Running head: SKIN AND SOFT TISSUE INFECTIONS

Skin and Soft Tissue Infections: Using a Severity Stratification Tool to Improve Knowledge
Chelsea A. Cason
Arizona State University

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SKIN AND SOFT TISSUE INFECTIONS
Abstract

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Skin and soft tissue infections (SSTI’s) are a significant health concern with serious potential
implications. Evidence suggests the importance of implementing a severity stratification tool to
improve early identification of SSTI’s. The aim of this evidence based project is to examine if
educating healthcare staff on the use of a severity stratification tool would increase staff
knowledge of SSTI's. The sample consisted of 18 participants, 12 healthcare providers and 6
healthcare staff at a correctional facility in the Southwestern United States. A pre-and posttest
design, including an educational session was implemented. A 14-item multiple choice selfdeveloped questionnaire was used to evaluate participants’ knowledge of identifying and ranking
SSTI’s using the CREST tool. A one tail paired t-test was performed to compare the pre-and
post-test case study scores for the healthcare provider group. A significant increase from pre-test
to post-test case study scores was found (t(13)= -6.19, p < 0.00). Of the healthcare providers,
57% found the tool “moderately helpful.” Of the non-provider sample, 50% found the tool
“extremely helpful” and plan to use the tool “all of the time.” The findings of this study suggest
that implementing an educational session on a wound severity stratification tool improves staff
knowledge and increases the likelihood of the tool being used in practice. Recommendations for
future research include larger sample sizes across a variety of regional correctional facilities to
further explore the use and knowledge of the tool in practice.
Keywords: skin and soft tissue infections, severity stratification tool, CREST tool

SKIN AND SOFT TISSUE INFECTIONS
Skin and Soft Tissue Infections: Using a Severity Stratification Tool to Improve Knowledge

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The incidence of primary care visits for skin and soft tissue infections (SSTI’s) have
substantially increased over recent years. This is of greater significance in high-risk patient
populations, such as immune compromised patients and inmates. Not only have SSTI’s
increased, but also the emergence of Methicillin Resistant Staphylococcus Aureus (MRSA) has
further complicated this issue. SSTI’s have had severe economic and health status consequences
that impact healthcare resources, patient safety and quality of care.
Problem Statement
Skin and soft tissue infections (SSTI’s) have presented problems for primary care patients
in recent years. SSTI’s account for nearly 14 million primary care visits each year in the United
States (U.S.), and this statistic has been climbing in the last decade (Ramakrishan, Salinas &
Higuita, 2015; Ray, Suaya & Baxter, 2013). Skin and soft tissue infections originate from a
bacterial invasion of the skin, resulting in a variety of skin presentations (Ramakrishan, Salinas
& Higuita, 2015; Lee et al., 2016). These particular presentations can be classified as either
simple (involving superficial tissue) or complicated (involving deep tissues) SSTI’s. Common
simple SSTI’s include cellulitis, impetigo, folliculitis, furuncles, carbuncles and abscesses.
Complicated SSTI’s include necrotizing fasciitis, gangrene and infections of animal or human
bites (Ramakrishan, Salinas & Higuita, 2015; Ray, Suaya & Baxter, 2013).
The emergence of Methicillin Resistant Staphylococcus Aureus (MRSA) has further
complicated the incidence of skin and soft tissue infections. MRSA is a gram-positive cocci
bacterium that presents as a break in the skin, abscess or boil that has become resistant to
standard antimicrobial treatment (Lee et al., 2016). In the United States, approximately 80-90%
of SSTI’s are found to contain the bacterium MRSA (Lee et al., 2016; Mullen & O’Keefe, 2015).

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SSTI’s affect all populations, but an increased incidence has been seen in high-risk patients,

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which includes correctional facility inmates (Mullen & O’Keefe, 2015). The rise of MRSA in
correctional facilities in the U.S. over the last decade has caused great concern not only for
inmates and correctional staff, but when individuals are released from prison back into society
(Mullen & O’Keefe, 2015). The number of incarcerated individuals in the United States has
increased by 300% since 1980, increasing the risk of acquiring MRSA in correctional facilities,
but also in the community upon release (Mullen & O’Keefe, 2015).
Purpose and Rationale
Skin and soft tissue infections are a significant health concern with serious potential
implications that can affect health status of communities and impact the healthcare system
through increased emergency room visits and hospitalizations related to complicated SSTI’s (Lee
et al., 2016; Ramakrishan, Salinas & Higuita, 2015; Ray, Suaya & Baxter, 2013). If an SSTI
goes untreated or treatment is delayed due to difficult diagnostic accuracy the condition may
progress to sepsis, deformity, skin grafts, necrotizing fasciitis, limb amputation or even death
(Lee et al., 2016; Ramakrishan, Salinas & Higuita, 2015; Ray, Suaya & Baxter, 2013). In 2005,
nearly 11,406 people died of MRSA infections in the U.S. (Mullen & O’Keefe, 2015). This is
more than the number of people in the U.S. that died of Acquired Immune Deficiency Syndrome
(AIDS) in that same year (Mullen & O’Keefe, 2015).
SSTI’s also pose a significant financial burden. The annual cost of community acquired
MRSA is 478 million to 2.2 billion U.S. dollars (Lee et al., 2013). This impacts the U.S.
department of corrections (DOC) as 15 to 36 million U.S. dollars are spent annually in the
American jail system on costs related to MRSA alone (Lee et al., 2013). By exploring the root

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cause of the increased incidence of SSTI’s, one can explore potential interventions to decrease

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the occurrence and severity of SSTI’s in the community and correctional setting.
Background/Significance
An emergence of SSTI’s, specifically MRSA, has been seen in distinct patient
populations that include: athletic teams, students in dormitories, military recruits, the homeless
population and the incarcerated (Malcolm, 2011). Overall though, the correctional facility inmate
population is at an even higher risk of MRSA then other population groups. Many of the
potential risk factors for SSTI’s and MRSA often directly correlate with this subgroups personal
demographics and residential setting (Malcolm, 2011; Mullen & O’Keefe, 2015). Factors such as
overcrowded living conditions, outdoor work assignments, self-draining of abscesses, sharing
soap, washing clothes by hand and close contact with persons known to have MRSA contribute
to the incidence and prevalence of this condition. Additional co-morbid health related issues
include obesity, diabetes mellitus and inadequate hygiene (Malcolm, 2011; Mullen & O’Keefe,
2015; Ray, Suaya & Baxter, 2013). In a three-year study of predictors of SSTI’s, authors Ray,
Suaya and Baxter (2013) determined that of the 376,262 patients studied with SSTI’s, 50% had a
diagnosis of diabetes mellitus.
Additionally, inmates generally have a higher incidence of past intravenous (IV) drug
use, alcohol abuse, prison tattoos, Human Immunodeficiency Virus (HIV), Hepatitis B, Hepatitis
C and tuberculosis, which are also high-risk factors for MRSA (Mullen & O’Keefe, 2015;
Ramakrishnan, Salinas & Higuita, 2015). Recent research demonstrates a correlation between
age, MRSA risk factors, and SSTI (Malcolm, 2011; Ramakrishnan, Salinas & Higuita, 2015). In
a study of correctional inmates, authors found that inmates ages 30-49 years were significantly
more likely to have MRSA infections than patients less than 29 or patients over the age of 50

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(Malcolm, 2011). There is no known cause for this increase in the prevalence of MRSA in ages

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30 to 49 years of age (Malcolm, 2011; Ramakrishnan, Salinas & Higuita, 2015).
Despite preventative efforts, the incidence of SSTI’s continues to escalate, and treatment
failure is widespread (Lee et al., 2016; Miller et al., 2015; Suaya et al., 2014). Potential barriers
to restrain this trend include a lack of standardized polices, screening and education. Research
demonstrates that a primary barrier is a lack of standardized policies or tools for early diagnosis
and treatment of SSTI’s (Flanagan, 2014; Marwick, 2011; Mullen & O’Keefe, 2015; Talan et al.,
2014). Wound management is often an area of expert opinion versus evidence-based practice,
making it difficult to formulate policies and standardized tools (Flanagan, 2014). The signs and
symptoms of SSTI’s can be masked or vague, making proper diagnosis difficult (Tiwari & Lal,
2014). SSTI’s also can be difficult to diagnose related to their considerable variability and
severity (Hashem, Hidayat, Berkowitz, & Venugopalan, 2016; Tiwari & Lal, 2014). In 2005, the
Infectious Disease Society of America (IDSA) developed guidelines for MRSA infection
treatment (Stevens et al, 2014). These guidelines were then updated by IDSA in 2014, but still no
other guidelines have been produced for SSTI’s in general and the actual implementation of
these guidelines in primary care is lacking (Stevens et al., 2014).
Inadequate education, screening and standardized policies have been noted in
correctional facilities across the U.S. (Malcolm, 2011; Mullen & O’Keefe, 2015). In a study of a
correctional facility in Omaha, Nebraska researchers discovered the facility to be deficient in a
specific treatment policy and guideline for MRSA SSTI’s (Mullen & O’Keefe, 2015). The
authors created and implemented a screening and treatment policy for 132 inmates with MRSA
SSTI (Mullen & O’Keefe, 2015). As a result, by improving the screening and treatment
procedure for SSTI’s they were able to improve staff education, patient education and patient

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outcomes (Mullen & O’Keefe, 2015). In another correctional facility in Georgia, after a MRSA

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outbreak staff implemented a multi-modal intervention and noted similar results (Malcolm,
2011). In this study the authors incorporated interventions to include screening for skin lesions,
personalized hygiene, antimicrobial therapy, and standardized wound care (Malcolm, 2011). As
a result, the MRSA rate of infection decreased from 11.6 per 10,000 inmates to 0 per 10,000
inmates in the post-study period (Malcolm, 2011).
The management of SSTI’s is primarily related to infection severity, location and patient
co-morbidities (Ramakrishnan, Salinas & Higuita, 2015; Stevens et al., 2014; Tiwari & Lal,
2014). Despite this being common practice for management and treatment, there is no
standardized severity scale for SSTI’s (Tiwari & Lal, 2014). Several studies have tested
established severity tools for accuracy and reliability. The CREST tool measures severity of
illness with SSTI’s (Hashem et al., 2016; Marwick, 2011). The CREST tool is based on the Eron
classifications (Marwick, 2011). In this study, the authors used the CREST hierarchical scoring
system to measure SSTI severity ranging from one to four, with one being least severe SSTI and
four being most severe SSTI (Hashem et al., 2016). Through the CREST scoring system,
researchers were able to accurately ascertain SSTI severity and determine proper antimicrobial
treatment (Hashem et al., 2016). Researcher Marwick (2011) also created his own tool based on
the Eron classification and CREST tool. This tool also used a severity scale of one to four, one
being least severe SSTI and four being most severe SSTI (Marwick, 2011). The researcher
looked at appropriate empirical antibiotic treatment of SSTI’s based on the SSTI severity
classification (Marwick, 2011). Marwick (2011) discovered there to be a lack of consistency in
antimicrobial treatment strategies. Over 43 different antimicrobials were selected in the treatment
of SSTI’s in this study (Marwick, 2011). In another study, the authors looked at using a severity

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stratification algorithm in formulating treatment strategies for SSTI’s (Tiwari & Lal, 2014). The

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authors created this algorithm and it has not yet been tested in any other studies. The algorithm
addressed whether the patient has redness, warmth, pain or swelling to wound, signs of sepsis,
and size of wound. The authors found this algorithm helpful with proper detection of severity
and subsequent treatment for SSTI’s (Tiwari & Lal, 2014).
SSTI’s and MRSA also present a significant financial burden to patients, third party
payers, and health care facilities (Lee et al., 2013; Suaya et al., 2014). In a study of inpatients and
outpatients with SSTI’s in the United States, the mean annual cost for treatment for a single SSTI
case was $8,865 (Suaya et al., 2014). The pharmacological treatment and medical/nursing care
for MRSA is complicated compared to a SSTI caused by another bacterium. This is reflected in
the mean cost for treatment for a single case of MRSA-SSTI being estimated at $40,046 U.S.
dollars (Suaya et al., 2014). The financial burden of SSTI’s have also been recognized in U.S.
correctional systems. In a study of the financial implications of MRSA and SSTI’s, the
researchers studied a U.S. correctional facility in Pittsburg (Lee et al., 2013). At Cook County
correctional facility, the authors determined the annual societal cost of SSTI’s to be $140,275
U.S. dollars, reflecting not only cost to patients and third-party payers but also to society (Lee et
al., 2013).
As a whole, the literature identifies risk factors for skin and soft tissue infections that
substantiate the high incidence for SSTI’s in the correctional system. These risk factors include:
overcrowded populations, poor hygiene, IV drug use, alcohol abuse, HIV, Hepatitis B and C, age
and co-morbid conditions such as diabetes. The literature also identifies barriers to decreasing
the incidence of SSTI’s, principally the lack of a standardized tool or policy for SSTI
identification, management and treatment. Thus far, studies have shown that implementing a

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standardized policy, algorithm or severity rating scale have improved patient outcomes and

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decreased the rate of SSTI’s and MRSA.
Internal Evidence
In a county correctional facility in the Southwestern U.S., the incidence and prevalence of
SSTI’s in this population has been of concern, especially related to MRSA SSTI early
identification and treatment. Because of the high incidence of MRSA SSTI’s, the organization’s
healthcare providers no longer culture wounds, but instead presume MRSA and treat skin and
soft tissue infections accordingly. This correctional facility has no standardized policy or
algorithm for how nursing staff or medical providers should treat SSTI’s. This inquiry has led to
the clinically relevant PICOT question: In adults at high risk for skin and soft tissue infections
how does a risk stratification severity tool compare to usual wound diagnosis, affect time to
healing and hospitalization rates?
Search Strategy
In order to address how a wound severity stratification tool can improve patient outcomes
an exhaustive search of the literature was undertaken. Databases searched for the literature
review included PubMed, Elton B Stephens Company host (EBSCO host) and Web of science.
Keywords searched included: “soft tissue skin infection,” “MRSA,” “correctional facilities,”
“community acquired” “primary care” and “management.” The following limits were placed:
Adult population, English language and date limits 2012 to 2017. Date limits were expanded to
1980 to capture landmark studies. Over 5,000 studies were initially yielded, so then Boolean
phrases and MESH terms were utilized. Inclusion criteria for the studies included adult
population and diagnosis of SSTI. Exclusion criteria included studies prior to 2012 (except
landmark studies) and patients younger than 18 years of age. The MESH terms search method

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for PubMed yielded 2,402 results (Appendix A). Using MESH term “skin and soft tissue

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infections” and title/abstract keyword “primary care”, the search was further narrowed and four
of the articles yielded were pertinent to this project. The search was then narrowed to MESH
term “skin and soft tissue infections” and title/abstract keyword “correctional facilities,” which
yielded five articles. Using hand search, other references to other articles were found. These
studies contained “MRSA” in their title. So a search was done for MESH term “MRSA” and
title/abstract keyword “correctional facilities” yielding three results pertinent to the topic of
interest.
The Boolean phrase term search for SSTI on EBSO host yielded 4,628 studies (Appendix
B). By using connected Boolean phrases results yielded were narrowed down to 727 when
combined terms of “MRSA” and “community acquired” were used. At this time, the search was
still yielding a large quantity of unrelated studies. Boolean phrases were then used for “MRSA”
and “management”, which resulted in 288 studies. Then “MRSA” and “correctional facility” was
searched which resulted in 14 studies. Two selected studies were repeated in above search of
PubMed, but the search also yielded one new study pertinent to topic. This study was a landmark
study and outside of 5-year date inclusion criteria. Web of science search for key word “soft
tissue skin infection” yielded 731 results (Appendix C). These results were then narrowed adding
“management” to current search, which resulted in 54 studies. Of these 54 studies three met
inclusion criteria for this topic. Roughly 20 articles were initially reviewed for inclusion, but 10
were ruled out due to poor documentation, inconclusive evidence, weak descriptive or statistical
data, misleading conclusions or impertinent data or results. The ten studies that were chosen for
inclusion met the criteria and were relevant to the stated PICO question. Each study was

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reviewed and the data was extracted and organized in an evidence table for examination

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(Appendix D).
Evidence Synthesis
Ten studies have been chosen for inclusion in this literature review. All studies were
evaluated using rapid critical appraisal and were placed in evaluation and synthesis tables
(Appendix D, Appendix E). Overall, the strength of the studies were moderate, encompassing six
level II evidence and four with level III evidence, in which three were retrospective cohort
studies, one observational cross-sectional study, and five prospective cohort studies (Appendix
D). The stated PICOT question was better answered by retrospective and prospective studies,
resulting in lower levels of evidence. However the ten included studies were substantiated by
statistically significant results (Appendix D). None of the studies stated theoretical frameworks
or models, but they were implied. A large majority of these implied models, focused on
prevention models to include: Levels of prevention model and Millio’s prevention framework.
All articles required a diagnosis of SSTI’s. Very few of the studies listed specificities or
sensitivities for their measuring instruments, but the majority of the studies used some form of a
risk stratification severity ranking system adopted for SSTI’s (Appendix D). This is suspected
due to no formally accepted severity scale of SSTI’s yet. Reliability and validity of these tools
was identified in six of the articles, showing statistical significance in using risk scales to reduce
poor patient outcomes (Appendix E). Possible bias was noted in three of the studies, all three of
which were funded by Pharmaceutical companies, but when researching these companies further
none of the antimicrobial treatment options utilized in these studies appeared to be their products
(Appendix D). Seven of the studies took place in hospital settings and the remaining three studies
took place in community settings. The selection of hospital settings appears to be related to

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current research focusing on causation and risk factors of SSTI’s in hospital admissions.

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Additionally, five of the studies took place in Europe and five took place in the U.S. Since the
CREST tool was created in Scotland, it is more highly utilized in Europe currently. Homogeneity
was present in demographic data. A large proportion of the studies saw an association between
increased age and comorbidities, resulting in poor patient outcomes. Patient outcomes were
measured across the studies as increased time to healing, death, re-occurrence of SSTI or rehospitalization (Appendix E). Several studies incorporated the CREST scoring or the authors
own adaptation of it. Overall the studies found the variables lesion size, fever and duration of
skin infection to be the most significant in determining the severity of the infection when using
the tool (Appendix D). Heterogeneity of the studies was noted especially in the selection of a
severity stratification tool. The studies used a variety of severity stratification tools. Some of the
studies used established scoring tools to include CREST, SEWS and Ki/Rothstein, while others
developed modified severity scoring tools or risk stratifications (Appendix E). Across the studies
though, all of the tools were shown to be statistically significant in improving utilization of
appropriate antimicrobial treatment measures and to improve patient outcomes.

Current practice in the identification and management of SSTI’s is inconsistent with
evidence based practice guidelines. Evidence suggests the importance of implementing a severity
stratification tool to improve early identification of SSTI’s, which in turn can improve long-term
patient outcomes. The research does not show evidence of one severity tool being highly
accurate over another, but the research collectively shows any severity tool utilized in SSTI
identification and treatment, results in improved patient outcomes. Although, the CREST tool
was the most frequently used tool and had more robust research compared to the other severity
stratification tools. A severity stratification tool is a quick, easy and low-cost item to prevent

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hospitalizations, surgical interventions and mortality related to SSTI’s. The proposed evidence
based project includes educating healthcare providers and nurses, in a Southwestern correctional
facility on the use of the CREST tool in an effort to increase staff knowledge of proper
identification and treatment of SSTI's, and for healthcare staff to have an increased rate of use of
this tool in their practice.
Conceptual Framework and EPB Model
Implementation of an SSTI severity stratification tool focuses on early detection to
prevent worsening of SSTI disease state. The Models of Prevention theory suits this project
design (Nilsen, 2015). This model emphasizes the idea that health is on a continuum, with health
at one end and advanced disease on the other. The model uses three levels of application of
preventative measures that can be used to promote health and stop the disease at different points
along the continuum. This continuum has four levels: primordial, primary, secondary and tertiary
(Nilsen, 2015). Secondary and tertiary levels would be appropriate for the severity stratification
tool for SSTI’s, given the SSTI is already established and the goal is early identification and
treatment, to prevent increased severity of disease.
The evidence based model (EBP) Advancing Research and Clinical Practice through
Close Collaboration (ARCC) facilitated the proposed practice change. This model resembles an
organizational plan for a department (Appendix F). The model first focuses on assessing the
organizational culture and the readiness for system wide implementation, then on identifying
organizational barriers and strengths to EBP implementation plan and follow through with
actually implementing the EBP intervention (Schaffer, M.A., Sandau, K.E. & Diedrick, L.,
2012). This is an appropriate model for a large organization. The organization where the project
was implemented is resistant to change. Using ARCC one could first evaluate specific barriers

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staff may have with implementation of this project. One of the nurse practitioners at this facility
was used as an EBP mentor. Throughout this project, the EBP mentor was utilized in this
capacity, which is consistent with this model.
Project Methods
Evidence based project approval was obtained through Arizona State University
Investigation Review Board (IRB) and through facility medical director approval. Participants
were recruited using flyers, email invitations and word of mouth. Inclusion criteria required the
participants to be 18 years or older, the ability to read and write the English language and a
current employee of the identified correctional facility. A consent letter was provided to the
participants prior to the intervention that included information on the project participation risks
and benefits. The potential benefits of this project included: potential increase in knowledge
related to skin and soft tissue infections and a potential increase in self-confidence to assess,
diagnose and treat skin and soft tissue infections. The only potential risk for participants
identified was time away from other work duties in order to complete the educational session,

which could potentially increase work burden. Participants were also aware that they could with
draw from the project at any time, as it was stated in the consent letter. Additionally, by
participants completing the educational session it was considered implied consent to participate
in the project. In order to protect the project participants’ identity, participants selected a unique
number of their choosing and were instructed to write this number on the top of their pre-test and
post-test. These unique identifiers functioned to maintain the participants’ privacy, while still
allowing for pre-and post-test data to be compared.
The project setting is an outpatient clinic in a correctional facility in the Southwestern
U.S. that houses over 2,440 inmates. This facility also houses a 60-bed medical infirmary and a

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primary care clinic. The outpatient clinic includes over 75 nurses and over 20 healthcare

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providers. This is a site with several barriers to change. Because of inmates being a high-risk
population, changes to policies and procedures have to go through a stringent and laborious
process before being implemented in this setting. Also, initially staff and key stakeholders were
not open to the idea of change. To these staff members, a practice change was viewed as a
burden to practice. Because of the many hurdles involved with invoking change at this site,
resistance to change was expected and occurred initially.
The final sample size consisted of 18 participants, 12 healthcare providers in the provider
group and 6 healthcare staff members in the non-provider healthcare group. The intervention
itself included a pre-and posttest design and an educational session. The interventional sessions
took place at two separate events in October 2017, one for the healthcare providers at their
monthly provider meeting and one for the nurses and other healthcare members at their monthly
educational meeting. At these two meetings, participants were initially provided with the consent
letter. After reading over the consent letter, participants were given a self-created pre-and posttest and a severity stratification tool algorithm. The pre-and post-test questionnaires were
reviewed by faculty for content and validity. The algorithm itself is a self-created tool that
incorporates the CREST tool into a helpful and easy to use algorithm for healthcare members to
use for easy identification, assessment and treatment of SSTI’s. This algorithm was determined
to be a reliable and accurate tool after review by several experts for accuracy. The participants
were directed to put a unique identifier number at the top of the pre-and post-test and to then
place the post-test to the side. Prior to the educational session, participants were given fifteen
minutes to complete the pre-test of knowledge. And after the educational session was complete,
participants were given fifteen minutes to complete the posttest of knowledge. Both the pre-test

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and post-test involved questions related to identifying SSTI’s and case study questions. The

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questions were the same on the pre-and post-test, except for three questions on the post-test
involving if the participant felt the tool was helpful and whether they would use it in practice.
Additionally, the demographic questions were only on the pre-test, not on the post-test. The
questions for the provider group versus the non-provider group slightly differed. The nonprovider group case study questions only included questions to identify and assess SSTI’s using
the CREST tool, whereas the health care provider case study questions included diagnosing and
treating SSTI’s using the CREST tool. Immediately following the pre-test, a thirty-minute
educational session was presented to the participants by the project leader using power point
slides. This power point presentation included information on the background and significance of
the issue, information on the CREST tool for assessing, diagnosing and treating SSTI’s.
Participants were also supplied with a copy of the CREST tool algorithm and time was allotted
for questions or clarifications. Immediately following the educational session, a fifteen-minute
post-test was administered.
Since there is currently no instrument specifically focused on improvements in
knowledge of SSTI’s, a self-developed questionnaire was used. The developed questionnaire is a
14-item scale, with multiple choice questions. Unanswered questions were treated as incorrect
answers, and were recorded accordingly. This scale was developed to measure healthcare staff’s
knowledge of severity rankings of SSTI’s using specifically the CREST tool. The measurement
includes questions on descriptions of SSTI’s, proper ranking of SSTI’s, participants’ comfort
level with managing patients with SSTI’s and whether participants plan to use the CREST tool in
practice. Reliability and validity on this instrument was determined by multiple revisions and
improvements through feedback from several research and data experts.

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At the conclusion of the intervention, all pre-and post-test sheets were collected and were
double checked to ensure the pre-and posttest for each participant had their unique identifying
number at the top of the tests. These completed pre-and post-tests were then scanned to a
password protected computer with no participant identifiers on them. Data was manually entered
into the statistical software SPSS. Questionnaires were broken down into several categories
based on the multiple-choice questions. Gender, education, license, age was recorded based on a
Likert scale. The questions on wound characteristics for non-providers and providers were
broken down into a pre-and post-test category. One point was awarded for each correct question
for these sections, and these questions were totaled to offer a score. Similarly, for pre-and post-

test case study scores for non-providers versus providers, one point was awarded for each correct
question and these questions were totaled to offer a score. A Likert scale was used for three
questions related to usefulness of the tool in practice and actual implementation of the tool in
practice. This was completed and rechecked by the project lead several times for correct data
entry and accuracy. It was then further verified for accuracy by the co-investigator and the
statistical consultant. Descriptive statistics and inferential statistics were completed to include a
one pair tailed t- test comparing pre-and post-test case study scores for healthcare provider group
and the non-healthcare provider group.
Outcomes/Project Results
Descriptive statistics were used to describe the sample and outcome variables. The age of
the subjects ranged from 25 to 74 years. The sample (n= 18) consisted of 12 (67%) females and 6
(33%) males. The majority (89%) of the sample had an educational degree of a Master’s degree
or higher. The sample size was made up of 12 healthcare providers and 6 individuals that were
not providers. The healthcare provider sample (n= 12) was made up of 2 (11%) nurse

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practitioners, 6 (33%) physician assistants and 4 (22%) physicians. There was no statistical

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significance in the descriptive statistics except for a significant increase in healthcare provider
pre-test to post-test case study scores. A one tail paired t-test was performed to compare the preand post-test case study scores. The mean of the pre-test case study scores was 3.57 (SD= 1.60),
and the mean of the post-test case study scores was 6.21 (SD= 1.97). A significant increase from
pre-test to post-test case study scores was found (t(13)= -6.19, p < 0.00). Of the healthcare
providers, over half (57%) found the tool “moderately helpful” and 61% of them plan to
incorporate the tool in practice at least “some of the time.” The non-provider sample (n=6) was
made up of 1 Licensed Practical Nurse, 1 Clinical Nurse Specialist and 4 unknown. The nonprovider sample was too small to run any inferential statistics. Of the non-providers, half (50%)
found the tool “extremely helpful” and plan to use the tool “all of the time.” The data suggests a
statistical significance in improvements in post test case study scores for healthcare providers,
showing an increase in knowledge from pre-intervention to post intervention. The data also
suggests that the majority of healthcare providers and nurses planned to use the tool in practice,
75% of the non-providers and 85% of the healthcare providers reported that they plan to use this
tool in practice.
Discussion
Through this study it was determined that by educating and implementing a severity
stratification tool, staff knowledge improves and staff have a plan for an increased use of the tool
in practice. At the provider and nurse level the impact of the project was seen through the data
that reflects improvements of case study scores and willingness to use the tool in practice. This
translated to the participants actually planning to implement the CREST tool into their own
practice. At a system level, after completion of the project, administration is considering making

SKIN AND SOFT TISSUE INFECTIONS
the CREST tool educational PowerPoint and algorithm accessible and mandatory to all

19

providers. Since changing policy in this public-sector organization is difficult, no plans are in
place at this time to make the CREST tool algorithm and its use a part of policy, but may at least
be making the education portion mandatory. Having the education portion mandatory for
providers could be a stepping stone to making the algorithm available as a procedural tool as
well. The project intervention is planned to be sustained by a second-year Doctor of Nursing
Practice student. This student plans to continue the project with her focus on implementing the
CREST tool and algorithm facility wide. Through implementing this tool facility wide in
practice, she plans to look at specific patient outcomes and financial spending. This is expected
to be implemented by next fall. The financial implications of this study were very minimal. The
only cost was minimal and was covered by the project leader as costs for snacks at the
educational session. As for longevity of this project the only cost would be paying employees to
undergo the training session, which could easily be completed in 30 minutes to an hour.
Implementing education on a severity stratification tool is a low cost and efficient intervention to
improve staff knowledge of SSTI’s.
This study had several limitations. By sharing in these experiences hopefully these
limitations will be avoided as the project is sustained and carried forward. One of the limitations
of this study is the small sample size (n= 18) and non-provider sample size (6). Only inferential
statistics could be conducted on the provider sample, not on the non-provider sample group,
since the non-provider sample was too small to run any inferential statistics. With a larger
sample size, inferential statistics in the non-provider group could be included. This leaves much
room for additional studies with larger sample sizes to further explore the use of the tool and
knowledge of the tool in practice. Another limitation to the study was that the participants were

SKIN AND SOFT TISSUE INFECTIONS

20

asked their job title as a multiple-choice question. Several participants left this section blank. It
was discovered later that several x-ray technicians and physical therapists attended the provider
meeting. This finding could have potentially skewed the data results. The strengths of the study

were the larger sample size of the healthcare providers, which allowed for inferential statistics to
be completed, determining that the health care provider group had a statistically significant
increase in knowledge from pre-intervention to post intervention. Another strength of the study
was the attrition rate, no participants exited the study early and all participants who completed a
pretest also completed a post test, improving the consistency of the data that was obtained.
Conclusion
Through this evidence based project it was determined that by implementing an
educational session on a severity stratification tool for SSTI’s, healthcare providers had an
improvement in knowledge related to diagnosing and treating SSTI’s using the CREST tool. Not
only was staff knowledge improved, but the majority of participants found this tool to be helpful
and had plans to incorporate it into their daily practice. Hopefully now this project can be moved
one step further to determine if it is actually being used by healthcare staff in the field. By
arming healthcare staff with the knowledge of the CREST tool, there is an opportunity to
diagnoses and treat SSTI’s early on to promote improved communication amongst health care
staff, improved assessment coordination and ultimately improved patient outcomes for this high
risk and vulnerable population.

SKIN AND SOFT TISSUE INFECTIONS
References

21

Figtree, M., Konecny, P., Jennings, Z., Goh, C., Krilis, S. A., & Miyakis, S. (2012). Risk
stratification and outcome of cellulitis admitted to hospital. Journal of Infection, 60(6),
431-439.
Flanagan, M. (2014). Barriers to the implementation of best practice in wound care. Wounds,
15(1), 74-82.
Hashem, N. G., Hidayat, L., Berkowitz, L., & Venugopalan, V. (2016). Management of skin and
soft-tissue infections at a community teaching hospital using a severity-of-illness tool.
Journal of Antimicrobial Chemotherapy, 71(11), 3268-3275.
Jenkins, T. C., Knepper, B. C., Sabel, A. L., Sarcone, E. E., Long, J. A., Haukoos, J. S., ... &
Mehler, P. S. (2011). Decreased antibiotic utilization after implementation of a guideline
for inpatient cellulitis and cutaneous abscess. Archives of internal medicine, 171(12),
1072-1079.
Lane, S., Johnston, K., Sulham, K. A., Syed, I., Pollack, C. V., Holland, T., & Nathwani, D.
(2016). Identification of patient characteristics influencing setting of care decisions for
patients with acute bacterial skin and skin structure infections: results of a discrete choice
experiment. Clinical therapeutics, 38(3), 531-544.
Lee, B. Y., Singh, A., David, M. Z., Bartsch, S. M., Slayton, R. B., Huang, S. S.,…Daum, R. R.
(2013). The economic burden of community‐ associated methicillin‐ resistant
Staphylococcus aureus (CA‐ MRSA). Clinical Microbiology and Infection, 19(6), 528536.
Lee, G.C., Hall, R. G., Boyd, N. K., Dallas, S. D., Du, L. S., Trevino, L. B.,…Trevino, S. B.
(2016). A prospective observational cohort study in primary care practices to identify

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factors associated with treatment failure in Staphylococcus aureus skin and soft tissue

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infections. Annals of Clinical Microbiology and antimicrobial, 15(58), 1-7.
Malcolm, B. (2011). The Rise of Methicillin-Resistant Staphylococcus aureus in U.S.
Correctional Populations. Journal of Correctional Health Care: The Official Journal of
the National Commission on Correctional Health Care, 17(3), 254–265.
doi.org/10.1177/1078345811401363
Marwick, C., Broomhall, J., McCowan, C., Phillips, G., Gonzalez-McQuire, S., Akhras, K., ... &
Davey, P. (2011). Severity assessment of skin and soft tissue infections: cohort study of
management and outcomes for hospitalized patients. Journal of Antimicrobial
Chemotherapy, 66(2), 387-397.
Marwick, C., Rae, N., Irvine, N., Davey, P., Marwick, C., Rae, N., ... Davey, P. (2011).
Prospective study of severity assessment and management of acute medical admissions
with skin and soft tissue infection. Journal of Antimicrobial Chemotherapy (JAC), 67(4),
1016-1019. doi:10.1093/jac/dkr554
Miller, L.G., Eisenberg, D.F., Liu, H., Chang, C., Wang, Y., Luthra, R., … Suaya, J.A. (2015).
Incidence of skin and soft tissue infections in ambulatory and inpatient settings 20052010. Bio Med Central Infectious Disease, 15, 362.
Mullen, L. A. & O’Keefe, C. (2015). Management of skin and soft tissue infections in a county
Correctional facility: A quality improvement project. Journal of Correctional Health
Care, 21(4), 335-364.
Nilsen, P. (2015). Making sense of implementation theories, models and
frameworks. Implementation Science, 10(1), 53.
Ramakrishan, K., Salinas, R.C. & Higuita, N.I. (2015). Skin and soft tissue infections. American

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Family Physician, 92(6), 474-483.

23

Ray, G.T., Suaya, J.T. & Baxter, R. (2013). Incidence, microbiology, and patient characteristics
of skin and soft-tissue infections in a US population: a retrospective population-based
study. BMC infectious diseases, 13(1), 252.
Schaffer, M. A., Sandau, K. E., & Diedrick, L. (2012). Evidence‐based practice models for
organizational change: overview and practical applications. Journal of advanced
nursing, 69(5), 1197-1209.
Stevens, D. L., Bisno, A. L., Chambers, H. F., Dellinger, E. P., Goldstein, J. C., Gorbach, S. L.,
… Wade, J.C. (2014). Practice guidelines for the diagnosis and management of skin
and soft tissue infections: 2014 update by the Infectious Diseases Society of America.
Clinical Infectious Diseases, 59, e10-e52.
Suaya, J.A., Mera, R.M., Cassidy, A., O’Hara, P., Amrine-Madsen, H., Burstin, S. & Miller,
L.G. (2014). Incidence and cost of hospitalizations associated with staphylococcus aureus
skin and soft tissue infections in the United States from 2001 through 2009. Bio Med
Central Infectious Disease, 14, 296.
Talan, D. A., Salhi, B. A., Moran, G. J., Mower, W. R., Hsieh, Y., Krishnadasan, A. & Rothman,
R. E. (2014). Factors associated with decision to hospitalize emergency department
patients with skin and soft tissue infections. Western Journal of Emergency Medicine, 1,
89-97.
Tiwari, A.K. & Lal, R. (2014). Study to evaluate the role of severity stratification of skin and
soft tissue infections in formulating treatment strategies and predicting poor prognostic
factors. International Journal of Surgery, 12, 125-133.
Wilson, S. E., Solomkin, J. S., Le, V., Cammarata, S. K., & Bruss, J. B. (2011). A severity score

SKIN AND SOFT TISSUE INFECTIONS
for complicated skin and soft tissue infections derived from phase III studies of
linezolid. The American journal of surgery, 185(4), 369-375.

24

SKIN AND SOFT TISSUE INFECTIONS
Appendix A
Search Strategy 1
PubMed

Appendix B

25

SKIN AND SOFT TISSUE INFECTIONS
Search Strategy 2
EBSO host

Appendix C

26

SKIN AND SOFT TISSUE INFECTIONS
Search Strategy 3
Web of Science

27

28

SKIN AND SOFT TISSUE INFECTIONS IN HIGH RISK POPULATIONS
Appendix D
Evaluation Table
Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

Major Variables &
Definitions

Measureme
nt/
Instrument
ation

Data
Analysis
(stats used)

Findings/
Results

Level/Quality of
Evidence;
Decision for
practice/
application to
practice

Figtree, M.
(2012). Risk
stratification
and outcome of
cellulitis
admitted to
hospital.

Millio’s
framework
for
prevention

Design:
Retrospective
cohort study

N= 395

IV1- Age>60

LOE: Level III

IV2- Impaired
mobility

Pearson’s
chi square,
Fischer’s
exact test,
One way
analysis of
variance,
univariate
analysis,
logistic
regression, 2
tailed tests
of
significance
P= <0.05

IV1P= <0.001*

n= 1

Predictive
risk tool
model
created by
authors

Funding:
None
Conflict/Bias:
None

Country:
Sydney

Purpose: To
identify risk
factors associated
with mortality and
adverse outcomes
of community
acquired cellulitis
requiring hospital
admission

SS= 1 650 bed
Tertiary referral
hospital in
Sydney, Australia

IV3- > Comorbidities
IV4- Albumin <30

M age= 70.6
F= 57%
IC=
Hospitalization
between January
1999 and
December 2006,
diagnosis code for
cellulitis

IV5= symptoms > 4
days
IV6= Antibiotics
delayed >8 hours

Iv2P= <0.001*
IV3=
P= 0.004*
IV4=
P=<0.001*
IV5=
P<0.001*
IV6=
P< 0.001*

DV1=Hospital stay
>7days

DV1=36.1%
Findings: Pt

W: Study design,
selection bias,
associations
detected are not
necessarily
causations but
associations,
residual
confounding
may have
occurred, study
pertains to
cellulitis SSTI’s
only.
S: Stringent
inclusion and
exclusion
criteria, practical

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
EC= Myositis,
necrotizing
fasciitis, diabetic
foot infections,
<16 years of age

Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

with admission
risk factor <4 at
a low risk of
adverse
outcomes, Risk
increased with
score.
Multivariate
factors
independently
associated with
hospital stay >
7days included
age >60,
symptom
duration>
4days,
Bacteremia.

Major Variables &
Definitions

Measureme
nt/
Instrument
ation

Data
Analysis
(stats used)

Findings/
Results

29

clinical
endpoints,
largest study of
in-hospital
mortality
APP:
Appropriate risk
stratification can
prioritize
patients for
timely antibiotic
administration.
This study can
help provider to
know using risk
stratification
whether patient
needs to be
admitted or can
remain in
community.
Level/Quality of
Evidence;
Decision for
practice/
application to
practice

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
Hashem, N.G.
(2016).
Management of
skin and soft
tissue infections
at a community
teaching
hospital using a
severity of
illness tool.
Funding: None
Conflict/Bias:
None
Country: USA

Levels of
prevention
model

Design:
Retrospective
Cohort study
Purpose: To
retrospectively
apply CREST to a
Cohort of
hospitalized
patients with
SSTI’s and to
assess the
relationship
between disease
severity and
appropriateness of
antimicrobial
management.

N= 200

IV1- Severity of
SSTI

n= 1
SS= one 416 bed
community
teaching hospital
in Brooklyn, NY
M age= 54
M= 53%
IC= Primary
discharge
diagnosis SSTI,
admitted from
January to
December 2011,
>18 years of age,
EC= Admitted
with cellulitis
associated with
DM, <18 years of
age.

IV2- Early
recognition of SSTI
DV1- Over-treatment
DV2-Undertreatment
DV3- Complete Final
response
DV4- Clinical
Stability

IV1CREST
scoring

ANOVA,
Fischer’s
Exact test

IV1Statistically
significant

IV2SEWS
Scoring
(Class I
least
severeclass 4
most
severe)

P<0.05

IV2Statistically
significant

DV1,
DV2,Measured
based on
antimicrobi
al route and
spectrum of
activity
based on
suspected
pathogens
in each
severity
class.

DV1Overtreatment
most common
in least severe
classes
P= <0.05*
DV2- 50% class
4 undertreated

DV3- 83% of
classes 1
through 3
achieved
complete
response.
DV4- Median
time 3 days in
classes 1 - 3

30

LOE: Level III
W: study design,
patient
classifications
were not
assessed by an
independent
viewer; CREST
is not applicable
to MRSA
patients.
S: Easy
application of
CREST tool to
clinical practice,
can use in
hospital setting
or outpatient
setting, low cost
APP: By using
severity scoring
for SSTI’s
providers can
ensure adequate
treatment for
SSTI.

DV3, DV4-

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

31

SKIN AND SOFT TISSUE INFECTIONS
measured
as number
of days

Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

Major Variables &
Definitions

Measureme
nt/
Instrument
ation

Data
Analysis
(stats used)

Findings/
Results

Level/Quality of
Evidence;
Decision for
practice/
application to
practice

Jenkins, T.C.
(2011).
Decreased
antibiotic
utilization after
implementation
of a guideline
for inpatient
cellulitis and
cutaneous
abscess.

Millio’s
framework
for
prevention,
Levels of
prevention
model

Design:
Retrospective Preintervention and
post-intervention
study

N= 344

IV= Without
standardized
treatment plan

SAS Data
analysis

Pearson X,
Fischer
exact,
Wilcoxon
rank sum

IV=
Statistically
significant

LOE: Level III

Funding:
Department of
patient safety
and quality,
Denver Health

Purpose: To
explore whether
implementing an
institutional
guideline to
standardize and
streamline the
evaluation and
treatment of
inpatient cellulitis

n=
CO n= 169
I n= 175
Median age:
CO=46
I=47
M:
CO= 75%
I= 66%
SS= Denver
health integrated
system (includes

IV2=With
standardized
treatment plan
DV1= Duration of
therapy
DV2= Days
antibiotic therapy
administered
DV3= Clinical failure
(Treatment failure,

DV1=
Number of
calendar
days Pt
received
treatment
DV2= Sum
of calendar
days of
each
antibiotic
administere
d

IV2=Statisticall
y Significant

P<0.5
DV1=
Decreased by 3
days in I group
P<0.001*
DV2=
P=Decreased in
I group
P<0.001*

W: Reviewer
bias due to study
design, Subject
to period effect,
study preformed
at a single
institution,
preformed on
only a subset of
patients with
SSTI’s
S: Intervention is
broadly
applicable, uses

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
medical center
Conflict/Bias:
None

and abscess would
decrease antibiotic
and health care
resource
utilization.

hospital, ED,
urgent care, subspecialties,
primary care,
public health)

recurrence of rehospitalization)

IC= Principal
discharge
diagnosis of
cellulitis or
cutaneous abscess
from January 1st
2007 to December
31st 2007 or July
9th 2009 to July 8th
2010, >19 years
of age

DV5=
Microbiological
cultures

DV4= Use of serum
ESR, CRP

Country: USA

EC= <19 years of
age, transferred
from another
hospital, leaving
against medical
advice, chronic
ulcer, peripheral
arterial disease,
animal/human
bite, necrotizing
fasciitis, or
hospital

DV6= Imaging
DV7= Length of
hospital stay

DV3=
Number of
patients
with
treatment
failure,
reoccurrenc
e rehospitalizat
ion in a 30
day period
DV4=
Number of
ESR, CRP
use
DV5=
Number
patient
cases that
used
cultures

DV3=
CO- 7.7%
I- 7.4%
P=0.93

DV4=
P= 0.006*
DV5=
Decreased with
I group
P=0.003*
DV6=
Decreased with
I group
P= 0.2*
DV7=
P=0.43

32

only a minimal
amount of
financial
resources
APP:
Implementation
of a clinical
practice
guideline for
cellulitis and
cutaneous
abscesses results
in shorter
durations of
more targeted
antibiotic
therapy, with a
decrease in use
of healthcare
resources
without
negatively
effecting pt.
outcomes.

DV6=
number of
cases
imaging
was used

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

33

SKIN AND SOFT TISSUE INFECTIONS
admission,
Nursing home or
surgery within last
90 days. Stay in
ICU

DV7= Sum
of calendar
days of
hospital
stay

Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

Major Variables &
Definitions

Measureme
nt/
Instrument
ation

Data
Analysis
(stats used)

Findings/
Results

Level/Quality of
Evidence;
Decision for
practice/
application to
practice

Lane, S. (2016).
Identification of
patient
characteristics
influencing
setting of care
decisions for
patients with
acute bacterial
skin and skin
structure
infections:
Results of a
discrete choice
experiment.

Multifactorial
Causation
Theory

Design:
Observational
Cross-sectional
study

N= 400

IV1- Severity of
ABSSSI

IV1- Mild,
moderate,
severe
depending
on presence
of fever,
abnormal
VS, SIRS
criteria,
Sepsis or
organ
failure

Multinomial
Model

IV1- Severe
ABSSSI
resulted in
decreased
percentage of
outpatient
treatment CI
0.02 – 0.07

LOE: Level II

Funding: The

Purpose: To
understand the
patient attributes
that affect clinical
decision-making
regarding the
setting of care for
ABSSSI
treatment.
Method: 3 part
Online Survey

M age=
USA- 45
UK- 42

IV2- Comorbidities
IV3- Age

M=
USA- 64.5%
UK- 70%
SS=USA, United
Kingdom
providers (50
emergency room
specialists, 50
infectious disease
specialists, 50

IV4- Lack of
adherence
DV1= Decision to
treat as inpatient or
outpatient

IV2classified
as minor or
major

CI: 95%
Mixed
effects
Model

Mild ABSSSI
resulted in
increased
probability of
outpatient
treatment CI
0.94 to 0.99

W: Convenience
sample and may
not represent
general
population,
nature of sample
could have
introduced
selection bias,
differing
definitions of
mild, moderate
and severe
ABSSSI’s exists
which could

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
Medicines
Company
Conflicts/Bias:
The author is an
employee of the
Medicines
Company,
which is
company in the
biopharmaceuti
cal industry
Country:
United
Kingdom

hospitalists, 50
nurse
practitioners)
IC= Medical
Providers
experienced with
treating
ABSSSI’s, >18
years of age, must
live in USA or
United Kingdom
EC= other
professions beside
medical provider,
Lives outside of
USA or United
Kingdom

IV4Identified
an nonadherent if
antimicrobi
al doses
missed or
missed
appointmen
t

IV2- Severe comorbidities
outpatient
treatment
probability
<50%
IV3- <75 years
of age
probability
range of
treating as
outpatient
decreased to
0.28 – 0.61.
IV4- Not
significant

Findings:
Severe
comorbidities
and age >75
years of age
were important
indicators of
inpatient
treatment

34

affect
interpretation of
results
S: Eliciting
treatment
preferences from
a variety of
medical
providers,
providers
recruited from a
variety of
practice types,
large SS,
experimental
design
demonstrated
orthogonality
and level
balance.
APP: Identifying
key factors that
affect ABSSSI’s
treatment plan.
Highlights
importance of
severity
stratification in

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

35

SKIN AND SOFT TISSUE INFECTIONS

determining
treatment of
ABSSSI’s.

Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

Major Variables &
Definitions

Measureme
nt/
Instrument
ation

Data
Analysis
(stats used)

Findings/
Results

Lee, G.C.
(2016). A
prospective
observational
cohort study in
primary care
practices to
identify
factors
associated with
treatment failure
in
Staphylococcus
aureus skin and
soft tissue
infections.

Normalizatio
n Process
Theory,
which is the
theory of
looking at
complex
interventions
that have
been put into
place and
evaluating
them. This
correlates
with the
treatment
failure rate
and risk

Design=
PCS

N= 106 patients
M age= 41
M= 50%
H= 74%
O= 54%

IV1- MRSA
phenotype

IV1, IV2- +
or negative
blood
cultures

Bivariate
Analysis

IV1- No
statistical
significance

IV3- >5cm
wound
measureme
nt

Breslow
Day test
95% CI

Funding: Pfizer

Purpose=To
identify risk
factors
contributing to
treatment failure
associated with
CA-MRSA
SSTI’s.

n= 84 (no
treatment failure)
M age= 40
M= 52%
H=76%
0= 50%
n= 22 (treatment
failure)
M age= 45
M= 41%
H= 64%

IV2-MSSA
phenotype
IV3- Largest size
diameter of the
wound
IV4- Duration of skin
infection
DV1- Treatment
failure

IV4number of
days >7
days
DV1Measured
by ER or

P=< 0.05
IV2- No
statistical
significance
IV3- CI: 1.5817.20. ES: 3.53
IV4- CI: 1.7419.61. ES: 4.80
DV1P= 0.32

Level/Quality of
Evidence;
Decision for
practice/
application to
practice
LOE: Level II
W: Limited SS,
social/behavioral
risk factors not
included,
compliance of
antimicrobials
was no assessed
S: First study to
look at wound
size as risk
predictor, no
bias, focused on
primary care
setting

Findings:

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS

Conflicts/Bias:
Pfizer could
present bias on
antibiotic
treatment plans
selected in this
study since it is
a
pharmaceutical
company.
Country: USA

factors for
this rate.

O= 71%
SS= 14 primary
care clinics in
South Texas
IC= Provided
informed consent,
at least 18 years
of age or older
and presented to
one of the
included clinics
with a SSTI.
EC= <18 years of
age, no informed
consent and no
SSTI diagnosis.

hospital
visit in post
90 days

MRSA
infections do
not have worse
outcomes than
MSSA
infections.
MRSA is not a
reliable
indicator of
SSTI severity.
Duration of
infection and
wound diameter
were strongest
risk predictors
for treatment
failure.

36

APP: Identify
predictors for
treatment failure
can help target
risk factors for
severity of
SSTI’s. This
study further
strengthens the
argument of time
to effective
treatment is a
major factor in
poor clinical
outcomes and
treatment failure

Treatment Failure
definition: need
for new antibiotic
course/therapy,
I&D, ER or
hospital visit in
post 90 days

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS

37

Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

Major Variables &
Definitions

Measureme
nt/
Instrument
ation

Data
Analysis
(stats used)

Findings/
Results

Level/Quality of
Evidence;
Decision for
practice/
application to
practice

Marwick, C.
(2011).
Prospective
study of
severity
assessment and
management of
acute medical
admissions with
skin and soft
tissue infection.

Levels of
prevention
model

Design:
Retrospective
cohort study

N= 79
N1= 37
N2= 4
N3= 27
N4= 11

IV1= Patients with
blood cultures

CREST
Severity
Scale

ANOVA

IV1- no
statistical
significance

LOE: Level III

Funding: None
Conflict/Bias:
None
Country:
Scotland

Purpose: To
identify patients
with SSTI’s to
explore clinical
management,
illness severity
and outcomes

IV2= Patients
without blood
cultures

n= 1
SS= 2 hospitals in
Scotland
M=65%
SI= 70% arm
AGR= 37-69
IC= Patient’s had
to be seen for
treatment between
April 2009 and
June 2010, >18
years of age,
primary diagnosis
of SSTI who
received
antimicrobials

IV3= Diabetes
Mellitus
IV4= Appropriate
Treatment
IV5= Inappropriate
Treatment
IV6= Age
IV7= Charlson Index
IV8= Severity Index
IV9= SEWS

Two-Way
KAPPA
Ki/Rostein test
Severity
Classificat P<0.5
CI= 95%
ions
SEWS
Classificat
ion

IV2- no
statistical
significance
IV3CI: 0.98 –
1.31. OR: 1.13
IV4CI: 0.41- 0.80
P<0.01.
OR: 1.0
IV5CI: 1.0730.09
OR: 0.57

W: Outside of
US, 2 sites,
Small SS, data
1 year, Level
III
S: significant
results
FE: Safe, easy
to use index
scales
APP:
Appropriate
treatment, age,
severity index,
SEWS scale,
SIRS scale
affect

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
IV10= SIRS
EC= Patients seen
out of date range,
< 18 years of age,
lacking a primary
diagnosis of SSTI

IV6P<0.07

DV1= Mortality

IV7P<0.27
IV8P<0.004

38

mortality.
Implementation
of these
index/scales
could decrease
incidence of
mortality in
SSTI’s.

IV9P<0.002
IV10P<0.266
DV1Statistically
significant
Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

Major Variables &
Definitions

Measureme
nt/
Instrument
ation

Data
Analysis
(stats used)

Findings/
Results

Level/Quality of
Evidence;
Decision for
practice/
application to
practice

Marwick. C.
(2011).
Prospective
study of

Levels of
Prevention
Model

Design:
Prospective Study

N= 79

IV1=Timeframe
antibiotics given

IV1hours

Fisher’s
Exact Test

IV1- P<0.05

LOE: Level II

IV2= Comorbidities

IV2, IV3,

Kappa

IV2- Not
significant

W: Small study
population,

n= 2
Purpose: To

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

39

SKIN AND SOFT TISSUE INFECTIONS
severity
assessment and
management of
acute medical
admissions with
skin and soft
tissue skin
infections.

prospectively
identify patients
with SSTI’s
presenting from
the community, to
document clinical
management,
illness severity
and outcome

M age= 52
M= 65%
SS= Acute
Medical
Admissions for 2
hospitals in
Scotland

Funding: None
Conflict/Bias:
None
Country:
Scotland

IC=>18 years of
age, primary
diagnosis of SSTI
who received
antibiotics,
admission
between April
2009 and June
2010
EC= <18 years of
age, no primary
diagnosis of SSTI,
admission outside
of April 2009 and
June 2010

IV3= Physiological
Parameters
IV4= Site of SSTI
IV5= Size of SSTI
IV6= Presence of
absence of
necrotizing fasciitis
IV7= Severity of
SSTI
DV1= Adverse
Patient outcomes
DV2=Appropriatenes
s of antibiotics

IV4, IV5,
IV6CREST
severity
classificati
on,
Ki/Rotstei
n criteria,
Tayside
Formulary
DV1:
measured
as death
within 30
days or readmission
to hospital
within 30
days

Statistic
CI= 95%

IV3- Not
significant
IV4- Not
significant
IV5- Not
significant
IV6- not
significant
IV7-not
significant
DV1= 6%

small number
of adverse
outcome events
S: Prospective
design,
ensuring the
appropriate
diagnosis for
enrollment and
ensuring no
sampling bias
FE: May not be
feasible to
apply to
primary care
setting

DV2= P<0.01
APP: Over
treatment of
mild infections
and undertreatment of
severe SSTI’s
is a common
practice that is

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

40

SKIN AND SOFT TISSUE INFECTIONS

identified in the
data. High
incidence of
wasteful
investigation,
time and
resources to
include blood
cultures.
Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

Major Variables &
Definitions

Measureme
nt/
Instrument
ation

Data
Analysis
(stats used)

Findings/
Results

Talan, D. A.
(2014). Factors
associated with
decision to
hospitalize
emergency
department
patients with
skin and soft
tissue
infections.

Multifactorial
Causation
Theory

Design:
Prospective
Cohort study

N= 619

IV1= History of
failed treatment

Survey for
physicians
on reasons
for
admission
and clinical
characterist
ics

Statpages 2
way
contingenc
y table
analysis

IV1=
CI1.38 -3.72*
ES: 1.76

Funding:
Center’s for

n=1
IV2= A

Purpose: To
identify factors
that influences the
physicians’
decision to
hospitalize a
patient with a
SSTI.

SS= 12 USA
ED’s
M age= 38.7
M= 57.5%
AA= 51.5%
CM= 32.5%

IV3= Comorbidity
(Prior MRSA
infection, Diabetes,
chronic ulcer, edema,
bed ridden)
IV4=Infection type
(abscess, cellulitis,
infected wound)

CI: 95%
Chi-Square
Binary
recursive
partitioning

IV2=
CI-1.11-5.08*
ES: 2.86
IV3= CI- 2.255.00*
ES: 3.35

Level/Quality of
Evidence;
Decision for
practice/
application to
practice
LOE: Level II
W: SS ED only,
instrumentation
used physician
survey, did not
take into account
admission
related to
inability to
preform wound
care due to adl
restrictions

IV4=

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
Disease Control
Conflicts/Bias:
None
Country: USA

SSTI type: 85.1%
abscess
IC= >18 years of
age, SSTI with
symptoms <1
week, purulent
exudate available
for culture, ED
patients only.
EC= < 18 years
of age, SSTI
symptoms >1
week,

IV5 =Infection
mechanism (chronic
wound, infection of
surgical wound)
IV6= Infection
location (groin, lower
extremity)
IV7=Symptoms
(fever, chills,
nausea/vomiting,
extreme
pain/tenderness, local
edema)
IV8=Size of wound
IV9=Abnormal
Imaging results

CI2.31- 5.22*
ES: 3.47
IV5=
CI2.06 -6.77*
ES: 6.77
IV6=
CI1.13-2.49*
ES: 1.69
IV7=
CI2.92- 5.96*
ES: 4.21
IV8= CI2.57 -6.62*
ES: 4.09

IV10=Vital signs
abnormalities (fever,
tachypnea,
tachycardia)

IV9=
CI- 1.42- 3.84*
ES: 1.74

DV1= Admitted with

IV10=
CI2.60-5.66*

41

S: Adequate
sample size,
variety of
significant
variables
FE: Safe, low
cost
APP: Reasons
physicians
choose to admit
a patient to the
hospital for SSTI
pertain to clinical
characteristics of
wound and
patient history. A
better
understanding of
reasoning behind
physicians to
hospitalize can
help to
streamline this
process and
admit
unnecessary
admissions from
occurring.

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
SSTI

ES: 4.02

DV2= Discharged
with SSTI

DV1=
Statistically
significant

42

DV2=
Statistically
significant

Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

Major Variables &
Definitions

Tiwari, A.K.
(2014). Study to
evaluate the role
of severity
stratification of
skin and

Incidence,
prevalence,
mortality
model

Design:
Prospective
cohort study

N= 105
n 1=35
n 2= 70

IV1= fever

Purpose: To
explore the

SS= 1 hospital in
New Delhi’s

Multifactorial

IV2= History of
wound (trauma,
animal or insect
bite)

Measureme
nt/
Instrument
ation

Data
Analysis
(stats used)

Findings/
Results

Department’s
Protocol
Severity
Stratification
classification

Chi-Square
Binary
recursive
partitioning

IV1= <0.0001*
IV2= 0.069*
IV3= <0.0004*

P<0.05

Level/Quality of
Evidence;
Decision for
practice/
application to
practice
LOE: Level II
W: Small sample
size, setting
surgical
outpatient,

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
soft tissue
infections in
formulating
treatment
strategies and
predicting poor
prognostic
factors.
Funding: None
Conflicts/Bias:
None
Country: New
Delhi

causation
theory

clinical profile of
CA SSTI’s, to
evaluate the
existing method
of severity
stratification and
to identify factors
for poor outcomes
and lengthened
hospitalizations.

surgical outpatient
department and
ED
AGR= 15-71
M age= 41.2
M= 83.81 %
IC= Presence of at
least 2 of the
following:
swelling,
erythema, severe
pain, induration,
abscess or
tenderness to
palpation.
Presence of 1 of
the following:
body temperature
> 40 Celsius <35
Celsius, WBC
counts >15,000 or
wound culture.
Diagnosis of
severe infection
such NF
EC= Patient with
trivial injury,
patient with
hospital acquired

IV4= <0.001*
IV3= Gangrene

43

outside of USA
population

IV5= <0.0365*
IV4= BSA
IV5= Loss of
sensation
DV1= Healthy
wound
DV2= Negative
culture wound
DV3= Unhealthy
wound
DV4= Positive
wound culture

S: No Bias, Well
described
intervention,
appropriate
statistical
analysis
FE: Safe, Low
cost
APP: The
existing severity
stratification
model does
decrease healing
time of SSTI’s,
but it could use
some
modifications
that the Severity
stratification
model identified
in this study.
Based on the
conclusions of
the study,
researchers

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

44

SKIN AND SOFT TISSUE INFECTIONS
infection, <12
years of age,
infection other
than skin or soft
tissue.
Citation

Theory/
Conceptual
Framework

Design/ Method

Sample/ Setting

Major Variables &
Definitions

Measurement
/
Instrumentati
on

Data
Analysis
(stats used)

Findings/
Results

Wilson, S.M.
(2011). A
severity score
for complicated
skin and soft
tissue infections
derived from
phase III studies
of Linezolid.

Incidence,
prevalence,
mortality
model

Design:
Prospective
comparative
Randomized trial

N=
Study A- 632
Study B- 334

IV1=
Comorbidities

IV1-IV7=
Modified
Fine Severity
Scoring
Model
IV2= >30
IV3= <130
IV4= <30%
IV5= <150cm

Logistic
Regression

IV1=
P= <0.1

P= <0.05

IV2=
P= 0.023*

Funding:
Pharmacia and
Upjohn
Conflict/Bias:
Study is funded
by
pharmaceutical

Purpose: To
develop and
implement a
severity scoring
system for SSTI’s
and to identify
factors
contributing to
treatment failure.

IV2=BUN
n=
A- 133
B- 38

IV3= Sodium
IV4= Hematocrit

M age=
A-16.8
B-49.2

IV5= Lesion size
IV6= Surgical
wound infection

C=
A- 56.8%
B- 88.5%
M=
A-63%
B-46.9%

IV7=Linezolid
intervention
IV8= Comparator
group

DV1=
measured by
number of
days/number
of doses of
treatment
until s/sx
improved.

IV3=
P= 0.036*
IV4=
P= 0.002*
IV5=
P= 0.009*
IV6=
P= 0.039*

created an
accurate
algorithm for
early detection,
diagnosis and
treatment.
Level/Quality of
Evidence;
Decision for
practice/
application to
practice
LOE: Level II
W: Study criteria
excluded
uncomplicated
SSTI’s,
necrotizing
fasciitis and
diabetic foot
ulcers. Not all
variables
included that
could influence
outcome in
severity scale.
Only applied to
hospital
population, not

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
companies
which may
influence their
recommendatio
ns on the use of
antimicrobial
therapy
Country: USA

DV1= Lower
Clinical Cure Rate
SS=
A- 133 sites
in USA,
Latin
America,
Asia
B- 38 sites
in
Europe,
South
Africa
IC= hospitalized,
Gram +
complicated SSTI,
signed consent
form
EC=
Uncomplicated
SSTI, Outpatient
setting, Other
organisms besides
Gram +

Complicated
Definition:
infections
extending into the
deeper tissue and
requiring systemic
antibiotic therapy
and surgical
interventions
Clinical Cure
definition:
Resolution of
baseline s/sx of
infection or
improvement after
at least 5 days and
20 doses of study
medication.
Clinical Failure
definition:
Persistence or
progression of s/sx
of infection after at
least 2 days and 8

IV7=
P= 0.001*
IV8- Not
significant
DV1=
P=0.001*
Findings:
Comorbidities
significantly
decrease cure
rate. BUN,
sodium,
hematocrit,
lesion size and
presence of
surgical wound
infection are
clinically
significant
factors in clinical
cure rates.
Clinical cure
rates were lower
in patients who
disease scores
below the
median and who

45

primary care.
S: Large SS,
Level II evidence
APP: This
scoring tool
provides a
practical tool for
assessing
patients with
complicated
SSTI’s and
identifying those
who may need
more aggressive
support. This
scoring system
can be used as an
overall guide to
overall prognosis
and to the level
of
medical/surgical
treatment.

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

SKIN AND SOFT TISSUE INFECTIONS
doses of
medication

were in the highrisk classes.

46

A: age, AA: African American, ABSSI: Acute bacterial skin and skin structure infections, ADL: activities of daily living, AGR: age range, AHRQ: Agency for research healthcare and quality, APP:
application, BSA: Body surface area, C: Caucasian, CA: cellulitis/abscess, CA-MRSA: Community acquired Methicillin resistant staphylococcus aureus, CCR: Clinical cure rate, CF: correctional
facility/facilities, CI: Confidence interval, CM: Co-morbidity, CR: Complete response, CR%: Caucasian race percentage, DAT: days of antibiotic therapy, DIA: Delayed Initiation antibiotics, DS:
Duration of symptoms, DT: Duration of therapy, DV: dependent variable, E: ethnicity, EC: exclusion criteria, ED: emergency department, ESM: Economic Simulation Model, F: female, FE:
Feasibility, G: gender, H: Hispanic, HCUP: healthcare cost and utilization project, HOW: history of wound, IC: inclusion criteria, I&D: incision and drainage, IPT: Inpatient treatment, IV:
independent variable, KPNC: Kaiser Permanente of Northern California, LHS: Length of hospital stay, LOA: lack of adherence, LOE: Level of evidence, M: male, MA: Metanalysis, M Age: Mean
age, MIDAS: Models of Infectious Disease Agent Study, MOR: Mortality, n: sample size studies, ME: Median, N: sample size cases/people, MRSA: Methicillin resistant staphylococcus aureus, NF:
necrotizing fasciitis, NI: Not identified in article, NIS: nationwide inpatient sample,, NWC: Negative wound culture, O: Obese, OP: outpatient, OPT: Outpatient treatment, OT: Over treatment, PC:
participant characteristics, PWC: Positive wound culture, PRTM: Predictive risk tool model, PCS: Prospective cohort study, RCS: Retrospective cohort study, REH: Re-hospitalization, RR:
Reference range, RSST: Risk severity stratification tool, SA: staphylococcus areus, SABSSSI: Severity of acute bacterial skin and skin structure infections, SI: site of infection, SEWS: Standardized
early warning score, SIRS: systemic inflammatory response syndrome, SR: Systematic Review, SS: Number of study sites, SS: Severity of SSTI, S/S: Signs/symptoms, SSTI’s: Skin and soft tissue
infections, STP: standardized treatment plan, TF: treatment failure, TS: type of SSTI, UK: United Kingdom, USA: United States of America, UT: Under treatment, VS: Vital signs abnormalities,
WBC: white blood cell count, WS: Wound size, Y: years, $: US dollars, $$: US dollars in Millions,

47

SKIN AND SOFT TISSUE INFECTIONS IN HIGH RISK POPULATIONS
Appendix E
Synthesis Table: Severity/Risk Stratification Scales in SSTI’s
Author
Figtree

Year:
Design:
Retrospective Cohort
Prospective Cohort
Cross Sectional

Hashem

Jenkins

2012

2016

2011

X

X

X

Lane

Lee

Study Characteristics
2016
2016

Marwick

Marwick

Talan

Tiwari

Wilson

2011

2012

2014

2014

2011

X

X

X

X

X
X
X

Setting:
Community
Inpatient

X

X

X
X

Sample:
Number of sites
N

1
395

1
200

Duration

7y

Demographics:
Age (mean)
Male Gender (%)

Tools used:
Risk severity
stratification tool
Standardized treatment
plan
CREST
SEWS
Severity of acute
bacterial SSTI’s
Ki/Rotstein

X
X
X
Population Demographics

X

X

X

X

X
X

1
344

N/A
400

14
106

2
79

2
79

12
619

1
105

133, 38
632, 334

1y

2y

NI

NI

15
Months

15 months

NI

NI

NI

70.6

54

AGR-3769
65%

52

*
38.7

41.2

16.8, 49.2

53%

USA-45
UK-42
USA-64.5%
UK- 70%

41

43%

CO-46
I- 47
CO- 75%
I- 66%

65%

57.5%

83.81%

63%, 46.9%

X

X

X

50%

X
X
X
X

X
X

X

X

X

X

CREST: Clinical resource efficiency support team, N: sample size, SEWS: Standardized early warning score, SSTI’s: skin and soft tissue infections, *: Statistically significant

48

SKIN AND SOFT TISSUE INFECTIONS IN HIGH RISK POPULATIONS
Systemic
inflammatory response
syndrome
Charlson Index

X

X

Age
Co-Morbidities
Duration of symptoms
S/SX
Wound Size
Vital signs

X*
X*
X*

Length of hospital stay
Complete response
Duration of therapy
Re-hospitalization rate
Outpatient treatment

X

Independent Variables
X*
X*
X*
X*
X*

X*
X*

X

X
X

X*
X*
X*
X*
X*

X

X*
X*

X
X
X

X*

Measurable Outcomes
X

X
X

X*
X*

X
X

X

X
X

CREST: Clinical resource efficiency support team, N: sample size, SEWS: Standardized early warning score, SSTI’s: skin and soft tissue infections, *: Statistically significant

SKIN AND SOFT TISSUE INFECTIONS IN HIGH RISK POPULATIONS
Appendix F
The Advancing Research and Clinical Practice Through Close Collaboration (ARCC) EBP Model

49

SKIN AND SOFT TISSUE INFECTIONS IN HIGH RISK POPULATIONS

50