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

Abstract

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.

Keywords: 1-year-mortality; clinical prediction rules; colonic neoplasms; colorectal surgery; prediction model; tree-based methods.

Figure 1

Variable importance for the top 30 predictors of 1-year mortality selected by the random forest. Abbreviations: ASA, American Society of Anesthesiologists; CA, carbohydrate antigen; CEA, carcinoembryonic antigen; CRC, colon or rectum cancer; ICU, intensive care unit; pTNM, histopathologic tumor–node–metastasis.

Figure 2

Results of the CART analysis for 1-year mortality in the derivation sample. Notes: Each branch shows the classification variable and each node shows the number of subjects and the estimated probability of 1-year mortality on that node. Final nodes are in bold using different line types for stratified risk groups: low (dotted), medium (dashed), high (dotted dash) and very high (solid). Application to the validation sample is shown below each node in light gray-colored boxes. Abbreviations: ASA, American Society of Anesthesiologists; CART, classification and regression trees; CCI, Charlson Comorbidity Index; Chem, adjuvant chemotherapy; IntraCom, intraoperative complications; pTNM, histopathologic tumor–node–metastasis; R1y, recurrence of the tumor; ResTum, residual tumor.

Figure 3

ROC curve for predicted 1-year mortality by the CART analyses. Notes: Solid line applies for derivation sample and dashed line for validation sample. AUC=0.896 and 95% CI is (0.856, 0.936) for derivation sample and AUC=0.835 and 95% CI is (0.776, 0.895) for validation sample. The cut-off point of estimated 1-year mortality risk dichotomization for optimal sensitivity–specificity combination for derivation sample is shown with the corresponding specificity and sensitivity values. Abbreviations: AUC, area under the receiver operating characteristic curve; CART, classification and regression trees; CI, confidence interval; ROC, receiver operating characteristic.