Friday December 13, 2019

Effective Treatment to Protect Cancer Patients From Blood Clots

Taking oral drugs daily can be an effective treatment for nearly 10 million cancer patients worldwide suffering from a deadly blood clot condition, results from a clinical trial have showed.

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Blood cells, pixabay

Taking oral drugs daily can be an effective treatment for nearly 10 million cancer patients worldwide suffering from a deadly blood clot condition, results from a clinical trial have shown.

People with cancer have an increased risk of developing blood clots, with roughly one in five experiencing venous thromboembolism (VTE) — either a blood clot in a deep vein or a condition in which one or more arteries in the lungs become blocked by a blood clot.

The results from the clinical trial called “select-d” suggested that prescribing the oral drug rivaroxaban significantly reduced VTE recurrence among patients with cancer.

“Clinicians were already adopting the oral drug into practice for non-cancer patients and now they have data from this study to indicate that this form of treatment is an alternative option for many cancer patients who have a clot,” said lead author Annie Young, Professor at the University of Warwick in Britain.

Although there are many causes and risk factors for VTE, cancer patients are particularly at risk due to a combination of factors such as immobility, pancreatic and gastric tumours as well as chemotherapy, the researcher said.

The reason for increased bleeding is not known. It may be because rivaroxaban is more 'potent', the paper published in the Journal of Clinical Oncology said. (IANS)
Representational image, pixabay

For the “select-d” trial, researchers enrolled 406 patients who had cancer and VTE; most (69 per cent) were receiving cancer treatment (typically chemotherapy) at the time of their VTE.

Half were randomly assigned to receive low-molecular-weight heparin (dalteparin) and half were given the oral drug rivaroxaban. After six months of treatment, the VTE recurrence rate was four per cent among those taking the tablet and 11 per cent in those receiving dalteparin.

The results for secondary outcomes were mixed, the researcher said.

In patients receiving rivaroxaban, there were around the same percentage of major bleeding events (6 per cent) as those receiving dalteparin (4 per cent) but a marked and significant increase in clinically relevant non-major bleeds (13 per cent) with rivaroxaban compared to those having low molecular weight heparin (4 per cent).

Also Read: Drug Used For Osteoporosis May Help in Reducing Heart Attack Risk

The reason for increased bleeding is not known. It may be because rivaroxaban is more ‘potent’, the paper published in the Journal of Clinical Oncology said. (IANS)

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Machine Learning Can Help Doctors to Improve End-Of-Life Conversation with Patients

A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families

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Machine Learning
A Research used Machine Learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients. Pixabay

Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication.

Some of the most important, and difficult, conversations in healthcare are the ones that happen amid serious and life-threatening illnesses.

Discussions of the treatment options and prognoses in these settings are a delicate balance for doctors and nurses who are dealing with people at their most vulnerable point and may not fully understand what the future holds.

“We want to understand this complex thing called a conversation. Our major goal is to scale up the measurement of conversations so we can re-engineer the healthcare system to communicate better,” said Robert Gramling, director of the Vermont Conversation Lab in the study published in the journal Patient Education and Counselling.

Gramling and his colleagues used machine learning algorithms to analyze 354 transcripts of palliative care conversations collected by the Palliative Care Communication Research Initiative, involving 231 patients.

They broke each conversation into 10 parts with an equal number of words in each, and examined how the frequency and distribution of words referring to time, illness terminology, sentiment and words indicating possibility and desirability changed between each decile.

“We picked up some strong signals,” said Gramling.

Conversations tended to progress from talking about the past to talking about the future, and from sadder to happier sentiments. “There was quite a range, they went from pretty sad to pretty happy,” Gramling added.

Machine Learning
Researchers at University of Vermont have used Machine Learning and natural language processing (NLP) to better understand conversations about death, which could eventually help doctors improve their end-of-life communication. Pixabay

The consistent results across multiple conversations show just how much people make meaning out of stories in healthcare.

“What we found supports the importance of narrative in medicine,” he said.

That knowledge could eventually help healthcare practitioners understand what makes a “good” conversation about palliative care, and how different kinds of conversations might require different responses.
That could help create interventions that are matched to what the conversation indicates the patient needs the most.

ALSO READ: Light Alcohol Consumption Might Also Increase Cancer Risk: Study

A deeper understanding of these conversations, which are often freighted with emotion and uncertainty, will also help reveal what aspects or behaviors associated with these conversations are more valuable for patients and families. (IANS)