Abstract
Patients with myeloma experience the longest diagnostic delays compared with patients with other cancers in the UK; 37% are diagnosed through emergency presentations. To identify and quantify the risk of myeloma from specific clinical features reported by primary care patients. Matched case-control study using General Practice Research Database primary care electronic records. Putative clinical features of myeloma were identified and analysed using conditional logistic regression. Positive predictive values (PPVs) were calculated for the consulting population. A total of 2703 patients aged ≥40 years, diagnosed with myeloma between 2000 and 2009, and 12 157 age, sex, and general practice-matched controls were identified. Sixteen features were independently associated with myeloma: hypercalcaemia, odds ratio 11.4 (95% confidence interval [CI] = 7.1 to 18), cytopenia 5.4 (95% CI = 4.6 to 6.4), raised inflammatory markers 4.9 (95% CI = 4.2 to 5.8), fracture 3.1 (95% CI = 2.3 to 4.2), raised mean corpuscular volume 3.1 (95% CI = 2.4 to 4.1), weight loss 3.0 (95% CI = 2.0 to 4.5), nosebleeds 3.0 (95% CI = 1.9 to 4.7), rib pain 2.5 (95% CI = 1.5 to 4.4), back pain 2.2 (95% CI = 2.0 to 2.4), other bone pain 2.1 (95% CI = 1.4 to 3.1), raised creatinine 1.8 (95% CI = 1.5 to 2.2), chest pain 1.6 (95% CI = 1.4 to 1.8), joint pain 1.6 (95% CI = 1.2 to 2.2), nausea 1.5 (95% CI = 1.1 to 2.1), chest infection 1.4 (95% CI = 1.2 to 1.6), and shortness of breath 1.3 (95% CI = 1.1 to 1.5). Individual symptom PPVs were generally <1%, although were >10% for some symptoms when combined with leucopenia or hypercalcaemia. Individual symptoms of myeloma in primary care are generally low risk, probably explaining diagnostic delays. Once simple primary care blood tests are taken, risk estimates change. Hypercalcaemia and leucopenia are particularly important abnormalities, and coupled with symptoms, strongly suggest myeloma.
Keywords
blood tests
diagnosis
hypercalcaemia
leucopenia
multiple myeloma
primary health care
MeSH Terms
Adult
Aged
Electronic Health Records
Female
Humans
Male
Middle Aged
Morbidity/trends
Multiple Myeloma/diagnosis,epidemiology
Odds Ratio
Primary Health Care
Referral and Consultation/statistics & numerical data
Retrospective Studies
Risk Assessment/methods
Risk Factors
Survival Rate/trends
United Kingdom/epidemiology
Authors & Affiliations
6 authors, click to expand affiliations / ORCID
Shephard Elizabeth A
University of Exeter Medical School, Exeter.
Neal Richard D
North Wales Centre for Primary Care Research, Bangor University, Wrexham.
Rose Peter
Department of Primary Care Health Sciences, University of Oxford.
Walter Fiona M
Department of Public Health and Primary Care, University of Cambridge, Cambridge.
Litt Emma J
Glyndwr University, Wrexham.
Hamilton William T
University of Exeter Medical School, Exeter.
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