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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.
To systematically review published studies on risk prediction models for sarcopenia in patients undergoing MHD.
Systematic review and meta-analysis of observational studies.
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.
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.
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.
CRD42023476067.
Previous research has demonstrated the effectiveness of virtual reality (VR) technology in many application areas. However, there is a clear gap in the literature regarding its effects on depression and anxiety in older adults with chronic diseases.
This review aimed to assess the effectiveness of VR interventions for depression and anxiety in older adults with chronic diseases.
Seven electronic databases were systematically searched from their inception to April 9, 2024. Two researchers evaluated methodological quality using RoB (version 2.0) and performed meta-analyses using RevMan (version 5.4) and Stata (version 16.0) software.
This review included 19 randomized controlled studies. Meta-analysis revealed that VR significantly improved depression (standard mean difference [SMD] = −0.67, 95% confidence interval [CI] [−0.90, −0.45], p < .00001) and anxiety (SMD = −0.76, 95% CI [−0.95, −0.57], p < .00001) in older adults with chronic diseases, improved their quality of life (SMD = 0.39, 95% CI [0.17, 0.61], p = .0006) and positive emotions (SMD = 5.65, 95% CI [3.61, 7.69], p < .00001), and relieved stress (SMD = −1.08, 95% CI [−1.52, −0.64], p < .00001). However, the difference in self-efficacy was statistically non-significant (SMD = 1.01, 95% CI [−0.48, 2.50], p = .19).
The results of this systematic review provide important evidence for developing interventions to improve the mental health of older adults with chronic diseases.
FreshRSS 