Dual-source CT stacks up to 64-slice CT for hard-to-image patients

Newer dual-source cardiac (DSC) CT scanners can provide accurate anatomic information to aid in the diagnosis and assessment of coronary artery disease (CAD) in patients who are difficult to image using older types of CT scanners, according to a study published online Feb. 7 in Radiology.

A systematic review of DSC CT studies found patient-level estimates of sensitivity ranged from 90 to 100 percent, according to Marie E. Westwood, PhD, of Kleijnen Systematic Reviews in York, England, and colleagues.

The authors aimed to assess the diagnostic performance of newer generation DSC CT scanners as an alternative to 64-slice CT. Diagnostic performance of 64-slice CT is well established, noted the authors, but certain patient groups—obese patients, those with irregular or fast heartbeats, those with artifacts reducing image quality—may not be candidates for this relatively older imaging technology.

Westwood and colleagues conducted a search of literature, trial registries and conference proceedings and found 25 studies that reported the accuracy of DSC CT for diagnosing CAD in difficult to image patients.

In 22 of the studies in which the DSC CT scanner was used to acquire data, pooled, per-patient estimates of sensitivity was 97.7 percent for both patients with arrhythmias and higher heart rates. Pooled estimates of specificity were 81.7 percent and 86.3 percent for patients with arrhythmias and high heart rates, respectively.

In studies of patients with high coronary calcium scores, previous bypass grafts or obesity, only per-segment or per-artery data were available, but the authors reported that sensitivity estimates remained over 90 percent in these patients in all but one study.

Westwood and colleagues wrote that one major limitation of the review was that no studies were found in which the effects of DSC CT scanning on patient treatment or outcomes was reported. “The ultimate aim of any research on clinical tests should be to determine the effect on patient treatment and outcome,” wrote the authors. “These data are essential to fully inform both clinical and policy decision making.”