CT continues to flex its muscles as a tool for predicting heart disease risk, in large measure due to its prowess for coronary artery calcium scoring. In JAMA, a new paper found CT-derived CAC scores to be more effective in predicting coronary heart disease than genetic scores when added to traditional risk scoring.
Traditional risk scoring – based on factors such as cholesterol levels, blood pressure, and smoking status – has done a good job of directing cholesterol-lowering statin therapy to people at risk of future cardiac events. But these scores still provide an imprecise estimate of coronary heart disease risk.
Two relatively new tools for improving CHD risk prediction are CAC scoring from CT scans and polygenic risk factors, based on genetic variants that could predispose people toward heart disease. But the impact of either of these tools (or both together) when added to traditional risk scoring hasn’t been investigated.
To answer this question, researchers analyzed the impact of both types of scoring on participants in the Multi-Ethnic Study of Atherosclerosis (1,991 people) and the Rotterdam Study (1,217 people). CHD risk was predicted based on both CAC and PRS and then compared to actual CHD events over the long term.
They also tracked how accurate both tools were in reclassifying people into different risk categories (higher than 7.5% risk calls for statins). Findings included:
- Both CAC scores and PRS were effective in predicting 10-year risk of CHD in the MESA dataset (HR=2.60 for CAC score, HR=1.43 for PRS). Scores were slightly lower but similar in the Rotterdam Study
- The C statistic was higher for CAC scoring than PRS (0.76 vs. 0.69; 0.7 indicates a “good” model and 0.8 a “strong” model)
- The improved accuracy in reclassifying patient risk was statistically significant when CAC was added to traditional factors (half of study participants moved into the high-risk group), but not when PRS was added
This study adds to the growing body of evidence supporting cardiac CT as a prognostic tool for heart disease, and reinforces CT’s prowess in the heart. The findings also support the growing chorus in favor of using CT as a screening tool in cases of intermediate or uncertain risk for future heart disease.
A new AJR study out of Toronto General Hospital highlighted the largely-untapped potential of non-gated chest CT CAC scoring, and the significant impact it could have with widespread adoption.
Current guidelines recommend visual CAC evaluations with all non-gated non-contrast chest CTs. However, these guidelines aren’t consistently followed and they exclude contrast-enhanced chest CTs.
The researchers challenged these practices, performing visual CAC assessments on 260 patients’ non-gated chest CT exams (116 contrast-enhanced, 144 non-contrast) and comparing them to the same patients’ cardiac CT CAC scores (performed within 12-months) and ~6-year cardiac event outcomes.
As you might expect, visual contrast-enhanced and non-contrast chest CT CAC scoring:
- Detected CAC with high sensitivity (83% & 90%) and specificity (both 100%)
- Accurately predicted major cardiac events (Hazard ratios: 4.5 & 3.4)
- Had relatively benign false negatives (0 of 26 had cardiac events)
- Achieved high inter-observer agreement (κ=0.89 & 0.95)
Considering that CAC scores were only noted in 37% of the patients’ original non-contrast chest CT reports and 23% of their contrast-enhanced chest CT reports, this study adds solid evidence in favor of more widespread CAC score reporting in non-gated CT exams.
That might also prove to be good news for the folks working on opportunistic CAC AI solutions, noting that AI has (so far) seen the greatest adoption when it supports processes that most radiologists are actually doing.
UCSF is now using AI to automatically screen all of its routine non-contrast chest CTs for elevated coronary artery calcium scores (CAC scores), representing a major milestone for an AI use case that was previously limited to academic studies and future business strategies.
UCSF’s Deployment – UCSF becomes the first medical center to deploy the end-to-end AI CAC scoring system that it developed with Stanford and Bunkerhill Health earlier this year. The new system automatically identifies elevated CAC scores in non-gated / non-contrast chest CTs, creating an “opportunistic screening pathway” that allows UCSF physicians to identify high-CAC patients and get them into treatment.
Why This is a Big Deal – Over 20m chest CTs are performed in the U.S. annually and each of those scans contains insights into patients’ cardiac health. However, an AI model like this would be required to extract cardiac data from the majority of CT scans (CAC isn’t visible to humans in non-gated CTs) and efficiently interpret them (there’s far too many images). This AI system’s path from academic research to clinical deployment seems like a big deal too.
The Commercial Impact – Most health systems don’t have the AI firepower of Stanford and UCSF, but they certainly produce plenty of chest CTs and should want to identify more high-risk patients while treatable (especially if they’re also risk holders). Meanwhile, there’s growing commercial efforts from companies like Cleerly and Nanox.AI to create opportunistic CAC screening pathways for all these health systems that can’t develop their own CAC AI workflows (or prefer not to).
A new study in Circulation used coronary CTA scans and CAC scoring to reveal a surprisingly high prevalence of “silent” coronary artery atherosclerosis in the general population, suggesting that this could “lay the foundation” for future CT-based cardiac screening programs.
The Study – The researchers analyzed CCTA and CAC exams from 25k randomly recruited Swedish participants (50-64yrs, none w/ known coronary heart disease) finding that:
- 42% had CCTA-detected atherosclerosis
- 8.3% had noncalcified plaques
- 5.2% had significant stenosis
- 1.9% had serious coronary artery diseases
- All participants with >400 CAC scores had atherosclerosis (yes, 100%), and 45.7% had significant stenosis
- Some participants with 0 CAC scores had atherosclerosis (5.5%) and significant stenosis (0.4%)
- So, CAC-based screening might still miss some at-risk patients
The Takeaway – 2021 brought a notable surge in academic and business efforts focused on CT-based cardiac screening, and this study’s revelation about “silent” atherosclerosis in the general population suggests that cardiac screening’s momentum will continue.