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Background/Objectives: Patient-specific diagnostic and therapeutic approaches are important in the care of people with chronic wounds. The heterogeneity of underlying disease profiles and the diversity of the wound micro-environment make generalized approaches difficult. While high-throughput molecular diagnostic methods are increasingly widespread and available, the analysis of objective biomolecular disease patterns has not found its way into everyday wound management. The aim of this study is to evaluate the use of wound swab samples for the analysis of biomarkers and disease patterns in people with chronic wounds. Methods: A sample cohort from the multicenter "Wound-BIOME" project was analyzed. The project aims to comprehensively investigate the local micro-environment of chronic wounds of various entities, healing tendencies and regeneration stages at the biomolecular level. A sample collection and handling protocol suitable for everyday use was tested and evaluated regarding feasibility for multiplex immunoassay, proteomics, small RNA sequencing (miRNA) and metagenome analyses (microbiomics). Results: It could be shown that standard wound swabs are well-suited for the analysis of the complex wound micro-environment using various high-throughput methods. Despite the sample heterogeneity, the quality was adequate to analyze biomolecular patterns. Conclusions: Initial analyses of protein signatures, microbial wound communities and miRNA patterns show promising results for future individualized diagnostics and targeted interventions. [ABSTRACT FROM AUTHOR]

Objective: Several national and international guidelines recommend lipidocolloid technology with a nano-oligosaccharide factor (TLC-NOSF) dressings (UrgoStart dressing range, Laboratoires Urgo, France) for treating patients with chronic wounds. However, these dressings are still often reported as second-line options, potentially leading to loss of opportunity for patients and additional costs for payers. This review aimed to explore the reported wound healing and patient outcomes as well as the related costs when the dressings were used as first-line treatment in patients with different types of chronic wounds. Method: A systematic review of the literature was conducted. Databases (MEDLINE, Embase, Emcare, and Google Scholar) were searched up to 1 February 2024, without any language or time period limitations. Studies were eligible if the evaluated dressings had been used as a first-line treatment for chronic wounds, that is, as an integral part of the standard of care (SoC) at the patient's first presentation and/or in recent wounds. The main evaluation criteria included: wound healing rate; time to reach wound closure; change in patients' quality of life (QoL); and associated costs. The quality of evidence of the included studies was appraised using well-recognised risk-of-bias tools suitable for different study designs. A narrative synthesis describes the findings in three sections depending on the type of comparison. This report followed the principles of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: A total of 17 studies published between 2017 and 2024 met the eligibility criteria. A comparative analysis between TLC-NOSF dressings and standard dressings, both of which were used as first-line treatment, was reported in nine studies. A comparative analysis between the use of TLC-NOSF dressings as first-line and second-line treatments was reported in eight studies, and five studies reported a systematic use of the TLC-NOSF dressing as first-line treatment without a control group. Overall, the included studies had a relatively low risk of bias for the respective types of evidence. Data of 10,191 patients of both sexes and different age groups with a total of 10,203 wounds (diabetic foot ulcers, leg ulcers, pressure injuries, and other types of chronic wounds) were included in the analysis: 7775 treated with the evaluated dressing and 2428 treated with a comparator dressing. The data suggested that using TLC-NOSF as a first-line treatment for chronic wounds consistently resulted in significantly higher healing rates, shorter healing times, and cost savings compared with standard dressings used under similar conditions. Real-life evidence confirmed the results obtained in clinical trials and economic models, within similar ranges, regardless of the settings involved or of the characteristics of the patients and wounds treated. The wound healing rates ranged around 70-80% by week 20/24 and time-to-heal was reported on average around seven weeks, with slightly longer times reported in wounds with a more severe prognosis. Furthermore, the dressings were shown to improve patient QoL, and were well tolerated and accepted, supporting a wider adoption approach. Conclusion: The results of this review are aligned with the current guidelines recommending the use of TLC-NOSF dressings in the treatment of patients with chronic wounds. They support its wider implementation as a first-line treatment and as an integral part of SoC for these wounds in the daily practice of all centres involved in their management. [ABSTRACT FROM AUTHOR]

Objective: Hard-to-heal (chronic) wounds are treated differently at varying healing stages and often require long periods of time to heal completely. Therefore, it is often very difficult to achieve this primary endpoint in clinical trials with wound products which are used temporarily. Thus, it seems reasonable to use valid wound healing parameters determined at an early stage, such as the percentage (wound) area reduction (PAR), as surrogates for predicting complete wound healing. Method: To identify studies on the association between potential surrogate endpoints and complete wound healing, a systematic literature search in MEDLINE, Embase and the Cochrane Library databases was performed. The findings of the studies were summarised qualitatively. Results: A total of 23 publications with a focus on hard-to-heal wounds (n=10 diabetic foot ulcers (DFUs), n=10 venous leg ulcers (VLUs), n=3 other ulcers) and various wound care regimens were identified. All studies showed a high bias potential. The available clinical evidence confirmed that the PAR at week 4 showed a good discriminatory capacity to predict which wounds would eventually heal and which would not. The chance to achieve complete wound healing was significantly higher for wounds achieving a PAR of >40% (VLU) or ≥50% (DFU) at week 4. Conclusion: The PAR thresholds of 40% (VLU) and 50% (DFU) can be used to identify the proportion of the wounds that will heal, and those that will not heal, to compare the efficacy of different wound dressings as early as week 4 in the setting of clinical trials. The available evidence may be limited by a potential risk of bias. [ABSTRACT FROM AUTHOR]

Introduction Automation bias poses a significant challenge to the effectiveness of Clinical Decision Support Systems (CDSS), potentially compromising diagnostic accuracy. Previous research highlights trust, selfconfidence, and task difficulty as key determinants. With the increasing availability of AI-enabled CDSS, automation bias attains new attention. This study therefore aims to identify factors influencing automation bias in a diagnostic task. Methods A quantitative intervention study with participants from different backgrounds (n = 210) was conducted, employing regression analysis to analyze potential factors. Automation bias was measured as the agreement rate with wrong AI-enabled recommendations. Results and Discussion Diagnostic performance, certified wound care training, physician profession, and female gender significantly reduced false agreement rates. Higher perceived benefit of the system was significantly associated with promoting false agreement. Strategies like comprehensive diagnostic training are pivotal in the prevention of automation bias when implementing CDSS. Conclusion Considering factors influencing automation bias when introducing a CDSS is critical to fully leverage the benefits of such a system. This study highlights that non-specialists, who stand to gain the most from CDSS, are also the most susceptible to automation bias, emphasizing the need for specialized training to mitigate this risk and ensure diagnostic accuracy and patient safety. [ABSTRACT FROM AUTHOR]