• Wei Xu, Yazhou He, Yuming Wang, Xue Li, Jane Young, IoannidisJohn P AJPADepartment of Medicine, School of Medicine, Stanford University, Stanford, CA, 94305, USA.Department of Epidemiology and Population Health, School of Medicine, Stanford University, Stanford, CA, 94305, USA.Department of Biomedical Data, Malcolm G Dunlop, and Evropi Theodoratou.
• Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh, EH8 9AG, UK.
• Bmc Med. 2020 Jun 26; 18 (1): 172172.

BackgroundThere is a clear need for systematic appraisal of models/factors predicting colorectal cancer (CRC) metastasis and recurrence because clinical decisions about adjuvant treatment are taken on the basis of such variables.MethodsWe conducted an umbrella review of all systematic reviews of observational studies (with/without meta-analysis) that evaluated risk factors of CRC metastasis and recurrence. We also generated an updated synthesis of risk prediction models for CRC metastasis and recurrence. We cross-assessed individual risk factors and risk prediction models.ResultsThirty-four risk factors for CRC metastasis and 17 for recurrence were investigated. Twelve of 34 and 4/17 risk factors with p < 0.05 were estimated to change the odds of the outcome at least 3-fold. Only one risk factor (vascular invasion for lymph node metastasis [LNM] in pT1 CRC) presented convincing evidence. We identified 24 CRC risk prediction models. Across 12 metastasis models, six out of 27 unique predictors were assessed in the umbrella review and four of them changed the odds of the outcome at least 3-fold. Across 12 recurrence models, five out of 25 unique predictors were assessed in the umbrella review and only one changed the odds of the outcome at least 3-fold.ConclusionsThis study provides an in-depth evaluation and cross-assessment of 51 risk factors and 24 prediction models. Our findings suggest that a minority of influential risk factors are employed in prediction models, which indicates the need for a more rigorous and systematic model construction process following evidence-based methods.

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