Start Date Jan 2021
Myeloma is one of the hardest cancers to diagnose because it is unlikely to be suspected by a GP. Once suspected, however, myeloma is relatively easy to diagnose. There are many reasons why myeloma is unlikely to be suspected. It is a rare cancer and patients present with a range of non-specific symptoms, including back pain, bone pain and fatigue, which are common in an aging population and mainly attributable to benign conditions. As a result, myeloma has been identified as having one of the longest diagnostic intervals compared to other cancers, with half of myeloma patients having three or more pre-referral consultations. Diagnosing myeloma earlier reduces the proportion of patients with end-organ dysfunction including spinal cord compression, pathological fractures, severe immunosuppression and renal failure in particular. Avoiding these common presenting complications has significant implications for patients’ quality of life, treatment tolerance and in some cases, long-term survival.
Alternative diagnostic strategies (to the current ‘symptomatic’ approach) to identify myeloma are needed. Strategies that diagnose people at earlier stage cancer with minimal impact on NHS resources are needed. This is particularly important given the COVID-19 pandemic with additional strains on NHS diagnostic and cancer clinics. Many myeloma patients receive numerous blood tests in the years before diagnosis, often for reasons unrelated to myeloma. In this study we propose to describe and compare results from routine blood tests conducted in myeloma and non-myeloma patients and develop an algorithm to identify people at highest risk of the disease.
Aims & objectives
The overall aim of this study is to develop a myeloma case-finding approach for higher-risk patients based on blood tests undertaken for other purposes.
1. Compare and describe results from routine blood tests conducted in patients subsequently diagnosed with myeloma, Monoclonal Gammopathy of Undetermined Significance (MGUS) and non-myeloma controls
2. To develop and internally validate a risk prediction model to identify people at higher risk of having myeloma based on routine blood test results and clinical data
In this study a nested case control study will be conducted using de-identified clinical and pathology data from Leeds Teaching Hospitals NHS Trust (LTHT). Data will be extracted from LTHT clinical and pathology systems to include all blood test results from 2011-2019 linked to clinical and demographic data. Cases will include all patients diagnosed with myeloma and five age- and sex- matched controls will be included for each case. All MGUS patients will be included as a separate group.
Routine blood tests results will be described and compared in patients subsequently diagnosed with myeloma, MGUS and non-myeloma controls. Trajectories of blood test results in the three groups will be described. Logistic regression will be used to develop a risk prediction model to identify people at higher risk of having myeloma based on routine blood test results and clinical data.
Outputs & impact
The main aim of this project is to develop an algorithm to identity patients at higher-risk of myeloma. This algorithm will be externally validated in alternative datasets to fully assess the developed model’s performance.
The main study findings will be written up for publication in high-impact peer-reviewed journal.
The algorithm development is the first stage of a wider project. Once this model has been developed and validated its application will be tested in a prospective study. The developed algorithm will be used to identify people at higher risk of having myeloma and then reflex testing of these blood samples will be undertaken to specifically test for myeloma. Further, a full health economic and capacity analysis will be undertaken to assess the impact of earlier diagnosis of myeloma using this strategy.
- Smith L, Carmichael J, Cook G, Shinkins B, Neal RD. Diagnosing myeloma in general practice: How might earlier diagnosis be achieved? British Journal of General Practice 2022; 72 (723): 462-463. doi: 10.3399/bjgp22X720737