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.
Potential Conflicts of Interest: This study was supported by a research grant from ConvaTec Inc, Skillman, NJ. The study was conducted, the data analyzed, and the manuscript written by the authors, who are responsible for the content of this publication. The authors do not have any financial interest in any products discussed in this publication.
Pressure ulcer (PU) prevention has become a litigious and fiscal imperative in contemporary healthcare. Considered a quality indicator that greatly impacts quality of life and cost of care,1 PU development can be interpreted as poor quality of care or even as a “medical error.”2
Knowledge about PU prevention has increased substantially in recent decades,3 and multiple guidelines targeting their prevention and treatment are available.4,5 However, research suggests that translating available knowledge into bedside clinical decision-making is complicated by many factors, including difficulties implementing lengthy, time-consuming care guidelines; faulty communication; limited education; and poor standardization.6,7 Devices or instruments that can help encapsulate larger amounts of information into a more usable process may assist with the transfer of research into clinical practice.8 Cognitive forcing strategies such as checklists and algorithms can aid optimal clinical decision making and actions, thus expediting safe patient care.9-11 The imperative for multicomponent PU prevention programs has become so strong that multiple publications have targeted it as a patient safety strategy that should be implemented immediately.12,13
The overall purpose of this work was to create a PU prevention algorithm for adults to be included 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. To that end, the first purpose of this study was to create an evidence-based PU prevention algorithm for adults; the second, to identify the strength of recommendation of each decision step based on currently available evidence; and the third, to obtain face validation of the algorithm and revise the instrument based on analytic input.
ConvaTec’s Solutions® program (ConvaTec, Skillman, NJ), developed in the 1990s, consists of a set of eight wound algorithms and a Pressure Ulcer Prevention Algorithm. The former have since been content- and construct-validated, are available in a digital format,14-16 and are currently included in the Agency for Healthcare Research and Quality (AHRQ) Guidelines Clearinghouse.17 The prevention algorithm has not been updated to reflect current standards of care and has not been formally tested, even though available evidence suggests implementation of the entire program reduces PU prevalence rates.18,19
Literature review. The algorithm development and literature review process was guided by recently published recommendations for guideline development.20 The project commenced in the fall of 2011 with a general review of the literature and development of a literature search strategy to capture both PU risk assessment and prevention intervention publications. A systematic review of the English literature on PU prevention published since 2007 and starting with highest level of evidence databases (meta-analysis) was conducted between January 2012 and May 2013 using the following databases: Medline® (US National Library of Medicine®, Bethesda, MD), CINAHL® (Western Adventist Health Services, Glendale, CA), JBI COnNECT+ (Joanna Briggs Institute Clinical Online Network of Evidence for Care and Therapeutics), The COCHRANE Library (The Cochrane Collaboration), and ProQuest Dissertation Abstracts. The search yielded 623 publications (see Table 1). First, all publications were reviewed for applicability. Because similar search terms were used in the different databases, a substantial number of citations were duplicates. After excluding duplicate, nonPU-related (other chronic wounds), preclinical studies as well as general opinion-based reviews, a total of 117 publications remained. Most salient publications were obtained from the Medline and CINAHL databases. Finally, using the same search terms, the PubMed (US National Library of Medicine), Clinical Trials.gov, and the Agency for Healthcare Research and Quality National Guideline Clearinghouse websites17 were reviewed and the most recent evidence based and/or content-validated PU clinical practice guidelines posted on guidelines.gov were retrieved.4,5 Because the Association for the Advancement of Wound Care Pressure Ulcer Guideline4 includes previously published guidelines, such as the European Pressure Ulcer Advisory Panel and the National Pressure Ulcer Advisory Panel (EPUAP/NPUAP) Pressure Ulcer Guidelines,21 it was not included separately in order to prevent skewing evidence levels.
Abstraction and study quality ratings. The initial literature review (n =117) yielded three major themes in PU prevention practice: 1) screening, diagnosis, and PU risk assessment; 2) PU prevention interventions; and 3) education related to all aspects of care. All publications were abstracted and entered into Excel® (Microsoft, Seattle, WA) spreadsheets based on these themes. In addition to publication information, the following variables were entered: publication type, study design and variables, intervention(s), contraindication(s), sample size, patient care setting, and study results or outcomes. With respect to risk assessment and screening instrument study results, emphasis was placed on modifiable PU risk factors. The quality of each individual study was rated using the Strength of Recommendation Taxonomy (SORT).22 The SORT meets the Institute of Medicine’s criteria for systematic reviews and guideline development20 and is based on patient-centered, not interim, outcomes. With respect to the PU prevention literature, the SORT was found to be most useful because it includes clear criteria for rating diagnostic studies, treatment, prevention, and screening studies, as well as studies related to prognosis. It also has been used in previous wound algorithm validation work.23,24
Algorithm development. All available evidence, including other validated and reliable instruments such as the Braden Scale for Predicting Pressure Sore Risk© (originally developed by Barbara Braden and Nancy Bergstrom), was organized and put into a flowchart format for algorithm development. Color schemes were included to bring users’ attention to critical steps in the process (see Figure 1). Users enter the algorithm at the point of patient assessment for evident risk or history of recent PU; if appropriate, users then are re-directed to either prevention interventions for the at-risk patient or reassessment for the patient with intact skin and/or low risk. If, for example, the person is an obstetrics patient or healthy ambulatory surgery patient, the algorithm is not appropriate or necessary. Because staff, caregiver, and patient education are an important component of all aspects of patient assessment, screening, and intervention, this step undergirds the entire algorithm. If the patient develops or already has skin breakdown, the clinician is directed to obtain a differential diagnosis (eg, PU versus incontinence-associated dermatitis) and implement an appropriate validated wound care algorithm set (eg, Solutions® Algorithms, ConvaTec, Skillman, NJ).
Literature-based evidence algorithm components. Each segment and step within the algorithm was described and the available abstracted evidence was grouped for each step to obtain an overall strength of recommendation where A = consistent, good quality patient level evidence; B = inconsistent or limited quality patient level evidence; and C = consensus, usual practice, opinion, disease-oriented evidence, or case series for studies of diagnosis, treatment, prevention, or screening.20,22 For the final strength of recommendations, studies with a study quality (SORT) rating of 3 (lowest level) were included only for steps with limited Level 1 or 2 quality studies, leaving a total of 55 publications upon which the algorithm strength of recommendations were based (see Table 2 and Addendum). Because some evidence-based reviews, meta-analyses, or practice guidelines contained evidence to support multiple algorithm components/steps,4,25 the Quality (SORT) rating of the study for the purpose of developing literature-based strength of recommendations was based on the strength of recommendation/level of evidence for the specific algorithm decision statement/step contained within that guideline.
Instrumentation. In order to obtain face validation for the newly constructed algorithm, a data collection instrument was developed by the researchers containing 17 questions regarding basic demographic data (age, gender, education, wound care experience) and the 26 items representing each segment/step previously developed for the literature-based evidence table. Using a process described by Lynn26 and Waltz and Bausell,27 participants were asked to review each decision statement/step and rate it according to the following scale: 1 = not relevant/appropriate, 2 = unable to assess relevance without revision, 3 = relevant but needs minor alteration, and 4 = very relevant and appropriate.
Processes. Before surveying participants, the Institutional Review Board of La Salle University (Philadelphia, PA) reviewed the proposed project in 2012 and deemed it exempt status due to its low risk level and educational focus; however, ethical considerations were fully addressed. Specifically, all participants received an explanatory cover letter and were asked to provide written informed consent. A multidisciplinary group of clinicians (N = 23) was contacted via telephone or email and asked to participate in the study. After agreeing to participate and providing informed consent, participants received a professionally produced copy of the Pressure Ulcer Prevention Algorithm and the researcher-designed data collection instrument. The cover sheet provided background information, described the objectives of the PU prevention algorithms, and described the participant’s role in helping validate the newly constructed algorithm. Specifically, it was emphasized that being a member of the review panel did not constitute authorship or endorsement of the algorithm. Twelve (12) of the 23 invited experts agreed to participate, a response rate of 52%.
Data analysis. Data analysis was conducted using Excel® version 2010 (Microsoft, Seattle, WA). Data were coded and entered into the database by an assistant. Summary statistics were calculated for demographic data (eg, mean age and frequencies).
Ratings of all 26 algorithm decision statements/steps were entered and mean scores were calculated. The Content Validity Index (CVI) also was calculated using processes described by Lynn26 and Waltz and Bausell27 and clarified by Polit and Beck28 and Polit et al.29
Qualitative comments regarding individual decision statements/steps and overall processes were transcribed and thematically analyzed using qualitative data reduction techniques. The “find” mechanism of Microsoft© Word assisted with thematic analysis.
Literature-based strength of recommendations. The number of level 1 and level 2 quality studies was substantial for the majority of the 26 steps (n = 18, 69%). Two of the decisions/steps had sufficient evidence to receive an A strength of recommendation; only eight had a C strength of recommendation. Most of the recommendations with lower level evidence pertained to common interventions related to skin protection and care (see Table 2).
Demographics. The majority (n = 9, 75%) of participants are female and Advanced Practice Nurses, Nurse Practitioners, or Clinical Nurse Specialists (n = 6, 50%) with an average age of 47.5 years (median 52). Five participants are Registered Nurses and one is a Medical Doctor (MD). All had received formal wound care education, were currently wound care-certified (certified wound, ostomy, continence nurse [CWOCN]/certified wound continence nurse [CWCN]: n = 10, 84%; certified wound specialist [CWS] or other: n = 2, 16%); and had a Baccalaureate (n = 4, 33%), Master’s (n = 6, 50%), or doctoral degree (n = 2, 17%). All had received their healthcare education in the US. Eleven out of 12 had 10 or more years of clinical experience, including four (33%) with more than 30 years experience. Several participants practice in more than one patient care setting, including acute care (n = 10, 83%), subacute care (n = 3, 25%), home care (n = 2, 17%) or long-term care (n = 2, 17%). Geographically, participants were widely dispersed. The most common practice areas (n = >1 per state) were Illinois (n = 2), Pennsylvania (n = 2), and Virginia (n = 2). Most (n = 9, 75%) indicated they encountered >200 wounds per year in their practice, with 11 (92%) indicating that PUs were the wounds they encountered most frequently. Respondents also managed patients with lower extremity wounds (n = 9), diabetic foot ulcers (n = 5), and surgical wounds (n = 3).
Quantitative analysis. The overall mean score of all algorithm decision statements/steps was 3.5 (SD 0.8), interpreted as meaning the components were very relevant to relevant and needed only minor alteration. Without the lowest score step — provide medical grade sheepskin — the mean score was 3.6 (SD 0.8). The CVI of the entire PU prevention algorithm was 0.89, well above the minimum of 0.7 or 0.80 suggested by researchers as minimally acceptable.26,28,29 A CVI of 0.89 suggests that participants viewed the overall content of the algorithm as strongly appropriate. Two steps questionable to reviewers and that received a less-than-acceptable CVI were following admission assessment if not at risk and with intact skin, assess for current or recent history of limited mobility (CVI 0.7) and provide medical grade sheepskin for bed and chair (CVI 0.18) (see Table 2). Table 2 displays the components’ evidence levels— ie, how each component or step of the algorithm was rated for CVI, mean score, and the study quality and strength of recommendation (patient-centered) based on available abstracted literature. In general, most steps were strongly rated except for those noted above.
Qualitative analysis. Respondents’ comments reflected concern about time issues (Does it have to be every 24 hours or less?), the use of medical grade sheepskins (issues with infection control and incontinence), and the appropriateness of using medical grade sheepskin. In the US, synthetic sheepskins are used, and study participants expressed concern about hygiene and the effect of these sheepskins on pressure.
Qualitative analysis generated themes regarding individual decision statements/steps: issues with timing, specifying the content of pertinent education, defining limited mobility, appearance of the algorithm (strengths and weaknesses), wording, and specification of products. Qualitative analysis of overall comments identified the following themes: terminology, specificity versus generality, expert versus nonexpert perspectives, litigation, and algorithm flow and appearance (see Table 3 and Table 4). In addition, one participant was confused about the entry step targeting risk versus nonrisk. Because most reviewers did not have this response, no changes were made to the language or format. Future content validation testing with a larger sample may help delineate needed changes.
Following a careful review of these results, minor modifications were made to algorithm wording for the two most frequently questioned and lower CVI-rated steps. In addition, concerns about the flow of information prompted slight revisions in the layout for the purpose of reducing text redundancy.
An evidence-based PU prevention algorithm for adults was created for inclusion in an existing evidence-based wound care program. The systematic literature review identified more than 600 publications, but many were duplicates, did not pertain to PUs, or were nonclinical studies or nonevidence-based reviews. However, the remaining 117 publications used in the creation of the algorithm and the 55 publications used to populate the evidence table yielded a reasonably good strength of recommendation (level B) for the majority of the 26 decision points/steps. The majority of the available studies utilized for each choice were quality Level 2 (limited quality clinical trials or retrospective cohort studies or studies with inconsistent results).22 Occasionally a high-quality, Level 1 study was available to support an algorithm step.
The CVI of the one-page algorithm for PU prevention in adults was strong (0.89 out of 1.0) with an overall mean of 3.50 (out of 1 to 4), suggesting the components were very appropriate to the purpose of the instrument. Although most validation average scores were >3.0, a few were not, yielding some observations that require further study or exploration. The strongest level of evidence available was for the use of medical grade sheepskin. Although used internationally and tested with good quality clinical trials (see Table 2), its use was not viewed positively by the experts, who cited current use of synthetic sheepskin. Most often, reference was made to problems with infection control and incontinence.
Another decision item with a relatively good strength of recommendation and an acceptable but low CVI was the need to evaluate for a current or recent history of immobility in patients not considered at risk according to the Braden Scale. Although the Braden Scale subscores include patient mobility and activity, there are concerns about that instrument’s ability to capture recent episodes of immobility due to lengthy surgical procedures, emergency room stays, or following trauma.4,30-33
On the other hand, several steps (interventions) with the lowest quality studies and level of evidence (C strength of recommendation) had the highest CVI. Specifically, most interventions related to skin care and preventing skin exposure to moisture, as well as the need to provide a differential diagnosis if an alteration in skin integrity is observed, had a CVI >0.9. Clearly, these interventions are considered important by clinicians but lack good quality evidence. Recently, two studies34,35 about the role of specialized fabrics that help manage moisture have been published, which may help increase evidence levels in this area.
Strong evidence supports the use of a high-quality support surface to redistribute pressure,36 but the exact nature of what surface is “best” is still to be determined; panel members expressed an interest in more specific directions for this step. Similarly, periodic reassessment of risk also is supported in the literature and received a high CVI, but the exact frequency of reassessment in various patient care environments remains vague. Although the expert panel considered more specific details about interventions to be beneficial, there was also concern about steps that were unambiguous. For example, although the literature generally supports assessment and reassessment within 24 hours and the CVI for that recommendation was high (0.9), some clinicians who participated in this study were concerned about the potential legal implications of timeframes. Use of a validated nutritional assessment tool also is well supported in the literature, but less evidence is available regarding how best to correct nutritional deficiencies. Finally, adequate quality evidence supports the use of valid, reliable instruments to assess PU risk, and the importance of staff, caregiver, and patient education about PU risk and prevention has relatively strong support.37 At the same time, there is little consensus in the literature on how to achieve these educational requirements. Current study participants identified a need for more specific guidelines on this topic.
The development and validation of the PU prevention algorithm was a challenging task but somewhat easier than previous algorithms developed, such as those for negative pressure wound therapy (NPWT).23,24 The level of available evidence was generally stronger for PU but not as high level as would be desirable for optimal evidence-based practice.
The great strength of the algorithm is that it captures much research evidence on various patient risk factors that are routinely collected and modifiable and places them in one succinct visual aid. Algorithms like the PU prevention algorithm can assist with promoting patient safety in a fast-paced clinical environment.9-11
The current algorithm was designed to focus on adult care only and cannot be guaranteed appropriate or effective for neonatal and/or pediatric populations. Related to this focus is the phenomenon of device-related PUs. Because pediatrics was not incorporated, device-related skin damage was not abstracted because this etiology is a major issue in pediatric skin protection. Another phenomenon, deep tissue injury, was not incorporated specifically because the research evidence base underlying cause and prevention is only beginning to emerge, and the PU algorithm focuses on prevention, not PU assessment or treatment strategies.
Although not strictly a limitation of the study, the issue of validation of algorithms, protocols, and guidelines is important. If skin integrity is altered, the PU prevention algorithm suggests obtaining a differential diagnosis and guides the user to a specific protocol of care. The PU prevention algorithm possibly could be used with other wound care protocols, but if those other algorithms, protocols, or decision trees are not research-based for content or construct validity, the evidence support for PU treatment potentially becomes weakened.
To ensure information is current, the literature review was conducted for the years 2007 to 2013. Earlier work was not included purposely, but pertinent earlier work was included in the evidence-based guidelines and meta-analysis used for the creation of the algorithm. Finally, face validation with expert review is not commensurate with full content validation. It is only a “step one” activity. Full content validation is needed for best evidentiary support and further algorithm refinement.
Following a systematic review of the literature, a one-page PU prevention algorithm was developed and face validated. More than 60% of the 26 decisions/steps contained in the algorithm had at least a B strength of recommendation. The average score for the algorithm was very good, but the individual CVI of two steps was <0.7, suggesting they need to be modified. To the authors’ knowledge, this is the first PU prevention algorithm based on a systematic literature review and face validation. Future research should target content and construct validation with a larger multidisciplinary group of wound care clinicians.
The valuable contributions of the following Pressure Ulcer Prevention Algorithm Face Validation Panel members are gratefully acknowledged:
Anita Thurman, MSN,FNP-C, CWOC-AP, Coordinator, Inpatient Wound Care, Southeastern Regional Medical Center, Lumberton, NC; Richard Schneider, MBA, RN, CWON, Department of Wound, Ostomy, and Continence Nursing, University of Virginia Health System, Charlottesville, VA; Deborah L. Gray, DNP, CRNP, Geriatric and Extended Service, James E. Van Zandt Veterans Administration Medical Center, Altoona, PA; Catherine T. Milne, APRN, MSN, BC-ANP/CSS, CWOCN, Connecticut Clinical Nursing Associates, LLC. Bristol, CT; Janet Johnson Prince, RN, BSN, CWOCN, Manager, Wound Care Services, St. Joseph Hospital, Nashua, NH; Anne Scheurich, BS, RN, CWOCN, Vice President of Clinical Operations, Telemedicine Solutions LLC, Schaumburg, IL; and Shawna Philbin, RN, BSN, CWOCN, WOC Department, Health First Palm Bay Hospital, Palm Bay, FL.
Ms. van Rijswijk is an Instructor, Holy Family University, School of Nursing and Allied Health Professions, Philadelphia, PA; and Clinical Editor, Ostomy Wound Management. Dr. Beitz is a Professor of Nursing, School of Nursing – Camden, Rutgers University, Camden, NJ. Please address correspondence to Lia van Rijswijk, MSN, RN, CWCN, Holy Family University, 9801 Frankford Avenue, Philadelphia, PA 19114-2009; email: firstname.lastname@example.org.