“Exciting” drug study could detect unrecognised cancer symptoms earlier

An innovative new study, funded by Cancer Research UK, aims to identify patients with early signs of cancer through medications they are prescribed before they are diagnosed with the disease.

It’s hoped the project could help identify various cancers at an early stage when treatment is more likely to be effective.

A team of researchers including Professor Chris Cardwell, Professor Carmel Hughes, Dr Sarah Baxter, Dr David Wright and Dr Blánaid Hicks of Queen’s University Belfast with Professor Peter Murchie of the University of Aberdeen, will study extensive anonymised medical information to identify treatments given to people who are then diagnosed with cancer.

The research will be the first of its kind to study prescription information comprehensively in the UK. Similar studies have been carried out overseas and smaller studies conducted looking at fewer cancer types in the UK.

Previous studies have already shown increased use of pain and indigestion medication in women with ovarian cancer up to eight months before diagnosis and increases in haemorrhoid treatments in patients with colorectal cancer up to 15 months before diagnosis.

Professor Chris Cardwell, of Queen’s University Belfast, said: “Our study has the potential to identify previously unrecognised medications which are newly used in the period up to two years before cancer diagnosis.

“These changes in specific medications could act as an alert for doctors to consider earlier cancer investigation or point to unrecognised symptom patterns.

“Diagnosing cancer as early as possible is key to ensuring treatment is as effective as possible and give patients the best chance of recovery.”

The study, which will receive £76,462 from Cancer Research UK, will focus on eight cancers: multiple myeloma, pancreatic, stomach, ovarian, lung, renal, colorectal and non-Hodgkin’s lymphoma – selected because these cancers are known to involve more GP consultations prior to diagnosis.

Currently, there are many symptoms and medical conditions known to be associated with cancer, but often symptoms can indicate a variety of conditions, not just cancer, making diagnosis harder.

Professor Peter Murchie, of the University of Aberdeen, said: “This is an exciting study which we hope will show how our increasingly sophisticated health records can be used for the maximum patient benefit.

“We know symptoms of cancer can develop slowly so changes in our prescription data could become a very important early warning signal to prompt busy GPs.”

Prescription data from the Secure Anonymised Information Linkage (SAIL) Databank at Swansea University, which works with the NHS to provide crucial information for researchers while keeping data anonymous and protected, will be used.

This requires permission from the Databank’s Information Governance Review Panel which oversees the safe and responsible use of population health records.

Codes used in the NHS to indicate medication prescriptions are easier to analyse and track than symptoms, for example an increase in dose or stronger medication can be flagged more easily to medical colleagues than notes on a patient’s record saying a symptom is worsening.

Cancer Research UK Director of Research, Dr Catherine Elliott, said: “Innovative approaches to tackling cancer are crucial to improving outcomes for patients. We have already made great strides in turning many types of cancer into a treatable disease if diagnosed at an early stage, and studies like this aim to help doctors identify people at risk of cancer much earlier.

“Earlier diagnosis takes us further along the path towards a world where cancer diagnosis is the start of the road to recovery and a less fearful prospect for patients.”

Nearly one in two people born in the UK will get cancer in their lifetime.*

With around 10,300 people being diagnosed with cancer each year in Northern Ireland (385,000 across the UK) finding new ways to diagnose cancer earlier, is vital.**

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davepickering

Edinburgh reporter and photographer

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