Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study

Constantinos Koshiaris; Ann Van den Bruel; Brian D Nicholson; Sarah Lay-Flurrie; FD Richard Hobbs; Jason L Oke

doi:10.3399/BJGP.2020.0697

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Clinical prediction tools to identify patients at highest risk of myeloma in primary care
Tomoyuki Kawada

Published on: 21 July 2022
Opacity of this article
Alastair W. Rigg

Published on: 04 May 2021

Published on: (21 July 2022)
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Clinical prediction tools to identify patients at highest risk of myeloma in primary care
Tomoyuki Kawada, Professor, Nippon Medical School
Koshiaris et al. presented an equation for predicting 495 patients with myeloma within 2 years, who were aged ≥40 years.¹ Older age, male sex, back, chest and rib pain, nosebleeds, low haemoglobin, platelets, white cell count, raised mean corpuscular volume, calcium, and erythrocyte sedimentation rate were selected as significant predictors. By using full blood count, an area under the curve (AUC) (95% confidence interval [CI]) was 0.84 (0.81 to 0.87), and sensitivity (95% CI) at the highest risk decile was 62% (55% to 68%). By using all-test model, AUC (95% CI) was 0.87 (0.84 to 0.90) and sensitivity (95% CI) at the highest risk decile was 72% (66% to 78%). Regarding the prediction model of myeloma, I understand that the independent variables may be limited for general physicians, and an interval period between medical check and diagnosis of myeloma may be important for prediction model.

On this point, Blair et al. conducted a 16-year follow-up study, and reported the significance of anthropometry for contributing diagnosis of myeloma in postmenopausal women.² In an age-adjusted model, weight and waist circumference significantly contributed to the risk of myeloma. In contrast, body mass index (BMI) did not relate to the risk of myeloma. This information was partly confirmed by reports by Hagström et al.³ During a median follow-up of 20 years, waist circumference and waist-hip ratio were significant predictors for myeloma,...
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Koshiaris et al. presented an equation for predicting 495 patients with myeloma within 2 years, who were aged ≥40 years.¹ Older age, male sex, back, chest and rib pain, nosebleeds, low haemoglobin, platelets, white cell count, raised mean corpuscular volume, calcium, and erythrocyte sedimentation rate were selected as significant predictors. By using full blood count, an area under the curve (AUC) (95% confidence interval [CI]) was 0.84 (0.81 to 0.87), and sensitivity (95% CI) at the highest risk decile was 62% (55% to 68%). By using all-test model, AUC (95% CI) was 0.87 (0.84 to 0.90) and sensitivity (95% CI) at the highest risk decile was 72% (66% to 78%). Regarding the prediction model of myeloma, I understand that the independent variables may be limited for general physicians, and an interval period between medical check and diagnosis of myeloma may be important for prediction model.

On this point, Blair et al. conducted a 16-year follow-up study, and reported the significance of anthropometry for contributing diagnosis of myeloma in postmenopausal women.² In an age-adjusted model, weight and waist circumference significantly contributed to the risk of myeloma. In contrast, body mass index (BMI) did not relate to the risk of myeloma. This information was partly confirmed by reports by Hagström et al.³ During a median follow-up of 20 years, waist circumference and waist-hip ratio were significant predictors for myeloma, and BMI did not significantly become a predictor of myeloma. Body composition may be a good predictor for long-term risk of myeloma.

References
1. Koshiaris C, Van den Bruel A, Nicholson BD, et al. Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study. Br J Gen Pract 2021;71(706): e347-e355.
2. Blair CK, Cerhan JR, Folsom AR, et al. Anthropometric characteristics and risk of multiple myeloma. Epidemiology 2005;16(5):691-694.
3. Hagström H, Andreasson A, Carlsson AC, et al. Body composition measurements and risk of hematological malignancies: A population-based cohort study during 20 years of follow-up. PLoS One 2018;13(8): e0202651.
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Competing Interests: None declared.
Published on: (4 May 2021)
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Alastair W. Rigg, GP semi-retired, Dumfries and Galloway Health Board
Is it me? I read this abstract in the paper form and couldn't understand it, so read the whole thing on-line and still couldn't understand it. Perhaps a statistician might. The table produced in the magazine was beautiful but what did it mean? The full article said that you would need software to use the prediction rules. Surely the "How this fits in" box should clearly state what we as GPs should DO. Am I correct in thinking that anaemia is the best predictor of high risk of myeloma? Does having 2 FBC's mean the risk of myeloma is raised? Well done the researchers for sifting through all these records but I remain confused.

Competing Interests: None declared.

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