Correction: External validation of risk prediction models for incident colorectal cancer using UK Biobank

Abstract Background Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. Methods We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. Results We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. Conclusions Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

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Risk Prediction Models of Locoregional Failure After Radical Cystectomy for Urothelial Carcinoma: External Validation in a Cohort of Korean Patients

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2014.04.049 ◽

2014 ◽

Vol 89 (5) ◽

pp. 1032-1037 ◽

Cited By ~ 12

Author(s):

Ja Hyeon Ku ◽

Myong Kim ◽

Chang Wook Jeong ◽

Cheol Kwak ◽

Hyeon Hoe Kim

Keyword(s):

Urothelial Carcinoma ◽

Radical Cystectomy ◽

Risk Prediction ◽

Prediction Models ◽

External Validation ◽

Risk Prediction Models ◽

Korean Patients ◽

Locoregional Failure

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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

Gut ◽

10.1136/gutjnl-2017-315730 ◽

2018 ◽

Vol 68 (4) ◽

pp. 672-683 ◽

Cited By ~ 6

Author(s):

Todd Smith ◽

David C Muller ◽

Karel G M Moons ◽

Amanda J Cross ◽

Mattias Johansson ◽

...

Keyword(s):

Colorectal Cancer ◽

Systematic Review ◽

Prediction Models ◽

Large Population ◽

External Validation ◽

Population Based ◽

Prognostic Models ◽

Uk Biobank ◽

Asymptomatic Individuals ◽

The Uk

ObjectiveTo systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.DesignModels 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).ResultsThe 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.ConclusionSeveral 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.

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External Validation and Recalibration of Risk Prediction Models for Acute Traumatic Brain Injury among Critically Ill Adult Patients in the United Kingdom

Journal of Neurotrauma ◽

10.1089/neu.2014.3628 ◽

2015 ◽

Vol 32 (19) ◽

pp. 1522-1537 ◽

Cited By ~ 11

Author(s):

David A. Harrison ◽

Kathryn A. Griggs ◽

Gita Prabhu ◽

Manuel Gomes ◽

Fiona E. Lecky ◽

...

Keyword(s):

Traumatic Brain Injury ◽

United Kingdom ◽

Brain Injury ◽

Critically Ill ◽

Risk Prediction ◽

Prediction Models ◽

External Validation ◽

Risk Prediction Models ◽

The United Kingdom ◽

Acute Traumatic Brain Injury

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External validation of risk prediction models for incident colorectal cancer using UK Biobank

British Journal of Cancer ◽

10.1038/bjc.2017.463 ◽

2018 ◽

Vol 118 (5) ◽

pp. 750-759 ◽

Cited By ~ 15

Author(s):

J A Usher-Smith ◽

A Harshfield ◽

C L Saunders ◽

S J Sharp ◽

J Emery ◽

...

Keyword(s):

Colorectal Cancer ◽

Prediction Models ◽

Characteristic Curve ◽

External Validation ◽

Risk Scores ◽

Prior History ◽

Uk Biobank ◽

Risk Models ◽

Potential Health ◽

Men And Women

Abstract Background: This study aimed to compare and externally validate risk scores developed to predict incident colorectal cancer (CRC) that include variables routinely available or easily obtainable via self-completed questionnaire. Methods: External validation of fourteen risk models from a previous systematic review in 373 112 men and women within the UK Biobank cohort with 5-year follow-up, no prior history of CRC and data for incidence of CRC through linkage to national cancer registries. Results: There were 1719 (0.46%) cases of incident CRC. The performance of the risk models varied substantially. In men, the QCancer10 model and models by Tao, Driver and Ma all had an area under the receiver operating characteristic curve (AUC) between 0.67 and 0.70. Discrimination was lower in women: the QCancer10, Wells, Tao, Guesmi and Ma models were the best performing with AUCs between 0.63 and 0.66. Assessment of calibration was possible for six models in men and women. All would require country-specific recalibration if estimates of absolute risks were to be given to individuals. Conclusions: Several risk models based on easily obtainable data have relatively good discrimination in a UK population. Modelling studies are now required to estimate the potential health benefits and cost-effectiveness of implementing stratified risk-based CRC screening.

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1227Colorectal cancer risk prediction models incorporating lifestyle and biomarker data: Results from the EPIC cohort

International Journal of Epidemiology ◽

10.1093/ije/dyab168.027 ◽

2021 ◽

Vol 50 (Supplement_1) ◽

Author(s):

Krasimira Aleksandrova ◽

Robin Reichmann ◽

Mazda Jenab ◽

Sabina Rinaldi ◽

Rudolf Kaaks ◽

...

Keyword(s):

Colorectal Cancer ◽

High Risk ◽

Risk Prediction ◽

Prediction Models ◽

Lifestyle Factors ◽

Health Concern ◽

Incidence Density ◽

Processed Meat ◽

Risk Prediction Models ◽

Biomarker Data

Abstract Background Colorectal cancer represents a major public health concern and there is a worrying tendency of increasing incidence rates among younger people in the last decades. Risk stratification of high-risk individuals may aid targeted disease prevention. We therefore aimed to evaluate the predictive value of a wide range of lifestyle and biomarker variables using data within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Methods A range of lifestyle, anthropometric and dietary variables in 329,885 participants in the EPIC cohort were evaluated as potential predictors for risk of colorectal cancer over 10 years. Biomarker measurements of 41 parameters were available for 1,320 CRC cases and 1,320 controls selected using incidence density matching. Best sets of predictors were selected using elastic net regularization with bootstrapping. Random survival forest was applied as a novel technique to validate the set of selected predictors taking variable interactions into account. Results The results suggested a set of lifestyle factors including age, waist circumference, height, smoking, alcohol consumption, physical activity, vegetables, dairy products, processed meat, and sugar and confectionary that showed good discrimination (Harrell's C-index: 0.710) and excellent calibration. The analyses further revealed a set of biomarkers that increased the predictive performance beyond age, sex and lifestyle factors. Conclusions Risk prediction models based on lifestyle and biomarker data may prove useful in the identification of individuals at high risk for colorectal cancer. Key messages Risk prediction models incorporating lifestyle and biomarker data could contribute to developing strategies for targeted colorectal cancer prevention.

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