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NCT Number: NCT06468306

Combined Molecular and Mechanistic Methods for Detection of Pressure Ulcers

This project aims to develop a novel method for identifying early tissue damage related to pressure ulcer (PU) development in vulnerable patients by measuring biomarkers of inflammation on the skin surface. PUs are common and costly injuries that result from prolonged pressure on the skin. Current methods to assess PU risk are unreliable, and the mechanisms of PU development are not well understood. This project contributes to new knowledge of PU etiology as well as the individual variability at a molecular level combined with new knowledge about nursing actions and clinical factors linked to PU progression and outcomes of prevention. The project will use non-invasive techniques and model-based analysis to identify specific biomolecules that reflect individual susceptibility to pressure exposure in different PU risk scenarios.

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Key information

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Linköping University

Linköping, 58183, Sweden

Location contact

Sara Bergstrand, PhD

PRINCIPAL_INVESTIGATOR

About this study

Purpose and aims A pressure ulcer (PU) is a localized injury to the skin and/or underlying tissue and develop from prolonged pressure on the skin. Such injuries are common in the healthcare setting, especially among vulnerable elderly. PUs greatly decrease the quality of life of individuals and are costly for the healthcare system. As many as 14% of the inpatients suffered from PUs in the Swedish country's municipalities and regions during 2022. The origin and timing of events leading to PUs are not fully understood, and current methods to assess the risk for an individual to develop a PU, are unreliable. Therefore, there is an urgent need to develop more objective, sensitive and specific methods for identifying early signs of tissue damage before they come visible and thus avoid development of PUs.

The investigators have previously identified a preliminary set of molecular biomarkers (cytokines and proteins), sampled non-invasively in the sebum, that reflects the inflammatory process under-pinning PU etiology and, possibly, individual susceptibility to pressure exposure. Therefore, it is hypothesize that non-invasive measurements of specific biomolecules on the skin surface, together with model-based analysis, can be used for individualized PU prediction. Accordingly, the purpose of this project is to confirm and expand on these preliminary findings in different PU risk scenarios to model the underlying inflammatory processes that reflect the individual vulnerability of the skin caused by pressure exposure and use modeling to extract a new layer of mechanistic insights of the underlying inflammatory process in different patient populations.

The specific aims of the project are:

  • To establish and validate optimal combinations of molecular biomarkers to identify individual susceptibility to pressure exposure during routine management regimes related to medical devises non-invasive ventilation (NIV) therapy.
  • To unravel key mechanisms in inflammatory processes related to early tissue damage by developing a mathematical model for the timing of events in the response to pressure, based on collected biomolecules, earlier data, and interaction databases
  • To identify risk factors of PU vulnerability on an individual level in routine clinical settings by combining biomolecules, model-based simulations, and clinical parameters

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • patients that use oronasal face masks in their ordinary care during routine management regimes of non invasive ventilation.

Exclusion criteria

  • acute respiratory failure
  • previous ICU care
  • pressure ulcer on measurement site

Treatment and study plan

non-invasive ventilation teraphy, NIV

Procedure

Routine management regimes of NIV

Primary outcomes

  1. CTACK

    Time frame: 2 minutes

    inflammatory biomarker

  2. ENA-78

    Time frame: 2 minutes

    inflammatory biomarker

  3. Eotaxin

    Time frame: 2 minutes

    inflammatory biomarker

  4. Eotaxin-2

    Time frame: 2 minutes

    inflammatory biomarker

  5. Eotaxin-3

    Time frame: 2 minutes

    inflammatory biomarker

  6. EPO

    Time frame: 2 minutes

    inflammatory biomarker

  7. FLT3L

    Time frame: 2 minutes

    inflammatory biomarker

  8. Fractalkine

    Time frame: 2 minutes

    inflammatory biomarker

  9. G-CSF

    Time frame: 2 minutes

    inflammatory biomarker

  10. GM-CSF

    Time frame: 2 minutes

    inflammatory biomarker

  11. GRO-alpha

    Time frame: 2 minutes

    inflammatory biomarker

  12. I-309

    Time frame: 2 minutes

    inflammatory biomarker

  13. IFN-α2a

    Time frame: 2 minutes

    inflammatory biomarker

  14. IFN-β

    Time frame: 2 minutes

    inflammatory biomarker

  15. IFN-γ

    Time frame: 2 minutes

    inflammatory biomarker

  16. IL-10

    Time frame: 2 minutes

    inflammatory biomarker

  17. IL-12/IL-23p40

    Time frame: 2 minutes

    inflammatory biomarker

  18. IL-12p70

    Time frame: 2 minutes

    inflammatory biomarker

  19. IL-13

    Time frame: 2 minutes

    inflammatory biomarker

  20. IL-15

    Time frame: 2 minutes

    inflammatory biomarker

  21. IL-16

    Time frame: 2 minutes

    inflammatory biomarker

  22. IL-17A

    Time frame: 2 minutes

    inflammatory biomarker

  23. IL-17A/F

    Time frame: 2 minutes

    inflammatory biomarker

  24. IL-17B

    Time frame: 2 minutes

    inflammatory biomarker

  25. IL-17C

    Time frame: 2 minutes

    inflammatory biomarker

  26. IL-17D

    Time frame: 2 minutes

    inflammatory biomarker

  27. IL-17E/IL-25

    Time frame: 2 minutes

    inflammatory biomarker

  28. IL-17F

    Time frame: 2 minutes

    inflammatory biomarker

  29. IL-18

    Time frame: 2 minutes

    inflammatory biomarker

  30. IL-1RA

    Time frame: 2 minutes

    inflammatory biomarker

  31. IL-1α

    Time frame: 2 minutes

    inflammatory biomarker

  32. IL-1β

    Time frame: 2 minutes

    inflammatory biomarker

  33. IL-2

    Time frame: 2 minutes

    inflammatory biomarker

  34. IL-21

    Time frame: 2 minutes

    inflammatory biomarker

  35. IL-22

    Time frame: 2 minutes

    inflammatory biomarker

  36. IL-23

    Time frame: 2 minutes

    inflammatory biomarker

  37. IL-27

    Time frame: 2 minutes

    inflammatory biomarker

  38. IL-29/IFN-L1

    Time frame: 2 minutes

    inflammatory biomarker

  39. IL-2Ra

    Time frame: 2 minutes

    inflammatory biomarker

  40. IL-3

    Time frame: 2 minutes

    inflammatory biomarker

  41. IL-31

    Time frame: 2 minutes

    inflammatory biomarker

  42. IL-33

    Time frame: 2 minutes

    inflammatory biomarker

  43. IL-4

    Time frame: 2 minutes

    inflammatory biomarker

  44. IL-5

    Time frame: 2 minutes

    inflammatory biomarker

  45. IL-6

    Time frame: 2 minutes

    inflammatory biomarker

  46. IL-7

    Time frame: 2 minutes

    inflammatory biomarker

  47. IL-8

    Time frame: 2 minutes

    inflammatory biomarker

  48. IL-9

    Time frame: 2 minutes

    inflammatory biomarker

  49. IP-10

    Time frame: 2 minutes

    inflammatory biomarker

  50. I-TAC

    Time frame: 2 minutes

    inflammatory biomarker

  51. MCP-1

    Time frame: 2 minutes

    inflammatory biomarker

  52. MCP-2

    Time frame: 2 minutes

    inflammatory biomarker

  53. MCP-3

    Time frame: 2 minutes

    inflammatory biomarker

  54. MCP-4

    Time frame: 2 minutes

    inflammatory biomarker

  55. M-CSF

    Time frame: 2 minutes

    inflammatory biomarker

  56. MDC

    Time frame: 2 minutes

    inflammatory biomarker

  57. MIF

    Time frame: 2 minutes

    inflammatory biomarker

  58. MIP-1α

    Time frame: 2 minutes

    inflammatory biomarker

  59. MIP-1β

    Time frame: 2 minutes

    inflammatory biomarker

  60. MIP-3α

    Time frame: 2 minutes

    inflammatory biomarker

  61. MIP-3β

    Time frame: 2 minutes

    inflammatory biomarker

  62. MIP-5

    Time frame: 2 minutes

    inflammatory biomarker

  63. SDF-1alpha

    Time frame: 2 minutes

    inflammatory biomarker

  64. TARC

    Time frame: 2 minutes

    inflammatory biomarker

  65. TNF-α

    Time frame: 2 minutes

    inflammatory biomarker

  66. TNF-β

    Time frame: 2 minutes

    inflammatory biomarker

  67. TPO

    Time frame: 2 minutes

    inflammatory biomarker

  68. TRAIL

    Time frame: 2 minutes

    inflammatory biomarker

  69. TSLP

    Time frame: 2 minutes

    inflammatory biomarker

  70. VEGF-A

    Time frame: 2 minutes

    inflammatory biomarker

  71. YKL-40

    Time frame: 2 minutes

    inflammatory biomarker

Study contacts

Contact information is provided by the study sponsor or research team.

Sara Bergstrand, PhD

CONTACT

[email protected]

+4613286773

Sponsors and collaborators

Lead sponsor

Linkoeping University

Other Gov

Collaborators

  • Region Östergötland

Registry information

Official study title

Combined Molecular and Mechanistic Methods for Early Detection and Individual Prevention of Pressure Ulcer Formation in Vulnerable Patients

Important dates

Study start
2024
Primary completion
2025
Study completion
2027
First posted
Jun 21, 2024
Registry last updated
Jun 21, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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