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The biomechanical efficacy of a dressing with a soft cellulose fluff core in prophylactic use.

Amit GefenMaja KrämerMaik BrehmSören Burckardt
Published in: International wound journal (2020)
In this work, we developed an experimental-computational analysis framework which facilitated objective, quantitative, standardised, methodological, and systematic comparisons between the biomechanical efficacies of two fundamentally different dressing technologies for pressure ulcer prevention: A dressing technology based on cellulose fibres used as the core matrix was evaluated vs the conventional silicone-foam dressing design concept, which was represented by multiple products which belong in this category. Using an anatomically-realistic computer (finite element) model of a supine female patient to whom the different sacral dressings have been applied virtually, we quantitatively evaluated the efficacy of the different dressings by means of a set of 3 biomechanical indices: The protective efficacy index, the protective endurance, and the prophylactic trade-off design parameter. Prior rigorous experimental measurements of the physical and mechanical behaviours and properties of each tested dressing, including tensile, compressive, and friction properties, have been conducted and used as inputs for the computer modelling. Each dressing was evaluated for its tissue protection performances at a new (from the package) state, as well as after exposure to moisture conditions simulating wet bedsheets. Our results demonstrated that the dressing with the fluff core is at least as-good as silicone-foams but importantly, provides the best balance between protective performances at its "new" condition and the performance after being exposed to moisture. We conclude that preventative dressings are not equal in their prophylactic performances, but rather, the base technology, the ingredients, and their arrangement in the dressing structure shape the quality of the delivered tissue protection.
Keyphrases
  • finite element
  • wound healing
  • deep learning
  • skeletal muscle
  • physical activity
  • high resolution
  • quality improvement
  • finite element analysis