Risk factors and risk prediction models for colorectal cancer metastasis and recurrence: an umbrella review of systematic reviews and meta-analyses of observational studies

Wei Xu, Yazhou He, Yuming Wang, Xue Li, Jane Young , John PA Ioannidis, Malcolm Dunlop, Evropi Theodoratou

Research output: Contribution to journalArticlepeer-review

Abstract

Background: There 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. Methods: We 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. Results: Thirty-four risk factors for CRC metastasis and 17 for recurrence were investigated. 12/34 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. Conclusions: This 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.
Original languageEnglish
Article number172
JournalBMC Medicine
Volume18
Issue number1
Early online date26 Jun 2020
DOIs
Publication statusE-pub ahead of print - 26 Jun 2020

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