Comorbidity index model to predict the death risk of hemodialysis patients: a single-center observational cohort study

Abstract Background Comorbidity prediction models have been demonstrated to offer more comprehensive and accurate predictions of death risk compared to single indices. However, their application in China has been limited, particularly among chronic kidney disease patients. Therefore, the objective of this study was to evaluate the utility of comorbidity index models in predicting mortality risk among Chinese maintenance hemodialysis (MHD) patients. Methods Take the MHD patients in the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine as the subjects, Claims-based Disease-specific refinements Matching translation to ICD-10 and Flexibility (CDMF-CCI) model and Liu model were selected as the candidate models for this verification research. Univariate and multivariate Cox regression calculation were used to analyze the independent predictive effect of the models on survival rate. Results Annually, nearly 500 patients undergo hemodialysis treatment. From January 2019 to June 2022, a total of 199 patients succumbed, with a mean age of 65.2 years. During these four years, the mortality rates were 13.04%, 9.68%, 11.69%, and 6.39%, respectively. The leading causes of death were sudden demise (82 patients, 41.2%), cardiovascular disease (48 patients, 24.1%), pulmonary infection (33 patients, 16.5%), and stroke (19 patients, 9.5%). When compared to individual indices, the CDMF-CCI model displayed more accurate and predictive results, with an HR of 1.1. Conversely, the Liu model failed to identify high-risk individuals. Conclusions The MHD patients face a significant risk of mortality. When compared to univariate parameters and the Liu model, the CDMF-CCI model exhibits superior predictive accuracy for mortality in MHD patients.