Use of the quick sequential organ failure assessment tool for sepsis screening
Submitted by
Rodney James Buchanan

A Direct Practice Improvement Project Presented in Partial Fulfillment
of the Requirements for the Degree
Doctor of Nursing Practice

PennWest University
Edinboro, Pennsylvania
December 5, 2022

© by Rodney James Buchanan, 2022
All rights reserved.

PENNWEST UNIVERSITY
Use of the quick sequential organ failure assessment tool for sepsis screening
by
Insert Rodney James Buchanan

has been approved

December 5, 2022

APPROVED:
Robin Bilan, DNP, CRNP, RN, Project Chairperson
______________________________________________
Megan Larson, DNP, CRNP, Project Committee Member
______________________________________________
Colleen Bessetti-Barrett, DNP, CRNP, FNP-BC
Project Committee Member
______________________________________________
_________________________________________
Date

Abstract
Sepsis is a clinical condition that requires early identification and initiation of evidencebased interventions to improve mortality and outcomes. A quality concern that has been
identified is that the emergency department at the practice site is not actively screening
for sepsis patients. This has resulted in difficulty meeting three-and six-hour sepsis
bundle metrics. The purpose of this quality improvement project is to implement a sepsis
screening methodology to correct this gap in practice. The door to lactic acid collection
metric will be used to determine if any statistical improvement was made after the
implementation of a sepsis screening tool. The screening tool selected for the project will
be the qSOFA tool as this is one of the easiest tools to use and implement. The quality
improvement project used a quantitative methodology with quasi-experimental design.
The results demonstrated a statistically significant improvement in the collection times of
lactic acid post-implementation. The results further support the use of sepsis screening in
the emergency department setting for rapid identification and treatment of sepsis
conditions. Emergency nursing plays a pivotal role in the identification and treatment of
this condition as nurses are often the first healthcare professional a septic patient will
encounter.

Keywords: qSOFA, lactic acid, sepsis screening

Table of Contents
Chapter 1 Introduction to the Project ...................................................................................1
Background of the Project .............................................................................................2
Problem Statement .........................................................................................................3
Purpose of the Project ....................................................................................................5
Clinical Question ...........................................................................................................5
Advancing Scientific Knowledge ..................................................................................7
Significance of the Project .............................................................................................8
Rationale for Methodology ............................................................................................9
Nature of the Project Design ........................................................................................10
Definition of Terms......................................................................................................10
Assumptions, Limitations, Delimitations ....................................................................11
Summary and Organization of the Remainder of the Project ......................................12
Chapter 2: Literature Review .............................................................................................13
Theme one……………………………………………………………………………15
Theme two………………………………………………………………………..….20
Theme three………………………………………………………………………….34
Chapter 3: Methodology ....................................................................................................44
Statement of the Problem .............................................................................................44
Clinical Question .........................................................................................................44
Project Methodology....................................................................................................45
Project Design ..............................................................................................................46
Population and Sample Selection.................................................................................46

Instrumentation or Sources of Data .............................................................................47
Validity ........................................................................................................................48
Reliability.....................................................................................................................48
Data Collection Procedures..........................................................................................49
Data Analysis Procedures ............................................................................................49
Potential Bias and Mitigation.......................................................................................50
Ethical Considerations .................................................................................................50
Limitations ...................................................................................................................51
Summary ......................................................................................................................52
Chapter 4: Data Analysis and Results ................................................................................53
Descriptive Data...........................................................................................................53
Comparison of Minutes to Lactate Pre and Post qSOFA Implementation ..................55
Results ..........................................................................................................................57
Chapter 5: Summary, Conclusions, and Recommendations ..............................................59
Summary of the Project ...............................................................................................59
Summary of Findings and Conclusion.........................................................................60
Implications..................................................................................................................61
Theoretical Implications ...............................................................................................62
Recommendations ........................................................................................................62
References ....................................................................................................................64
Appendix A ..................................................................................................................72
Appendix B ..................................................................................................................73
Appendix C ..................................................................................................................74

Appendix D…………………………………………………………………………75

List of Tables
Table 1. Comparison of Gender Pre- and Post-Intervention…………………………….54
Table 2. Comparison of Age Pre- and Post-Intervention………………………………..55
Table 3. Normality Test (Shapiro-Wilk) All Cases……………………………………..56
Table 4. Normality Test (Shapiro-Wilk) Outlier Removed Dataset………………….…57
Table 5. Homogeneity of Variances Test (Levene’s)…………………………………...57
Table 6. Independent Samples t-Test…………………………….………………….….58

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Chapter 1 Introduction to the Project
The purpose of this project is to improve the identification of potential sepsis
patients at the author’s practice site in the emergency department (E.D.) setting. Early
sepsis identification is a critical part in the management of sepsis as none of the
recommended sepsis measures can begin if the patient has not been identified. This is not
a new concept as emergency departments have been using early identification
methodologies to identify ST segment elevated myocardial infarction (STEMI) and
stroke patients for years to begin early life saving or disability reducing measures.
However, the adoption of early sepsis recognition has been slow and therefore the
management of this patient population has resulted in high mortality rates and disability
post-acute phase.
Furthermore, much research has been done to show that the sooner patients
receive intravenous (IV) fluids and antibiotics, the lower the mortality. As this knowledge
has become so common, the Centers for Medicare and Medicaid Services (CMS) has
established sepsis guidelines that require hospitals to attain. These metrics are divided
into two areas, three-hour bundles (Sepsis-1) and six-hour bundles (Sepsis-2). The threehour bundle focuses on the initial care of the patient and includes door to blood culture
collection, door to lactic acid collection, door to first-dose antibiotic administration and
door to correct IV fluid administration. Understanding these metrics all begin at the point
of registration; it becomes easy to understand why a sepsis screening tool is so important.
Unlike STEMI or stroke patients where an electrocardiogram (EKG) or
computerized tomography (CT) scan can be used to make a quick and early diagnosis,
sepsis presentations do not have a traditional presentation all the time and the source of
the infection can be from many different sites such as bladder, lung, gastrointestinal (GI)

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or post-surgical. Therefore, a physical exam in conjunction with early lab studies are
needed for the rapid identification of a potential sepsis patient. The practice site currently
does not use a sepsis screening tool, which has resulted in difficulty meeting all the CMS
three-hour bundle sepsis metrics. Implementation of an evidence-based screening tool
such as the quick sequential organ failure assessment (qSOFA) in the triage phase of care
will assist in the early identification of potential sepsis patients arriving at the practice
site’s emergency department. Permission to use the qSOFA tool was obtained from Dr.
Seymour (Appendix A).
Background of the Project
The background from this problem stems from the knowledge gained that the
septic disease process itself has resulted in a steady increase in volume of septic patients
to the emergency department year over year (Moore et al., 2019). Additionally, delays in
first-dose antibiotics have resulted in increased mortality rates (Mitzkewich, 2018). The
steady increase in sepsis presentation and known mortality rates simply forces hospitals
and regulatory bodies to track sepsis data and strive to perform better.
The practice site only uses a standard triage screening tool called the Emergency
Severity Index (ESI) to triage patients and rank them by acuity, so that providers know
which patients to select first. This is a common triage scoring system used in the United
States. Kwak et al. (2018) completed a study and demonstrated the use of ESI alone is not
sufficient to adequately screen for sepsis and supplemented the use of the ESI score with
the qSOFA screening tool and found the tool improved mortality prediction by 5.7% for
ESI I patients and 8.6% with ESI II patients. These are the most critical ESI levels, but it
is not uncommon to have multiple ESI II patients at once, so having an additional
differentiator is important to call out the specific septic patient. It is important to note that

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a proper ESI II score can also be applied to a cardiac or stroke patient too, which is why
having a sepsis screening process in place is needed. This is an important gap to identify
as the use of ESI alone does not work. Finally, Husabo et al. (2020) completed a study of
Norwegian emergency departments and found the emergency departments not using a
sepsis screening tool had markedly worse performance with sepsis metrics than other
emergency departments that were using a screening tool.
Another significant gap noted is general knowledge deficit about sepsis,
treatments, and processes. Dewalder and Hulton (2020) identified that sepsis education is
not required by most hospitals or licensing boards. Again, much knowledge has been
required about cardiac emergencies or neurologic emergencies, which has been shown to
reduce the mortality in these clinical disease processes. However, given that sepsis
mortality rates are hovering around 50% demonstrates that significant gaps in knowledge
that exist for this disease process (Dewalder & Hulton, 2020).
Problem Statement
The problem of no sepsis screening at the practice site is clear when assessing the
data. Therefore, a well written problem statement is needed to help guide the project,
which is explained now. It was not known if or to what degree the implementation of
quick sequential organ failure sepsis screening tool would impact Centers for Medicare &
Medicaid services Sepsis-1 door to lactic acid collection data when compared to current
practice among adult emergency department patients arriving through triage or by
ambulance. The project will serve to solve the sepsis screening gap that exists at the
practice site. Current overall compliance rates with Sepsis-1 measures are 47% at the
practice site and the identified root cause of this problem is no sepsis screening
methodology.

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Meyer et al. (2018) had completed a study comparing sepsis patients from 2010
against sepsis patients in 2015 and found that sepsis related mortality rates decreased
from 24.1% in 2010 to 14.8% in 2015 as more hospitals were adopting sepsis screening
tools as a standard in the emergency department setting. Seymour et al. (2016) completed
the original study to develop the qSOFA tool based on issues with the prior commonly
used tool named systemic inflammatory response syndrome (SIRS) tool. The SIRS tool is
highly sensitive and therefore screened many patients’ positive that were not. This led to
overwhelming emergency departments with multiple patients undergoing expensive
work-ups and not having sepsis. The practice site also used to use the SIRS tool and
experienced much of the same issues, which is why they stopped using the tool, but never
implemented a new methodology. Seymour et al. (2016) developed an easy-to-use tool
that measures only three items, which are mental status, systolic blood pressure and
respiration rate. A score of two or more is a positive score that should prompt an
evaluation by a provider.
The reason this tool was selected from other tools in the literature is because it
was the recommended tool to use by the Third International Sepsis Taskforce (Seymour
et al., 2016). Additionally, the tool is simple to use by collecting information that is
already gathered in the triage phase of care and ease of its use is a critical factor with
compliance (Singer et al., 2017). Other tools that may perform better as it relates to
sensitivity or specificity are also more difficult to calculate or require electronic health
record (EHR) reform making them cost prohibitive for this project.

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Purpose of the Project
The purpose of this project is to improve the quality of sepsis care being
delivered at the practice site. A strong purpose statement is needed in order to capture and
frame the project, which is reviewed next. The purpose of this quantitative, quasiexperimental project is to determine if or to what degree the implementation of the
University of Pittsburgh quick sequential organ failure sepsis screening tool would
impact the targeted Centers for Medicare & Medicaid Services Sepsis-1 metric of door to
lactic acid collection when compared to current practice among adult patients in an urban
emergency department in Pennsylvania over four weeks. As stated, prior, the compliance
rates with the Sepsis-1 bundle is only performing at 47%. This project assisted the
practice site to return to using a screening methodology to improve patient outcomes.
This project was accomplished by using a quantitative method and a quasiexperimental design. The dependent variable will be the lactic acid collection time and
the independent variable will be the qSOFA tool. It is expected that by implementing the
qSOFA screening tool in the emergency department, that a reduction in the time needed
to collect a lactic acid level will be recognized. This is an important concept as lactic acid
is one of the needed data points for a provider to diagnosis a sepsis condition. The sooner
this value can be obtained, the sooner a provider can take their physical exam and add
context to what they are seeing to make a diagnosis and begin treatment.
Clinical Question
The clinical question for this project is to what degree does the implementation of
The University of Pittsburgh quick sequential organ failure sepsis screening tool decrease
the lactic acid collection time when compared to current practice among adult patients in
an urban emergency department in Pennsylvania over four-weeks? This question narrows

6
the focus of the project to evaluating the effectiveness of the qSOFA tool as it relates to
timely collection of lactic acid in the emergency department.
The variables that will be included as part of this project include an independent
variable, which is the qSOFA tool and a dependent variable, which is the door to lactic
acid collection time. The reason for selecting the dependent variable is related to the
importance of lactic acid values in the diagnosis of sepsis (Ferando et al., 2018). By
improving the time to obtaining the lactic acid level, it allows the treating provider to
make decisions about completing the other Sepsis-1 metrics to improve compliance.
Using this clinical question allows the team in the emergency department to
understand their focus and understand the importance of timely collection of the lactic
acid level. The clinical question also defines the population being used and both the
independent and dependent variables that are being measured. Lastly, the clinical
question sets a defined measurement period for the project.

Variable

Variable Type

qSOFA screening tool

Independent

Level of
Measurement
Nominal

Door to lactic acid minutes

Dependent

Ratio

Quantitative (Clinical question and Variables)
Q1: To what degree does the implementation of The University of Pittsburgh
quick sequential organ failure sepsis screening tool impact Centers for Medicare

7
& Medicaid Services Sepsis-1 door to lactic acid draw collection when compared
to current practice among adult patients arriving through triage or ambulance in
an urban emergency department in Pennsylvania over four-weeks?
Variable 1: qSOFA screening tool is the independent variable that is a scoring
system identifying patients as either positive or negative making the scoring a
nominal variable.
Variable 2: Door to lactic acid measurement is a time measurement starting at
zero minutes and working its way up. Having an absolute zero point makes this a
ratio variable.
Advancing Scientific Knowledge
The project is focused on improving care of the sepsis patient in the emergency
department setting. At the same time, the project will also provide advancement in
scientific knowledge by implementing evidence into practice and measuring outcomes to
add to the body of evidence that already exist related to using qSOFA sepsis screening in
a real-world setting. This is an important concept as the translation of evidence into
practice is the final step to advancing the scientific knowledge regarding a particular
topic or intervention.
In order to advance scientific knowledge, it is needed to have a theoretical base to
build upon. This project will use Imogene King’s systems theory. King (1968) developed
a grand theory called systems theory. This theory was founded on the principles of
human interaction with their environment. This project recognizes the current
environment does not subscribe to any type of sepsis screening methodology. This allows
the nursing and physician staff to work within this environment without feeling the need
for screening. However, by changing the environment and adding the expectation of

8
sepsis screening, this changes how the nursing and physician staff respond. Overtime,
sepsis screening will become part of the environment and much like screening for a
STEMI or stroke is rote behavior, so too will sepsis screening.
As previously reviewed, the sepsis population continues to grow as our average
age increases and as persons over the age of 65 become greater. Septic conditions
secondary to pneumonia and pressure ulcers are common in the elderly. This means at the
micro-level; every practice site needs to be focused on sepsis identification and treatment
in the emergency department. The proper implementation of qSOFA sepsis screening
process will at a minimum set the standard for using a sepsis screening methodology in
an emergency department that in their current practice does not screen for. Solving for
this gap in knowledge at the practice site will further advance the scientific knowledge
within the local emergency department setting.
Significance of the Project
The project has great clinical significance as it is centered on the current clinical
problem of sepsis and it focuses on a real gap in practice. The implications of this project
are many as this could be the beginning of a culture change within the local emergency
department setting by actively screening for and identifying potential sepsis patients.
Evidence has shown that early identification has led to early treatment and decreased
mortality. This means, the local practice site could see an increase in early sepsis
identification leading to early treatment and reduced intensive care unit (ICU)
admissions, shorter length of stay (LOS) and the ability to further the sepsis screening
process by completing EHR reform to include automated sepsis screening technology.
This project also falls in line with current evidence-based standards, which rely on
early identification of sepsis patients to reduce mortality. This is an important concept to

9
note as it keeps the project in line with current evidence. By completing this project, it
adds to the body of evidence not only regarding the use of the qSOFA tool, but also with
the importance of sepsis screening in general.
Finally, this project allowed for the incorporation of front-line and newer nursing
staff with the implementation and show them the importance of completing a quality
improvement project needed for sustainable change. By moving through this
implementation process, this fostered the interest of and knowledge for improving
practice at the bedside. Fostering this professional desire will only yield benefit for other
projects or gaps in knowledge that are discovered.
Rationale for Methodology
The methodology for this project is quantitative. Quantitative methodology is best
when implementing an independent variable and measuring the outcome (Melnyk &
Fineout-Overholt, 2019). For the purposes of the project, the independent variable is the
qSOFA screening tool and it will be implemented to measure impact on lactic acid
collection times (dependent variable). This project is not seeking the opinion of the staff
for ease of use or the qSOFA tool or their feelings about sepsis screening, which would
be a qualitative methodology.
The clinical question that was reviewed prior also leads to the need for a
quantitative methodology. This is because the clinical question is asking if the
implementation of the tool will demonstrate a measurable outcome. By virtue of the
clinical question, one must use the quantitative methodology. Finally, quantitative
methodology is appropriate when answering questions such as what is the problem,
frequency of the problem or will this intervention improve the problem (Melnyk &
Fineout-Overholt, 2019)? This project answers these questions in that the problem is poor

10
compliance with sepsis metrics and a literature review show using a screening
intervention helps to improve compliance.
Nature of the Project Design
The design of this project is a quasi-experimental design. In an experimental
design, the dependent variables are randomly separated and only the control group would
receive the intervention. However, it would not be ethical to withhold an evidence-based
intervention from some patients while allowing others to benefit from being screened.
Therefore, a quasi-experimental design will be used to allow for the intervention to be
applied to all patients and measure the outcome using all patients.
The quasi-experimental design is also a good design to use for a quality
improvement project. This is because a quality improvement project is not developing
original research. It is translating existing research into practice and measuring the
outcomes to add to the body of literature. Again, when following the clinical question, it
calls for application of qSOFA screening to all patients, which further supports the quasiexperimental design.
Definition of Terms
The Definition of terms will be explained in this section.
qSOFA
Quick sequential organ failure assessment is a sepsis screening tool developed to
screen for potential sepsis patients using three commonly used assessments to include
mental status, blood pressure and respiration rate (Seymour et al., 2016).
Clinical Significance.
Clinical significance (also known as clinical relevance) indicates whether the
results of a project are meaningful or not for when applied to patient care.

11
Statistical Significance
Statistical significance indicates if the intervention demonstrated a measurable
improvement by using descriptive statistics. This also means that the measurable
improvement did not happen by chance by setting a p-value of <0.05.
Intervention Group.
The intervention group is the adult population that will receive the screening tool
in the emergency department. As this is a quasi-experimental design, there is no control
group.
Assumptions, Limitations, Delimitations
The assumptions of this project include a pre-existing knowledge of how to obtain
vital signs and an understanding of their meaning. It is assumed that when a patient is
screened positive, this triggers an action to begin obtaining things like blood cultures,
lactic acid as well as other lab values needed to make a diagnosis. Additionally, it is
assumed that actions such as IV fluid administration and appropriate radiological studies
will also be obtained.
Limitations of the study include training that may not have occurred with visiting
staff in the emergency department, conflicting clinical presentations such as a stroke
presentation with a positive qSOFA screening. The stroke workup would take precedence
over the sepsis work up. An additional limitation was human error by not completing the
paper qSOFA screen as this is not automatically incorporated into the medical record.
The delimitations of the project include using the emergency department as the
location for the project in order to have the greatest opportunity to use the screening tool
and using the adult population as this is the most readily available population to use.
Another delimitation is using a paper tool over an electronic tool since the emergency

12
department involved is part of a network of eight emergency departments and the use of
the EHR would require changes to all the documentation for all the emergency
departments even though they are not in scope.
Summary and Organization of the Remainder of the Project
To summarize, the reason for project was a gap in clinical practice was found that
has resulted in the practice site not being able to meet some of their sepsis goals. After a
literature review was completed, it was noted that evidence shows sepsis screening is the
best way to improve sepsis metrics. A sepsis screening tool called the qSOFA was
selected as this is the easiest and most cost-effective tool to implement to meet the needs
of the practice site. A theoretical framework was also selected to help guide the
development of the project and a change model was selected to help guide the change in
the emergency department. Next a detailed review of the literature will be presented
specific to the qSOFA tool and the relationship it has with lactic acid. This will show the
connection between why lactic acid was chosen to be the metric to measure.

13
Chapter 2: Literature Review
The purpose of this quantitative quasi-experimental project is to determine if or to
what degree the implementation of the Quick Sequential Organ Failure Assessment
would impact sepsis screening when compared to current practice of no sepsis screening
among adult patients arriving through triage or ambulance in an urban E.D. in Northwest
Pennsylvania over four-weeks. CMS has developed three-hour bundle metrics that each
hospital in the United States is to work to attain. These metrics include door to lactic acid
collection, door to blood culture collection, door to first dose antibiotic administration,
door to 30 ml/kg IV fluid bolus. The first step in being able to meet these metrics is
identification of a potential sepsis patient presenting to the E.D. The international
consensus sepsis task force is the international organization that researches sepsis and
meets to update guidelines every four-years. During the meeting in 2016, the task force
made a recommendation to begin using the qSOFA sepsis screening tool instead of the
SIRS tool that was recommended in the 2012 meeting. Seymour et al. (2016) completed
the original study that developed the qSOFA tool and found that qSOFA had a much
higher specificity for predicting 30-day mortality than SIRS, however qSOFA was noted
to be less sensitive than SIRS. These findings led the sepsis task force to make the
recommendation to transition from a high sensitivity tool to a high specificity tool.
Theoretical Foundations
This project seeks to correct a systems issue. Therefore, Imogene King’s system’s theory
will serve as the grand theory to guide the project. King (1968) explains that nursing is a
process of action, reaction, interaction, and transaction by which care to humans is
delivered. These actions all occur within a social institution resulting in three distinct
levels of operation which are, the individual, the group and society (King, 1968). This

14
theory fits nicely within this project as the nursing staff are the group using the tool. The
individual nurse functioning as an individual will make personal choices on many things
from how they see themselves as part of the solution, commitment to learning the
functioning of the tool and accurate application of the tool. However, the nurse is not
alone in this project, they will also be functioning as a group. Nurses will be using the
tool, but also relying on other nurses for support, validation, and mastery of competence.
Finally, the nurse will be functioning within the societal structure of the hospital that
currently does not use a screening methodology for sepsis. These levels of operation will
be occurring fluidly and in conjunction with each other as a system.
Change is also an important aspect to consider when implementing this project.
Kotter (1996) developed an eight-step change model used in leadership to implement
change. The first step is to establish urgency by identifying realities and acknowledging
major opportunities, second is creating a guiding coalition by establishing a group with
the authority to make change and lead a team, third is developing a vision so the team
understands the end goal and can develop strategy to implement change, fourth is
communication of the vision to the department, fifth is empowering broad-based action
by eliminating obstacles and encouraging out of the box thinking, sixth is generation of
short term wins to fuel motivation, seventh is consolidating gains from lessons learned
and capitalizing on results and finally the last step is anchoring the new approach by
linking the new outcomes/results with the new behaviors (Kotter, 1996).
Review of Literature
A literature review was conducted to identify the problem and establish themes.
Google Scholar, Pubmed and TRIP database were used with the following search terms
“qSOFA,” “qSOFA screening”, “sepsis screening ED”, “sepsis mortality reduction”,

15
“qSOFA and lactic acid” and “sepsis alert”. The publication range was set 2017-2021 and
full text methodology was used to find articles. The search yielded 370 articles, but only
40 were selected. Of the 40, 10 were determined to be quasi-experimental studies and
were discarded from the literature review.
While reviewing the 30 articles several themes began to emerge beginning with
the importance of lactic acid and what it tells clinical providers when caring for a sepsis
patient. The second theme was the various amounts of screening tools to use. The final
theme was the low sensitivity of the qSOFA screening tool compared to many of the
other tools that have been developed.
Theme 1, Lactic Acid
The first emerging theme was the importance of lactic acid. As stated earlier, this
is one of the CMS metrics in the three-hour bundle (door to lactic acid collection).
Gattinoni et al. (2019) completed a study to determine what causes the elevation of lactic
acid in sepsis, which they found to be impaired tissue oxygen use, instead of impaired
oxygen transport. This is an important concept in the management of sepsis, because this
pathophysiology can be reversed with IV fluid resuscitation, but only if sepsis is
recognized and lactic acid is measured. For these reasons, the learner’s project will be
focused on the door to lactic acid measurement time metric.
Subtheme 1, Lactic Acid Levels
Understanding the importance of lactic acid values. This subtheme
emerged from literature regarding the understanding about various levels of lactic
acid. This understanding leads to a deeper knowledge base regarding sepsis.
Fernando et al. (2018) completed a study to determine if lactic acid levels of 2-3.9

16
in septic patients would result in a lower level of acuity and allow for placement
on a lower acuity floor instead of an ICU. The study was a prospective
observational cohort design that enrolled patients over the age of 21 who met the
1992 society of critical care medicine (SCCM) definition for sepsis. The sample
size was 985. Criteria set up for inclusion included meeting SCCM criteria, lactic
acid level of 3.9 or less and not receiving any critical care interventions in the
E.D. All clinical data was reported using 95% confidence intervals and area under
receiver curves (AUC) were used to determine the predictive power of the lactic
acid level. Limitations of the study included the decision to use critical care
definitions from 1992. Another limitation of the study was that approximately
20% of the enrolled patients did not have a lactic acid level drawn due to
physician choice. The results showed that patients with lactic acid levels of 2-3.9
had a specificity rate of 66% for deterioration within 72 hours of arrival. These
results show that even at lower levels of lactic acid in sepsis patients, the risk of
clinical deterioration remains high, which is why obtaining a timely lactic acid is
so important for this population. Recommendations for future research would be
to complete a study determining lactic acid clearance by following serial lactic
acid levels.
As stated earlier, Gattinoni et al. (2019) completed a study to better
understand and explain how elevated lactic acid performs from a
pathophysiological perspective. This was a post hoc analysis of a random control
trial completed analyzing the impact of using albumin for fluid resuscitation in
sepsis. Taking the patient sample of 1741 ICU patients, the authors already had
lactic acid and central venous oxygen saturation (Scvo2) information. Using this

17
information, the base excess was calculated to better understand the relationship
between hyperlactatemia and acidemia. The authors used AUC were used
compare continuous variables and chi-square test were used to compare
differences between groups. A limitation of the study was since the authors had
used the sample from a different study, they did not know the volume of IV fluids
that had been administered in the E.D. setting, which could impact the lactic acid
levels of the population, which was an ICU population only. The results showed
that elevated lactic acid levels in sepsis were mainly caused by impaired tissue
oxygen use instead of impaired oxygen transport. This demonstrates that sepsis
causes impairment at the tissue level which is an important concept to understand
in the management of sepsis. This also validates why CMS has placed the door to
lactic acid metric in the three-hour bundle.
Subtheme 2, Enhancing Quality of Screening Tools
The second subtheme determined from the lactic acid theme is the
important role lactic acid plays in enhancing the quality of screening tools. This is
a novel concept that has been emerging in the literature. This concept stems from
having a better understanding of the pathophysiology of elevated lactic acid
levels. Baumann et al. (2020) completed a study assessing positive qSOFA scores
and elevated lactic acid levels. The authors used a retrospective study design with
a sample size of 3743. The data was then sorted by lactic acid level of greater than
two and greater than four. The authors used AUC for qSOFA positive patients
with both separated levels of lactic acid. The results showed that qSOFA scores
greater than 1 and a lactic acid level of greater than 2 showed a sensitivity of
94%. This is much higher than using qSOFA alone. The sensitivity rate of qSOFA

18
score three and lactic acid level greater than four was 7.1%, which makes sense as
a patient would have to attain all qSOFA points and have a very high lactic acid in
order to be positive. Limitations identified included missing data due to the
retrospective design, patients discharged from the E.D. were excluded from the
study, which means the combination of qSOFA and lactic acid could not be used
on all patients arriving to the E.D. The authors suggest that additional research
needs to occur to establish a relationship between lactic acid and qSOFA.
Jung et al. (2018) completed a study to determine if changing the qSOFA
scoring tool by adding an additional point for lactic acid level would improve the
sensitivity of the tool. This was a retrospective study with a sample size of 1226
patients that required emergency abdominal surgery for a complicated intraabdominal infection. These records were used to determine qSOFA and SOFA
scores. The points of measurement for qSOFA are respiration rate greater than 22,
systolic blood pressure less than 100 and altered mental status defined as a
Glasgow Coma Score (GCS) of less than 15. The patient receives one point for
each of these measures and a score of 2 or more is considered positive. The
authors gathered lactic acid measurements for each of these patients and made an
additional point if the lactic acid level was greater than two. The t-test and MannWhitney test were used to measure continuous variables. The chi-square test was
used to establish relationship between categorical variables. To determine the
sensitivity, the AUC was used. Limitations of the study included a surgical
population presenting the E.D. that typically are ranked higher for acuity and
often do not follow a standard protocol and this was a retrospective study and the
results may not be able to be generalized to all hospitals. The results showed the

19
qSOFA sensitivity rate was 46% in this patient population. The qSOFA plus lactic
acid tool had a sensitivity rate of 72%, which aligned with other like studies. The
authors suggest that prospective studies of patients presenting to the E.D. with
suspected abdominal infection should be performed to gain further insight on
modifying the qSOFA tool.
Subtheme 3, Latic Acid and Clinical Decision Making
The third subtheme determined from the lactic acid theme is the
importance lactic acid levels play in clinical decision making. This is an important
subtheme as lactic acid levels assist in the decision making for providers on how
aggressive they should treat and placement of the patient after the E.D. episode of
care. Aksu, et al. (2018) completed a study by taking the qSOFA screening tool,
which has three measured items which are, altered mental status, elevated
respiration rate and low blood pressure and they added a “fourth” point being a
lactic acid level of 2.3. The results showed by adding the additional lactic acid
point, this improved the qSOFA tool’s ability to predict 30-day mortality by 50%
and enhanced the clinical decision making for the treating provider to treat more
aggressive and more accurate placement of a patient for inpatient admission. This
was an observational, prospective study that used a population of adult patients 65
years and older that were admitted to the intensive care unit (ICU). Their sample
size was 799. The authors then determined lactic acid levels that would function
as another point on the scoring, which were 2.4 and 4.0. The authors used MannWhitney tests to compare the groups and chi-square test to summarize the
categorical variables. Then to perform weighing of the new score, they used a
multiple regression analysis for the outcome of mortality. Finally, AUC was used

20
to determine the statistical significance. The results showed that when the lactic
acid level of 2.3 was added to the score for both the qSOFA and SOFA score,
both tools demonstrated a statistically significant higher predictive value to
predict mortality. Specific to the qSOFA tool, a lactic acid level of at least 2.3 and
a score of 2 resulted in a 50% mortality rate. Understanding these results allows
providers to treat these patients with urgency, much like a stroke or trauma
patient. Limitations identified was that this was a single site study and the results
could not be generalized to all facilities and this facility also saw a higher-thanaverage amount of cardiac patients than other facilities around them, which could
have skewed data due to the higher than usual groin access procedures. The
authors recommend additional research on this topic as this would be a change to
the design of the qSOFA tool.
Shetty et al. (2018) completed a study is to determine at what point the
lactic acid level should be considered positive when a patient is presenting with
possible sepsis to an E.D. This was a retrospective study using the Australian
SEPSIS KILLS government program that oversees sepsis care in Australia
including an electronic sepsis registry. Data was obtained from this registry for
adult patients 18 years and older that were cared for in the E.D. The limitation
identified in the study was that data was used from SIRS screening, which is no
longer the recommended screening tool. The results revealed a mortality rate of
8.4% for a lactic acid level of greater than 2 even with systolic blood pressure
greater than 100. Mortality rates increased to 19.7% with a lactic acid level
greater than two and systolic blood pressure less than 100. The analysis of the
data shows that lactic acid levels of two and higher should be used as a positive

21
cutoff point to consider potential sepsis especially in the presence of hypotension.
This should prompt clinical providers to have an increased awareness when lactic
acid levels are present. The authors suggest completing the study using qSOFA
data to establish a relationship between lactic acid and qSOFA scores.
Theme 2, Importance of Screening Tools
The second theme that emerged from the literature review was the importance of
screening tools in the recognition and treatment of sepsis. Without recognition, sepsis
care cannot begin. When reviewing evidence, it becomes clear that many different tools
exist and each of them have their positive and negative attributes. Seymour et al. (2016)
completed the original study that produced the qSOFA tool and it is noted that this tool is
easy to use and a good starting point for a facility that currently does not do sepsis
screening. Therefore, this tool was selected for the learner’s project. Several subthemes
have been identified and will be reviewed.
Subtheme 1, qSOFA Screening Tool
The first subtheme is the use of the qSOFA tool itself. This is the largest
subtheme under the second theme for obvious reasons as this is the tool that has
been selected for implementation. During the review of literature under this
subtheme, it will be noted that many studies are comparing the qSOFA tool
against other tools such as SIRS, sequential organ failure assessment (SOFA) and
national early warning score (NEWS). Other studies simply review the
performance of qSOFA independently.
Barbara et al. (2018) completed a study to evaluate the use of qSOFA in
the pre-hospital environment. This study is a retrospective study that sampled
adult patients 18 years and older that were transported to one of the two hospitals

22
in the study. These patients were already identified as qSOFA positive (score of
two) by the ambulance crews. The sample size was 271. These patients were then
compared to their admitting diagnosis to determine if they were admitted for
sepsis, severe sepsis, or septic shock. The patients were then separated into two
groups, one group being sepsis and the other being non-sepsis. Differences in the
groups were assessed using the t-test and comparisons of skewed data was
analyzed using the Mann-Whitney test. Finally, the chi-squared test was used to
compare the variables between the two groups. The largest limitation to the study
was not having denominators in both groups, meaning not having patients that
were septic or non-septic, but not screened pre-hospital as positive. This
prevented the authors from determining sensitivity and specificity data. The
results showed that 66.67% of the patients that were scored positive by prehospital services were indeed admitted with sepsis, leading the authors to suggest
further study of the qSOFA tool in the pre-hospital setting.
Burnham & Kollef (2018) completed a study to determine if the use of the
qSOFA tool has resulted in inappropriate use of initial antibiotics. This is a
retrospective study that collected data from records on adult patients hospitalized
with a Enterobacteriaceae infection. qSOFA scores were determined based on the
worst vital signs in the record 24 hours prior to becoming blood culture positive.
All antibiotics prescribed and administered were collected. The chi-square test
was used to determine categorical values and the t-test, Mann-Whitney and oneway ANOVA was used to determine continuous variables. The sample size was
510. Limitations of the study include being a single-center study leading to results
that may not be generalizable and altered mental status may have only been

23
documented in the most severe cases, which could inflate mortality. The
conclusion of this study reveals the qSOFA can be used as predictor of mortality
and the results of the study show no impact of a positive score related to
inappropriate antibiotic selection. The authors suggest that further research should
occur regarding the importance of lactic acid levels in the sepsis population.
Canet et al. (2018) completed a study using the same study methodology
that Seymour et al. (2016) used, but only using E.D. patients to determine if the
qSOFA tool can predict mortality upon presentation in the E.D. setting. Seymour
et al. (2016) used an in-hospital population, to develop the qSOFA tool, so the
results could not be generalizable to the E.D. population. This study is a
retrospective cohort study that developed a query of the EHR to obtain all E.D.
records of adult patients 18 years and older that were screened for suspected
infection. The sample size was 165,912. The statistical tests used were the
Wilcoxon rank sum test. Limitations of the study included this being a singlecenter study, so the results cannot be generalizable to all centers. The results of
the study showed that for patients who screened positive in the E.D., the qSOFA
tool had a 61% sensitivity rate and 80% specificity rate combined with a high
mortality rate. The authors recommend further research on evaluating the qSOFA
tool in the E.D. setting.
Goulden et al. (2018) completed a study is to compare predictive mortality
rates of qSOFA, SIRS and NEWS screening tools. This is a retrospective cohort
study using adult patients that presented to the E.D. with suspected sepsis. The
sample size was 2158. The statistical analysis used the AUC to determine
sensitivity and specificity as well as positive and negative predictive value for

24
mortality. Limitation of the study was the authors did not have data on
comorbidities or cause of death, so this makes it impossible to determine that
patients who died, did so from sepsis specifically. The results showed that for
predicting mortality qSOFA was most specific followed by NEWS then SIRS.
However, SIRS was the most sensitive followed by NEWS then qSOFA. This
determines that none of these tools score high for both sensitivity or specificity.
The authors suggest further research be conducted comparing the various scoring
tools for sepsis.
Kievlan et al. (2018) completed a study is to determine if completing the
qSOFA score in six-hour increments during the first 48-hours would demonstrate
improved predictive validity of the tool. This was a retrospective cohort study that
used the original qSOFA study population of 1,309,025 patients. These patient
records were then used to develop trajectory modeling using their vital signs data
to apply a qSOFA score every six-hours. The standard deviation and median were
calculated for the continuous variables. To compare predictive validity the authors
used the Transparent Reporting of a multivariable prediction model for Individual
Prognosis or Diagnosis (TRIPOD) recommendations. Finally, AUC were used to
show differences in scores at each measurement interval. The authors identified
limitations of using only an in-hospital population and they did not compare
predictive validity against other screening tools. The results showed that the
repeated measurements improved the AUC curve from 0.79 to 0.85 in predictive
validity to predict mortality suggesting that qSOFA should not be considered a
one-time use only scoring tool. Future suggestions for research were to study the

25
repeating of the qSOFA score until discharge to determine the trajectory of
qSOFA throughout the hospital stay.
LeGuen et al. (2018) completed a study to determine how many rapid
response patients will have a positive qSOFA score, how antibiotics were started
based on the score and if positive qSOFA patients also had an infection. This was
a prospective observational study that reviewed every rapid response call from
June 6th, 2016 to July 10th, 2016 for adult patients over the age of 18. The sample
size was 258. Statistical analysis used the chi-square test to compare variables
between groups and two-sided p-value to indicate any statistical significance. The
authors identified the limitations of a small sample size and the possibility of
selection bias secondary to the time of the year. The results showed that qSOFA
positive patients had a mortality rate of 39% compared to non-positive patients of
16% regardless if they had an infection or not. For patients that did have an
infection and were scored positive their mortality rate was 26% compared to
qSOFA negative at 15%. The analysis of the data demonstrated the qSOFA
scoring tool was useful in assisting the rapid response team in the identification of
sepsis and critical patients alike. The authors suggest further study of the qSOFA
tool at other facilities for rapid response patients.
Seymour et al. (2016) completed a study to develop a screening tool that
had a better specificity than the currently used SIRS tool. This was because the
third international consensus sepsis task force had determined the current
recommendation of using the SIRS tool was yielding a very low specificity,
making it an unreliable tool. This was a retrospective cohort study that collected
data from 2010 to 2012 using databases from 164 hospitals with a sample size of

26
4,885,558. The authors used three different sepsis scoring methodologies SIRS,
SOFA and Logistic Organ Dysfunction System (LODS) and scored the records
based on established algorithm. Using these scores and measuring the datapoints
that makes these scores, the authors were able to assign weights to the specific
clinical criteria that were most significant to each score to predict mortality. The
three things that weighed the most was elevated respiration rate, low blood
pressure and altered mental status. This aligned the closest with the SOFA scoring
and the authors coined the term "quick" SOFA. The statistical analysis of used
was quite complex using such a large database, but the authors used AUC to
measure sensitivity and specificity of the new tool. The authors identified several
limitations. The first was using only patients with a diagnosis of sepsis. Secondly,
the tool does not have any lab values in it to make the sensitivity higher and
finally using altered mental status can be difficult with patients presenting with
dementia. The results showed an AUC for qSOFA of 0.71 specificity to predict
30-day mortality in the non-ICU patients. qSOFA scores of two or three
accounted for 70% of deaths and 70% of ICU stays longer than three days. The
authors suggest studies of the tool need to be completed internationally to
determine its real-world usability.
This subtheme reveals the many types of screening tools that are available
to be used. This subtheme also demonstrates that not a single tool being used is
perfect, including the tool being recommended. However, as with any type of
clinical scoring tool, this does not replace clinical judgement and physical exam.

27
It simply calls the patient out, so that the physical exam can occur in a timelier
manner.
Subtheme 2, qSOFA and Special Populations
The second subtheme that emerged from the second theme was qSOFA’s
performance in special patient populations. Discussions about sepsis in the E.D.
setting are generally broad, because the E.D. is designed to be an all-comer
department. This raises the question about how qSOFA would perform
specifically with pneumonia and cancer patients. These sub-populations are of
particular interest, because pneumonia can mimic congestive heart failure (CHF)
or congestive obstructive pulmonary disease (COPD), neither of which is an
infectious process. Cancer is also a population that will often have blunted sepsis
responses secondary to chemotherapeutic treatments and steroid therapy. Asai et
al. (2019) completed a study is to determine if the qSOFA tool can evaluate the
severity and prognosis of patients with pneumonia against traditional pneumonia
scoring systems. The population was all patients that presented to the hospital
between 2014-2017 with either community-acquired or healthcare-acquired
pneumonia (nursing home or rehab facilities) were entered into the study. This
was a retrospective study with a sample size of 257. Each record was scored using
the SOFA, qSOFA and traditional pneumonia screening tools used by the authors
using admission data found in the record. The authors used a chi-square test to
compare the categorical variables. The Mann-Whitney test was used to compare
continuous variables and finally a logic regression was used to identify
independent risk factors associated with 30-day mortality. The authors identified
limitations such as small sample size from a single-facility and the population had

28
a higher amount of healthcare-acquired pneumonia versus community-acquired
pneumonia. The results showed that a positive qSOFA score of greater than two
correlated well with other validated pneumonia scoring tools. However, qSOFA
scores of zero or one did not correlate as well. The authors felt that qSOFA should
not be used solely to determine severity and prognosis of pneumonia patients, but
when a qSOFA score is positive, the clinical team should also calculate a SOFA
score, because their data showed a positive qSOFA and positive SOFA correlated
with high levels of mortality. The authors also explain the ease of calculating a
qSOFA score making it an easy tool to begin with upon arrival in an E.D. and
suggest that further research using these tools to identify pneumonia should be
completed.
Costa et al. (2018) completed a study is to assess the qSOFA, SOFA and
SIRS screening tools as it relates to mortality for cancer patients admitted with
sepsis. This is a retrospective study that reviewed records for cancer patients
admitted to the ICU setting with a diagnosis of potential sepsis. The sample size
was 485 patients. Chi-squared test were used to compare categorical differences
between groups and AUC were used to calculate results and display performance
of each tool. The authors identified limitations of the study such as this being a
single-center study, so results cannot be generalized to all facilities. Additionally,
the authors acknowledge they were only focused on cancer patients, so
comparative performance of qSOFA, SOFA and SIRS was not able to be done
with other patients. The results showed that the SOFA score was more sensitive,
but less specific than SIRS in its ability to predict ICU mortality. qSOFA was
more sensitive, but performed equally regarding specificity compared to SIRS in

29
its ability to predict ICU mortality. There was no difference between SOFA and
qSOFA to predict out of ICU mortality. The authors feel that SOFA and qSOFA
scoring tools perform better than SIRS to predict mortality in cancer patients
presenting with sepsis. The authors also feel that additional research needs to be
completed in this population using these tools as they are only aware of one other
study like theirs.
Mecham et al. (2018) completed a study to examine the predictability of
30-day mortality rates of pneumonia patients using the SIRS, qSOFA and SOFA
scores. This is a retrospective study that evaluated patient records of adult patients
that presented to the E.D. and diagnosed with pneumonia from three 12-month
time frames December 2009 - November 2010, December 2011 - November 2012
& November 2014 - October 2015. The sample size was 11,051. The authors used
the AUC to determine the 30-day mortality for all three scores. t-test and
Wilcoxon rank sum test were used for all time metrics and chi-square test was
used to compare variables between groups. Limitations discussed in the study
included missing data to calculate the SOFA score on some patients and the
authors used a GCS score less than 14 to determine altered mental status, when
the tool is designed to score altered mental status with a score less than 15. The
results showed for 30-day mortality qSOFA had an AUC of 0.7 and SIRS 0.61
showing that qSOFA outperformed SIRS in its ability to predict mortality. Using
the scores to predict admission form the E.D. qSOFA scored an AUC of 0.7 and
SIRS 0.67, this translates to 42% of positive qSOFA patients being admitted and
20% of SIRS patients being admitted. These results show the qSOFA only has a
moderate sensitivity, but a much higher specificity that SIRS in the recognition of

30
pneumonia. The authors suggest further research regarding changing the scoring
thresholds of the qSOFA tool to improve its sensitivity.
Tokioka et al. (2018) completed a study is to compare the qSOFA tool
against two known pneumonia screening tools called the Confusion, Urea
nitrogen, Respiratory rate, Blood pressure, age 65 (CURB-65) and the Pneumonia
Severity Index (PSI) to determine the qSOFA's ability to predict ICU admission
and mortality. This is a secondary study using data from a prospective study of
adult patients 18 years and older that were admitted with a diagnosis of
pneumonia. The sample size was 1954. Categorical variables were calculated
using the chi-square and the C statistic was used to determine the probability.
Limitations presented in the study included this being a single-center study, so
results cannot be generalized to other centers. Additionally, at the time this article
was published only four studies had been completed regarding the use of qSOFA
in pneumonia patients leading to insufficient literature review by the authors. The
results showed that for predicting ICU admission, positive qSOFA was 23.4%,
CURB-65 was 60.8% and PSI was 59.9%. C statistics for predicting mortality for
qSOFA was 0.69, CURB-65 was 0.75 and PSI was 0.74. The sensitivity of
qSOFA was 39% and specificity was 88% for predicting mortality. CURB-65 and
PSI both showed high sensitivity and moderate specificity, but actual percentages
for these scores was not published. This means the CURB-65 and PSI scores do a
better job at predicting pneumonia in the E.D. setting, but these tools are more
difficult to apply to every patient coming to the E.D., therefore the use of the
qSOFA was determined to be a good starting point and then apply other more
specific tools during the care of the patient. The authors suggest prospective

31
observational studies need to be completed to fully understand the application of
qSOFA in the pneumonia patient.
This subgrouping of research articles shows that qSOFA can be used to
identify potential sepsis in cancer and pneumonia patients. A common theme is
that once identified, the clinician should also complete a more comprehensive
scoring tool to further narrow down the predictive mortality. However, since the
qSOFA is easy to use, this does offer a starting point in the E.D. setting.
Subtheme 3, Importance of Using a Screening Tool
The third subtheme under screening tools is the importance of using a
screening tool. All the new antibiotics, ICU therapies and evidence-based
interventions will mean nothing if the patient is not identified. While this sounds
simple, it can be quite difficult in an E.D. setting, because all clinical
presentations and age group arrive to the E.D. in an unpredictable manner
requiring an E.D. to have some sort of screening methodology to detect sepsis.
This is an important subtheme for the learner’s project as the practice site does not
use a screening methodology to screen for sepsis.
Husabo et al. (2020) completed a study is to determine if delays occur in
Norwegian E.D.'s that prolong the identification of sepsis and the following
treatment. This was an observational study involving 24 E.D.'s in Norway. The
study used SIRS criteria to determine if a patient was positive for sepsis and this
information was retrieved from a national patient registry. The sample size was
1559 patients. All data was calculated to confidence intervals of 95%. Linear
regression models were used to determine variables in time metrics. A logic
regression model was used to estimate the 30-day mortality rates. Limitations

32
identified were the use of the SIRS screening tool, when the recommendation is to
use qSOFA. Additionally, the authors did not have data on the severity of sepsis
to establish correlation between antibiotic delays and mortality. The results
showed that 72.9% of patients were triaged in the first 15 minutes of arrival,
44.9% were evaluated by a provider in accordance with triage level, 83.6% had a
set of vital signs obtained within the first hour. 80% of patients had regular blood
work drawn in the first hour, only 48.6% of patients had a lactic acid level drawn
in the first hour. 25.4% of patients received antibiotics in the first hour and 55.5%
of patients received antibiotics in two hours. This poor performance was related to
either no screening or ineffective processes in their E.D.’s. This study highlights
the importance of leadership establishing evidence-based practices in E.D.'s to
properly screen patients for potential sepsis.
Meyer et al. (2018) completed a study to better understand the relationship
over time of post-discharge outcomes and hospital readmission since the hospital
readmissions reduction program (HRRP) has been put into place. This study is a
retrospective design that used International Classification of Disease, version-9
(ICD-9) codes to pull patient records that were end-coded as sepsis, severe sepsis,
and septic shock at three network hospitals. The sample size was 275,600.
Limitations identified included the study hospitals were using different tools for
identifying sepsis patients in their E.D.’s and the ICD-9 codes only represented
the sickest and not all sepsis patients that visited the E.D. and discharged. The
results showed that sepsis hospitalizations increased from 3.9% in 2010 to 9.4%
in 2015. However, in-hospital mortality has decreased from 24.1% in 2010 to
14.8% in 2015. The study shows that advancements in early recognition and

33
improved clinical guidelines encouraged by the HRRP initiative have resulted in
an overall decrease in mortality. The authors suggest further research at other
centers to correlate their findings.
Rosenqvist et al. (2020) completed a study is to measure the results of
modifying the local Swedish triage scoring methodology by adding fever of 38
degrees Celsius as a trigger to initiate an evaluation by a provider to decrease the
door to antibiotic metric in sepsis patients. This was an interventional study
comparing outcomes before and after changes were made, but also calculating
sensitivity for their tool. Patients were identified and included in the study before
the change to the triage score from January 1st, 2015 to March 31st, 2015. Then a
second group was studied from January 1st, 2017 to March 31st, 2017 post
changes to their scoring system. The sample population for the 2015 group was
443 and the sample population for the 2017 group was 533. The authors used ttest to measure continuous variables and chi-square test to compare variables
between groups. Limitations identified included adding a temperature may not
pick up cancer patients or immunosuppressed patients and the authors were using
sepsis definitions from 2012 despite new definitions being released in 2016. The
results showed that the 2015 sample of patients received their antibiotics within
one hour 68.3% of the time. The 2017 sample of patients received their antibiotics
within an hour 89.3% of the time demonstrating the importance of screening. The
authors suggest further research on screening tools and their criteria.
Shah et al. (2018) completed a study is to measure three-hour sepsis
bundle compliance before and after implementing an institution-based sepsis
screening methodology and determine if this tool has any impact on 30-day

34
mortality. This was a retrospective cohort study that included all adult patients 18
years and older that were admitted with a diagnosis of severe sepsis or septic
shock. The pre-implementation period was August 2012 to January 2013 and the
post-implementation period was January 2015 to June 2015. The sample size for
the pre-implementation group was 58 and post-implementation group was 57.
Continuous variables were measured using the t-test, differences between data
sets were measured using the chi-square test. A logistic regression analysis was
conducted with p-values to determine mortality rates. Limitations identified
included using an unvalidated screening tool and small sample size. The results
showed no difference in three-hour bundle compliance for any metric except the
door to first dose antibiotic metric. Pre-implementation antibiotic administration
in one-hour was 10.3% and post-implementation was 89.5%. The screening tool
did not show any statistical difference in 30-day mortality rate preimplementation or post-implementation. The authors suggest comparing their tool
against the qSOFA tool for future research.
This subtheme shows the importance of using a screening methodology.
Facilities that did not use any screening noted to have unacceptable delays in
treatment and antibiotics. Other facilities that did use screening tools showed
superior performance. These findings in literature support the learner’s project.
Theme 3, Sensitivity and Specificity of Screening Tools
The final theme from the literature review is the sensitivity and specificity of the
sepsis screening tools, but in particular, the qSOFA tool. Multiple studies including the
original study that developed the tool have demonstrated a low-sensitivity, but highspecificity for qSOFA. SIRS screening tool was the opposite, the tool has a high-

35
sensitivity, but low-specificity (Seymour et al., 2016). This resulted in a high volume of
patients screening positive thus leading to increased resource demand, but not having
sepsis. Literature reviewing the low sensitivity, high specificity and ease of use will
reviewed.
Subtheme 1, Low Sensitivity of qSOFA Tool
The first subtheme is low sensitivity of the qSOFA tool. Literature will be
reviewed demonstrating this finding about qSOFA. Implications of having low
sensitivity will be discussed throughout the literature review.
Abdullah et al. (2020) completed a study is to evaluate the role of qSOFA
as a prognostic factor for 30-day mortality in patients with suspected or proven
infection fulfilling the SIRS criteria for sepsis on admission to the E.D. This study
used a retrospective review of patients with sepsis diagnosis and positive SIRS
criteria. These records were then reviewed and assessed to determine what the
qSOFA score would be. The population size was 464 patients. Limitations
identified included selection bias as the authors were using a historical database
and the authors did not always have GCS data to calculate a qSOFA score. The
results showed that 16.8% of the patients had a positive qSOFA score upon
presentation to the E.D. Of the qSOFA positive patients, 81.8% of them needed
ICU level care within the first 24 hours of their admission. The qSOFA positive
patients had a 21.9% in-hospital mortality rate and a 32.9% 30-day mortality rate.
A 16.8% capture rate is low for recognition of sepsis. The authors do suggest

36
completing a similar study with a larger sample size to determine if the results
would be the same.
Filbin et al. (2018) completed a study is to evaluate the Shock Precautions
on Triage (SPoT) and qSOFA sepsis screening tools and to determine the
importance that vital signs have in the early identification of sepsis in the E.D.
setting. This was a retrospective observational study using ICD-9 records that
were end coded for sepsis in 2009. These records were then assigned a SPoT
score and qSOFA score at the time of their first set of positive vital signs. The
sample size was 2669. Limitations identified included not always having the GCS
score available to calculate the qSOFA score and a single-center study, so results
cannot be generalized to all facilities. The results showed that the SPoT scoring
yielded a 56% sensitivity rate when scored in the triage phase (initial phase) of the
E.D. visit. qSOFA sensitivity was 28% in triage. The authors suggest additional
research on the impact of antibiotic delays as this is a metric in the three-hour
bundle, as there is not a lot of research on therapeutic benefit of this metric.
Ortega et al. (2019) completed a study is to compare the prognostic value
of the qSOFA tool to identify potential sepsis patients arriving in the E.D. setting
as compared to using SIRS, NEWS or ESI scoring. This was a prospective allcomer study that used patients that presented to the E.D. over the age of 18 except
obstetric and ophthalmologic patients. The sample size was 2523. Limitations
identified include only 1.6% of the patients in the sample had sepsis and 32.3% of
their sample had incomplete qSOFA scores. The results showed that 33.6% of the
patients required admission to the hospital and 5.4% of those needing admitted
required ICU level care. 1.5% died in the hospital and 2.5% died within 30 days.

37
The AUC for prognostic ability was as follows: qSOFA-0.79, SIRS-0.81, NEWS0.85 and ESI-0.77. These results show that qSOFA had the lowest sensitivity
scores besides the emergency severity index (ESI), which is not a sepsis screening
tool. The authors suggest further research to find a tool that has improved
sensitivity and specificity for the E.D. setting.
Perman et al. (2020) completed a study to take a known population of
patients that have already been diagnosed with sepsis and have a high rate of
mortality and assign a qSOFA score to them for the purposes of determining
sensitivity within a known population. This was a retrospective, observational
study that enrolled patients diagnosed with severe sepsis that were admitted from
the E.D. The sample size was 2859. Continuous data was calculated using the ttest. Categorical variables were analyzed using the chi-square test. Sensitivity and
specificity results were calculated using AUC. Limitation identified included
using the worst vital signs and not the triage vital signs when the tool would be
used and this was a single-center study, so the results cannot be generalizable.
The results showed the qSOFA performed in triage demonstrated an AUC of 0.59,
which the authors felt was not very high given these were already known sepsis
patients. The authors suggest further research to develop better screening tools.
This subtheme shows that the qSOFA tool has a low sensitivity, meaning
patients presenting with sepsis can be missed. However, the practice site currently
does not use any type of screening methodology, so even a low sensitivity tool

38
theoretically would yield results. It is also clear that additional research needs to
be completed to develop a tool that has a higher sensitivity that is practical to use.
Subtheme 2, High Specificity of qSOFA
The second subtheme under sensitivity and specificity is the high
specificity of qSOFA. In the prior subtheme, it was noted that qSOFA has a low
sensitivity that can result in missing septic patients. However, qSOFA does have a
high sensitivity, meaning that when a patient is screened as positive, there is a
high likelihood they will be septic. This section will review literature discussing
this feature of qSOFA.
Brink et al. (2019) completed a study is to evaluate the 10 and 30-day
mortality predictability rates for the qSOFA, SIRS and NEWS scores. This was a
retrospective study that collected all E.D. records for patients presenting to the
E.D. with suspected sepsis. The sample size was 75,428. Patient characteristics
were compared by using a two-sampled t-test and Mann-Whitney test. A chisquared test was used to compare the distribution of the data. The AUC was used
to display observed mortality. The Youden's J statistic was used to determine
sensitivity, specificity and positive or negative predictive value for each test.
Limitations identified included the facility where the study was completed
specializes in congenital and acquired immunodeficiencies thus altering
presentations and not being able to generalize the results of the study to all
facilities. The results showed that the NEWS score performed best in predicting
10 and 30-day mortality, qSOFA showed the highest specificity and SIRS had the
highest sensitivity. The authors suggest future research to include possibly

39
changing the scoring cut off of the qSOFA from 2 to 1 to improve sensitivity as
using the NEWS tool is complex and could lead to non-compliance.
Rodriguez et al. (2018) complete a study is to determined sensitivity and
specificity for both the qSOFA and SIRS screening tools. This is a retrospective
study that was conducted using five network connected E.D.'s on the west coast of
the U.S. Patients age 17 and older that were admitted through any of these E.D.'s
for suspected sepsis were identified as the study population. The sample size was
3743. Statistical analysis used AUC to calculate sensitivity and specificity.
Limitations identified were the inclusion of patients that needed vasopressor
support, which would favor qSOFA as a blood pressure less than 100 is one of the
criteria. The results showed that a positive qSOFA had a 53.5% sensitivity rate,
positive SIRS had an 86.7% sensitivity rate. For specificity, positive qSOFA had
an 89.1% specificity rate, Positive SIRS had a 45.6% specificity rate. These
results support the high specificity of qSOFA, which the authors deem valuable
when making disposition decisions in the E.D. setting. A future research question
proposed was comparing qSOFA against other scoring tools such as NEWS.
Tian et al. (2019) completed a study is to determine clinical outcomes of
patients that would have been scored falsely positive or falsely negative using the
qSOFA scoring system. This is a retrospective cohort study that enrolled all adult
patients over the age of 18 that were hospitalized with sepsis. The sample size
was 21,191. Univariate and multivariate logistic regression analyses were used to
identify independent risk factors associated with mortality. AUC was used to
calculate the prognostic value of qSOFA. Sensitivity and specificity were
calculated for false-positives and false-negatives. Finally, all categorical variables

40
were measured using chi-square. Limitations identified were using a database that
was not originally designed for the study of qSOFA and qSOFA scores were
calculated on the worst vital signs throughout admission and not upon
presentation. The calculated sensitivity rate for the qSOFA tool was 50.2% and
specificity rate was 78.1%. This supports other studies that the qSOFA has a high
specificity rate. The authors suggest further prospective studies be completed to
validate their findings.
This subtheme highlights that when the qSOFA score is positive, there is a
high potential that patient is septic. At a minimum, the patient needs urgent
evaluation for another emergency. This evidence supports the initiation of the
qSOFA tool in this particular E.D. setting as they do not currently use any type of
screening tool.
Subtheme 3, Ease of Use Regarding qSOFA
The third subtheme is the ease of use with qSOFA. It has been reviewed
that many tools exist to screen for sepsis. Some of these tools are validated and
others are facility specific, but none of them are as easy to use as the qSOFA
making compliance easier to attain. The other tools require lab studies or have
several questions that need answered and when a nurse is in triage seeing five to
ten patients per hour, they do not have time for complex screening tools. For this
reason, qSOFA was selected as the intervention for the project. Two studies will
be reviewed in this subtheme.
Kwak et al. (2018) completed a study to test the combination of the ESI
score and qSOFA score to predict mortality. The ESI score is calculated at the
practice site on every patient before a provider can see a patient to pick up for

41
their caseload. The same items needed to calculate qSOFA are also incorporated
into the ESI score making the use of qSOFA easier in a triage setting. This is a
retrospective study that used patient records for patients age 15 and older. The
sample size was 43,748. Patients were then divided into four groups for ESI 1,
ESI 2, ESI 3 & ESI 4/5. Statistical tests used to measure continuous variables
were t-test and one-way ANOVA. Categorical variables were tested using the chisquare test. Limitations identified included the specific diagnosis of patients was
not available, which means some qSOFA positive patients were quickly seen
based on the positive score, but did not have sepsis, but rather had other critical
etiology occurring. The results showed that when the qSOFA score was positive it
correlated with a higher mortality for ESI scores 1, 2 & 3. Positive qSOFA plus
ESI 1 improved mortality prediction from 14.7% (ESI only) to 20.4% (qSOFA
plus ESI). Positive qSOFA plus ESI 2 improved mortality prediction from 2.7%
(ESI only) to 11.3% (qSOFA plus ESI). Positive qSOFA plus ESI 3 improved
mortality prediction from 0.4% (ESI only) to 2.3% (qSOFA plus ESI). No
changes were noted with ESI levels 4/5 and qSOFA. These results not only
support the use of this tool for this project from an ease-of-use perspective, but
they could also enhance overall care in the E.D. setting as an incidental finding.
The authors have suggested further studies should be completed related
specifically to sepsis and ESI plus qSOFA.
Singer et al. (2017) completed a study is to determine the qSOFA scoring
tool ability to predict inpatient mortality. This is a retrospective study that used
adult patients over the age of 18 that presented to the E.D. with sepsis. The
sample size was 22,530. The chi-square test was used to measure all categorical

42
variables. t-test and ANOVA were used to compare continuous variables. AUC
was used to measure sensitivity and specificity. Limitations identified included
the study was using inpatients who are sicker, thus qSOFA may have performed
higher than expected since the acuity of the patient was higher. The results
showed a positive qSOFA score of 2 was associated with a 12.8% mortality and
qSOFA score of 3 was associated with 25% mortality. Sensitivity for qSOFA of 2
was 29% and specificity for qSOFA of 2 was 97%. These results validate prior
studies of sensitivity and specificity, but the authors do make a point to explain
how easy the scoring tool is to complete leading to increased compliance with
usage. Future questions for research include further testing of qSOFA on E.D.
only patients as the original study did not use any E.D. patients.
This subtheme reveals how using the qSOFA tool is not difficult. While
the tool may not be as sensitive as many would like, it does have a high
specificity rating. Since the tool only needs three things to determine a score and
these items are already assessed in triage to determine the ESI score, this tool
aligns well with the project.
Summary of Literature Review
To conclude, a comprehensive literature review needs to be completed prior to the
implementation of any project. This allows for the doctorate of nursing practice (DNP)
prepared nurse to have a full understanding of the evidence that has been published and
gives insight into how interventions will work within one’s respective practice site. This
literature review supports the use of the qSOFA tool at the author’s practice site, as it has
been identified that the organization elected to not use a screening methodology after the
recommendation was made to stop using the SIRS tool. As described, this tool does have

43
a low to moderate sensitivity, but a high specificity. Given that the practice site is not
using any screening process, even a low sensitivity tool is expected to produce an
improvement. Additionally, the tool is easy to use, which makes compliance easier. Other
tools such as NEWS which have better sensitivity and specificity are also more difficult
to use for a site that is new to sepsis screening, which was documented in the literature
that compliance with using the tool was not always 100%. No tool will work if it is not
used. Understanding all these concepts leads to supporting the use of qSOFA for the
practice improvement project.

44
Chapter 3: Methodology
To review, the focus of this project is to improve the identification of potential
sepsis patients in the emergency department setting. This will be accomplished by
implementing the qSOFA sepsis screening tool during the triage phase of care in the
emergency department. The facility has a goal of 30 minutes to collect a lactic acid, but
they are currently not meeting that goal. This will serve as the metric that is measured to
demonstrate how the qSOFA screening tool improved this collection time. This chapter
will further explain the data collection procedures that will be used to measure statistical
significance of the project.
Statement of the Problem
Current practice at the facility is not using any type of sepsis screening allowing
sepsis to be found later during care and delaying treatment. The project will improve the
collection time of the lactic acid level, which is one of the Sepsis-1 metrics. Reducing the
time of lactic acid collection will allow for faster recognition of a sepsis condition and
subsequent treatment.
Clinical Question
The following clinical question guides this quantitative project:
Q1: To what degree does the implementation of The University of Pittsburgh
quick sequential organ failure sepsis screening impact Centers for Medicare &
Medicaid Services Sepsis-1 door to lactic acid collection when compared to
current practice among adult patients arriving through triage or by ambulance in
an urban emergency department in Pennsylvania over four-weeks?

45
Variable 1: qSOFA screening tool is the independent variable that is a scoring
system identifying patients as either positive or negative making the scoring a
nominal variable.
Variable 2: Door to lactic acid measurement is a time measurement starting at
zero minutes and working its way up. Having an absolute zero point makes this a
ratio variable.
Variable

Variable Type

qSOFA screening tool

Independent

Level of
Measurement
Nominal

Door to lactic acid measurement

Dependent

Ratio

The variables will be a within-subjects (or repeated-measures) design as the same
data is in all collections both before and after the intervention. This design was selected,
so that an accurate comparison to pre-implementation and post-implementation can be
reviewed for statistical significance. This gives an objective view to how well the tool
performed to improve the metric.
Project Methodology
The project used a quantitative methodology. Melnyk and Fineout-Overholt
(2019) define a quantitative methodology as a study that examines the relationships
between two more variables. Quantitative methodology can have several types of designs
such as experimental design, quasi-experimental design, causal-comparative design, or
time-series design, which will be discussed in the next section (Houser, 2018).

46
Quantitative methodology is appropriate when answering questions such as what
is the problem, frequency of the problem or will this intervention improve the problem
(Melnyk & Fineout-Overholt, 2019)? This project answers these questions in that the
problem is poor compliance with sepsis metrics and a literature review that recommends
using a screening intervention to improve compliance. This project does not measure how
staff feel about using a sepsis screening tool or how patients react to being assessed for
the clinical data being obtained, which would then define this as a qualitative
methodology.
Project Design
The research question for this project calls for the introduction of an intervention
to be applied to all patients. This means there is not a control group or a group that does
not receive an intervention. Melnyk and Fineout-Overholt (2019) explain that the quasiexperimental design is one that does not have a control group, because random
assignment is often not possible due to ethical concerns. In the case of this project,
withholding the application of sepsis screening to random patients may cause harm by
delaying care. The sample that was investigated was all adult patients presenting to the
emergency department. The process to collect the data was the nurse labeling a paper
qSOFA form that is positive and placing it in a bin to be collected, counted, and scanned
into the EHR. This needed be a paper process as this tool is not built into the EHR.
Population and Sample Selection
The setting for the population was an urban E.D. that sees an annual volume of
around 55,000. The population used for the study was the adult population age 18 and
over that arrived by either private vehicle or ambulance. The qSOFA screening tool was
used during the triage phase of care, which is the first phase of care. During this phase of

47
care, the nurse assessed for the criteria included in the qSOFA tool. If the patient met
criteria, a paper qSOFA form was be completed and scanned into the EHR.
No consent was needed for patients as the same clinical information being
collected during the triage phase to assign an ESI score is the same information needed to
collect for qSOFA. Patients were not be subjected to any different processes nor did they
need to endure any undue hardship as a result of the quality improvement project. All
patient confidentiality measures that are currently in place in the facility were maintained
throughout the quality improvement project.
Instrumentation or Sources of Data
This project used data collected from the EHR and from a paper qSOFA tool. The
use of a paper tool was be required due to cost and ease of collection. The data collected
included time, gender, patient medical record number (MRN), zip code, qSOFA score
and door to lactic acid collection time. A health insurance portability and accountability
act (HIPAA) waiver was not be sought as patients were not contacted for permission to
use their data and patient identifier data was not be displayed in the results.
qSOFA Tool
Details of the qSOFA tool will be discussed here. The qSOFA tool is a threequestion tool (Appendix B). The way to score points with qSOFA is to record one point
for each abnormal area. The three areas are mental status, systolic blood pressure and
respiration rate. To score a point for mental status the patient must have either a GCS of
14 or less or have a more pronounced altered state from their baseline for patients with
underlying cognitive conditions such as dementia. To score a point for blood pressure,
the systolic pressure must be less than 100. To score a point for respiration rate, the rate
must be greater than 22. If a patient arrived with a GCS of 15, blood pressure of 118/62

48
and respiration rate of 18, their qSOFA score would be zero. If a patient arrived with a
GCS of 14, blood pressure of 92/50 and respiration rate of 20, their score would be two.
Scores of two or more are considered positive.
Validity
Sylvia and Terhaar (2018) define validity as an instrument that measures what it
is intended to measure. To ensure validity for this project, this project followed the
intended guideline and use for the qSOFA tool, which was to identify potential sepsis
patients outside of the ICU setting. The tool was not being altered and any way and was
only used on patients in the E.D. setting. By doing this, it preserves the integrity and
validity of the tool as it was intended.
In order to support internal validity, the project was been designed to implement
one intervention (independent variable) and measure one metric (dependent variable).
This was to reduce the possibility of having other variables that could impact the
outcome. The project had an expectation to add to the external validity of the qSOFA tool
by adding the generalizability to the out of ICU population.
Reliability
Sylvia and Terhaar (2018) define reliability as an instrument that yields the same
results through repeated administrations of the test. Reliability has been established
throughout the literature with an understanding of low to moderate sensitivity and high
specificity. This information was reviewed in chapter two under the literature review.
Therefore, this project implemented the qSOFA tool using the same out of ICU
population with an expectation of the same results.

49
Data Collection Procedures
To collect data, the project used data already being collected by the practice site
for sepsis metrics by utilizing the certified and trained sepsis abstractors that are
employed to abstract sepsis data for the practice site. The abstractors are required to have
completed training approved by CMS to accurately abstract sepsis data using a
standardized abstraction tool (CMS, 2021). Additionally, a quarterly sample of their
abstractions are sent to a third-party vendor to be verified for accuracy. As this data
collection process is already in place, the project continued to use this resource. The
additional step of collecting the paper qSOFA forms was the responsibility of the
emergency department quality assurance nurse, so the data could be counted and
quantified to be used specifically for the project. This occurred daily.
The population targeted was all adult patients over the age of 18 entering the
emergency department. Only positive patients had a qSOFA form completed. For this
data collection to begin for the project the organization and university required
institutional review board (IRB) approval (Appendix C & D). The following items had to
be completed to obtain approval.
•

CITI training completion

•

Practice site informed consent training

•

The qSOFA tool being used uploaded for review

Data Analysis Procedures
The descriptive statistics used for this project include mean, median and standard
deviation for all variables. The independent variable was measured as yes or no. The
dependent variable data was displayed in minutes. Inferential statistics needed to test the
independent variable was be the t-test to test the differences in two related groups (pre-

50
intervention vs. post-intervention). The results were used to determine if any statistical
change was noted to draw inference from.
The data for this project was collected and the raw numbers were converted into
mean, median and standard deviation by using frequencies to make the numbers
meaningful. The data included registration time and lab collection time. To do this, first
the data was abstracted and categorized accordingly. Next descriptive statistics were used
to obtain the mean, median and standard deviation. Using a parametric test called the ttest, the difference between the pre-intervention group and post-intervention group was
able to be calculated with the independent variable to determine statistical significance. A
p-value of <0.05 was used to determine statistical significance.
Potential Bias and Mitigation
Potential bias that existed for this project was if a nurse decided to not screen a
patient or a sample size that is not adequate. To mitigate these problems, education was
completed with nearly every nurse, so they had an understanding on how to use the tool
and why it is important to screen for sepsis. This did not guarantee elimination of bias,
but did mitigate it. Sampling bias was mitigated by not randomly selecting patients and
applying the screening tool to all adult patients entering the E.D. Finally, the data was
evaluated by a statistician to eliminate a data analysis bias.
Ethical Considerations
Ethical considerations for the project evolved around data integrity. Since the
same clinical data was collected is the same data currently collected in triage, no
additional strain occurred on a patient. Additionally, this intervention did not involve
any type physical task, it was merely an assessment. However, data including
information about the patient such as an MRN, date and time of service and gender

51
was collected giving rise to the possibility that someone could be identified.
Understanding this, management of the data followed the practice site data
management plan and was also reviewed by the practice site IRB. Since the data being
used was already captured by employees at the practice site, the needed safeguards
were already in existence in the form of secured and encrypted servers and data
warehouses. Additionally, any data being shared with the school in the form of the
project was only minimally necessary data only such as time metric results. Vital signs
information and MRN information was not shared in the results.
Limitations
Broad limitations were discussed in chapter one, but a more detailed review of
limitations will be presented here. The qSOFA tool has its own set of limitations
documented in the literature. It is known that some sepsis patients will be missed and the
recognition of sepsis will have to occur via physical exam and clinical prudence. The data
collection process was a manual process, which opened the opportunity for human error
by not completing the screening or misunderstanding of the tool resulting in either a false
negative or false positive score. Since this tool was only used on all adult patients, it was
possible for intoxicated patients with a high respiration rate to be screened positive.
Further clinical assessment and decision making will need to occur to determine if they
are truly a septic patient. While these limitations were present, mitigation initiatives such
as education, the use of established sepsis order sets and daily check in to assess progress
and answer questions was completed.
To better help with the change, the use of a change model was also used. Kotter
(1996) developed his eight-step change model to assist in making change that is
sustainable. These steps include developing a sense of urgency, establishment of a team,

52
developing vision, communicating the vision, empowering the staff to make the change,
celebrating short-term wins, consolidating the wins to show improvement, and finally
anchoring the change as daily culture and practice. These steps had already begun and
will continued to be touched on not only before the implementation, but also during the
implementation.
Summary
To summarize chapter three, this project answered the clinical question by
implementing an evidence-based tool into practice and measuring the outcome and
results. The project was a quantitative methodology with a quasi-experimental design, so
that the intervention was applied to all adult patients entering the E.D. Data collection
occurred using a paper qSOFA tool that was completed with a positive screen and the
initiation of a sepsis order set. Descriptive statistics were used to determine statistical
significance using the door to lactic acid collection time as the metric determining
statistical significance. Next will be chapter four and a review of the data analysis and
results.

53
Chapter 4: Data Analysis and Results
This chapter will review the data analysis and results from the project. This
review will also include the sample comparisons both pre- and post-intervention as well
as explanations about the data. Finally, this chapter will review the statistical analysis
used to answer the PICOT question. All statistical analysis was completed using
Microsoft® Excel and Jamovi Statistics, version 2.3 (2022).
Descriptive Data
The descriptive data used to compare the samples were gender and age. The preimplementation sample was representative from January 2022 to July 2022. It consisted
of 197 patients with an ICD-10 code of sepsis, however eight cases were missing
collection times and needed to be removed bringing the sample size to 189. The postimplementation data consisted of all qSOFA positive patients with a sample size of 50.
Pre- and post-implementation gender related data were compared to ensure comparable
samples. Chi square was calculated and noted no significant difference between samples
(see Table 1).

54

Table 1

Comparison of Gender Pre/Post qSOFA

gender

PRE/POST

F

M

Total

PRE

92

97

189

POST

24

26

50

Total

116

123

239

X2 (1, N=239) = 0.01, p = .932
Pre and post implementation age related data was compared to ensure comparable
samples. t-test was used to determine no significant difference in age related data was
found (see Table 2).

55
Table 2

Comparison of Ages Pre/Post qSOFA

Age

Group

N

Mean

Median

SD

PRE

189

69.1

70.0

14.6

POST

50

66.6

69.5

17.5

t(237) = 1.0, p = .316
Comparison of Minutes to Lactate Pre and Post qSOFA Implementation
Review of the clinical question is important to better understand this section. To
what degree does the implementation of The University of Pittsburgh quick sequential
organ failure sepsis screening impact Centers for Medicare & Medicaid Services Sepsis-1
door to lactic acid draw collection when compared to current practice among adult
patients arriving through triage or ambulance in an urban emergency department in
Pennsylvania over four-weeks?
As stated before, the original sample was an n=197 and eight of these cases were
missing a collection time when completing a retrospective record review. These cases
needed to be removed. The post-implementation sample was from October 2022 and
consisted of 50 patients that were screened with a positive qSOFA screening. The eight
cases in the retrospective data that were missing collection times were removed leaving
an n=189 pre-intervention and n=50 postintervention.
The comparison of minutes from registration time to lactic acid collection for both
pre- and post-implementation was compared next. Pre-implementation data included 189
patients that had an ICD-10 code of sepsis. No sepsis screening was being completed

56
during this time. Post-implementation data included 50 patients that were all qSOFA
positive, but final end-coding with ICD-10 codes has not been complete at the time of
writing this manuscript.
The statistical test used in the analysis was the t-test. Assumptions for the t-test are
independence, normality and equal variances. Independence meaning both groups were
independent of each other was evaluated and both groups were found to be independent.
The t-test assumption for independence was met.
Normality assumption was evaluated using the Shapiro-Wilk normality test (See
Table 3).
Table 3
Normality Test (Shapiro-Wilk) – All Cases

MINUTES

W

p

0.601

< .001

Note. A low p-value suggests a violation of the assumption of normality

Based on Shapiro-Wilk test the data does not meet the normality assumption. In
order to improve normality of the data, eight data points in the pre-data were excluded as
outliers. These outliers had lactic acid collection times greater than 350 minutes and of
note, all were qSOFA negative. As a result, the pre-implementation sample size will be
181 and the post-implementation sample size will be 50. The Shapiro-Wilk test was
repeated with outliers removed and was found to have not met normality, but was noted
to be improved. Fien et al. (2022) explains the dependent variable should be normally or
near-normally distributed for each group. The t-test is robust for minor violations in
normality (Fien et al., 2022). Given this information, the decision was made to use the ttest for the analysis (See Table 4).

57
Table 4
Normality Test (Shapiro-Wilk)

MINUTES

W

p

0.839

< .001

Note. A low p-value suggests a violation of the assumption of normality

The third assumption for t-test is equal variances. The homogeneity of variance
test was completed (Levene’s Test). See table 5.
Table 5

Homogeneity of Variances Test (Levene's)

MINUTES

F

df

df2

p

10.0

1

229

0.002

Note. A low p-value suggests a violation of the assumption of equal variances

Homogeneity of variance assumption was not met. However, to deal with the
violation of the homogeneity the Welch’s t-test will be used (Glen, 2022).
Results
A t-test with outliers removed was completed to answer the PICOT question. The
null hypothesis is the average minutes pre-intervention equals average minutes postintervention. Table 7 shows the results of the t-test. The results of the t-test demonstrated
a statistically significant difference (t(137) = 2.43, p = 0.017). This rejects the null
hypothesis and accepts the alternative hypothesis that there is a difference between preand post-minutes. Finally, the Cohen’s d was used to determine the effect size. Fien et al.
(2022) defines a small, medium, and large effects for the t-test to be .2, .5 and .8
respectively. The Cohen’s d result was 0.329 revealing a small to medium effect (see

58
Table 6).
Table 6
Independent Samples T-Test
Statistic

df

p

137

0.017

Effect
Size

MINUTES
Welch's t

2.43

Cohen's d

0.329

ᵃ Levene's test is significant (p < .05), suggesting a violation of the assumption of equal variances

Summary
To summarize the data, the t-test was the parametric test used to determine
statistical significance. However, in order to better normalize the distribution of the data,
eight outlier cases needed to be removed from the pre-intervention data. Doing this
allowed for a more normal distribution of the data. The results using Welch’s t-test and
was found to have a p-value of 0.017 demonstrating statistical significance between the
pre-intervention data and post-intervention data with a Cohen’s d indicating a small to
medium effect.

59
Chapter 5: Summary, Conclusions, and Recommendations
After completion of the project and analysis of the data, this chapter will review
the summary of the project, conclusion of results and recommendations for future action.
As previously reviewed in chapter four, the project did yield statistical significance, but
more importantly the project exhibited clinical significance and improvement in the
clinical management of the septic population in this E.D.
Summary of the Project
The sepsis screening project was focused on the implementation of the qSOFA
sepsis screening tool and determination if that single action would result in improvement
of the lactic acid collection time. Several tools were evaluated to be used and some were
also noted to have better outcomes, but higher complexity as it related to implementation.
For this reason, the qSOFA tool was chosen when considering the post-pandemic staffing
situation, increased observation of boarding patients in the E.D. and leadership turnover
within the department. Implementation of a more robust, but complex tool would risk
non-compliance.
Critical to the project was staff acceptance. The nursing staff felt the tool was
very simple to use and recognized its value when positive that something was clinically
wrong with the patient and the patient needed to be brought to a provider quickly. Many
of the nursing staff likened a positive screen to a positive stroke or STEMI situation to at
least get the appropriate labs ordered and properly leveled using the ESI system.
The provider staff also found value in having positively screened patients either
presented directly to them or flagged with a high ESI level for expedient pick up.
Additionally, mid-level providers in triage found use in taking the qSOFA positive
patient and being able to call the charge nurse or attending physician to quickly room the

60
patient and begin their clinical workups with sepsis being a differential diagnosis. In
many cases, this resulted in bypassing a three-hour wait in the waiting room.
Summary of Findings and Conclusion
The answer to the PICOT question, the implementation of the qSOFA tool did
yield statistical significance in the collection times of the lactic acid. As described in the
literature, the qSOFA tool did demonstrate a low sensitivity and high specificity for this
project as well. Moreover, the project did expand upon the scientific knowledge of the
staff related to sepsis knowledge and management. As discussed in the themes,
knowledge deficit regarding sepsis is a significant concern. By virtue of the project,
nearly the entire nursing staff were able to be educated on the basics of sepsis
pathophysiology and understand the treatments for sepsis. Additionally, the project
answered many of the “whys” that staff may have had regarding the three-hour bundle
and six-hour bundles.
The project also recognized clinical significance with 23 patients that screened
positive and were found to have other clinical etiology that needed to be addressed
urgently. The ability to take purposeful action based on the positive screen brought a
sense of satisfaction to many of the staff as well. This is largely in part, because it was
not just their “gut reaction” that something was not right, but they had an objective
positive score to back their findings and move the patient to the next phase in care. This
makes conversations between attending physicians and nurses easier too. Finally, this
project also brought forth the value in accurate measurement of respiratory rates. It was
found during the retrospective review of the sepsis patients that many had documented
respiratory rates of 16 or 18, but consistently monitored respiratory rates of 25 from the
bedside monitor. The project was able to address this clinical measurement concern and

61
provide an opportunity to educate nursing staff regarding the significance of elevated
respiratory rate in the presence of acidosis.
While the project demonstrated statistical significance related to the PICOT
question, it also exposed many of the underpinning short falls needed to have a successful
sepsis program. This ended up being the most significant part of the project and
demonstrates the importance of understanding the culture and education levels of the staff
that will be tasked with identification and caring of these patients. Now at the completion
of this project, the path forward is much clearer.
Implications
The implications this project had surrounded the importance of using evidencebased practice to guide clinical actions. Prior to initiation of the screening tool,
hemodynamically stable sepsis patients were simply triaged, evaluated according to ESI
level and time of arrival. However, now that an evidence-based process has been
implemented (sepsis screening), these patients are being called out and moved to the front
of the line for timely evaluation and treatment.
Teamwork was another implication that evolved from this project. Emergency
medicine is a team sport, but so is sepsis management. The project facilitated the focus
on sepsis in the E.D., but it also spilled over to include the hospitalist and to some extent,
the inpatient nursing staff.
The final implication from this project was trust. The staff began to learn to
strengthen their trust in each other when they had a positive patient and trust in the tool
itself when they saw a positive patient achieve all the three-hour bundle metrics. This is
clearly an important factor when working in a E.D. setting.

62
Theoretical Implications
As reviewed in chapter two, the project was following King’s system theory as
the lack of sepsis screening was identified as a systems issue. The use of this theory
turned out to be quite valuable, because it forced cultural change for the whole
organization. Not only did the project change the culture of the E.D., it also received
attention from the executive leadership team and the quality department, which resulted
in reestablishing the hospital-wide sepsis committee. Furthermore, this project allowed
for sepsis management to be placed on performance dashboards at the physician and
nursing levels, so sustainability and expectations of continued improvement can occur.
Recommendations
The first recommendation would be the implementation of a screening tool that
was incorporated in the EHR. Automation and leveraging technology would
hypothetically improve sepsis recognition and early management. While this practice site
did note improvement in their sepsis data using the qSOFA tool, it would be wise to
explore other screening tools based on recent literature (Evans et al., 2021).
The most significant recommendation from this project is to establish a sepsis
alert process. While the data point of order set entry was not measured, the quality
department did note improvement in the launching of sepsis order sets early during care.
Even though improvement in collection times was noted, more improvement could be
discovered if an actionable process was put into place. This idea is already being
discussed at the sepsis committee and is part of the sustainability of the project.
The final recommendation would be the use of the qSOFA tool with the local
paramedic providers. Identification of a potential sepsis patient in the pre-hospital
environment has potential to be impactful. The qSOFA would be an easy tool for pre-

63
hospital providers to learn and use. After a sepsis alert process is established internally,
the pre-hospital staff could be activating that process before the patient arrives thus
improving the timeliness of interventions.
Conclusion
To conclude, low performance in CMS sepsis metrics and clinical outcomes was
identified as a clinical problem that needed to be addressed. Through literature review, it
was found that the use of a sepsis screening tool was one of the primary methods to
resolve this problem. Since the practice site did not use any type of sepsis screening
methodology, it became clear that this was a systems issue that created and supported the
culture of no screening. King’s systems theory was used to build the framework of the
project and it was decided to implement an easy-to-use sepsis screening tool to assist in
changing the culture of the department. The qSOFA tool was the best fit for this situation
to help change the culture, educate staff, and begin to make improvements in sepsis
management. Post-implementation, both clinical and statistical significance was noted in
the management of the septic population. Additionally, clear next steps emerged as a
result of the project to reinforce sustainability and continuous improvement. This entire
project demonstrates the importance of finding evidence-based practices that can be
implemented to improve actual clinical outcomes.

64
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Appendix A

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Appendix B

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Appendix C

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Appendix D