Evaluation methods of pressure injury stages: A systematic review and meta-analysis

Pressure injury is prevalent in clinical settings and demands precise staging for optimal care. Subjectivity and imprecision in traditional visual assessments have sparked the creation of advanced technology-based evaluation tools. To systematically assess pressure injury staging methods, analyze their evaluation results, and provide reference for clinical practice. Systematic review and meta-analysis. PubMed, Embase, Cochrane Library, Web of Science, CINAHL, and manual searches of academic journals and conference proceedings were utilized. The study conducted a systematic search of databases in April 2024, utilizing Endnote X9 to document findings. Two reviewers independently extracted data and evaluated its quality using the QUADAS-2 tool. The meta-analysis, conducted in Meta-disc, focused on metrics such as AUC, sensitivity, and specificity. Heterogeneity among the studies was assessed using Cochran's Q and I 2 tests. This review screened 15312 articles and ultimately included 15 studies. These studies described methods for pressure injury staging, including visual assessment, 29 machine learning models, and human-model integrated evaluation. The accuracy of traditional visual assessment was relatively low and showed significant variability. Eight studies involving 24 machine learning models were included in the meta-analysis, demonstrating significantly high accuracy, with an AUC of 0.93, and the combined sensitivity, specificity, and diagnostic odds ratio were 0.81, 0.87, and 20.48, respectively. The review underscores the advantages of machine learning in diagnosing pressure injuries, offering higher accuracy over traditional methods. Integrating clinical expertise with machine learning enhances medical service quality and efficiency. CRD42023462951. crd.york.ac.uk/prospero/display_record.php?ID=CRD42023462951 . • This study evaluates staging methods. Visual assessment is traditionally used for pressure injury staging. • Machine learning models improve the accuracy of pressure injury diagnosis. • Machine learning models demonstrate higher sensitivity and specificity in staging. • The integration of human expertise with machine learning optimizes healthcare service quality.