Month: October 2020

Both cancer patients and their medical teams found it beneficial when patients shared their symptoms in real time using a web- or telephone-based reporting system, according to a national multi-institutional study.

Ethan Basch, MD, MSc, at the University of North Carolina Lineberger Comprehensive Cancer Center, and his colleagues reported in the journal JCO Clinical Cancer Informatics findings from the PRO-TECT trial, which is evaluating the use of electronic patient-reported outcomes (ePROs) among adults receiving outpatient treatment for advanced and metastatic cancers.

“Our prior research showed that using a web-based system for patients to self-report symptoms to their cancer care team improves patient satisfaction, quality of life, physical function, reduces emergency room visits and lengthens survival,” said Basch, director of UNC Lineberger’s Cancer Outcomes Research Program and the Richard M. Goldberg Distinguished Professor and chief of oncology at the UNC School of Medicine. “However, it has not been clear whether this approach could be widely used in cancer practices across the U.S. or be seen as useful or valuable by patients and providers. It is essential with any strategy for improving care to make sure that people will actually use it and find it valuable.”

In the new study, the researchers conducted a cluster-randomized controlled study at 52 community-based oncology practices across the United States. Half of the practices were assigned to use ePROs as part of the standard of care.

Participants in the study’s intervention arm were prompted every week for a year to report their symptoms and well-being. This involved using a website or an automated telephone program to answer a series of questions about their symptoms, such as pain, nausea and depression, as well as their physical functioning and financial health. The responses had a pre-assigned value on a five-point scale. When

Researchers from Skoltech and their US colleagues have designed a new machine learning-based approach for detecting atrial fibrillation drivers, small patches of the heart muscle that are hypothesized to cause this most common type of cardiac arrhythmia. This approach may lead to more efficient targeted medical interventions to treat the condition that is estimated to affect more than 33 million people worldwide, according to the American Heart Association. The recent paper was published in the journal Circulation: Arrhythmia and Electrophysiology.

The mechanism behind atrial fibrillation (AF), a type of abnormal heart rhythm that is associated with increased risk of heart failure and stroke, is yet unclear. Research suggests it may be caused and maintained by what’s called reentrant AF drivers, highly localized sources of repetitive rotational activity that lead to irregular heart rhythm. These drivers can be burnt via a surgical procedure, which can mitigate the condition or even restore the normal functioning of the heart.

To locate these reentrant AF drivers for subsequent destruction, doctors use multi-electrode mapping, a technique that allows them to record multiple electrograms inside the heart (this is done with a catheter) and build a map of electrical activity within the atria. However, clinical applications of this technique often produce a lot of false negatives, when an existing AF driver is not found, and false positives, when a driver is detected where there really is none.

Recently, researchers have tapped machine learning algorithms for the task of interpreting ECGs to look for atrial fibrillation; however, these algorithms require labeled data