Thomas Jefferson University researchers have demonstrated that a 64-slice CT triple rule-out exam can, with 99.3% certainty, dismiss the presence of acute coronary syndrome in the emergency room for chest pain patients at mild or intermediate risk for ACS. The test also diagnosed pulmonary emboli and other noncardiac sources of the patients’ discomfort.
Thomas Jefferson University researchers have demonstrated that a 64-slice CT triple rule-out exam can, with 99.3% certainty, dismiss the presence of acute coronary syndrome in the emergency room for chest pain patients at mild or intermediate risk for ACS. The test also diagnosed pulmonary emboli and other noncardiac sources of the patients' discomfort.
The finding was drawn from a prospective clinical trial of 172 consecutive ER patients, said Dr. Ethan Halpern, professor of radiology and urology at the university. Dr. Kevin Takakuwa, an assistant professor and director of Thomas Jefferson's chest pain center, also managed the trial. Results were presented Tuesday at the 2007 RSNA conference.
Only one false-negative finding, stemming from a misreading of results, kept the protocols from accurately ruling out ACS for all patients.
"The study suggests that patients with normal coronaries or mild disease, as shown with the triple rule-out exam, can be safely discharged home," Halpern said. "Patients with moderate to severe disease should undergo further cardiac testing."
The triple rule-out protocol involves contrast-enhanced CTA of the coronary artery. Full-view imaging acquisition of the chest enables a rapid evaluation of lungs to rule out pulmonary emboli and the aorta and pulmonary arteries for dissection.
Imaging was performed on a 64-slice Philips Brilliance Pro scanner. Beta blockers were administered to the keep subjects' heartbeats under 65 per minute. Patients were also administered sublingual nitroglycerine and 100 cc of contrast at 5 cc per second. Imaging was interpreted by a single reader. Scans were reformatted primarily as axial and slab maximum intensity projection images. Wall motion was examined in every case. Scans were classified as no coronary disease, mild disease (1% to 49% stenosis), moderate disease (50% to 70% stenosis) or mild disease with a corresponding a wall motion abnormality, and severe (greater than 70% stenosis).
Follow-up involved stress testing in all patients with more than minimal coronary artery disease. Cardiac catheterization was carried out when clinically warranted. All patients received a 30-day clinical follow-up.
Coronary CTA was negative for coronary artery disease for 61%, or 104 of 172 patients. They were sent home once the interpretation was rendered. Mild disease was found in 25% (44 of 172) of patients, moderate disease in 8% (14), and severe disease in 5% (eight). Diagnoses were rendered in all cases, with 90% of exams considered technically adequate for all vessels.
Twenty-two percent of patients had clinically significant noncoronary findings, but a noncoronary finding explained the patient's symptoms for only 10%. Those conditions involved pneumonia for four patients, hiatal hernia and pulmonary embolism for three patients each, cardiomyopathy for two patients, and one case each for aortic dissection, chronic obstructive pulmonary disease, myocarditis, Barrett's esophagitis, and pancreatitis.
No poor outcomes were identified during clinical follow-up exams performed 30 days after the CCT studies.
As an experienced echocardiographer, Halpern supports the use of CT wall motion studies with coronary artery imaging. He described a case of mild stenosis of the lateral anterior descending artery. Wall motion observed in the short axis indicated an anteroseptal wall contraction. Because of that finding, the patient was shifted into the moderate category. Catheterization revealed a 70% stenosis that was subsequently stented.
The trial was limited because of its small size and its setting in a single institution. Radiation exposure rates were not described.
Can AI Enhance PET/MRI Assessment for Extraprostatic Tumor Extension in Patients with PCa?
December 17th 2024The use of an adjunctive machine learning model led to 17 and 21 percent improvements in the AUC and sensitivity rate, respectively, for PET/MRI in diagnosing extraprostatic tumor extension in patients with primary prostate cancer.
Can Radiomics Bolster Low-Dose CT Prognostic Assessment for High-Risk Lung Adenocarcinoma?
December 16th 2024A CT-based radiomic model offered over 10 percent higher specificity and positive predictive value for high-risk lung adenocarcinoma in comparison to a radiographic model, according to external validation testing in a recent study.