AI Article Synopsis
- The study focuses on the rising issue of end-stage renal disease (ESRD) in developing countries and emphasizes the need for advanced statistical models to address patient variability.
- Researchers analyzed data from 170 hemodialysis patients over a decade, utilizing a gamma frailty mixed cure Weibull model (MC-WG) to investigate factors affecting patient survival and time to death.
- Findings revealed that nearly half of the patients died, with identifiable risk factors including age, gender, and serum urea levels; the MC-WG model showed significant impacts from diabetes and other variables that differed from traditional Cox PH model results.
Article Abstract
Background: Along with the increasing prevalence of ESRD in developing countries, the use of more up-to-date statistical models is highly recommended. It is crucial to control potential cure pattern and heterogenicity among patients.
Methods: In this longitudinal study, the data of 170 hemodialysis patients who visited the dialysis department of Shafa Hospital in Kerman from 2006 to 2016 were collected. To provides robust estimates the time to event data (death) were analyzed with a gamma frailty mixed cure Weibull model (MC-WG) using Bayesian inference.
Results: About 49% of patients experienced the death and median survival time was 37.5 months. Older patients (0.264), female patients (0.269), and patients with higher mean serum urea levels (0.186) had a higher risk of death. Moreover, we observe a decrease in death with increase in Creatine (Cr).
Conclusion: In the MC-WG Bayesian model, the diabetes, AST, calcium, phosphorus and uric acid variables had a significant effect on the survival of hemodialysis patients, while they were not significant in the Cox PH model. The results of MC-WG Bayesian model are more consistent with other studies.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11490321 | PMC |
| http://dx.doi.org/10.18502/ijph.v53i9.16464 | DOI Listing |
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Long-Term Survival of Patient with End-Stage Renal Disease Using Bayesian Mixture Cure Rate Frailty Models.
Iran J Public Health
September 2024
Department of Biostatistics and Epidemiology, School of Public Health, Kerman University of Medical Sciences, Kerman, Iran.
Article Synopsis
- The study focuses on the rising issue of end-stage renal disease (ESRD) in developing countries and emphasizes the need for advanced statistical models to address patient variability.
- Researchers analyzed data from 170 hemodialysis patients over a decade, utilizing a gamma frailty mixed cure Weibull model (MC-WG) to investigate factors affecting patient survival and time to death.
- Findings revealed that nearly half of the patients died, with identifiable risk factors including age, gender, and serum urea levels; the MC-WG model showed significant impacts from diabetes and other variables that differed from traditional Cox PH model results.
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