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Despite the fact that machine learning (ML) algorithms to construct predictive models for pressure injury development are widely reported, the performance of the model remains unknown. The goal of the review was to systematically appraise the performance of ML models in predicting pressure injury. PubMed, Embase, Cochrane Library, Web of Science, CINAHL, Grey literature and other databases were systematically searched. Original journal papers were included which met the inclusion criteria. The methodological quality was assessed independently by two reviewers using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed with Metadisc software, with the area under the receiver operating characteristic curve, sensitivity and specificity as effect measures. Chi-squared and I 2 tests were used to assess the heterogeneity. A total of 18 studies were included for the narrative review, and 14 of them were eligible for meta-analysis. The models achieved excellent pooled AUC of 0.94, sensitivity of 0.79 (95% CI [0.78–0.80]) and specificity of 0.87 (95% CI [0.88–0.87]). Meta-regressions did not provide evidence that model performance varied by data or model types. The present findings indicate that ML models show an outstanding performance in predicting pressure injury. However, good-quality studies should be conducted to verify our results and confirm the clinical value of ML in pressure injury development.
Deep tissue injuries (DTIs) are a serious type of pressure injuries that mainly occur at the bony prominences and can develop rapidly, making prevention and treatment more difficult. Although consistent research efforts have been made over the years, the cellular and molecular mechanisms contributing to the development of DTIs remain unclear. More recently, ferroptosis, a novel regulatory cell death (RCD) type, has been identified that is morphological, biochemical and genetic criteria distinct from apoptosis, autophagy and other known cell death pathways. Ferroptosis is characterized by iron overload, iron-dependent lipid peroxidation and shrunken mitochondria. We also note that some of the pathological features of DTI are known to be key features of the ferroptosis pathway. Numerous studies have confirmed that ferroptosis may be involved in chronic wounds, including DTIs. Here, we elaborate on the basic pathological features of ferroptosis. We also present the evidence that ferroptosis is involved in the pathology of DTIs and highlight a future perspective on this emerging field, desiring to provide more possibilities for the prevention and treatment of DTIs.
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