Development and Internal Validation of a Risk Prediction Model to Identify Myeloma Based on Routine Blood Tests: A Case-Control Study

Simple Summary Due to the rarity and non-specific symptoms of myeloma, a key challenge in diagnosing myeloma in primary care is the clinician considering myeloma and initiating appropriate investigations. We developed an algorithm to identify those at high-risk of having undiagnosed myeloma based on results from routine blood tests taken for other reasons. We demonstrated that it is possible to combine signals and abnormalities in several routine blood test parameters to identify individuals at high-risk of having undiagnosed myeloma with high predictive accuracy. Identified high-risk individuals could then undergo further automatic testing in the laboratory (a reflex test) to test specifically for myeloma. We estimate the number of additional tests needed to diagnose one case at different model thresholds. Further work is needed to explore the full potential of such a strategy. If successful, automatic reflex testing would enable large-scale, low-cost case-finding for myeloma with a significant impact on the diagnosis of myeloma. Myeloma is one of the hardest cancers to diagnose in primary care due to its rarity and non-specific symptoms. A rate-limiting step in diagnosing myeloma is the clinician considering myeloma and initiating appropriate investigations. We developed and internally validated a risk prediction model to identify those with a high risk of having undiagnosed myeloma based on results from routine blood tests taken for other reasons. A case-control study, based on 367 myeloma cases and 1488 age- and sex-matched controls, was used to develop a risk prediction model including results from 15 blood tests. The model had excellent discrimination (C-statistic 0.85 (95%CI 0.83, 0.89)) and good calibration (calibration slope 0.87 (95%CI 0.75, 0.90)). At a prevalence of 15 per 100,000 population and a probability threshold of 0.4, approximately 600 patients would need additional reflex testing to detect one case. We showed that it is possible to combine signals and abnormalities from several routine blood test parameters to identify individuals at high-risk of having undiagnosed myeloma who may benefit from additional reflex testing. Further work is needed to explore the full potential of such a strategy, including whether it is clinically useful and cost-effective and how to make it ethically acceptable.

Automated summary

This study developed an algorithm to identify individuals at high-risk of undiagnosed myeloma based on routine blood test results. Further testing can then be conducted to specifically diagnose myeloma. This method has the potential to significantly impact the diagnosis of myeloma on a large scale and at a low cost.