Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Dr Constantinos Koshiaris has developed clinical prediction models for use in primary care with the aim of accelerating myeloma diagnoses

Dial from green to red, with pointer that says Myeloma

Myeloma is a cancer of the bone marrow that caused 117,077 deaths worldwide in 2020 (International Agency for Research on Cancer). Earlier diagnosis improves the rate of survival but unfortunately, delays in myeloma diagnosis are common and result in poorer patient outcomes.

One of the reasons for the diagnostic delay is that myeloma symptoms are non-specific and relatively common in people without cancer. For example, back pain is associated with myeloma yet there are many other non-myeloma causes of this symptom. Additional measures are therefore needed to highlight the possibility of myeloma in patients where GPs do not originally suspect this disease.

GPs frequently order simple laboratory tests, such as the full blood count, to investigate patients presenting with non-specific symptoms. Previous work by Dr Constantinos KoshiarisDr Jason OkeDr Brian Nicholson and colleagues from Oxford’s Nuffield Department of Primary Care Health Sciences and the University of Exeter identified certain abnormalities in blood test results that indicate a higher risk of myeloma, such as low haemoglobin which can be observed up to 2 years before a myeloma diagnosis.

In this paper published recently in the British Journal of General Practice, the Oxford researchers have developed new clinical prediction models for myeloma that incorporate both symptoms and blood test results. Using the Clinical Practice Research Datalink (GOLD version), a primary care database containing electronic health records for more than 11 million patients in the UK, the team identified the most common symptoms and full blood count results recorded for patients with myeloma. The most predictive of these were included in the models they developed and the new tools were validated against a set of test data. Decisions made using their prediction models resulted in fewer false positives and more true positives when compared to single tests or symptoms alone.

By identifying patients at highest risk of myeloma in primary care, these new prediction rules have the potential to reduce diagnostic delays by a substantial amount. Further research is now needed to understand more about the feasibility and implementation of this tool in the primary care setting and the impact it will have on the diagnostic pathway and patient outcomes.

Similar Stories

Unique Clinical Imaging Dataset Released for Artificial Intelligence Research to Accelerate Diagnosis of Prostate Cancer

The National Cancer Imaging Translational Accelerator (NCITA) in partnership with the ReIMAGINE Consortium have announced the release of a unique clinical imaging dataset from the Prostate MRI Imaging Study (PROMIS)).

CRUK funding to investigate the molecular drivers of stomach cancer

Dr Francesco Boccellato wins a CRUK Early Detection and Diagnosis primer award to study tissue shape changes in the pre-cancerous stomach conditions, atrophic gastritis and intestinal metaplasia

First patient diagnosed earlier using liquid biopsy technology as part of the AI-REAL programme in sub-Saharan Africa

The AI-REAL programme led by Professor Anna Schuh and research teams in Tanzania and Uganda is improving the early detection and outcomes of childhood lymphoma in the region by increasing the speed and precision of diagnosis.