The study aims to follow up on the analysis of Pressure injuries (PIs) prevalence conducted between 2007 and 2014 and after the new methodological requirements for PIs surveillance establishment at the national level. A retrospective, nationwide cross-sectional analysis of data regarding the STROBE checklist was collected by the National Health Information System (NHIS). The International Classification of Diseases (ICD-10) diagnoses L89.0-L89.9 for PIs were used in the period 2010-2019. A total of 264 442 records of patients with diagnoses of L89.0-L89.9 were identified from 2010 to 2019 (26 444 patients per year on average). The numbers are increasing every year, and there is a 40% increase between 2010 and 2019. When comparing recorded PIs, the percentage of PIs occurrence in category I decreased, and the number of PIs in category IV increased in the second analysed period. Still, in absolute numbers, there is an increase across all categories. The age of patients with recorded PIs also rose slightly in the second analysed period. We have proven the PIs prevalence increase in an ageing population.

• Keywords
• epidemiology, national registry, pressure injuries, pressure ulcers, prevalence,
• MeSH
• Pressure Ulcer * MeSH
• Incidence MeSH
• Humans MeSH
• Follow-Up Studies MeSH
• Prevalence * MeSH
• Cross-Sectional Studies MeSH
• Registries MeSH
• Retrospective Studies MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Geographicals
• Czech Republic epidemiology   MeSH

Increasingly available open medical and health datasets encourage data-driven research with a promise of improving patient care through knowledge discovery and algorithm development. Among efficient approaches to such high-dimensional problems are a number of machine learning methods, which are applied in this paper to pressure ulcer prediction in modular critical care data. An inherent property of many health-related datasets is a high number of irregularly sampled time-variant and scarcely populated features, often exceeding the number of observations. Although machine learning methods are known to work well under such circumstances, many choices regarding model and data processing exist. In particular, this paper address both theoretical and practical aspects related to the application of six classification models to pressure ulcers, while utilizing one of the largest available Medical Information Mart for Intensive Care (MIMIC-IV) databases. Random forest, with an accuracy of 96%, is the best-performing approach among the considered machine learning algorithms.

• Keywords
• MIMIC database, MIMIC-IV, artificial neural network, machine learning, open data, pressure injury, pressure ulcer, random forest,
• Publication type
• Journal Article MeSH

Non-healing wounds are usually colonised by various types of bacteria. An alternative to antibiotic treatment in patients with infected wounds with local signs of inflammation may be medical-grade honey (MGH), which favourably affects the healing process with its antimicrobial, antioxidant, anti-inflammatory, and immunomodulatory properties. The objective of this study was to evaluate the effect of MGH therapy on the healing process of non-healing wounds of various aetiologies and different wound colonisations. Prospective, observation-intervention case studies (n = 9) of patients with wounds of various aetiologies (venous leg ulcers, diabetic foot ulcers, surgical wound dehiscence) are presented. All wounds were treated with MGH and the healing trajectory was rigorously and objectively monitored. In all cases, pain, odour, and exudation were quickly resolved, which led to an improvement in the quality of life of patients. Despite the proven bacterial microflora in wounds, antibiotic treatment was not necessary. The effects of MGH alleviated the signs of local infection until their complete elimination. In eight out of nine cases, the non-healing wound was completely healed. MGH has antimicrobial, anti-inflammatory, and antioxidant effects in wounds of various aetiologies and forms an effective alternative for the use of antibiotics for treating locally infected wounds.

• Keywords
• antibiotic replacement, infections, medical grade honey, objective wound assessment, wounds,
• Publication type
• Journal Article MeSH