Appropriate Use Criteria for Cardiac Computed Tomography: Does Computed Tomography Have Incremental Value in All Appropriate Use Criteria Categories?

Cardiac CT: Competition, complimentary or confounder

Coronary CT angiography (CCTA) has been gradually adopted into clinical practice over the last two decades. CCTA has high diagnostic accuracy, prognostic value, and unique features such as assessment of plaque composition. CCTA-derived functional assessment techniques such as fractional flow reserve and CT perfusion are also available and can increase the diagnostic specificity of the modality. These properties propound CCTA as a competitor of functional testing in diagnosis of obstructive CAD, however, utilizing CCTA in a concomitant fashion to potentiate the performance of the latter can lead to better patient care and may provide more accurate prognostic information. Although multiple diagnostic challenges such as evaluation of calcified segments, stents, and small distal vessels still exist, the technologic developments in hardware as well as growing incorporation of artificial intelligence to daily practice are all set to augment the diagnostic and prognostic role of CCTA in cardiovascular disorders.

Deep Learning-Based Automated Labeling of Coronary Segments for Structured Reporting of Coronary Computed Tomography Angiography in Accordance With Society of Cardiovascular Computed Tomography Guidelines

PURPOSE

To evaluate a novel deep learning (DL)-based automated coronary labeling approach for structured reporting of coronary artery disease according to the guidelines of the Society of Cardiovascular Computed Tomography (CT) on coronary CT angiography (CCTA).

PATIENTS AND METHODS

A retrospective cohort of 104 patients (60.3 ± 10.7 y, 61% males) who had undergone prospectively electrocardiogram-synchronized CCTA were included. Coronary centerlines were automatically extracted, labeled, and validated by 2 expert readers according to Society of Cardiovascular CT guidelines. The DL algorithm was trained on 706 radiologist-annotated cases for the task of automatically labeling coronary artery centerlines. The architecture leverages tree-structured long short-term memory recurrent neural networks to capture the full topological information of the coronary trees by using a two-step approach: a bottom-up encoding step, followed by a top-down decoding step. The first module encodes each sub-tree into fixed-sized vector representations. The decoding module then selectively attends to the aggregated global context to perform the local assignation of labels. To assess the performance of the software, percentage overlap was calculated between the labels of the algorithm and the expert readers.

RESULTS

A total number of 1491 segments were identified. The artificial intelligence-based software approach yielded an average overlap of 94.4% compared with the expert readers' labels ranging from 87.1% for the posterior descending artery of the right coronary artery to 100% for the proximal segment of the right coronary artery. The average computational time was 0.5 seconds per case. The interreader overlap was 96.6%.

CONCLUSIONS

The presented fully automated DL-based coronary artery labeling algorithm provides fast and precise labeling of the coronary artery segments bearing the potential to improve automated structured reporting for CCTA.

Comparison of Coronary Computed Tomography Angiography Image Quality With High-concentration and Low-concentration Contrast Agents: The Randomized CONCENTRATE Trial

PURPOSE

To confirm that the image quality of coronary computed tomography (CT) angiography with a low tube voltage (80 to 100 kVp), iterative reconstruction, and low-concentration contrast agents (iodixanol 270 to 320 mgI/mL) was not inferior to that with conventional high tube voltage (120 kVp) and high-concentration contrast agent (iopamidol 370 mgI/mL).

MATERIALS AND METHODS

This prospective, multicenter, noninferiority, randomized trial enrolled a total of 318 patients from 8 clinical sites. All patients were randomly assigned 1: 1: 1 for each contrast medium of 270, 320, and 370 mgI/mL. CT scans were taken with a standard protocol in the high-concentration group (370 mgI/mL) and with 20 kVp lower protocol in the low-concentration group (270 or 320 mgI/mL). Image quality and radiation dose were compared between the groups. Image quality was evaluated with a score of 1 to 4 as subject image quality.

RESULTS

The mean HU, signal-to-noise ratio, and contrast-to-noise ratio of the 3 groups were significantly different (all P<0.0001). The signal-to-noise ratio and contrast-to-noise ratio of the low-concentration groups were significantly lower than those of the high-concentration group (P<0.05). However, the image quality scores were not significantly different among the 3 groups (P=0.745). The dose length product and effective dose of the high-concentration group were significantly higher than those of the low-concentration group (P<0.0001 and 0.003, respectively).

CONCLUSIONS

The CT protocol with iterative reconstruction and lower tube voltage for low-concentration contrast agents significantly reduced the effective radiation dose (mean: 3.7±2.7 to 4.1±3.1 mSv) while keeping the subjective image quality as good as the standard protocol (mean: 5.7±3.4 mSv).

Trends and concerns of potentially inappropriate medication use in patients with cardiovascular diseases

Introduction: The use of potentially inappropriate medications (PIM) is an alarming social risk factor in cardiovascular patients. PIM administration may result in iatrogenic disorders and adverse consequences may be attenuated by limiting PIM intake.Areas covered: The goal of this review article is to discuss the trends, risks, and concerns regarding PIM administration with focus on cardiovascular patients. To find data, we searched literature using electronic databases (Pubmed/Medline 1966-2021 and Web of Science 1975-2021). The data search terms were cardiovascular diseases, potentially inappropriate medication, potentially harmful drug-drug combination, potentially harmful drug-disease combination, drug interaction, deprescribing, and electronic health record.Expert opinion: Drugs for heart diseases are the most commonly prescribed medications in older individuals. Despite the availability of explicit and implicit PIM criteria, the incidence of PIM use in cardiovascular patients remains high ranging from 7 to 85% in different patient categories. Physician-induced disorders often occur when PIM is administered and adverse effects may be reduced by limiting PIM intake. Main strategies promising for addressing PIM use include deprescribing, implementation of systematic electronic records, pharmacist medication review, and collaboration among cardiologists, internists, geriatricians, clinical pharmacologists, pharmacists, and other healthcare professionals as basis of multidisciplinary assessment teams.

Interobserver Reliability of the Coronary Artery Disease Reporting and Data System in Clinical Practice

PURPOSE

This study aimed to evaluate interobserver reproducibility between cardiothoracic radiologists applying the Coronary Artery Disease Reporting and Data System (CAD-RADS) to describe atherosclerotic burden on coronary computed tomography angiography.

METHODS

Forty clinical computed tomography angiography cases were retrospectively and independently evaluated by 3 attending and 2 fellowship-trained cardiothoracic radiologists using the CAD-RADS lexicon. Radiologists were blinded to patient history and underwent initial training using a practice set of 10 subjects. Interobserver reproducibility was assessed using an intraclass correlation (ICC) on the basis of single-observer scores, absolute agreement, and a 2-way random-effects model. Nondiagnostic studies were excluded. ICC was also performed for CAD-RADS scores grouped by management recommendations for absent (0), nonobstructive (1 to 2), and potentially obstructive (3 to 5) CAD.

RESULTS

Interobserver reproducibility was moderate to good (ICC: 0.748, 95% confidence interval [CI]: 0.639-0.842, P<0.0001), with higher agreement among cardiothoracic radiology fellows (ICC: 0.853, 95% CI: 0.730-0.922, P<0.0001) than attending radiologists (ICC: 0.711, 95% CI: 0.568-0.824, P<0.0001). Interobserver reproducibility for clinical management categories was marginally decreased (ICC: 0.692, 95% CI: 0.570-0.802, P<0.0001). The average percent agreement between pairs of radiologists was 84.74%. Percent observer agreement was significantly reduced in the presence (M=62.22%, SD=15.17%) versus the absence (M=80.91%, SD=17.97%) of modifiers, t(37.95)=3.566, P=0.001.

CONCLUSIONS

Interobserver reliability and agreement with the CAD-RADS terminology are moderate to good in clinical practice. However, further investigations are needed to characterize the causes of interobserver disagreement that may lead to differences in management recommendations.

Evaluation of a Tube Voltage-Tailored Contrast Medium Injection Protocol for Coronary CT Angiography: Results From the Prospective VOLCANIC Study

OBJECTIVE. The purpose of this study was to prospectively evaluate, using software support, the feasibility and the quantitative and qualitative image quality parameters of a tube voltage-tailored contrast medium (CM) application protocol for patient-specific injection during coronary CT angiography (CCTA). SUBJECTS AND METHODS. In the Voltage-Based Contrast Media Adaptation in Coronary Computed Tomography Angiography (VOLCANIC-CTA) study, a single-center trial, 120 patients referred for CCTA were prospectively assigned to a tube voltage-tailored CM injection protocol. Automated tube voltage levels were selected in 10-kV intervals and ranged from 70 to 130 kV, and the iodine delivery rate (IDR) was adapted to the tube voltage level using dedicated software. The administered CM volume (370 mg I/mL) ranged from 33 mL at 70 kV (IDR, 0.7 g I/s) to 65 mL at 130 kV (IDR, 1.7 g I/s). Attenuation was measured in the aorta and coronary arteries to calculate quantitative signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and 5-point scales were used to evaluate overall image quality. Radiation metrics were also assessed and compared among the protocols. RESULTS. The mean age of the study patients was 62.5 ± 11.9 (SD) years. Image quality was rated as diagnostic in all patients. Contrast attenuation peaked at 70 kV (p < 0.001), whereas SNR and CNR parameters showed no significant differences between tube voltage levels (p ≥ 0.085). Additionally, no significant differences in subjective image quality parameters were found among the different protocols (p ≥ 0.139). The lowest radiation dose values were observed in the group assigned to the 70-kV protocol, which had a median radiation effective dose of 2.0 mSv (p < 0.001). CONCLUSION. The proposed tube voltage-tailored injection protocol allows individualized scanning of patients undergoing CCTA and significantly reduces CM and radiation dose while maintaining a high diagnostic image quality.

More holes, more contrast? Comparing an 18-gauge non-fenestrated catheter with a 22-gauge fenestrated catheter for cardiac CT

Objective

To compare the performance of an 18-gauge nonfenestrated catheter (18-NFC) with a 22-gauge fenestrated catheter (22-FC) for cardiac CT angiography (CCTA) in patients with suspected coronary heart disease.

Subjects and methods

74 consecutive patients imaged on a 2^nd generation dual-source CT with arterial phase CCTA were included in this retrospective investigation to either an 18-NFC or 22-FC. In comparison to the 18-NFC, the 22-FC has three additional perforations for contrast agent dispersal proximal to the tip. We examined the two groups for differences in their average attenuation in the right and left ventricles (RV, LV) and in the atrium (RA, LA) as well as in the proximal right coronary artery (RCA) and the left main coronary artery (LM). The averages were calculated for both the 18-NFC and 22-FC.

Results

Catheters were successfully placed on the first attempt 97% (36/37) for 18-NFC and 95% (35/37) for the 22-FC. The following enhancement levels were measured: 22-FC (in Hounsfield-Units (HU)): RV = 203±29, LV = 523±36, RA = 198±29, LA = 519±38, RCA = 547±26, LM = 562±25; 18-NFC: RV = 146±26, LV = 464±32, RA = 141±24, LA = 438±35, RCA = 501±23, LM = 523±23; RV (p = 0,03), LV (p = 0.12), RA (p = 0.02), LA (p = 0.04), RCA (p = 0.3), LM (p = 0.33).

Conclusion

No significant differences in attenuation levels as well as in image quality of the coronary arteries were found between NFC and FC. Nevertheless, the 22-gauge FC examinations showed significantly higher attenuation in the left and right atrium as well as the right ventricle. Patients with poor venous access may benefit from a smaller gauge catheter that can deliver sufficiently high flow rates for CCTA.

Prognostic Value of Coronary Computed Tomography Angiography-derived Morphologic and Quantitative Plaque Markers Using Semiautomated Plaque Software

PURPOSE

In this study, we analyzed the prognostic value of coronary computed tomography angiography-derived morphologic and quantitative plaque markers and plaque scores for major adverse cardiovascular events (MACEs).

MATERIALS AND METHODS

We analyzed the data of patients with suspected coronary artery disease (CAD). Various plaque markers were obtained using a semiautomated software prototype or derived from the results of the software analysis. Several risk scores were calculated, and follow-up data concerning MACE were collected from all patients.

RESULTS

A total of 131 patients (65±12 y, 73% male) were included in our study. MACE occurred in 11 patients within the follow-up period of 34±25 months.CAD-Reporting and Data System score (odds ratio [OR]=11.62), SYNTAX score (SS) (OR=1.11), Leiden-risk score (OR=1.37), segment involvement score (OR=1.76), total plaque volume (OR=1.20), and percentage aggregated plaque volume (OR=1.32) were significant predictors for MACE (all P≤0.05). Moreover, the difference of the corrected coronary opacification (ΔCCO) correlated significantly with the occurrence of MACE (P<0.0001). The CAD-Reporting and Data System score, SS, and Leiden-risk score showed substantial sensitivity for predicting MACE (90.9%). The SS and Leiden-risk score displayed high specificities of 80.8% and 77.5%, respectively. These plaque markers and risk scores all provided high negative predictive value (>90%).

CONCLUSION

The coronary computed tomography angiography-derived plaque markers of segment involvement score, total plaque volume, percentage aggregated plaque volume, and ΔCCO, and the risk scores exhibited predictive value for the occurrence of MACE and can likely aid in identifying patients at risk for future cardiac events.

Long-Term Prognostic Value of Coronary Computed Tomography Angiography in an Asymptomatic Elderly Population

Background

The prognostic value of coronary computed tomographic angiography (CCTA) for evaluating coronary artery disease in asymptomatic older adults is controversial. We investigated the prognostic value of CCTA in community‐dwelling elderly Koreans.

Methods and Results

Participants (n=470; mean age: 75.1±7.3 years) who underwent CCTA were enrolled from KLoSHA (Korean Longitudinal Study on Health and Aging), a community‐based prospective cohort. Using CCTA, coronary artery disease was classified as normal, nonobstructive, or obstructive according to the presence of 0%, <50%, or ≥50% stenosis, respectively. Coronary artery calcium scores were investigated together with Framingham risk score, atherosclerotic cardiovascular disease score, and individual risk factors. Major adverse cardiac events (MACE) were defined as a composite of cardiac event–related death or nonfatal myocardial infarction. During a median follow‐up of 8.2 years (interquartile range: 7.7–10.1 years), MACE occurred in 24 participants (5.1%). Compared with the normal group, participants in the obstructive group showed higher incidence of MACE (hazard ratio: 5.65; 95% CI, 1.22–26.16; P=0.027), whereas there were no significant differences in MACE between the normal and nonobstructive groups. The 8‐year event‐free survival rates were 98.1±1.1%, 94.9±1.6%, and 81.7±4.8% in the normal, nonobstructive, and obstructive groups, respectively. Compared with the Framingham risk score and coronary artery calcium score model, CCTA improved risk prediction by C‐index (from 0.698 to 0.749) and category‐free net reclassification index (0.478; P=0.022).

Conclusions

CCTA showed better long‐term prognostic value for MACE than coronary artery calcium score in this asymptomatic older population.

Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry

OBJECTIVES

This study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-FFR).

BACKGROUND

CT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated.

METHODS

A total of 482 vessels from 314 patients (age 62.3 ± 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and ≥400) on a per-vessel level with invasive FFR as the reference standard.

RESULTS

The diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC ≥400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57 to 0.85] vs. 0.85 [95% CI: 0.82 to 0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC ≥ 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001).

CONCLUSIONS

Machine-learning-based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621).

Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning

Background Coronary CT angiography contains prognostic information but the best method to extract these data remains unknown. Purpose To use machine learning to develop a model of vessel features to discriminate between patients with and without subsequent death or cardiovascular events. Performance was compared with that of conventional scores. Materials and Methods Coronary CT angiography was analyzed by radiologists into four features for each of 16 coronary segments. Four machine learning model types were explored. Five conventional vessel scores were computed for comparison including the Coronary Artery Disease Reporting and Data System (CAD-RADS) score. The National Death Index was retrospectively queried from January 2004 through December 2015. Outcomes were all-cause mortality, coronary heart disease deaths, and coronary deaths or nonfatal myocardial infarctions. Score performance was assessed by using area under the receiver operating characteristic curve (AUC). Results Between February 2004 and November 2009, 6892 patients (4452 men [mean age ± standard deviation, 51 years ± 11] and 2440 women [mean age, 57 years ± 12]) underwent coronary CT angiography (median follow-up, 9.0 years; interquartile range, 8.2-9.8 years). There were 380 deaths of all causes, 70 patients died of coronary artery disease, and 43 patients reported nonfatal myocardial infarctions. For all-cause mortality, the AUC was 0.77 (95% confidence interval: 0.76, 0.77) for machine learning (k-nearest neighbors) versus 0.72 (95% confidence interval: 0.72, 0.72) for CAD-RADS (P < .001). For coronary artery heart disease deaths, AUC was 0.85 (95% confidence interval: 0.84, 0.85) for machine learning versus 0.79 (95% confidence interval: 0.78, 0.80) for CAD-RADS (P < .001). When deciding whether to start statins, if the choice is made to tolerate treating 45 patients to be sure to include one patient who will later die of coronary disease, the use of the machine learning score ensures that 93% of patients with events will be administered the drug; if CAD-RADS is used, only 69% will be treated. Conclusion Compared with Coronary Artery Disease Reporting and Data System and other scores, machine learning methods better discriminated patients who subsequently experienced an adverse event from those who did not. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Schoepf and Tesche in this issue.

Prognostic value of CT myocardial perfusion imaging and CT-derived fractional flow reserve for major adverse cardiac events in patients with coronary artery disease

OBJECTIVES

The purpose of this study was to analyze the prognostic value of dynamic CT perfusion imaging (CTP) and CT derived fractional flow reserve (CT-FFR) for major adverse cardiac events (MACE).

METHODS

81 patients from 4 institutions underwent coronary computed tomography angiography (CCTA) with dynamic CTP imaging and CT-FFR analysis. Patients were followed-up at 6, 12, and 18 months after imaging. MACE were defined as cardiac death, nonfatal myocardial infarction, unstable angina requiring hospitalization, or revascularization. CT-FFR was computed for each major coronary artery using an artificial intelligence-based application. CTP studies were analyzed per vessel territory using an index myocardial blood flow, the ratio between territory and global MBF. The prognostic value of CCTA, CT-FFR, and CTP was investigated with a univariate and multivariate Cox proportional hazards regression model.

RESULTS

243 vessels in 81 patients were interrogated by CCTA with CT-FFR and 243 vessel territories (1296 segments) were evaluated with dynamic CTP imaging. Of the 81 patients, 25 (31%) experienced MACE during follow-up. In univariate analysis, a positive index-MBF resulted in the largest risk for MACE (HR 11.4) compared to CCTA (HR 2.6) and CT-FFR (HR 4.6). In multivariate analysis, including clinical factors, CCTA, CT-FFR, and index-MBF, only index-MBF significantly contributed to the risk of MACE (HR 10.1), unlike CCTA (HR 1.2) and CT-FFR (HR 2.2).

CONCLUSION

Our study provides initial evidence that dynamic CTP alone has the highest prognostic value for MACE compared to CCTA and CT-FFR individually or a combination of the three, independent of clinical risk factors.