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Improving survival in end-stage renal disease: A case study
M H Elsensohn1,2,3, E Dantony1,2,3, J Iwaz1,2,3
1Hospices Civils de Lyon, Pôle Santé Publique, Service de Biostatistique-Bioinformatique, Lyon, France.
Statistical Methods in Medical Research
|November 10, 2018
Summary
This study shows that multi-state models and crude probability of death can assess end-stage renal disease (ESRD) treatment benefits. This helps in managing patient survival and understanding ESRD-related versus unrelated deaths.
Area of Science:
- Nephrology
- Biostatistics
- Public Health
Background:
- Increasing life expectancy leads to more end-stage renal disease (ESRD) patients.
- Renal replacement therapies (RRTs) have improved survival rates for ESRD patients.
- Current RRT practices' impact on patient survival requires investigation.
Purpose of the Study:
- To investigate how current RRT practices affect patient survival.
- To estimate ESRD-related and ESRD-unrelated death proportions.
- To quantify the impact of RRT on patient survival using restricted mean survival time (RMST).
Main Methods:
- Utilized a multi-state model for RRT transitions and death.
- Employed the "crude probability of death" concept to differentiate death causes.
- Solved Kolmogorov differential equations to predict patient trajectories.
- Quantified survival benefit using RMST compared to healthy individuals.
Main Results:
- ESRD-unrelated deaths were minimal in young patients but significant in older adults (≥70 years).
- Older patients without diabetes had a higher proportion of expected death due to longer lifespans.
- For 75-year-old men starting RRT, RMST was 61% (with diabetes) and 69% (without diabetes) of healthy men's RMST.
Conclusions:
- Multi-state models combined with "crude probability of death" are effective for ESRD research.
- This approach aids in evaluating RRT benefits and optimizing long-term patient management.
- Understanding death causes is crucial for personalized ESRD care and survival prediction.
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