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

Pressure ulcer (PU) prevention is a care imperative supported by substantive evidence, but translating that knowledge into clinical decision-making at the point of care remains challenging. The purpose of this study was to create a succinct, evidence-based algorithm for inclusion in an existing industry-sponsored, evidence-based wound care program that will: 1) help clinicians assess and document overall patient PU risk; 2) help clinicians assess and address modifiable PU risk factors; and 3) guide clinicians toward an evidence-based protocol of care for patients with impaired skin integrity. First, using a systematic literature review and the Strength of Recommendation Taxonomy (SORT), a one-page algorithm containing 26 distinct decision points/steps was developed with study quality ratings for all publications identified. Second, based on the quality-of-evidence ratings, the strength of each recommendation was obtained for each decision point/ step. Lastly, face validation and subsequent instrument revision based on analytic input occurred. Twelve (12) wound care experts were asked to review each decision step and rate its appropriateness/relevance on a 4-point Likert scale, where 1 = not relevant/appropriate and 4 = very relevant and appropriate. Average scores and a content validity index (CVI) were calculated for the algorithm and each individual component. Two components, the use of high-quality foam and medical grade sheepskin for at-risk patients, had sufficient evidence to receive an A strength of recommendation. However, the latter had a very low CVI (0.18). One other step, frequency of assessment for current or recent history of limited mobility (B strength of recommendation), had a low CVI (0.7). The overall literature-based level of evidence was good, but overall evidence gaps remain. The overall mean score was 3.6 (SD 0.8) with a CVI of 0.89 (out of 1). Both scores indicate strong face validity. This is the first PU prevention algorithm based on systematic literature review and face validation. Future content and construct validation is needed to refine the algorithm.