Development and validation of risk prediction algorithms to estimate future risk of common cancers in men and women: prospective cohort study

Julia Hippisley-Cox, Carol Coupland, Julia Hippisley-Cox, Carol Coupland

Abstract

Objective: To derive and validate a set of clinical risk prediction algorithm to estimate the 10-year risk of 11 common cancers.

Design: Prospective open cohort study using routinely collected data from 753 QResearch general practices in England. We used 565 practices to develop the scores and 188 for validation.

Subjects: 4.96 million patients aged 25-84 years in the derivation cohort; 1.64 million in the validation cohort. Patients were free of the relevant cancer at baseline.

Methods: Cox proportional hazards models in the derivation cohort to derive 10-year risk algorithms. Risk factors considered included age, ethnicity, deprivation, body mass index, smoking, alcohol, previous cancer diagnoses, family history of cancer, relevant comorbidities and medication. Measures of calibration and discrimination in the validation cohort.

Outcomes: Incident cases of blood, breast, bowel, gastro-oesophageal, lung, oral, ovarian, pancreas, prostate, renal tract and uterine cancers. Cancers were recorded on any one of four linked data sources (general practitioner (GP), mortality, hospital or cancer records).

Results: We identified 228,241 incident cases during follow-up of the 11 types of cancer. Of these 25,444 were blood; 41,315 breast; 32,626 bowel, 12,808 gastro-oesophageal; 32,187 lung; 4811 oral; 6635 ovarian; 7119 pancreatic; 35,256 prostate; 23,091 renal tract; 6949 uterine cancers. The lung cancer algorithm had the best performance with an R(2) of 64.2%; D statistic of 2.74; receiver operating characteristic curve statistic of 0.91 in women. The sensitivity for the top 10% of women at highest risk of lung cancer was 67%. Performance of the algorithms in men was very similar to that for women.

Conclusions: We have developed and validated a prediction models to quantify absolute risk of 11 common cancers. They can be used to identify patients at high risk of cancers for prevention or further assessment. The algorithms could be integrated into clinical computer systems and used to identify high-risk patients.

Web calculator: There is a simple web calculator to implement the Qcancer 10 year risk algorithm together with the open source software for download (available at http://qcancer.org/10yr/).

Keywords: PRIMARY CARE; QResearch; cancer.

Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Figures

Figure 1
Figure 1
Showing graphs of the adjusted HRs for the fractional polynomial terms for age for each cancer.
Figure 2
Figure 2
Showing graphs of the adjusted HRs for the fractional polynomial terms for body mass index for each cancer.
Figure 3
Figure 3
Showing graphs of the adjusted HRs for fractional polynomial terms for Townsend deprivation score for each cancer.
Figure 4
Figure 4
Showing graphs of the adjusted HRs for the interactions between age and family history for each relevant cancer.
Figure 5
Figure 5
Showing graphs of the adjusted HRs for the interactions between age and smoking status for each relevant cancer.
Figure 6
Figure 6
Showing the mean predicted risks and observed risks at 10 years by tenth of predicted risk applying each algorithm to all women in the validation cohort.
Figure 7
Figure 7
Showing the mean predicted risks and observed risks at 10 years by tenth of predicted risk applying each algorithm to all men in the validation cohort.
Figure 8
Figure 8
Showing the web calculator for an example patient.

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