A Predictive Nomogram Model for Overall Survival in Obstructive Colorectal Cancer Based on Clinical and Laboratory Indicators

Background Obstructive colorectal cancer (oCRC) correlates with advanced disease and poor outcomes. This study aimed to identify independent prognostic factors using clinical and laboratory data and construct a predictive nomogram for oCRC patients' individualized survival estimation and clinical decision-making. Methods A retrospective cohort of 167 patients with histologically confirmed oCRC admitted to Fujian Medical University Union Hospital between February 2010 and February 2021 was analyzed. Patients were randomly divided into a training cohort (n = 116) and a validation cohort (n = 51) in a 7:3 ratio. Prognostic variables were identified using univariate and multivariate Cox proportional hazards regression analyses. A nomogram was developed based on independent prognostic factors. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) to evaluate its discrimination, calibration, and clinical utility, respectively. Results Multivariate Cox regression analysis identified five independent prognostic factors: M stage (HR = 1.917, 95% CI:1.005–3.657, P = 0.048), tumor grade (HR = 0.229, 95% CI: 0.096–0.543, P < 0.001), CA19-9 (HR = 3.919, 95% CI: 2.038–7.538, P < 0.001), albumin-to-globulin ratio (AGR; HR = 2.103, 95% CI:1.158–3.817, P = 0.015), and platelet-to-lymphocyte ratio (PLR; HR = 1.873, 95% CI: 1.013–3.464, P = 0.045). These variables were incorporated into a prognostic nomogram. The model showed good discriminatory ability (AUC = 0.721 in the training set; 0.776 in the validation set), reliable calibration, and strong clinical applicability as demonstrated by DCA. Conclusion The nomogram (incorporating M stage, tumor grade, CA19-9, AGR, PLR) provides accurate, individualized prognosis for oCRC patients, and may aid clinical risk stratification and therapeutic decision-making.