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Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
Calcium scoring: a personalized probability assessment predicts the need for additional or alternative testing to
Judit Simon1, Lili Száraz1, Bálint Szilveszter1
1MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Semmelweis University, Budapest, Hungary.
Insights
Coronary artery calcium score (CACS) significantly improves prediction of further testing after coronary CT angiography (CTA). Individualized CACS cutoffs help personalize decisions on whether to perform or defer CTA for coronary artery disease (CAD) management.
Area of Science:
- Cardiovascular Imaging
- Radiology
- Preventive Cardiology
Background:
- Coronary CT angiography (CTA) is crucial for diagnosing coronary artery disease (CAD).
- Non-diagnostic image quality or uncertain stenosis severity can necessitate further testing after CTA.
- Predicting the need for additional testing is vital for efficient patient management.
Purpose of the Study:
- To evaluate the predictive capability of anthropometrics, clinical risk factors, and coronary artery calcium score (CACS) for downstream testing post-coronary CTA.
- To determine if CACS can identify patients who might benefit from deferring CTA.
- To establish individualized CACS thresholds for predicting the likelihood of further testing.
Main Methods:
- Retrospective analysis of 4120 patients undergoing coronary CTA for suspected CAD.
- Multivariate logistic regression and ROC analysis incorporating anthropometrics (BMI, heart rate, rhythm irregularity), pre-test likelihood factors (age, sex, angina type), and total CACS.
- Comparison of predictive models (Model 1: anthropometrics; Model 2: pre-test likelihood; Model 3: Model 2 + CACS).
Main Results:
- Model 3 (including CACS) significantly outperformed Models 1 and 2 in predicting downstream testing (AUC 0.84 vs. 0.56 and 0.72).
- Specific CACS cutoffs were identified for varying probabilities of further testing, stratified by angina type, heart rate, and rhythm.
- Higher heart rates and arrhythmias were found to decrease the predictive value of CACS cutoffs.
Conclusions:
- Total CACS is a powerful independent predictor for identifying patients who may not require coronary CTA for definitive CAD management.
- Individualized CACS cutoff values, considering patient-specific factors, can guide decisions on performing or deferring coronary CTA.
- The findings support personalized strategies to optimize the use of coronary CTA and subsequent diagnostic pathways.
Objective:
To assess whether anthropometrics, clinical risk factors, and coronary artery calcium score (CACS) can predict the need of further testing after coronary CT angiography (CTA) due to non-diagnostic image quality and/or the presence of significant stenosis.
Methods:
Consecutive patients who underwent coronary CTA due to suspected coronary artery disease (CAD) were included in our retrospective analysis. We used multivariate logistic regression and receiver operating characteristics analysis containing anthropometric factors: body mass index, heart rate, and rhythm irregularity (model 1); and parameters used for pre-test likelihood estimation: age, sex, and type of angina (model 2); and also added total calcium score (model 3) to predict downstream testing.
Results:
We analyzed 4120 (45.7% female, 57.9 ± 12.1 years) patients. Model 3 significantly outperformed models 1 and 2 (area under the curve, 0.84 [95% CI 0.83-0.86] vs. 0.56 [95% CI 0.54-0.58] and 0.72 [95% CI 0.70-0.74], p < 0.001). For patients with sinus rhythm of 50 bpm, in case of non-specific angina, CACS above 435, 756, and 944; in atypical angina CACS above 381, 702, and 890; and in typical angina CACS above 316, 636, and 824 correspond to 50%, 80%, and 90% probability of further testing, respectively. However, higher heart rates and arrhythmias significantly decrease these cutoffs (p < 0.001).
Conclusion:
CACS significantly increases the ability to identify patients in whom deferral from coronary CTA may be advised as CTA does not lead to a final decision regarding CAD management. Our results provide individualized cutoff values for given probabilities of the need of additional testing, which may facilitate personalized decision-making to perform or defer coronary CTA.
Key Points:
• Anthropometric parameters on their own are insufficient predictors of downstream testing. Adding parameters of the Diamond and Forrester pre-test likelihood test significantly increases the power of prediction. • Total CACS is the most important independent predictor to identify patients in whom coronary CTA may not be recommended as CTA does not lead to a final decision regarding CAD management. • We determined specific CACS cutoff values based on the probability of downstream testing by angina-, arrhythmia-, and heart rate-based groups of patients to help individualize patient management.
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