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Risk Prediction Models for Sarcopenia in Patients Undergoing Maintenance Haemodialysis: A Systematic Review and
Qing Yang1, Wenting Ji1, Julan Guo2
1College of Nursing, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China.
Background:
The number of risk prediction models for sarcopenia in patients undergoing maintenance haemodialysis (MHD) is increasing. However, the quality, applicability, and reporting adherence of these models in clinical practice and future research remain unknown.
Objective:
To systematically review published studies on risk prediction models for sarcopenia in patients undergoing MHD.
Design:
Systematic review and meta-analysis of observational studies.
Methods:
This systematic review adhered to the PRISMA guidelines. Search relevant domestic and international databases, which were searched from the inception of the databases until November 2023. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist was used to extract data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was used to assess the risk of bias and applicability. The Transparent Reporting of a Multivariate Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) was used to assess the reporting adherence.
Results:
A total of 478 articles were retrieved, and 12 prediction models from 11 articles were included after the screening process. The incidence of sarcopenia in patients undergoing MHD was 16.38%-37.29%. The reported area under the curve (AUC) ranged from 0.73 to 0.955. All studies had a high risk of bias, mainly because of inappropriate data sources and poor reporting in the field of analysis. The combined AUC value of the six validation models was 0.91 (95% confidence interval: 0.87-0.94), indicating that the model had a high discrimination.
Conclusion:
Although the included studies reported to some extent the discrimination of predictive models for sarcopenia in patients undergoing MHD, all studies were assessed to have a high risk of bias according to the PROBAST checklist, following the reporting guidelines outlined in the TRIPOD statement, and adherence was incomplete in all studies.
Registration Number:
CRD42023476067.
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