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

BACKGROUND: Patients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis. AIM: To generate a clinical prediction rule to identify primary care patients who are at highest risk of myeloma. DESIGN AND SETTING: Retrospective open cohort study using...

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Autores principales: Koshiaris, Constantinos, Van den Bruel, Ann, Nicholson, Brian D, Lay-Flurrie, Sarah, Hobbs, FD Richard, Oke, Jason L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Royal College of General Practitioners 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049204/
https://www.ncbi.nlm.nih.gov/pubmed/33824161
http://dx.doi.org/10.3399/BJGP.2020.0697
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author Koshiaris, Constantinos
Van den Bruel, Ann
Nicholson, Brian D
Lay-Flurrie, Sarah
Hobbs, FD Richard
Oke, Jason L
author_facet Koshiaris, Constantinos
Van den Bruel, Ann
Nicholson, Brian D
Lay-Flurrie, Sarah
Hobbs, FD Richard
Oke, Jason L
author_sort Koshiaris, Constantinos
collection PubMed
description BACKGROUND: Patients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis. AIM: To generate a clinical prediction rule to identify primary care patients who are at highest risk of myeloma. DESIGN AND SETTING: Retrospective open cohort study using electronic health records data from the UK’s Clinical Practice Research Datalink (CPRD) between 1 January 2000 and 1 January 2014. METHOD: Patients from the CPRD were included in the study if they were aged ≥40 years, had two full blood counts within a year, and had no previous diagnosis of myeloma. Cases of myeloma were identified in the following 2 years. Derivation and external validation datasets were created based on geographical region. Prediction equations were estimated using Cox proportional hazards models including patient characteristics, symptoms, and blood test results. Calibration, discrimination, and clinical utility were evaluated in the validation set. RESULTS: Of 1 281 926 eligible patients, 737 (0.06%) were diagnosed with myeloma within 2 years. Independent predictors of myeloma included: older age; male sex; back, chest and rib pain; nosebleeds; low haemoglobin, platelets, and white cell count; and raised mean corpuscular volume, calcium, and erythrocyte sedimentation rate. A model including symptoms and full blood count had an area under the curve of 0.84 (95% CI = 0.81 to 0.87) and sensitivity of 62% (95% CI = 55% to 68%) at the highest risk decile. The corresponding statistics for a second model, which also included calcium and inflammatory markers, were an area under the curve of 0.87 (95% CI = 0.84 to 0.90) and sensitivity of 72% (95% CI = 66% to 78%). CONCLUSION: The implementation of these prediction rules would highlight the possibility of myeloma in patients where GPs do not suspect myeloma. Future research should focus on the prospective evaluation of further external validity and the impact on clinical practice.
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spelling pubmed-80492042021-04-22 Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study Koshiaris, Constantinos Van den Bruel, Ann Nicholson, Brian D Lay-Flurrie, Sarah Hobbs, FD Richard Oke, Jason L Br J Gen Pract Research BACKGROUND: Patients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis. AIM: To generate a clinical prediction rule to identify primary care patients who are at highest risk of myeloma. DESIGN AND SETTING: Retrospective open cohort study using electronic health records data from the UK’s Clinical Practice Research Datalink (CPRD) between 1 January 2000 and 1 January 2014. METHOD: Patients from the CPRD were included in the study if they were aged ≥40 years, had two full blood counts within a year, and had no previous diagnosis of myeloma. Cases of myeloma were identified in the following 2 years. Derivation and external validation datasets were created based on geographical region. Prediction equations were estimated using Cox proportional hazards models including patient characteristics, symptoms, and blood test results. Calibration, discrimination, and clinical utility were evaluated in the validation set. RESULTS: Of 1 281 926 eligible patients, 737 (0.06%) were diagnosed with myeloma within 2 years. Independent predictors of myeloma included: older age; male sex; back, chest and rib pain; nosebleeds; low haemoglobin, platelets, and white cell count; and raised mean corpuscular volume, calcium, and erythrocyte sedimentation rate. A model including symptoms and full blood count had an area under the curve of 0.84 (95% CI = 0.81 to 0.87) and sensitivity of 62% (95% CI = 55% to 68%) at the highest risk decile. The corresponding statistics for a second model, which also included calcium and inflammatory markers, were an area under the curve of 0.87 (95% CI = 0.84 to 0.90) and sensitivity of 72% (95% CI = 66% to 78%). CONCLUSION: The implementation of these prediction rules would highlight the possibility of myeloma in patients where GPs do not suspect myeloma. Future research should focus on the prospective evaluation of further external validity and the impact on clinical practice. Royal College of General Practitioners 2021-04-07 /pmc/articles/PMC8049204/ /pubmed/33824161 http://dx.doi.org/10.3399/BJGP.2020.0697 Text en © The Authors https://creativecommons.org/licenses/by/4.0/This article is Open Access: CC BY 4.0 licence (http://creativecommons.org/licences/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Research
Koshiaris, Constantinos
Van den Bruel, Ann
Nicholson, Brian D
Lay-Flurrie, Sarah
Hobbs, FD Richard
Oke, Jason L
Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study
title Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study
title_full Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study
title_fullStr Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study
title_full_unstemmed Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study
title_short Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study
title_sort clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049204/
https://www.ncbi.nlm.nih.gov/pubmed/33824161
http://dx.doi.org/10.3399/BJGP.2020.0697
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