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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.
The first step in preventing pressure injuries (PIs), which represent a significant burden on intensive care unit (ICU) patients and the health care system, is to assess the risk for developing PIs. A valid risk assessment scale is essential to evaluate the risk and avoid PIs.
To compare the predictive validity of the Braden scale and Waterlow scale in ICUs.
A multicentre, prospective and cross-sectional study.
We conducted this study among 6416 patients admitted to ICUs in Gansu province of China from April 2021 to October 2022. The incidence and characteristics of PIs were collected. The risk assessment of PIs was determined using the Braden and Waterlow scale. The sensitivity, specificity, positive and negative predictive values, and the area under the receiver operating characteristic curve of the two scales were compared.
Out of 5903 patients, 72 (1.2%) developed PIs. The sensitivity, specificity, positive and negative predictive, and the area under the curve of the Braden scale were 77.8%, 50.9%, 0.014 and 0.996, and 0.689, respectively. These values for the Waterlow scale were 54.2%, 71.1%, 0.017, 0.994 and 0.651.
Both scales could be used for risk assessment of PIs in ICU patients. However, the accuracy of visual inspection for assessment of skin colour, nursing preventive measures for patients and scales inter-rater inconsistency may limited the predictive validity statistics.
Both scales could be used for PIs risk assessment. The low specificity of the Braden scale and low sensitivity of the Waterlow scale remind medical staff to use them in combination with clinical judgement and other objective indicators.
This study was designed to enhance the management of PIs. Patients and the general public were not involved in the study design, analysis, and interpretation of the data or manuscript preparation.
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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