(GS2) Sleeping Under The Stars The WOCN Support …wocnconference.com/wocn2015/CUSTOM/handouts/(GS2)...

Sunday, June 7, 2015

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Sleeping Under The Stars: The

WOCN Support Surfaces Algorithm

Carolyn Watts MSN, RN, CWON

Laurie McNichol, MSN, RN, GNP, CWOCN, CWON-AP

Dianne Mackey, RN, MSN, CWOCN

Mikel Gray, PhD, RN, FNP, PNP, CUNP, CCCN

June 7, 2015

Faculty Disclosures

Mikel Gray, Carolyn Watts, Laurie McNichol and

Dianne Mackey report no relevant disclosures.

Developing the Algorithm:

Initial Steps• Background

– Current best evidence strongly supports efficacy of

support surfaces for prevention of PU

– Evidence concerning use of support surfaces for

treatment of PU is sparse but supportive

– WOC nurses are key decision-makers in:• Selection of support surfaces for pressure ulcer prevention based

on multiple factors such as risk assessment

• Selection of support surfaces for treatment of pressure ulcers based

on multiple factors such as stage and location of pressure ulcer


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Initial Steps• Background

– Despite limited but consistent evidence

demonstrating efficacy of selective use of support

surfaces for prevention and treatment of PU no

national guidance for their use exists

– As a result, decisions about selecting the right

support surface for the right patient at the right

time tend to be based on reimbursement policies,

local factors, or tradition


Initial Steps• Challenge

– Society was challenged to provide guidance for

support surface selection based on current best

evidence

– The need for creating a guideline for decision

making related to support surface selection

– Several options were discussed and Society

leadership elected to develop an algorithm for

support surface selection


Initial Steps

• Algorithm: flow chart that

provides a highly visual aid for

managing multiple factors that

go into decision making for

support surface selection

• Criteria for algorithm: based on

current best evidence whenever

possible, based on consensus

based best practices when

evidence is lacking


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Initial Steps• Where to begin?

– WOCN leadership identified 3 society members with expertise in pressure ulcer prevention and management including support surface selection and charged them with task of developing algorithm

– Goal of algorithm: construct a valid, reliable, and clinically useful algorithm using current best

evidence and consensus based best practices as a guide for clinical practice


Initial Steps• Identifying current best evidence

– Task force completed literature review to identify

current best evidence essential for construction of

algorithm

– Initial search of MEDLINE and CINAHL databases

retrieved 1309 references….immediate question

arose, ‘What do we do with this mountain of

information?’


Initial Steps• 3 member task force recruited 2 methodological experts to aid

with task of synthesizing existing evidence and linking it to

decisional steps essential to algorithm

• 3 options for managing evidence

– Generate systematic review: extract data from multiple studies, pool

findings and analyze using meta-analytic techniques

– Generate comprehensive review: review all available literature on

subject, synthesize major points and generate background for algorithm

– Generate integrative review: blending of original research and existing

systematic reviews to determine decisional steps that are supported by

current best evidence versus those lacking adequate support to be

deemed “evidence-based”


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Initial Steps• Task Force chose…

– Option 3: integrative review method that blended

latest original research and best systematic reviews

of topic

– Step 1: Narrow literature review to latest research

on use of support surfaces for pressure ulcer

prevention and treatment• Title review of 1309 retrieved references narrowed pool to 342

articles

• Abstract review further narrowed pertinent references to 72


Initial Steps• Determining levels of evidence: taxonomy derived from WOCN

CPG for PU Prevention and Management and SORT statements

from American Academy of Family Practice

– Level A: consistent findings from 2 or more RCTs or a

systematic review with meta-analysis of pooled data from

multiple studies

– Level B: consistent findings from 1 RCT or >1 nonrandomized

clinical trial or mixed evidence from 2 or more systematic

reviews with meta-analysis

– Level C: expert opinion based on consensus among clinical

experts or findings from clinical case series, case studies


Initial Steps• Consensus based best practice statements

– Statements generated by task force

– 20 member consensus panel identified by task

force

– Consensus based statements based on 80%

agreement requiring discussion and formal voting

using anonymous electronic system led by

experienced moderator


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Supporting Statements

• Articles with level A or B evidence were

determined to be evidence based

• Level C evidence were deemed “consensus

statements”

• Task force developed statements to be reviewed

and voted on at a consensus conference by panel

of 20 experts

• Statements were generated from the 4 systematic

reviews and key publications reviewed


• Task force acknowledged that skin and

pressure ulcer risk assessment were essential

but other risk factors would be incorporated

into the algorithm (e.g. intrinsic and extrinsic

risk factors)

• General principles supporting these were

derived from existing clinical practice

guidelines (WOCN, NPUAP, AAWC)


• Inconsistencies in SS technology terms and

descriptions were noted during literature

review

• Identified and used uniform terms and

definitions based on NPUAP Support Surface

Standards Initiative (S3I, 2007)

• Additional terms used are defined in glossary.


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Development of Algorithm

• Developed draft algorithm via web-based

conference calls

• One face-to-face meeting was held; Dr Janice

Beitz was invited to attend that meeting

• Developed face validity of the algorithm at

various points by looking at hypothetical

patient scenarios and working through the

algorithm


• Reviews with scenarios ensured that

processes followed, decision points, interim

and end results were comprehensive, feasible

and appropriate.


• The algorithm was designed for specific

categories of support surfaces (overlays,

mattresses, mattress replacements, and

integrated bed systems) used for prevention

and treatment of pressure ulcers, excluding

medical device related PU.


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• Target audience included

– Nurses

– Specialty and advanced practice nurses

– Physicians

– Physicians assistants

– Physical therapists

– Occupational therapists


• Algorithm was designed to be used with adult

patients (including bariatric) in:

– Acute care

– Long term acute care

– Long term care/skilled nursing facilities

– Home care


• Not to be used with patients <16 years old

• Not to be used in selected settings where LOS

was < 24 hrs, e.g.

– OR

– Interventional services (cath lab, GI lab,

interventional radiology)


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• Selected surfaces were not incorporated into

the algorithm

– Seating surfaces/cushions

– CLRT mattresses

– Proning beds

– Other specialty surfaces (multiple fractures,

unstable spine, etc.)

Selection of the Consensus Panel

• 17 additional members for Consensus Panel (Task Force members filled the remaining 3 seats)

• All members had demonstrated expertise in support surface selection in addition to geographic and practice setting diversity

-Advanced practice nurses

-Certified WOC nurses

-Physical therapists

-Physicians

-Researchers and engineers with expertise in design and use of support surfaces

Consensus

Definition:

• Consensus decision-making is a group decision

making process that seeks the consent (agreement)

of all participants.

• Consensus may be defined professionally as an

acceptable resolution, one that can be supported,

even if not the "favorite" of each individual.


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Consensus Conference

• 2-day conference began with a presentation

summarizing preconference activities and a state of

the science presentation on support surface

selection

• Presentation of evidence-based statements

• Panel members provided recommendations and

modifications for clarity

Consensus Conference

• Statements supported by Level C evidence were

subjected to formal consensus validation

• Interactive software and wireless response system

allowed for anonymous interactive voting

• Consensus criterion required 80% agreement in

accordance with general principles outlined by

Murphy and colleagues(1998)

Consensus Building

• Initial vote held, if no consensus then

• Open facilitated discussion with proposed

modifications/edits

• Second and sometimes third votes taken

• If consensus not reached (or if proposed statement

deemed irrelevant to algorithm development)

consensus regarding deletion of statement was

obtained


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Draft Algorithm

• Reviewed in detail by consensus panel

• Modifications were made based on the

evidence-based and consensus statements

and additional discussion

Content Validation

• Based on procedures originally proposed by

Lynn (1986) and Waltz and Bausell (1981) and

modified by Grant and Davis (1997)

• A form was developed by the task force to

evaluate content validity of the algorithm

• Contained 18 questions regarding panel

demographics and pertinent professional

credential data

Content Validation (continued)

• 29 items representing each pathway and decision

points in the algorithm appeared on the form

• Following revision of the algorithm during the

consensus conference, panel members were asked to

rank individual items on the degree of relevancy and

appropriateness of the statement

• All members agreed to participate


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Content Validation (continued)

• Panel members were also asked to provide

qualitative feedback (written comments and

suggestions) on the comprehensiveness, omissions

of essential content and suggest changes to improve

clarity, parsimony and relevance.

• A content validity index (CVI) was calculated using

processes described by Polit and Beck (2006)

• Qualitative comments were transcribed and

thematically analyzed

Content Validity Index (CVI)

• The overall mean score was 3.72 =/- 0.48 out

of 4 (mean =/- SD) indicating components of

the algorithm were ranked as “very relevant

and appropriate” or “relevant and needed

only minor alteration”

• The CVI for the entire algorithm was 0.95, well

above the minimum (0.70 or 0.80) considered

acceptable.

An Evidence-and Consensus-Based

Support Surface Algorithm

• JWOCN, 2015;42(1):19-37. Identifying the Right

Surface for the Right Patient at the Right Time:

Generation and Content Validation of an Algorithm

for Support Surface Selection


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An Evidence-and Consensus-Based Support

Surface Algorithm

• Mobile app launched March 31st algorithm.wocn.org.

• Google Chrome or Mozilla Firefox

• Internet Explorer 11 if Chrome or Firefox not

available

• Fully compatible with smartphone/tablet browsers

(both Apple and Android).

An Evidence-and Consensus-Based Support

Surface Algorithm

• From March 31 launch date to May 6, the algorithm

has been accessed more than 2,500 times by nearly

2,000 individuals.

Case Scenario

• Mrs. Ingalls (82yo) is admitted to a medical unit in a large urban hospital with a fractured left hip due to a fall in her home.

• PMH: CHF, HTN, and Urinary Incontinence. She is 5’2” and weighs 145 lbs.

• Admission lab values : WBC’s 14.4, Hgb 11, HCT 23, electrolytes WNL. Albumin is 3.0.

• Nursing admission notes : Skin is intact. Braden score is 13 with a mobility subscale score of 2 and a moisture subscale score of 2.

• What support surface would you recommend?

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