Abstract
BACKGROUND: Comorbidities may complicate medical situations and have an impact on the treatment decisions and poor survival of cancer patients. How comorbidities cluster together and ultimately affect patients' outcomes in gastrointestinal tract cancer (GTC) is a poorly understood area. METHODS: In a multicenter prospective observational study from 2012 to 2021, we grouped the comorbidities of patients with GTC by latent class analysis, obtaining two comorbidity classes. Cox regression models were initially used to predict mortality. LASSO techniques were used to reduce the dimension. The final model included the comorbidity classes and nine more predictors. Additionally, the performance of different simple multimorbidity measures were compared using the Bayesian information criterion (BIC), ROC curves and C-index. Finally, the performance of the final model was analyzed using ROC curves, calibration curves and decision curves. The nomogram was drawn to evaluate the model. RESULTS: We included 10,019 patients and obtained two comorbidity classes. Class 2 patients have a higher incidence of comorbidities, and a lower survival rate compared to Class 1 (P < 0.001). Compared to models containing the number of comorbidities or only a single comorbidity, the final model with the comorbidity classes has the highest AUC and C-index, as well as the lowest BIC, indicating this model has the best predictive performance. CONCLUSION: We identified two classes of comorbidities that were associated with overall survival in patients with GTC. The combination of different comorbidities class plays a vital role in the prognosis of GTC.