Better interpretation of heart diseases by random results
10 April 2014
Random findings in the performance of a computer tomography (CT-scan) may contribute to the prediction of three major diseases: cardiovascular diseases, chronic obstructive pulmonary disease (COPD), and hip fractures. Pushpa Jairam, researcher at the Julius Center, shows in her PhD research that sorts CT abnormalities, which are not directly related to the indication and the patient's symptoms, still can play an important role in future problems. Moreover, the study shows that 82% of the patients studied, would like to know the risk factors for themselves.
Due to the ever increasing quality and use of the CT-scan of the chest cavity radiologists are regularly confronted with findings whose meaning is not clear. This was the reason to start the Prognostic Value of unrequested Information in Diagnostic Imaging (PROVIDI ) study. Lung disease such as emphysema and airway wall thickening, which are detected by diagnostic CT-scans of the chest cavity, can be used as signals for early detection of future hospitalizations or death resulting from an attack of chronic obstructive pulmonary disease.
Organize risk categories
There is also a risk score developed and tested for accuracy, which is based on chance findings on the CT scan suggestive of coronary calcification. This risk can be properly classify patients into risk categories used in the current guidelines for the treatment of cardiovascular diseases. The reporting of these findings can be used as an effective strategy to early identify where active treatments are needed to solve, or better yet, avoid the problem. To say that the use of these findings on CT-scans of the chest cavity are cost effective and leads to fewer hospitalizations or death, more research is needed in practice and more research into practice required. "So we need to look at ways radiologists should report their findings in order to get the best results," says Pushpa Jairam.
Pushpa Jairam is a researcher at the Julius Center, and defense her thesis on April 10 at 16:15 pm in the Academy Building in Utrecht. Her supervisors are prof. Y. van der Graaf, MD, PhD, professor W.P.T.M. Mali, MD, PhD, H. M. Verkooijen, MD, PhD, and P. A. de Jong, MD, PhD.