Evidence map›Paper›PMID 33824161›Full record

ArticleThe British journal of general practice : the journal of the Royal College of General Practitioners2021

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

Open access · hybridAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The British journal of general practice : the journal of the Royal College of General Practitioners, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.2field-weighted citation impact, top 12% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 20 citations in OpenAlex.

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  13. Approaches to diagnosing cancer earlier in general practice.The British journal of general practice : the journal of the Royal College of General Practitioners · 2021
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 2 countries.

Constantinos KoshiarisNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Ann Van den BruelAcademic Centre for Primary Care, KU Leuven, Leuven, Belgium.
Brian D NicholsonNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Sarah Lay-FlurrieNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Fd Richard HobbsNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Jason L OkeNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
University of Oxford · GBVIB-KU Leuven Center for Microbiology · BE

Funding

Wellcome Trust 211182/Z/18/Z
6 · The paper itself

Abstract

backgroundPatients with myeloma experience substantial delays in their diagnosis, which can adversely affect their prognosis.

aimTo generate a clinical prediction rule to identify primary care patients who are at highest risk of myeloma. DESIGN AND

settingRetrospective 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.

methodPatients 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.

resultsOf 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%).

conclusionThe 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.

Indexed as

Multiple MyelomaAgedCohort StudiesHumansMalePrimary Health CareProspective StudiesRetrospective StudiesRisk Assessmentcancerdiagnosisepidemiologymyelomaprimary care

Identifiers

PMID33824161
PMCPMC8049204
OpenAlexW3146902039

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.