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Creating a pressure ulcer prevention algorithm: systematic review and face validation.

Lia van Rijswijk1, Janice M Beitz

  • 1Holy Family University, School of Nursing and Allied Health Professions, Philadelphia, PA; Clinical Editor, Ostomy Wound Management;

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Summary
This summary is machine-generated.

This study developed an evidence-based algorithm for pressure ulcer (PU) prevention. The algorithm aids clinicians in assessing PU risk and guiding care protocols for patients with impaired skin integrity.

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Area of Science:

  • Clinical Nursing
  • Evidence-Based Practice
  • Wound Care Management

Background:

  • Translating evidence into clinical practice for pressure ulcer (PU) prevention remains a challenge.
  • Existing wound care programs require tools to aid point-of-care decision-making.
  • Standardized risk assessment and intervention protocols are crucial for effective PU prevention.

Purpose of the Study:

  • To develop a succinct, evidence-based algorithm for PU prevention.
  • To assist clinicians in assessing and documenting patient PU risk.
  • To guide clinicians toward evidence-based care protocols for patients with impaired skin integrity.

Main Methods:

  • Systematic literature review utilizing the Strength of Recommendation Taxonomy (SORT).
  • Development of a 26-step algorithm with study quality ratings and recommendation strengths.
  • Face validation by 12 wound care experts using a 4-point Likert scale and calculation of Content Validity Index (CVI).

Main Results:

  • The algorithm demonstrated strong face validity with an overall mean score of 3.6 and a CVI of 0.89.
  • High-quality foam and medical-grade sheepskin received an 'A' strength of recommendation, though sheepskin had a low CVI.
  • Frequency of assessment for limited mobility (B strength) had a low CVI, indicating potential areas for refinement.

Conclusions:

  • The developed algorithm is the first of its kind, based on systematic review and expert face validation for PU prevention.
  • While overall evidence quality is good, gaps in evidence remain.
  • Further content and construct validation are necessary to refine the PU prevention algorithm for clinical use.