Clinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort study
<strong>Background</strong> Patients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis. <br> <strong>Aim</strong> To generate a clinical prediction rule to identify primary care patients who are at highest risk of...
| Автори: | , , , , , |
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| Формат: | Journal article |
| Мова: | English |
| Опубліковано: |
Royal College of General Practitioners
2021
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| _version_ | 1826262064996286464 |
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| author | Koshiaris, C Van den Bruel, A Nicholson, BD Lay-Flurrie, S Hobbs, FR Oke, JL |
| author_facet | Koshiaris, C Van den Bruel, A Nicholson, BD Lay-Flurrie, S Hobbs, FR Oke, JL |
| author_sort | Koshiaris, C |
| collection | OXFORD |
| description | <strong>Background</strong> Patients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis.
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<strong>Aim</strong> To generate a clinical prediction rule to identify primary care patients who are at highest risk of myeloma.
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<strong>Design and setting</strong> 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.
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<strong>Method</strong> 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.
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<strong>Results</strong> 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%).
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<strong>Conclusion</strong> 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. |
| first_indexed | 2024-03-06T19:30:28Z |
| format | Journal article |
| id | oxford-uuid:1d475b2e-7dd2-4a87-aa17-42ff911565f7 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T19:30:28Z |
| publishDate | 2021 |
| publisher | Royal College of General Practitioners |
| record_format | dspace |
| spelling | oxford-uuid:1d475b2e-7dd2-4a87-aa17-42ff911565f72022-03-26T11:09:58ZClinical prediction tools to identify patients at highest risk of myeloma in primary care: a retrospective open cohort studyJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1d475b2e-7dd2-4a87-aa17-42ff911565f7EnglishSymplectic ElementsRoyal College of General Practitioners2021Koshiaris, CVan den Bruel, ANicholson, BDLay-Flurrie, SHobbs, FROke, JL<strong>Background</strong> Patients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis. <br> <strong>Aim</strong> To generate a clinical prediction rule to identify primary care patients who are at highest risk of myeloma. <br> <strong>Design and setting</strong> 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. <br> <strong>Method</strong> 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. <br> <strong>Results</strong> 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%). <br> <strong>Conclusion</strong> 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. |
| spellingShingle | Koshiaris, C Van den Bruel, A Nicholson, BD Lay-Flurrie, S Hobbs, FR Oke, JL 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 |
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