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Research project: CANDID (Clinical prediction rules for colorectal/lung cancer) CI: Paul Little

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The CANDID Study (CI - Paul Little) is to use the IPCARD self-completion questionnaire to supplement symptom and risk information collected during the GP consultation.  This information will provide predictive values of symptoms for lung cancer diagnosis, and inform the development of clinical prediction rules.

Lay Summary

In primary care the key areas of concern for both doctor and patients are delay in diagnosing cancer, getting high risk patients referred first, and keeping investigation to a minimum. There have been few valid studies to assist decision-making in primary care, either to get a patient referred quickly or to assist in making sure an anxious patient is effectively reassured. This study seeks to work out which of the symptoms and examination findings are the most effective in predicting lung or colon cancer. To decide the best clinical information to collect in the study we will interview patients and also get consensus from a group of experts. Then we will recruit 20,000 patients who consult their GP - half with lung symptoms and the other half with low bowel symptoms. Clinical information will be collected using standardised internet based forms. Willing patients will complete lifestyle questionnaires and provide blood or saliva samples (including for genetic analysis). The National Cancer Registry will then be monitored to see which patients develop cancer, and statistical analysis will determine the most important clinical variables that predict cancer. The clinical prediction 'rules' or decision aids developed from these studies will then be tested with a further 2000 patients for each condition for validity.

Project Team

Multi-centre study.  Team at Southampton University:
Paul Little
Gerry Leydon
Sue Broomfield
Lucy Brindle (co-investigator)

Project Funder

NIHR NSPCR

Associated research themes

Clinical Prediction rules for cancer diagnosis
Early Cancer diagnosis

Related research groups

Complex Healthcare Processes
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