Combining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon cancer

Inmaculada Arostegui, Nerea Gonzalez, Nerea Fernández-De-Larrea, Santiago Lázaro-Aramburu, Marisa Baré, Maximino Redondo, Cristina Sarasqueta, Susana Garcia-Gutierrez, José M. Quintana

    Research output: Contribution to journalArticleResearchpeer-review

    5 Citations (Scopus)


    © 2018 Arostegui et al. Introduction: Colorectal cancer is one of the most frequently diagnosed malignancies and a common cause of cancer-related mortality. The aim of this study was to develop and validate a clinical predictive model for 1-year mortality among patients with colon cancer who survive for at least 30 days after surgery. Methods: Patients diagnosed with colon cancer who had surgery for the first time and who survived 30 days after the surgery were selected prospectively. The outcome was mortality within 1 year. Random forest, genetic algorithms and classification and regression trees were combined in order to identify the variables and partition points that optimally classify patients by risk of mortality. The resulting decision tree was categorized into four risk categories. Split-sample and bootstrap validation were performed. Identifier: NCT02488161. Results: A total of 1945 patients were enrolled in the study. The variables identified as the main predictors of 1-year mortality were presence of residual tumor, American Society of Anesthesiologists Physical Status Classification System risk score, pathologic tumor staging, Charlson Comorbidity Index, intraoperative complications, adjuvant chemotherapy and recurrence of tumor. The model was internally validated; area under the receiver operating characteristic curve (AUC) was 0.896 in the derivation sample and 0.835 in the validation sample. Risk categorization leads to AUC values of 0.875 and 0.832 in the derivation and validation samples, respectively. Optimal cut-off point of estimated risk had a sensitivity of 0.889 and a specificity of 0.758. Conclusion: The decision tree was a simple, interpretable, valid and accurate prediction rule of 1-year mortality among colon cancer patients who survived for at least 30 days after surgery.
    Original languageEnglish
    Pages (from-to)235-251
    JournalClinical Epidemiology
    Publication statusPublished - 6 Mar 2018


    • 1-year-mortality
    • Clinical prediction rules
    • Colonic neoplasms
    • Colorectal surgery
    • Prediction model
    • Tree-based methods


    Dive into the research topics of 'Combining statistical techniques to predict postsurgical risk of 1-year mortality for patients with colon cancer'. Together they form a unique fingerprint.

    Cite this