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Published on: September 16, 2022
Predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients: A
Yanna Yu1,2, Fen Li3, Zhan Wang4
1Department of Nephrology The First Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou China.
Insights
The Claims-based disease-specific refinements matching translation to ICD-10 and flexibility (CDMF-CCI) model accurately predicts mortality risk in Chinese maintenance hemodialysis (MHD) patients. This comorbidity index model outperforms the Liu model and individual indices for predicting death risk in MHD patients.
Area of Science:
- Nephrology
- Public Health
- Biostatistics
Background:
- Maintenance hemodialysis (MHD) patients face significant mortality risks.
- Comorbidity prediction models offer improved death risk assessment over single indices.
- Application of these models in Chinese MHD populations remains limited.
Purpose of the Study:
- To evaluate the predictive utility of comorbidity index models for mortality risk in Chinese MHD patients.
- To compare the performance of the CDMF-CCI model and the Liu model in this population.
Main Methods:
- A cohort of Chinese MHD patients was studied retrospectively.
- The Claims-based disease-specific refinements matching translation to ICD-10 and flexibility (CDMF-CCI) model and the Liu model were assessed.
- Univariate and multivariate Cox regression analyses were employed to evaluate predictive effects on survival rates.
Main Results:
- The CDMF-CCI model demonstrated superior predictive accuracy for mortality, with a hazard ratio of 1.190 (P = 0.037).
- The Liu model did not effectively identify high-risk individuals among MHD patients.
- Leading causes of death included sudden demise, cardiovascular disease, pulmonary infection, and stroke.
Conclusions:
- The CDMF-CCI model exhibits superior predictive accuracy for mortality in MHD patients compared to univariate parameters and the Liu model.
- This comorbidity index model can aid in risk stratification and management of Chinese MHD patients.
Objectives:
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 maintenance hemodialysis (MHD) patients. Therefore, the objective of this study was to evaluate the utility of comorbidity index models in predicting mortality risk among Chinese MHD patients.
Methodology:
The MHD patients in the First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine were taken 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 calculations 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 4 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 1.190 (P = 0.037). Conversely, the Liu model failed to identify high-risk individuals.
Conclusion:
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.
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