Predicting Sarcopenia Risk in Dialysis Patients
Author Information
Author(s): Hou Zhuoer, Li Xiaoyan, Yang Lili, Liu Ting, Lv Hangpeng, Sun Qiuhua
Primary Institution: Zhejiang Chinese Medical University
Hypothesis
The study aims to systematically review and critically evaluate currently available predictive models for sarcopenia in dialysis patients.
Conclusion
Future research should focus on validating and improving existing predictive models or developing new models using rigorous methods.
Supporting Evidence
- 104,454 studies were screened, resulting in 13 predictive models being included.
- The incidence of sarcopenia in dialysis patients ranged from 6.6% to 34.4%.
- Common predictors included age and body mass index.
- Reported AUCs for the models ranged from 0.81 to 0.95.
- All studies had a high risk of bias, mainly due to poor reporting.
- Future research should focus on improving model validation.
- Most models are presented in the form of a nomogram for easier use.
- More high-quality research is needed to advance this field.
Takeaway
This study looked at different ways to predict muscle loss in patients on dialysis, finding that many models exist but need better testing.
Methodology
The authors systematically searched five databases for observational studies that developed or validated predictive models for sarcopenia in dialysis patients.
Potential Biases
Twelve studies had a high risk of bias mainly due to poor reporting of outcomes and analysis domains.
Limitations
Most studies had a high risk of bias, and many lacked large sample sizes and multi-center external validation.
Participant Demographics
The included studies focused on adults (≥ 18 years) undergoing dialysis, with varying age ranges.
Digital Object Identifier (DOI)
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