A predictive nomogram model for overall survival in obstructive colorectal cancer based on clinical and laboratory indicators.

Abstract

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 hospital between February 2019 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 [hazard ratio (HR) =1.917, 95% confidence interval (CI): 1.005-3.657, P=0.048], tumor grade (HR =0.229, 95% CI: 0.096-0.543, P<0.001), carbohydrate antigen 19-9 (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=0.02), 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 demonstrated good discriminatory ability, with area under the curve (AUC) values of 0.721 in the training cohort and 0.776 in the validation cohort. Additionally, the model exhibited satisfactory calibration and clinical utility, as evidenced by DCA.

Conclusions: The nomogram (incorporating M stage, tumor grade, CA19-9, AGR, PLR) provides individualized prognosis for oCRC patients, and may aid clinical risk stratification and therapeutic decision-making.