PT  - JOURNAL ARTICLE
AU  - Todd Smith
AU  - David C Muller
AU  - Karel G M Moons
AU  - Amanda J Cross
AU  - Mattias Johansson
AU  - Pietro Ferrari
AU  - Guy Fagherazzi
AU  - Petra H M Peeters
AU  - Gianluca Severi
AU  - Anika Hüsing
AU  - Rudolf Kaaks
AU  - Anne Tjonneland
AU  - Anja Olsen
AU  - Kim Overvad
AU  - Catalina Bonet
AU  - Miguel Rodriguez-Barranco
AU  - Jose Maria Huerta
AU  - Aurelio Barricarte Gurrea
AU  - Kathryn E Bradbury
AU  - Antonia Trichopoulou
AU  - Christina Bamia
AU  - Philippos Orfanos
AU  - Domenico Palli
AU  - Valeria Pala
AU  - Paolo Vineis
AU  - Bas Bueno-de-Mesquita
AU  - Bodil Ohlsson
AU  - Sophia Harlid
AU  - Bethany Van Guelpen
AU  - Guri Skeie
AU  - Elisabete Weiderpass
AU  - Mazda Jenab
AU  - Neil Murphy
AU  - Elio Riboli
AU  - Marc J Gunter
AU  - Krasimira Jekova Aleksandrova
AU  - Ioanna Tzoulaki
TI  - Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies
AID  - 10.1136/gutjnl-2017-315730
DP  - 2019 Apr 01
TA  - Gut
PG  - 672--683
VI  - 68
IP  - 4
4099  - http://gut.bmj.com/content/68/4/672.short
4100  - http://gut.bmj.com/content/68/4/672.full
SO  - Gut2019 Apr 01; 68
AB  - Objective To systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.Design Models were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability).Results The systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC.Conclusion Several of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained.