Evidence of metabolic imbalance and oxidative stress among patients suffering from pressure ulcers

Khlifi, Latifa; Graiet, Hajer; Sahli, Sondes; Ben-Hadj-Mohamed, Manel; Khelil, Souhir; Bouzidi, Nouha; Miled, A

doi:10.1080/09546634.2018.1527991

Cited by 10 publications

(12 citation statements)

References 45 publications

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“… 29 At the same time, recent studies have shown that oxidative stress is associated with PI and suggest that antioxidant therapy plays an important role in the prevention of PI. 30 , 31 Thus, RDW was confirmed as an independent risk factor for PI in this study, suggesting that it is important to limit the oxidative and inflammatory responses of critically ill patients in the prevention of PI.…”

Section: Discussionsupporting

confidence: 66%

“…The reason for this may be that the inflammatory response inhibits the production of erythropoietin by affecting iron metabolism, thus affecting the survival time of red blood cells and increasing the RDW value 29 . At the same time, recent studies have shown that oxidative stress is associated with PI and suggest that antioxidant therapy plays an important role in the prevention of PI 30,31 . Thus, RDW was confirmed as an independent risk factor for PI in this study, suggesting that it is important to limit the oxidative and inflammatory responses of critically ill patients in the prevention of PI.…”

Section: Discussionsupporting

confidence: 62%

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A nomogram prediction of pressure injury in critical ill patients: A retrospective cohort study

Jiang

et al. 2021

International Wound Journal

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Pressure injury (PI) is still a significant public health problem to be solved. Accurate prediction can lead to timely prophylaxis and therapy. However, the currently used Braden score shows insufficient predictive validity. We aimed to develop a nomogram to predict PI development in critically ill patients. We extracted data from Medical Information Mart for Intensive Care-IV v1.0. Variable selection was based on univariate logistic regression and all-subset regression. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the nomogram and Braden score. Decision curve analysis (DCA) was performed to identify and compare the clinical usefulness between the nomogram model and Braden score. We have developed a novel and practical nomogram that accurately predicts pressure ulcers. The AUC of the new model was better than that of the Braden score (P < .001). DCA showed that the nomogram model had a better net benefit than the Braden score at any given threshold. This finding needs to be confirmed by external validation as well as multicentre prospective studies.

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Section: Discussionsupporting

confidence: 66%

Section: Discussionsupporting

confidence: 62%

A nomogram prediction of pressure injury in critical ill patients: A retrospective cohort study

Jiang

et al. 2021

International Wound Journal

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“…In the comprehensive nomogram, advanced age, bilirubin, peripheral vascular disease, diabetes mellitus, surgical history and GCS were added to the Braden scale, which was consistent with the previous analysis to a large extent 27‐30 . A physiologic risk factor like bilirubin was presumed as a marker of hepatic and cystic abnormalities, accumulating inflammatory infiltration in the serum and in turn arousing inflammatory and oxidative responses 22 . The imbalance of immunity homeostasis dampened the output of the healing process and inclined toward a wounded outcome.…”

Section: Discussionmentioning

confidence: 66%

“… 27 , 28 , 29 , 30 A physiologic risk factor like bilirubin was presumed as a marker of hepatic and cystic abnormalities, accumulating inflammatory infiltration in the serum and in turn arousing inflammatory and oxidative responses. 22 The imbalance of immunity homeostasis dampened the output of the healing process and inclined toward a wounded outcome.…”

Section: Discussionmentioning

confidence: 99%

“…The standard evaluation process began with the metrics, named sensitivity, specificity, accuracy, precision, positive predictive value (PPV), negative predictive value (NPV) and area under the curve (AUC). Besides, accuracy was reflected in the comparison of the index integrated discrimination improvement 22 and the net reclassification improvement (NRI). In terms of calibration capacity, calibration curves were plotted to explore the relevancy to which predicted probabilities approximated actuality, where a near 45° line denoted the well‐performed model.…”

Section: Methodsmentioning

confidence: 99%

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Development and validation of a machine learning algorithm–based risk prediction model of pressure injury in the intensive care unit

Chen

Deng

Pan

et al. 2022

International Wound Journal

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The study aimed to establish a machine learning-based scoring nomogram for early recognition of likely pressure injuries in an intensive care unit (ICU) using large-scale clinical data. A retrospective cohort study design was employed to develop and validate a top-performing clinical feature panel accessibly in the electronic medical records (EMRs), which was in the mode of a quantifiable nomogram. Clinical factors regarding demographics, admission cause, clinical laboratory index, medical history and nursing scales were extracted as risk candidates. The performance improvement was based on the application of the machine learning technique, comprising logistic regression, decision tree and random forest algorithm with five-fold cross-validation (CV) technique. The comprehensive assessment of sensitivity, specificity and the area under the receiver operating characteristic curve (AUROC) was considered in the evaluation of predictive performance. The receiver operating characteristic curves revealed the top performance for the logistic regression model in respect to machine learning improvement, achieving the highest sensitivity and AUC among three types of classifiers. Compared against the 23-point Braden scale routinely recorded online, an incorporated nomogram of logistic regression model and Braden scale achieved the best performance with an AUC of 0.87 ± 0.07 and 0.84 ± 0.05 in training and test cohort, respectively.Our findings suggest that the machine learning technique potentiated the limited predictive validity of routinely recorded clinical data on pressure injury development during ICU hospitalisation. Easily accessible electronic records held the potentials to substitute the traditional Braden score in the prediction of pressure injury in intensive care unit. Preoperative prediction of pressure injury facilitates the exemption from the severe consequences.

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Redox signaling in pressure ulcers within older population: Role of the of antioxidants in their prevention and management

Rojas-Solé,

Pinilla-González,

Lillo-Moya

et al. 2024

Advances in Biogerontology

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Evidence of metabolic imbalance and oxidative stress among patients suffering from pressure ulcers

Cited by 10 publications

References 45 publications

A nomogram prediction of pressure injury in critical ill patients: A retrospective cohort study

A nomogram prediction of pressure injury in critical ill patients: A retrospective cohort study

Development and validation of a machine learning algorithm–based risk prediction model of pressure injury in the intensive care unit

Redox signaling in pressure ulcers within older population: Role of the of antioxidants in their prevention and management

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