There were 380 deaths from all causes, including 70 from coronary artery disease. In addition, 43 patients reported heart attacks. Compared to CAD-RADS and other scores, the ML approach better discriminated which patients would have a cardiac event from those who would not.
When deciding whether to start statins, the ML score ensured that 93 per cent of patients with events would receive the drug, compared with only 69 per cent if CAD-RADS were relied on.
"The risk estimate that you get from doing the machine learning version of the model is more accurate than the risk estimate you're going to get if you rely on CAD-RADS," Johnson said.
"Both methods perform better than just using the Framingham risk estimate. This shows the value of looking at the coronary arteries to better estimate people's risk," he said. If machine learning can improve vessel scoring, it would enhance the contribution of noninvasive imaging to cardiovascular risk assessment.
The ML-derived vessel scores could be combined with non-imaging risk factors such as age, gender, hypertension and smoking to develop more comprehensive risk models. This would benefit both physicians and patients.
"Once you use a tool like this to help see that someone's at risk, then you can get the person on statins or get their glucose under control, get them off smoking, get their hypertension controlled, because those are the big, modifiable risk factors," he said.
Source: dailypioneer.com
