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Chest CT Accurate in Estimating All-Cause COPD Mortality Risk


The use of soft-tissue biomarkers identified on chest CT can be a reliable way to accurately assess risk.

Providers can get valuable body composition information from chest CT scans that can lead to a better understanding of overall health for patients with chronic obstructive pulmonary disease (COPD), including their risk of all-cause mortality.

In a study published on April 6 in Radiology, investigators from Johns Hopkins University examined scans from patients with this condition and determined the added detail could lead to earlier intervention and reduced risk.

While CT is typically used to evaluate the lung health of patients with COPD, it could also play a role in evaluating the impact of other associated conditions, such as obesity and sarcopenia, through soft-tissue biomarkers. This could be particularly helpful because obesity has the opposite effect in COPD patients than would be expected – it is associated with lower mortality.

“Chest CT scans have long focused on the lungs or heart,” said study co-author David A. Bluemke, M.D., Ph.D., from the University of Wisconsin School of Medicine and Public Health. “Few prior investigators have evaluated muscle quality, bone density, or degeneration of the spine as an index of overall health. These are readily available and quantifiable in these CT examinations.”

Along with Johns Hopkins School of Medicine colleagues Farhad Pishgar, M.D., MPH, and Shadpour Demehri, M.D., Bluemke, who is also Radiology editor, examined chest CT scans to pinpoint associations between soft-tissue markers picked up on images and the all-cause mortality in COPD.

For their study, they included 2,994 participants who were involved in the Multi-Ethnic Study of Atherosclerosis (MESA), a large trial that looks at the role of these biomarkers in predicting outcomes that are relevant to cardiopulmonary diseases. Of the study population, 265 patients had COPD, 49 (18 percent) of whom died during the follow-up period.

In addition, the team determined that having more intermuscular fat, which is linked to diabetes and insulin resistance, was associated with higher mortality rates. However, having more subcutaneous fat tissue was associated with lower all-cause mortality risk. This contrast showed, they said, that fat in the muscles is a more effective predictor of bad outcomes.

“Compared with subcutaneous adipose tissue quantification, intermuscular adipose tissue may be a better marker for predicting all-cause mortality in patients with COPD,” said the team, pointing out that intermuscular adipose tissue accumulation occurs before any loss of muscle volume and the index is less affected by weight fluctuations. “The [intermuscular adipose tissue] index is an indicator of other underlying co-morbidities (e.g., diabetes and hypertension) and may predict the all-cause mortality better.”

Based on these results, the team determined that benefit could come from body composition assessment in patients with COPD who have chest CT scans. Incorporating artificial intelligence algorithms could facilitate automatically adding risk assessment to the imaging report.

“I expect that more studies in the future will begin looking at all information on the CT, rather than just one organ at a time,” Bluemke said. “Clinicians will need thresholds when to intervene when fat or bone abnormalities become severe.”

Outside experts re-iterated that assessment. In an accompanying editorial, Nicola Sverzellati, M.D., Ph.D., and Filippo Cademartiri, M.D., Ph.D., from the University of Parma in Italy, agreed that this study’s outcomes point to the efficacy of using soft-tissue markers visualized on chest CT in estimating COPD patient’s mortality risk. The potential exists for chest CT to become an important arrow in the quiver for personalized medicine, they said.

“Whether it is incorporated into routine assessment for patients with COPD will ultimately depend on our ability to demonstrate that this information changes treatment and improves outcomes,” they concluded.

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