Predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients: A comprehensive single‐center observational cohort study
Abstract 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 obj...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Aging Medicine |
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| Online Access: | https://doi.org/10.1002/agm2.12384 |
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| author | Yanna Yu Fen Li Zhan Wang Zhibin Ni Shu Zhang Weihong Zhao Xiaohua Pei |
| author_facet | Yanna Yu Fen Li Zhan Wang Zhibin Ni Shu Zhang Weihong Zhao Xiaohua Pei |
| author_sort | Yanna Yu |
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| description | Abstract 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. |
| format | Article |
| id | doaj-art-dd1d0f881f2f4156a276b03d267afafd |
| institution | Kabale University |
| issn | 2475-0360 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Aging Medicine |
| spelling | doaj-art-dd1d0f881f2f4156a276b03d267afafd2025-01-04T08:39:02ZengWileyAging Medicine2475-03602024-12-017673774310.1002/agm2.12384Predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients: A comprehensive single‐center observational cohort studyYanna Yu0Fen Li1Zhan Wang2Zhibin Ni3Shu Zhang4Weihong Zhao5Xiaohua Pei6Department of Nephrology The First Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou ChinaDepartment of Nephrology The Eighth People's Hospital of Qingdao Qingdao ChinaDepartment of Epidemiology, Center for Global Health, School of Public Health Nanjing Medical University Nanjing ChinaDepartment of Nephrology The First Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou ChinaDepartment of Nephrology The First Affiliated Hospital of Guangzhou University of Chinese Medicine Guangzhou ChinaDepartment of Geriatric Nephrology The First Affiliated Hospital of Nanjing Medical University Nanjing ChinaDepartment of Geriatric Nephrology The First Affiliated Hospital of Nanjing Medical University Nanjing ChinaAbstract 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.https://doi.org/10.1002/agm2.12384chronic kidney diseaseend‐stage kidney diseasemaintenance hemodialysismortalityrenal function |
| spellingShingle | Yanna Yu Fen Li Zhan Wang Zhibin Ni Shu Zhang Weihong Zhao Xiaohua Pei Predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients: A comprehensive single‐center observational cohort study Aging Medicine chronic kidney disease end‐stage kidney disease maintenance hemodialysis mortality renal function |
| title | Predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients: A comprehensive single‐center observational cohort study |
| title_full | Predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients: A comprehensive single‐center observational cohort study |
| title_fullStr | Predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients: A comprehensive single‐center observational cohort study |
| title_full_unstemmed | Predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients: A comprehensive single‐center observational cohort study |
| title_short | Predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients: A comprehensive single‐center observational cohort study |
| title_sort | predictive accuracy of comorbidity index models in assessing mortality risk among hemodialysis patients a comprehensive single center observational cohort study |
| topic | chronic kidney disease end‐stage kidney disease maintenance hemodialysis mortality renal function |
| url | https://doi.org/10.1002/agm2.12384 |
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