Prediction of cancer-specific survival and overall survival in middle-aged and older patients with rectal adenocarcinoma using a nomogram model

•Summarise the established knowledge on this subject.1Middle-aged and older patients are at high risk of rectal adenocarcinoma; however, studies comprehensively analysing its predictors and the construction of visual nomogram models are limited.2Most studies that reported on the prediction of colorectal cancer-related survival models had limited samples and included data from a single centre. The included predictors were limited, or the evaluation indicators were not easy to obtain, greatly limiting clinical application.3With the advancement of medical care, the clinical outcomes of patients with rectal adenocarcinoma have changed. Therefore, new, more comprehensive, and practical indicators are required for constructing clinical prediction models to effectively determine the prognosis of patients.•What are the significant and/or new findings of this study?1We included demographic and clinicopathological data from thousands of middle-aged and elderly patients with rectal adenocarcinoma to find relevant prognostic factors. New cut-offs were developed and used for the construction of nomograms.2The nomogram constructed this time has excellent predictive ability and clinical decision-making ability, and has good clinical practicability.3The nomogram survival prediction model constructed this time can effectively help evaluate the prognosis of middle-aged and elderly patients with rectal adenocarcinoma and guide the selection of clinical treatment measures. To develop a new nomogram tool for predicting survival in middle-aged and elderly patients with rectal adenocarcinoma. A total of 6,116 patients were randomly assigned in a 7:3 ratio to training and validation cohorts. Univariate and multivariate Cox proportional hazards regression analyses were used to identify independent prognostic factors associated with overall survival (OS) and cancer-specific survival (CSS) in the training set, and two nomogram prognostic models were constructed. The validity, accuracy, discrimination, predictive ability, and clinical utility of the models were assessed based on the concordance index (C-index), area under the receiver operating characteristics (ROC) curve, time-dependent area under the ROC curve (AUC), Kaplan-Meier survival curve, and decision curve analyses. Predictors of OS and CSS were identified, and nomograms were successfully constructed. The calibration discrimination for both the OS and CSS nomogram prediction models was good (C-index: 0.763 and 0.787, respec