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To investigate the preventive effect of different dressings on pressure injuries related to non-invasive ventilation equipment and to screen the efficacy of dressings. Systematic review and network meta-analysis. PubMed, the Cochrane Library, Web of Science, EMBASE, Cumulative Index to Nursing & Allied Health Literature (CINAHL), China Knowledge Resource Integrated Database (CNKI), Wanfang Database, Chinese Biomedical Database (CBM) and Weipu Database (VIP) were used for the search from the date of inception of each database to 15 October 2023. The quality of the data was assessed using the Cochrane Risk of Bias tool. Stata 16.0 Software was used to analysis and ranking of different types of dressings. A total of 23 randomized controlled trials on 7 interventions were included in the final analysis. The effectiveness of these in preventing the overall incidence of pressure injuries is ranked from best to worst as follows: hydrogel dressing > foam dressing > petroleum jelly gauze dressing > hydrocolloid dressing > film dressing > clean gauze dressing > sterile gauze. Sixteen studies reported the incidence of Stage I pressure injuries, the effectiveness in preventing the incidence of Stage I pressure injuries was ranked from best to least effective: foam dressing > hydrogel dressing > petroleum jelly gauze dressing > hydrocolloid dressing > film dressing > clean gauze dressing > sterile gauze dressing. Fourteen studies reported the incidence of Stages I/II pressure injuries, the effective in preventing the incidence of Stages I and II pressure injuries was ranked from best to least effective: foam dressing > hydrogel dressing > petroleum jelly gauze dressing > hydrocolloid dressing > clean gauze dressing > sterile gauze dressing. Considering the advantages and disadvantages of different dressings, both hydrogel and foam dressings are effective in preventing pressure injuries related to non-invasive ventilation equipment.
To compare the predictive properties of the Jackson/Cubbin scale and Waterlow scales in intensive care unit patients. A multi-centre study. This study was conducted between April 2021 and February 2023 in 72 intensive care units of 38 tertiary hospitals in Gansu Province, China. All adults admitted to the intensive care unit for 24 hours or more without pressure injury on admission were screened using the Waterlow scale and Jackson/Cubbin scales in intensive care. Additionally, the negative predictive value, positive predictive value, sensitivity, specificity and receiver operating characteristic curve with area under the curve of the Waterlow scale and Cubbin/Jackson scales were determined. The participant population for this study included 6203 patients. Predictive properties for the Jackson/Cubbin scales and Waterlow scales, respectively, were as follows: Cut-off scores, 28 versus 22; AUC, 0.859 versus 0.64; sensitivity, 92.4% versus 51.9%; specificity, 67.26% versus 71.46%; positive predictive value, 35% versus 23%; negative predictive value, 99.9% versus 99.1%. Both Waterlow scales and Jackson/Cubbin scales could predict pressure injury risk for patients in the intensive care unit. However, the Jackson/Cubbin scale demonstrated superior predictive properties than the Waterlow scale.
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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