To systematically evaluate and compare the diagnostic accuracy of pressure injury risk assessment tools in critically ill adult patients through a network meta-analysis.
Systematic review and network meta-analysis.
A comprehensive literature search was conducted across PubMed, Embase, Web of Science, and the Cochrane Library from inception to November 2025. Studies reporting the sensitivity and specificity of the Braden, Waterlow, Norton, Cubbin & Jackson, COMHON, and machine learning-based tools in ICU patients were included. A Bayesian network meta-analysis was performed to estimate pooled sensitivity, specificity, diagnostic odds ratio, and summary receiver operating characteristic curve. The methodological quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool.
51 studies involving 30,246 patients were included. The Cubbin & Jackson scale demonstrated relatively higher diagnostic accuracy in the network meta-analysis (e.g., sensitivity 0.81, specificity 0.71), although direct pooled estimates showed a different trade-off (sensitivity 0.90, specificity 0.73). According to the results from network meta-analysis, the pooled diagnostic odds ratio and the summary receiver operating characteristic curve (SORC) for Cubbin & Jackson was 11.64 and 0.74 respectively, but with wide credible intervals, indicating substantial uncertainty. Machine learning-based model and the COMHON scale also exhibited balanced performance, although estimates for COMHON were based on only three studies and should be interpreted cautiously. Substantial heterogeneity was observed across studies.
The Cubbin & Jackson scale may offer relatively better diagnostic accuracy for pressure injury risk assessment in critically ill adults compared with generic scales, possibly due to its inclusion of ICU-specific clinical indicators. However, indirect comparisons and wide uncertainty limit definitive conclusions. These findings support the use of context-specific assessment tools in the ICU, but head-to-head studies are needed to confirm any single tool as the most accurate.