A novel risk prediction nomogram for early death in patients with resected synchronous multiple primary colorectal cancer based on the SEER database

Abstract

Abstract Background: Synchronous multiple primary colorectal cancer (SMPCC) involves the simultaneous occurrence of 2 or more independent primary malignant tumors in the colon or rectum. Although SMPCC is rare, it results in a higher incidence of postoperative complications and mortality compared to patients with single primary colorectal cancer (SPCRC). Methods: The clinical factors and survival outcomes of SMPCC patients registered on the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2017 were extracted. The patients were divided into the training and validation cohorts using a ratio of 7:3. Univariate and multivariate logistic regression analyses were used to identify the independent risk factors for early death. The performance of the nomogram was evaluated using the concordance index (C-index), calibration curves, and the area under the curve (AUC) of a receiver operating characteristics curve (ROC). A decision curve analysis (DCA) was used to evaluate the clinical utility of the model in relation to the standard TNM system. Results: A total of 4386 SMPCC patients were enrolled in the study and randomly assigned to the training (n = 3070) and validation (n = 1316) cohorts. The multivariate logistic analysis identified age, chemotherapy, radiotherapy, T stage, N stage, and M stage as independent risk factors for all-cause and cancer-specific early death. The marital status was associated with all-cause early death, and the tumor grade was associated with cancer-specific early death. In the training cohort, the nomogram achieved a C-index of 0.808 (95% CI, 0.784-0.832) and 0.843 (95% CI, 0.816-0.870) for all-cause and cancer-specific early death, respectively. Following validation, the C-index was 0.797 (95% CI, 0.758-0.837) for all-cause early death and 0.832 (95% CI, 0.789-0.875) for cancer-specific early death. The ROC and calibration curves indicated that the model had good stability and reliability. The DCA showed that the nomogram had a better clinical net value than the TNM staging system. Conclusion: Our nomogram can provide a simple and accurate tool for clinicians to predict the risk of early death in SMPCC patients undergoing surgery and could be used to optimize the treatment according to the patient's needs.