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

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. ClinicalTrials.gov 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.We are grateful for the support of the 22 participating hospitals, as well as the clinicians and staff members of the various services, research, quality units and medical records sections of these hospitals. We also gratefully acknowledge the patients who participated in the study. We would like to thank Editage (www.editage.com) for English language editing. We also wish to thank the anonymous referees for providing comments, which led to substantial improvement of the article. Financial support for this study was provided, in part, by grants from the Instituto de Salud Carlos III (PS09/00314, PS09/00910, PS09/00746, PS09/00805, PI09/90460, PI09/90490, PI09/90453, PI09/90441, PI09/90397 and the thematic network REDISSEC - Red de Investigacion en Servicios de Salud en Enfermedades Cronicas), co-funded by European Regional Development Fund/European Social Fund (ERDF/ESF "Investing in your future"); the Research Committee of the Hospital Galdakao; the Department of Health and the Department of Education, Language Policy and Culture from the Basque Government (2010111098, IT620-13 and BERC 2014-2017 program); the Spanish Ministry of Economy and Competitiveness MINECO and FEDER (MTM2013-40941-P, MTM2016-74931-P and BCAM Severo Ochoa excellence accreditation SEV-2013-0323). The funding agreement ensured the authors' independence in designing the study, interpreting the data, writing and publishing the report