Clinical features of prostate cancer before diagnosis: a population-based, case-control study.
Аннотация
BACKGROUND
Even in areas where screening is available, many prostate cancers are diagnosed after the symptoms begin. However, the risk posed by particular symptoms is largely unknown, especially in unselected populations such as primary care.
AIM: To identify and quantify the features of prostate cancer before diagnosis, both individually and in combination.
DESIGN OF STUDY: Population-based case-control study.
SETTING
All 21 general practices in Exeter, Devon, UK.
METHODS
We studied all 217 prostate cancer patients diagnosed between 1998 and 2002, and 1080 male controls, matched by age and general practice. The full medical record for 2 years before diagnosis was coded, using the International Classification of Primary Care. We calculated odds ratios for variables independently associated with cancer, using conditional logistic regression, and calculated the positive predictive values for these, both individually and in combination.
RESULTS
Eight features were associated with prostate cancer before diagnosis. Their positive predictive values against a background risk of 0.35% were: urinary retention 3.1% (95% confidence interval [CI] = 1.5 to 6.0); impotence 3.0% (95% CI = 1.7 to 4.9); frequency 2.2% (95% CI = 1.3 to 3.5); hesitancy 3.0% (95% CI = 1.5 to 5.5); nocturia 2.2% (95% CI = 1.2 to 3.6); haematuria 1.0% (95% CI = 0.57 to 1.8); weight loss 0.75% (95% CI = 0.38 to 1.4); abnormal rectal examination, deemed benign 2.8% (95% CI = 1.6 to 4.6); abnormal rectal examination, deemed malignant 12% (95% CI = 5.0 to 37): all P <0.001, except for hesitancy P = 0.032, nocturia P = 0.004 and haematuria P = 0.009. Loss of weight, impotence, frequency and abnormal rectal examination remained associated with cancer after excluding the final 180 days from analysis.
CONCLUSION
Most men with prostate cancer present with symptoms. The predictive values for these symptoms will help guide GPs and patients about the value of further investigation.
Предметные рубрики MeSH
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