adverse atherosclerotic cardiovascular events in asymptomatic individuals. However, limited data exist as to how CAC compares ...with functional testing (FT) in estimating prognosis in symptomatic patients.
In the PROMISE trial (Prospective Multicenter Imaging Study for Evaluation of Chest Pain), patients with stable chest pain (or dyspnea) and intermediate pretest probability for obstructive coronary artery disease were randomized to FT (exercise electrocardiography, nuclear stress, or stress echocardiography) or anatomic testing. We evaluated those who underwent CAC testing as part of the anatomic evaluation (n=4209) and compared that with results of FT (n=4602). We stratified CAC and FT results as normal or mildly, moderately, or severely abnormal (for CAC: 0, 1-99 Agatston score AS, 100-400 AS, and >400 AS, respectively; for FT: normal, mild=late positive treadmill, moderate=early positive treadmill or single-vessel ischemia, and severe=large ischemic region abnormality). The primary end point was all-cause death, myocardial infarction, or unstable angina hospitalization over a median follow-up of 26.1 months. Cox regression models were used to calculate hazard ratios (HRs) and C statistics to determine predictive and discriminatory values.
Overall, the distribution of normal or mildly, moderately, or severely abnormal test results was significantly different between FT and CAC (FT: normal, n=3588 78.0%; mild, n=432 9.4%; moderate, n=217 4.7%; severe, n=365 7.9%; CAC: normal, n=1457 34.6%; mild, n=1340 31.8%; moderate, n=772 18.3%; severe, n=640 15.2%;
<0.0001). Moderate and severe abnormalities in both arms robustly predicted events (moderate: CAC: HR, 3.14; 95% confidence interval, 1.81-5.44; and FT: HR, 2.65; 95% confidence interval, 1.46-4.83; severe: CAC: HR, 3.56; 95% confidence interval, 1.99-6.36; and FT: HR, 3.88; 95% confidence interval, 2.58-5.85). In the CAC arm, the majority of events (n=112 of 133, 84%) occurred in patients with any positive CAC test (score >0), whereas fewer than half of events occurred in patients with mildly, moderately, or severely abnormal FT (n=57 of 132, 43%;
<0.001). In contrast, any abnormality on FT was significantly more specific for predicting events (78.6% for FT versus 35.2% for CAC;
<0.001). Overall discriminatory ability in predicting the primary end point of mortality, nonfatal myocardial infarction, and unstable angina hospitalization was similar and fair for both CAC and FT (C statistic, 0.67 versus 0.64). Coronary computed tomographic angiography provided significantly better prognostic information compared with FT and CAC testing (C index, 0.72).
Among stable outpatients presenting with suspected coronary artery disease, most patients experiencing clinical events have measurable CAC at baseline, and fewer than half have any abnormalities on FT. However, an abnormal FT was more specific for cardiovascular events, leading to overall similarly modest discriminatory abilities of both tests.
URL: https://www.clinicaltrials.gov. Unique identifier: NCT01174550.
Coronary artery calcium is an accurate predictor of cardiovascular events. While it is visible on all computed tomography (CT) scans of the chest, this information is not routinely quantified as it ...requires expertise, time, and specialized equipment. Here, we show a robust and time-efficient deep learning system to automatically quantify coronary calcium on routine cardiac-gated and non-gated CT. As we evaluate in 20,084 individuals from distinct asymptomatic (Framingham Heart Study, NLST) and stable and acute chest pain (PROMISE, ROMICAT-II) cohorts, the automated score is a strong predictor of cardiovascular events, independent of risk factors (multivariable-adjusted hazard ratios up to 4.3), shows high correlation with manual quantification, and robust test-retest reliability. Our results demonstrate the clinical value of a deep learning system for the automated prediction of cardiovascular events. Implementation into clinical practice would address the unmet need of automating proven imaging biomarkers to guide management and improve population health.
Abstract Objectives This study sought to determine prognostic value of nonobstructive coronary artery disease (CAD) for atherosclerotic cardiovascular disease (ASCVD) events and to determine whether ...incorporation of this information into the pooled cohort equation reclassifies recommendations for statin therapy as defined by the 2013 guidelines for cholesterol management of the American College of Cardiology and American Heart Association (ACC/AHA). Background Detection of nonobstructive CAD by coronary computed tomography angiography may improve risk stratification and permit individualized and more appropriate allocation of statin therapy. Methods This study determined the pooled hazard ratio of nonobstructive CAD for ASCVD events from published studies and incorporated this information into the ACC/AHA pooled cohort equation. The study calculated revised sex- and ethnicity-based 10-year ASCVD risk and determined boundaries corresponding to the original 7.5% risk for ASCVD events. It also assessed reclassification for statin eligibility by incorporating the results from meta-analysis to individual patients from a separate cohort. Results This study included 2 studies (2,295 subjects; 66% male; prevalence of nonobstructive CAD, 47%; median follow-up, 49 months; 67 ASCVD events). The hazard ratio of nonobstructive CAD for ASCVD events was 3.2 (95% confidence interval: 1.5 to 6.7). Incorporation of this information into the pooled cohort equation resulted in reclassification toward statin eligibility in individuals with nonobstructive CAD, with an original ASCVD score of 3.0% and 5.9% or higher in African-American women and men and a score of 4.4% and 4.6% or higher in Caucasian women and men, respectively. The absence of nonobstructive CAD resulted in reclassification toward statin ineligibility if the original ASCVD score was as 10.0% and 17.9% or lower in African-American women and men and 13.7% and 14.3% or lower in Caucasian women and men, respectively. Reclassification is observed in 14% of patients. Conclusions Detection of nonobstructive CAD by coronary computed tomography angiography improves risk stratification and permits individualized and more appropriate allocation of statin therapy across sex and ethnicity groups.
Background
The threshold model represents an important advance in the field of medical decision‐making. It is a linchpin between evidence (which exists on the continuum of credibility) and ...decision‐making (which is a categorical exercise – we decide to act or not act). The threshold concept is closely related to the question of rational decision‐making. When should the physician act, that is order a diagnostic test, or prescribe treatment? The threshold model embodies the decision theoretic rationality that says the most rational decision is to prescribe treatment when the expected treatment benefit outweighs its expected harms. However, the well‐documented large variation in the way physicians order diagnostic tests or decide to administer treatments is consistent with a notion that physicians' individual action thresholds vary.
Methods
We present a narrative review summarizing the existing literature on physicians' use of a threshold strategy for decision‐making.
Results
We found that the observed variation in decision action thresholds is partially due to the way people integrate benefits and harms. That is, explanation of variation in clinical practice can be reduced to a consideration of thresholds. Limited evidence suggests that non‐expected utility threshold (non‐EUT) models, such as regret‐based and dual‐processing models, may explain current medical practice better. However, inclusion of costs and recognition of risk attitudes towards uncertain treatment effects and comorbidities may improve the explanatory and predictive value of the EUT‐based threshold models.
Conclusions
The decision when to act is closely related to the question of rational choice. We conclude that the medical community has not yet fully defined criteria for rational clinical decision‐making. The traditional notion of rationality rooted in EUT may need to be supplemented by reflective rationality, which strives to integrate all aspects of medical practice – medical, humanistic and socio‐economic – within a coherent reasoning system.
Deep learning convolutional neural network (CNN) can predict mortality from chest radiographs, yet, it is unknown whether radiologists can perform the same task. Here, we investigate whether ...radiologists can visually assess image gestalt (defined as deviation from an unremarkable chest radiograph associated with the likelihood of 6-year mortality) of a chest radiograph to predict 6-year mortality. The assessment was validated in an independent testing dataset and compared to the performance of a CNN developed for mortality prediction. Results are reported for the testing dataset only (n = 100; age 62.5 ± 5.2; male 55%, event rate 50%). The probability of 6-year mortality based on image gestalt had high accuracy (AUC: 0.68 (95% CI 0.58-0.78), similar to that of the CNN (AUC: 0.67 (95% CI 0.57-0.77); p = 0.90). Patients with high/very high image gestalt ratings were significantly more likely to die when compared to those rated as very low (p ≤ 0.04). Assignment to risk categories was not explained by patient characteristics or traditional risk factors and imaging findings (p ≥ 0.2). In conclusion, assessing image gestalt on chest radiographs by radiologists renders high prognostic accuracy for the probability of mortality, similar to that of a specifically trained CNN. Further studies are warranted to confirm this concept and to determine potential clinical benefits.
Abstract Objectives The aim of this study was to describe the role of contrast-enhanced cardiac magnetic resonance (CMR) in the workup of patients with aborted sudden cardiac arrest (SCA) and in the ...prediction of long-term outcomes. Background Myocardial fibrosis is a key substrate for SCA, and late gadolinium enhancement (LGE) on a CMR study is a robust technique for imaging of myocardial fibrosis. Methods We performed a retrospective review of all survivors of SCA who were referred for CMR studies and performed follow-up for the subsequent occurrence of an adverse event (death and appropriate defibrillator therapy). Results After a workup that included a clinical history, electrocardiogram, echocardiography, and coronary angiogram, 137 patients underwent CMR for workup of aborted SCA (66% male; mean age 56 ± 11 years; left ventricular ejection fraction 43 ± 12%). The presenting arrhythmias were ventricular fibrillation (n = 105 77%) and ventricular tachycardia (n = 32 23%). Overall, LGE was found in 98 patients (71%), with an average extent of 9.9 ± 5% of the left ventricular myocardium. CMR imaging provided a diagnosis or an arrhythmic substrate in 104 patients (76%), including the presence of an infarct-pattern LGE in 60 patients (44%), noninfarct LGE in 21 (15%), active myocarditis in 14 (10%), hypertrophic cardiomyopathy in 3 (2%), sarcoidosis in 3, and arrhythmogenic cardiomyopathy in 3. In a median follow-up of 29 months (range 18 to 43 months), there were 63 events. In a multivariable analysis, the strongest predictors of recurrent events were the presence of LGE (adjusted hazard ratio: 6.7; 95% CI: 2.38 to 18.85; p < 0.001) and the extent of LGE (hazard ratio: 1.15; 95% CI: 1.11 to 1.19; p < 0.001). Conclusions Among patients with SCA, CMR with contrast identified LGE in 71% and provided a potential arrhythmic substrate in 76%. In follow-up, both the presence and extent of LGE identified a group at markedly increased risk of future adverse events.
Semi-automated software can provide quantitative assessment of atherosclerotic plaques on coronary CT angiography (CTA). The relationship between established qualitative high-risk plaque features and ...quantitative plaque measurements has not been studied. We analyzed the association between quantitative plaque measurements and qualitative high-risk plaque features on coronary CTA. We included 260 patients with plaque who underwent coronary CTA in the Rule Out Myocardial Infarction/Ischemia Using Computer Assisted Tomography (ROMICAT) II trial. Quantitative plaque assessment and qualitative plaque characterization were performed on a per coronary segment basis. Quantitative coronary plaque measurements included plaque volume, plaque burden, remodeling index, and diameter stenosis. In qualitative analysis, high-risk plaque was present if positive remodeling, low CT attenuation plaque, napkin-ring sign or spotty calcium were detected. Univariable and multivariable logistic regression analyses were performed to assess the association between quantitative and qualitative high-risk plaque assessment. Among 888 segments with coronary plaque, high-risk plaque was present in 391 (44.0%) segments by qualitative analysis. In quantitative analysis, segments with high-risk plaque had higher total plaque volume, low CT attenuation plaque volume, plaque burden and remodeling index. Quantitatively assessed low CT attenuation plaque volume (odds ratio 1.12 per 1 mm
3
, 95% CI 1.04–1.21), positive remodeling (odds ratio 1.25 per 0.1, 95% CI 1.10–1.41) and plaque burden (odds ratio 1.53 per 0.1, 95% CI 1.08–2.16) were associated with high-risk plaque. Quantitative coronary plaque characteristics (low CT attenuation plaque volume, positive remodeling and plaque burden) measured by semi-automated software correlated with qualitative assessment of high-risk plaque features.
Heavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated ...the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium.
We investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9-12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke).
Here we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score.
Our findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population.
The purpose of this study was to compare Coronary Artery Disease Reporting and Data System (CAD-RADS) to traditional stenosis categories and the coronary artery calcium score (CACS) for predicting ...cardiovascular events in patients with stable chest pain and suspected coronary artery disease (CAD).
The 2016 CAD-RADS has been established to standardize the reporting of CAD on coronary CT angiography (CTA).
PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) trial participants’ CTAs were assessed by a central CT core laboratory for CACS, traditional stenosis-based categories, and modified CAD-RADS grade including high-risk coronary plaque (HRP) features. Traditional stenosis categories and CAD-RADS grade were compared for the prediction of the composite endpoint of death, myocardial infarction, or hospitalization for unstable angina over a median follow-up of 25 months. Incremental prognostic value over traditional risk factors and CACS was assessed.
In 3,840 eligible patients (mean age: 60.4 ± 8.2 years; 49% men), 3.0% (115) experienced events. CAD-RADS (concordance statistic C-statistic 0.747) had significantly higher discriminatory value than traditional stenosis-based assessments (C-statistic 0.698 to 0.717; all p for comparison ≤0.001). With no plaque (CAD-RADS 0) as the baseline, the hazard ratio (HR) for an event increased from 2.43 (95% confidence interval CI: 1.16 to 5.08) for CAD-RADS 1 to 21.84 (95% CI: 8.63 to 55.26) for CAD-RADS 4b and 5. In stepwise nested models, CAD-RADS added incremental prognostic value beyond ASCVD risk score and CACS (C-statistic 0.776 vs. 0.682; p < 0.001), and added incremental value persisted in all CACS strata.
These data from a large representative contemporary cohort of patients undergoing coronary CTA for stable chest pain support the prognostic value of CAD-RADS as a standard reporting system for coronary CTA.
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