Development of a model based on the age-adjusted Charlson comorbidity index to predict survival for resected perihilar cholangiocarcinoma.

Background: Perihilar cholangiocarcinoma (pCCA) has a poor prognosis and urgently needs a better predictive method. The predictive value of the age-adjusted Charlson comorbidity index (ACCI) for the long-term prognosis of patients with multiple malignancies was recently reported. However, pCCA is one of the most surgically difficult gastrointestinal tumors with the poorest prognosis, and the value of the ACCI for the prognosis of pCCA patients after curative resection is unclear.

Aim: To evaluate the prognostic value of the ACCI and to design an online clinical model for pCCA patients.

Methods: Consecutive pCCA patients after curative resection between 2010 and 2019 were enrolled from a multicenter database. The patients were randomly assigned 3:1 to training and validation cohorts. In the training and validation cohorts, all patients were divided into low-, moderate-, and high-ACCI groups. Kaplan-Meier curves were used to determine the impact of the ACCI on overall survival (OS) for pCCA patients, and multivariate Cox regression analysis was used to determine the independent risk factors affecting OS. An online clinical model based on the ACCI was developed and validated. The concordance index (C-index), calibration curve, and receiver operating characteristic (ROC) curve were used to evaluate the predictive performance and fit of this model.

Results: A total of 325 patients were included. There were 244 patients in the training cohort and 81 patients in the validation cohort. In the training cohort, 116, 91 and 37 patients were classified into the low-, moderate- and high-ACCI groups. The Kaplan-Meier curves showed that patients in the moderate- and high-ACCI groups had worse survival rates than those in the low-ACCI group. Multivariable analysis revealed that moderate and high ACCI scores were independently associated with OS in pCCA patients after curative resection. In addition, an online clinical model was developed that had ideal C-indexes of 0.725 and 0.675 for predicting OS in the training and validation cohorts. The calibration curve and ROC curve indicated that the model had a good fit and prediction performance.

Conclusion: A high ACCI score may predict poor long-term survival in pCCA patients after curative resection. High-risk patients screened by the ACCI-based model should be given more clinical attention in terms of the management of comorbidities and postoperative follow-up.