Chest CT Scanning Can Predict Cardiovascular Disease Risk

Jairam, MD, PhD, from the University Medical Center Utrecht (Utrecht, the Netherlands). “With this study, we have taken a new perspective by providing a different approach for cardiovascular disease risk prediction strictly based on information readily available to the radiologist.”

Individuals at high risk for cardiovascular events are currently identified through risk stratification tools based on conventional risk factors, such as age, gender, blood pressure, cholesterol levels, diabetes, smoking status, or other factors thought to be related to heart disease. However, a significant number of cardiovascular events occur in individuals with no conventional risk factors, or in patients with undetected or underdiagnosed risk factors.

“Extensive literature has clearly documented the uncertainty of prediction models based on conventional risk factors,” Dr. Jairam said. “With this study, we address to some extent, the need for a shift in cardiovascular risk assessment from conventional risk factors to direct measures of subclinical atherosclerosis.”

By utilizing chest CT scanning, radiologists are consistently confronted with findings that are unrelated or unsuspected to the CT indication, known as incidental findings. Incidental findings indicating early indications of atherosclerosis are quite common and could play a role in a population-based screening strategy to identify individuals at high risk for cardiovascular events. However, there is currently no guidance on how to assess these findings in routine practice.

Dr. Jairam and colleagues set out to develop and confirm an imaging-based prediction model to more effectively assess the contribution of incidental findings on chest CT in detecting patients at high risk for cardiovascular disease. The retrospective study, published online May 27, 2014, in the journal Radiology, examined follow-up data from 10,410 patients who underwent diagnostic chest CT scanning for noncardiovascular indications. During a mean follow-up period of 3.7 years, 1,148 cardiovascular events occurred among these patients.

CT scans from these patients and from a random sampling of 10% of the remaining patients in the group were visually graded for several cardiovascular findings. The final prediction model included gender, age, CT indication, left anterior descending coronary artery calcifications, mitral valve calcifications, descending aorta calcifications, and cardiac diameter. The model was found to have accurately placed individuals into clinically pertinent risk categories.

The findings revealed that radiologic data may complement conventional clinical approaches in cardiovascular risk screening and may improve diagnosis and treatment in eligible patients. “Our study provides an innovative strategy to identify patients at high risk for cardiovascular disease, irrespective of the conventional risk factor status, based on freely available incidental information from a routine diagnostic chest CT,” Dr. Jairam said. “The resulting prediction rule may be used to assist clinicians to refer these patients for timely preventive cardiovascular risk management.”

Dr. Jairam emphasized, however, that a prospective, multicenter trial is needed to validate the impact of these findings.

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University Medical Center Utrecht