The number of adverse events related to contrast-enhanced CT are not statistically different between patients who fasted or who ate beforehand.
It may not be necessary for cancer patients to fast prior to undergoing contrast-enhanced CT, according to a study published in the American Journal of Roentgenology.
Researchers from Brazil performed a prospective study to evaluate the effects of preparative fasting prior to contrast-enhanced CTs for patients with cancer. A total of 3,206 patients were randomly assigned to one of two groups; 1,619 fasted for at least 4 hours prior to their procedure and 1,587 consumed a light meal before undergoing the scan.
The results showed 45 patients (1.5 percent) in the fasting group reported adverse events, compared with 30 patients (0.9 percent) in the non-fasting group. The most common adverse events were:
• Nausea: 32 patients
• Weakness: 12 patients
• Vomiting: 5 patients
The authors noted that the frequency of symptoms between the two groups did not differ statistically significantly.
They concluded that among their sample group, there were few adverse reactions associated with eating a light meal before undergoing a CT scan with an IV contrast agent. “These results support the idea that preparation for contrast-enhanced CT can be simplified, decreasing the discomfort and inconvenience experienced by patients,” they wrote.
Can AI Enhance CT Detection of Incidental Extrapulmonary Abnormalities and Prediction of Mortality?
September 18th 2024Emphasizing multi-structure segmentation and feature extraction from chest CT scans, an emerging AI model demonstrated an approximately 70 percent AUC for predicting significant incidental extrapulmonary findings as well as two-year and 10-year all-cause mortality.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Study Assesses Lung CT-Based AI Models for Predicting Interstitial Lung Abnormality
September 6th 2024A machine-learning-based model demonstrated an 87 percent area under the curve and a 90 percent specificity rate for predicting interstitial lung abnormality on CT scans, according to new research.