ArticlePDF AvailableLiterature Review

Abstract

Introduction: Sepsis is a common condition observed outside critical care areas. The purpose of this review was to examine the application of sepsis screening tools for early recognition of sepsis in general hospitalised patients to: (i) identify the accuracy of these tools; (ii) determine the outcomes associated with their implementation and; (iii) describe the implementation process. Method: A systematic review method was used. PubMed, CINAHL, COCHRANE, SCOPUS, Web of Science and EMBASE databases were systematically searched for primary articles, published from January 1990 to June 2016, that investigated screening tools or alert mechanisms for early identification of sepsis in adult general hospitalized patients. The review protocol was registered with PROSPERO (CRD42016042261). Results: Over 8 thousand citations were screened for eligibility after duplicates were removed. Six articles met the inclusion criteria testing two types of sepsis screening tools. Electronic tools can capture, recognise abnormal variables and activate an alert in real time. However accuracy of these tools was found inconsistent across studies with only one demonstrating high specificity and sensitivity. Paper-based nurse-led screening tools appear to be more sensitive in the identification of septic patients but were only studied in small samples and particular populations. While process of care measures appears to be enhanced, demonstrating improved outcomes is more challenging. Implementation details are rarely reported. Heterogeneity of studies prevented meta-analysis. Conclusion: Clinicians, researchers and health decision makers should consider these findings and limitations when implementing screening tools, research or policy on sepsis recognition in general hospitalised patients.
Accepted Manuscript
Screening for sepsis in general hospitalised patients: a systematic review
Laura Alberto, Andrea P. Marshall, Rachel Walker, Leanne M. Aitken
PII: S0195-6701(17)30275-X
DOI: 10.1016/j.jhin.2017.05.005
Reference: YJHIN 5101
To appear in: Journal of Hospital Infection
Received Date: 30 January 2017
Accepted Date: 7 May 2017
Please cite this article as: Alberto L, Marshall AP, Walker R, Aitken LM, Screening for sepsis in
general hospitalised patients: a systematic review, Journal of Hospital Infection (2017), doi: 10.1016/
j.jhin.2017.05.005.
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Screening for sepsis in general hospitalised patients: a systematic review
Laura Alberto (corresponding author)
School of Nursing & Midwifery, Menzies Health Institute Queensland, Griffith University,
Brisbane, Australia
Postal address: 170 Kessels Road, Nathan Campus, QLD 4111, Australia
Email: laura.alberto@griffithuni.edu.au
Phone: +54911 4915 8403
Andrea P. Marshall
National Centre of Research Excellence in Nursing, Menzies Health Institute Queensland,
School of Nursing and Midwifery, Griffith University
Gold Coast University Hospital, Gold Coast Hospital and Health Service, Gold Coast,
Australia.
Rachel Walker
NHMRC Centre of Research Excellence in Nursing (NCREN) Menzies Health Institute
Queensland, Griffith University, Brisbane, Australia.
Nursing Practice Development Unit Princess Alexandra Hospital, Brisbane, Australia.
Leanne M. Aitken
School of Nursing & Midwifery, Griffith University, Brisbane, Australia.
School of Health Sciences, City, University of London, London, UK
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Key words
Sepsis, sepsis screening, hospital wards, general hospitalised patients
Contributorship statement:
All authors were involved in the conception of the review and methodological design. LA
conducted the literature search and article retrieval assisted by a librarian. LA and either RW,
AM or LMA independently screened citations for eligibility. LA and LMA extracted data and
reproduced accuracy tests. LA drafted the article. All authors revised critically the
manuscript, provided important intellectual contribution and approved the final manuscript.
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Screening For Sepsis In General Hospitalised Patients: A Systematic Review
Summary
Introduction
Sepsis is a common condition observed outside critical care areas. The purpose of this
review was to examine the application of sepsis screening tools for early recognition
of sepsis in general hospitalised patients to: (i) identify the accuracy of these tools; (ii)
determine the outcomes associated with their implementation and; (iii) describe the
implementation process.
Method
A systematic review method was used. PubMed, CINAHL, COCHRANE, SCOPUS,
Web of Science and EMBASE databases were systematically searched for primary
articles, published from January 1990 to June 2016, that investigated screening tools
or alert mechanisms for early identification of sepsis in adult general hospitalized
patients. The review protocol was registered with PROSPERO (CRD42016042261).
Results
Over 8 thousand citations were screened for eligibility after duplicates were removed.
Six articles met the inclusion criteria testing two types of sepsis screening tools.
Electronic tools can capture, recognise abnormal variables and activate an alert in real
time. However accuracy of these tools was found inconsistent across studies with only
one demonstrating high specificity and sensitivity. Paper-based nurse-led screening
tools appear to be more sensitive in the identification of septic patients but were only
studied in small samples and particular populations. While process of care measures
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appears to be enhanced, demonstrating improved outcomes is more challenging.
Implementation details are rarely reported. Heterogeneity of studies prevented meta-
analysis.
Conclusion
Clinicians, researchers and health decision makers should consider these findings and
limitations when implementing screening tools, research or policy on sepsis
recognition in general hospitalised patients.
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Introduction
Sepsis is a physical response to a source of infection that triggers mechanisms that
compromise organ function leading to death if not treated early. Over the past 25
years there has been an increasing interest in providing recommendations to diagnose
and manage this condition.
1,2
In spite of these efforts sepsis mortality remains
unacceptably high. Global mortality rates based on data collected in 37 countries
averaged 39%, but ranged from 22% in Australia to 56% in Brazil.
3
Other recent
studies have reported rates of 38% across Americas and Europe,
4
32% in Uganda
5
and
24% in Australia and New Zealand.
6
Given these high mortality rates, timely
recognition of sepsis is crucial to enable early and adequate intervention.
Septic patients were previously predominantly cared for in intensive care units
(ICU),
7,8
but this is now changing with more septic patients being cared for in hospital
wards. In various countries across North American and Europe it is reported that 14-
80% of patients in medical surgical wards develop sepsis.
9-11
Furthermore, within
acute medical and surgical ward settings, sepsis is frequently the cause of organ
failure,
9
and clinical deterioration leading to rapid response activation
12,13
or death.
10
This growing evidence suggests identification of septic patients in hospital wards is
paramount.
The earlier sepsis is identified the sooner the patient can be rescued from clinical
deterioration.
14,15
Timely recognition of this condition is a perennial concern stressed
by clinicians and researchers.
1,16,17
To address the issue, hospital wide quality
improvement initiatives on sepsis recognition have been implemented, with some
resulting in improved patient outcomes.
18,19
Sepsis alerts mediated by technology
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embedded in electronic medical records have also been proposed as an effective
screening mechanism.
20,21
The most effective method of screening patients in acute
care is not clear, therefore the purpose of this review was to examine the application
of sepsis screening tools or alert mechanisms for early recognition of sepsis in general
hospitalised patients to: (i) identify the accuracy of these screening tools; (ii)
determine the outcomes associated with their implementation and; (iii) to describe the
implementation process.
Methods
A systematic review method was used to search, identify, and appraise the available
literature. The review was previously registered with the international prospective
register of systematic reviews (PROSPERO registration number CRD42016042261).
Inclusion and exclusion criteria
Primary research that tested a screening tool or alert mechanism for early
identification of sepsis in hospitalized general medical, surgical and trauma (including
intermediate care) patients aged 16 years. Outcomes of interest included accurate
diagnosis, early implementation of the 6-hour bundle,
2
shorter ICU and hospital
length of stay and lower rates of mortality. Studies conducted in the emergency
department and ICU were excluded, as were studies in patients aged 15 years,
pregnant, obstetrics, haemodialysis, oncology and inmuno-compromised (HIV, Bone
Marrow Transplant, neutropenia) patients as these patients may have an altered
response to sepsis and therefore not be representative of general hospitalised
populations. Languages of publications were limited to English and Spanish. The
search was limited to publications from January 1990 to June 2016. This time frame
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was considered adequate as it preceded the publication of first sepsis consensus
conference results.
1
Search strategy
US National Library of Medicine National Institutes of Health (PubMed), Cumulative
Index to Nursing and Allied Health (CINAHL), COCHRANE, SCOPUS, Web of
Science and EMBASE databases were systematically searched (Appendix 1). Medical
subject headings and key words used were: screen, screening, early warning system,
early identification, early diagnosis, mass screening, early detection, early
recognition, sepsis, septic shock, severe sepsis, hospital, inpatient, hospital ward,
hospitalised patient. The article search and retrieval process was undertaken by one
author (LA) assisted by a librarian. Others articles were identified through manual
searching reviewing the reference list section of relevant publications, and using the
“cited by” function of Google Scholar with details of those publications. Identified
citations were screened for eligibility by two independent reviewers (LA and either
RW, AM or LMA). Disagreements were discussed and resolved within the entire
team.
Appraisal and data extraction
An appraisal and data extraction tool was developed (Appendix 2) based on the BMJ
Diagnostic test studies and critical appraisal,
22
the Critical Appraisal Skills
Programme (CASP) Diagnostic Test Study Checklist
©
,
23
the STARD checklist for
reporting of studies of diagnostic accuracy
24
and the template for intervention
description and replication (TIDieR) checklist and guide.
25
The tool was used to
assess the study validity, adequacy of population, blinding, testing and accuracy,
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methods for the screening test, implementation of the test, its results and process if
reported. Accuracy tests of the screening tools were reconstructed using the reported
number of patients that did and did not activate the alerts, and the number of patients
that were actually diagnosed as septic in both groups. If more than one cohort or
group were studied, accuracy tests were combined when the sample characteristics
and results of the groups were similar. If relevant information was not available in the
publication, the author was contacted.
Results
The search resulted in 14 771 citations retrieved from 6 search engines and manual
searching. After eliminating duplicates, 8456 citations including titles and abstracts
were screened for eligibility (Figure 1).
26
Six articles met the inclusion criteria,
including two prospective observational pilot studies,
27,28
one prospective
observational study,
29
two pre-post studies
30,31
and one retrospective cohort study
32
(Table I). Heterogeneity of studies in terms of instruments used to screen patients and
outcomes measured (Table I, II and III) prevented meta-analysis and minimal detail
was reported on the implementation of the screening tools.
Variables of screening tools and alert mechanism
The reviewed sepsis screening tools and alert mechanisms varied. Four of 6 tools
were mediated by technology, with the alert criteria and mechanism embedded in
electronic medical records.
28,30-32
In one study it was not clear if the tool was paper or
electronic.
29
The remaining study introduced a paper based screening tool.
27
The
variables of all the alert/screening tools identified are summarised in Table II.
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The electronic tools collected, in real time, a set of laboratory values and vital signs.
One prediction tool was based on an algorithm with five levels of decision-criteria,
with some variables used twice in different levels.
32
The same prediction tool was
later applied in the same setting.
28
The alerts were sent to a nurse who reviewed the
patient and activated further referral to physicians in order to inform alert and patient
condition.
28
Similarly, an electronic algorithm-based sepsis surveillance, provided
additional prompts of isolated clinical changes, diagnostic variables and treatment
reminder alerts.
31
Nurses received the alerts and referred the patient to a physician.
31
Another electronic sepsis alert using additional vital signs was investigated. The Early
Warning and Response System (EWRS) for sepsis, comprised of a set of 6-point risk
criteria, activated an alert when 4 of 6 criteria were met.
30
Similar clinical variables
were applied in a three tier nurse-led paper-based screening tool.
27
Nurses assessed
patients against the tool evaluating vital signs and inflammatory indicators (first tier),
clues of infection (second tier), and tissue perfusion and organ dysfunction variables
(third tier). If the screening process was positive, the nurse initiated a protocol and
called the treating physician. Finally, based on vital signs the sepsis until proven
otherwise (SUPO) protocol was examined.
29
If a positive screen was identified, nurses
referred the patient to a medical provider and collected blood cultures and lactate
samples unless advised otherwise. Screening processes are summarised in Table III.
Accuracy of screening tools
The accuracy of screening tools tested in these studies differed. Reference standards
that were used varied across the studies and included ICD9 codes for sepsis,
27
ICD9
codes for acute infection matched to codes for acute organ dysfunction, and the need
for vasopressors within 24 hours of ICU transfer,
32
Systemic Inflammatory Response
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Syndrome plus presence of infection,
29
and Surviving Sepsis Campaign definition.
31
One study reported accuracy tests calculated against (1) any ICU transfer, (2) rapid
response call, (3) death, or a composite of (1, 2 and 3) variables.
30
One study was not
able to be included in this analysis because only positively screened patients were
included and no data regarding patients who screened negative were available.
28
The sensitivity, specificity and predictive values of each of the screening tools are
summarised in Table 1. In one case, the reproduced specificity (0.94) and positive
predictive value (0.56) resulted in higher values than those reported by authors
(specificity 0.88, positive predictive value 0.10).
29
High levels of accuracy were
reported in the studies
27,31
and reproduced for the purpose of this review
29
with the
screening tools used in three studies. However two studies had small sample sizes
with accuracy tests calculated on random numbers of negatively screened
participants.
27,29
The remaining study reported control data collected retrospectively
outside of the study period.
31
Lower sensitivity and positive predictive values were
reproduced
30
and reported
32
in the larger studies where arguably more robust designs
were used. The more complex screening tools
30,32
appear to be more effective in
ruling out patients with sepsis, but they performed poorly in correctly identifying
septic patients.
Response to sepsis alerts
Nurses were always the first responders to sepsis alerts
27-31
although sometimes the
rapid response coordinator
30
and the covering medical provider
30
were also alerted at
this time. Nurses were also responsible for initiating a sepsis protocol
29
or escalating
the care
27,28,31
(Table III). Sepsis management, mainly related to the 6-hour bundle,
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including antibiotics prescription or escalation, fluid resuscitation and diagnostic tests
were frequently specified
27-29,31
and further consultation or transfer to ICU were
outlined in one protocol.
27
Frequency of screening and review periods for variables to screen
The screening tools were used to identify clinical indicators of sepsis in two ways:
continuously and at intervals (Table III). Tools that were applied continuously were
electronically mediated and integrated into electronic medical records.
28,30,31
In
contrast, a paper based screening tool was used by nurses at the beginning of their
shift.
27
SUPO was universally used across the study hospital but the format of the tool
and frequency of screening were unclear.
29
In terms of the review periods for
variables to be searched for when screening, different times were incorporated and
ranged from two to 72 hours, with the most common being 24 to 48 hours.
Patient outcomes
Important improvements in sepsis management were identified in the reviewed
studies and these are summarised in Table 1. Overall the frequency and time to use of
diagnostic measures (lactate orders, blood cultures) improved significantly while
results pertaining to treatment (fluids and vasopressors) were inconsistent across
studies with some but not all demonstrating improvement. One study reported
significant decrease in mortality and risk of death.
31
Other studies showed positive
trends in hospital mortality,
28,30
hospital
28,30
and ICU length of stay,
30
and ICU
transfer.
27,28,30
Implementation of screening tools
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The process used to implement the screening tools into routine practice was rarely
reported. Gyang et al described support provided to nurses before and during the
intervention.
27
Clinical nurse specialists, assistant nurse managers and educators
provided more than 8 hours of education on infection and sepsis related topics six
months before the implementation. In addition a sepsis education module was
available with completion being optional. An extra hour of self-study time was
provided a month before data collection was initiated where clinicians could learn
about severe sepsis. In addition, designated champions conducted in-service training
on completion of the screening tools the month prior to implementation. Manaktala et
al reported the governance process was led by nursing and physician steering
committees and a ward nurses team.
31
They were responsible for defining, training
and following-up implementation processes, including conducting changes in the
nursing documentation procedures that contained variables to be captured by the
surveillance system.
31
A “standardized education strategy” delivered during
physicians and nurses meetings prior to the alert system going live was identified in
other study.
28
Data about process compliance after sepsis alerts were reported in only
one study and included the name of the provider, notifications sent to the provider,
nurse review alert, nursing tasks, team presence at bedside within 30 mins, team
awareness of sepsis before alert and changes in management.
30
Compliance results
ranged from a low of 32% (any change in management) to 99% (nursing task verified:
vital signs assessment).
30
Strengths and limitations of studies
The studies identified have some strengths and limitations to consider. Strengths
included large sample sizes,
30,32
common laboratory variables
27,28,30,32
and vital signs
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used for developing the tools,
27,29,30
inter-rater agreement for sepsis diagnosis
evaluated,
31
details about implementation process,
27,31
and details about process
evaluation.
30
Limitations comprised small sample size,
27-29
particular populations
studied such as intermediate care
27
and patient’s having abdominopelvic surgery,
29
a
random sample of true negative patients studied,
27,29
control group data collected out
of the study period,
31
and incomplete or lack of implementation details.
28-30
Discussion
The evidence related to sepsis screening in acute care is examined in this review. Six
studies were identified that investigated predominantly electronic tools, with only one
paper-based tool reported. While process of care measures appear to be improved,
demonstrating improved outcomes is more challenging. Electronic tools assisted by
computing systems were able to capture, recognise abnormal variables and activate an
alert immediately,
28,30,31
or even facilitate prediction of organ dysfunction.
32
However
these tools performed poorly in identifying septic patients.
30,32
When tools did
perform better, comparisons were based on control data collected out of the study
period.
31
Paper-based nurse-led tools and alert mechanisms appeared to be more
sensitive in the identification of septic patients
27,29
but were only studied in small
samples and particular populations. Further investigation is needed to determine the
effectiveness of the types of alerts, whether they are electronic
33
or health practitioner
mediated.
Screening tools were comprised of a combination of laboratory indicators of organ
dysfunction, hemodynamic, inflammatory, tissue perfusion, vital signs, and other
variables. When considering the performance of a given combination of variables in
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screening instruments, evidence is not consistent regarding accuracy. For example, a
tool based on vital signs appears to perform better (sensitivity 1, specificity 0.94)
29
than a more complex prediction tool based on laboratory values (sensitivity 0.17,
specificity 0.97)
32
or a combination of laboratory and vital signs variables (sensitivity
0.23, specificity 0.98). It has been argued that sepsis has no gold standard for
identification and diagnosis,
34,35
with early signs and symptoms being non-specific.
Thus the underlying spectrum of clinical variables may be difficult to capture
36
by the
tools resulting in limitations in accuracy. Thus, the most accurate set of variables for
sepsis screening is yet to be elucidated.
Nurses were the primary responders to sepsis alerts, even though on occasion rapid
response system and medical providers also responded. Nurses’ involvement in timely
identification and response to sepsis alerts hospital-wide has been previously reported
as decreasing overall mortality by 43% (p <0.01) in a multicentre quality
improvement program in the USA.
37
The initiative was based on (i) sepsis screening,
(ii) diagnostic testing, and (iii) timely treatment. Nurses apply complex clinical
reasoning about patient condition, respond according to protocols and serve as a
safety mechanism.
27
Evidence favours nurses in responding to sepsis alerts, but to
what extent their response influences patient outcomes in other settings merits further
investigation.
Evidence identified was limited to hospital ward settings,
28,30-32
intermediate care
27
or
a particular type of surgery (abdominopelvic) patients
29
in the context of a developed
economy, specifically the USA. The technology
28,30-32
and the staff available such as
the nurse patient ratio
27
and the supporting steering committees,
31
played a pivotal
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role in developing a strategy for sepsis screening in these studies. While quality
improvement initiatives are frequently being implemented in developed health
systems
4
and technology is changing the way clinicians identify sepsis in well-
resourced hospital ward settings,
28,30-32
little is known about sepsis screening practices
in less developed settings. For example, in Brazil, a hospital-wide paper-based sepsis
screening strategy led by nurses resulted in a reduction in patient mortality from
61.7% to 36.5% (p < 0.001).
19
Importantly, when technology is not available for
assisting real time surveillance in hospital wards, nurses, physicians and other health
care practitioners are the only safety mechanism patients have. However, health
system decision makers play a key role in allocating resources for sepsis care. Whilst
a nation wide “sepsis six” initiative has been implemented in the UK
39
low and
middle-income countries decision makers are challenged by different priorities.
40,41
Research to help understand the role of health care providers in sepsis care in diverse
settings is urgently needed.
Details about implementation of screening tools and alert mechanisms were
infrequently reported. Education on sepsis screening and care prior to, and throughout
the implementation period,
27,31
as well as compliance to the process
30
were the main
components reported. Sepsis screening and response are complex processes of care
that involve various disciplines necessitating roles of each of the professionals be
made explicit. Details about implementation (such as activities for staff engagement
and follow-up) provide evidence about intervention fidelity,
38
help to gain
understanding of the setting, and promote future reproducibility.
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This systematic review addressed early sepsis identification in acute care settings. It
has a number of strengths and limitations. The review is limited to studies that tested
a screening tool, were published in English and Spanish, and included quantitative
analysis of accuracy and outcome measures. No publication that met the inclusion
criteria was identified in Spanish. There may be strategies published in different
languages that were not identified. The search was undertaken in six search engines
only, but the key words and medical subject headings were purposively broad to
capture as many studies as possible. Finally, studies identified were heterogeneous in
terms of the settings resources, patients, and outcomes defined,
36
which prevented
meta-analysis.
Implication for practice and research
The evidence examined uncovered important implications for practice and research.
Reviewed screening tools have different levels of sensitivity and specificity which
need to be considered prior to identifying an instrument for implementation; this
applies not only to the variables incorporated in the instrument but also the medium
that is used, specifically either electronic or paper based. If technology were available,
electronic tools may be preferred over paper-based tools. However, given the
resource-limited settings around the globe, implementation of paper based, nurse
driven tools could make a difference in sepsis care. Frequency of screening practice
and review periods of variables to screen may depend on patient characteristics,
staffing and available technology. The roles of health professional within the multi-
disciplinary team particularly nurse/physician to patient ratios and supporting staff,
should be made explicit to promote optimal sepsis screening processes. Strategies to
implement a new instrument should be carefully considered and explicitly described.
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Robust prospective designs should be encouraged, as should hybrid trials. Larger
sample sizes, across health settings, with differing levels of resource allocation should
be studied, as should be the implementation process in these contexts.
Conclusion
Six studies were identified that examined predominantly electronic tools, with only
one paper-based tool reported. Variables utilised were a combination of vital signs,
laboratory indicators of organ dysfunction, inflammatory, tissue perfusion and other
variables. After alert activation, nurses were the first responders and responsible for
initiating a sepsis protocol to escalating the care. Electronic tools assisted by
computing systems captured, recognised abnormal variables and activated alerts in
real time and facilitated prediction of organ dysfunction. However these tools
performed poorly in identifying septic patients. Only one tool performed better, but
findings were based on control data collected prior to the study period. Paper-based
nurse-led tools and alert mechanisms appeared to be more sensitive in the
identification of septic patients but were only studied in small samples and in
particular patient populations. The evidence regarding sepsis screening in hospitalised
patients is limited. Clinicians, researchers and health decision makers should consider
these findings and limitations when implementing screening tools, future research or
policy on sepsis recognition in general hospitalised patients.
Funding
This systematic review is part of Ms LA PhD candidature, which is funded by Griffith
University International Postgraduate Research Scholarship and Griffith University
Postgraduate Research Scholarship.
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Acknowledgements
Authors acknowledge Ms Katrina Henderson, Health Librarian from Library and
Learning Services, Griffith University for assisting article search and retrieval
process.
Competing Interests
Authors have no competing interests to declare.
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(Sepsis-3). JAMA 2016;315:801-10. doi:10.1001/jama.2016.0287
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29. MacQueen IT, Dawes AJ, Hadnott T, Strength K, Moran GJ, Holschneider C, et
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Figure 1. PRISMA Flow diagram
Records identified through
database searching
(n = 14 752)
Screening
Included Eligibility Identification
Additional records identified through
manual search of reference lists &
forward citations
(n = 19)
Records after duplicates removed
and screened
(n = 8456)
Records excluded
(n = 8429)
Full-text articles assessed for
eligibility
(n = 27)
Full-text articles excluded
with reasons
(n = 21)
No subgroup analysis
(n=9),
Sample diagnosed /
screened by the point of
recruitment (n=6),
Intensive Care
Unit/Emergency
Department settings (n=4),
Not primary research
(n=2)
Studies included in qualitative
synthesis
(n = 6)
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Table I. Study characteristics
Author (year)
country
Aim Design Setting, Sample Definition of sepsis Accuracy tests
Outcomes NOT
significant
Significant Outcomes
(p)
Gyang et al,
2015,
27
USA
To examine the
performance of a
nurse-driven sepsis
screening tool
Prospective pilot
study
26-bed medical/surgical
intermediate care unit, 613-
bed university tertiary
referral hospital,
n= 245
ICD-9 codes for
sepsis, severe
sepsis, or septic
shock
Medical pts
Se= 1
Sp= 0.96
PPV= 0.70
NPV= 1
Surgical pts
Se= 0.93
Sp= 0.90
PPV= 0.48
NPV= 0.99
Fluids
ICU transfer
ATB (0.006),
Lactate (0.018),
Blood culture (0.002)
MacQueen et
al, 2015,
29
USA
To evaluate the
usage of a vital
sign–based
screening protocol
for identifying
sepsis
Observational,
prospective screen
Non monitored, general
surgical units, hospital
network,
n= 478 (abdominopelvic
surgery only)
Systemic
Inflammatory
Response
Syndrome plus
presence of
perioperative
infection
Se
a
= 1
Sp
a
= 0.94
PPV
a
= 0.56
NPV
a
= 1
NR NR
Manaktala et al,
2016,
31
USA
To develop and
implement a
clinical decision
support system, and
to evaluate its test
characteristics and
the resultant sepsis-
related outcomes.
Quasi-
experimental,
with pre- and
post-test analysis
Two hospital floors,
respiratory and general
medicine, 941 bed tertiary
care hospital,
n= 778 (pre and post)
ICD 9 codes for
sepsis
Se= 0.95
Sp= 0.82
PPV= 0.50
NPV= 0.98
ICD 9 sepsis
diagnosis
Readmission
rate
Length of stay
in the study
units
Mortality (0.03)
Lower risk of death
(0.04)
Sawyer et al,
2011,
28
USA
To evaluate
whether the
implementation of
an automated sepsis
screening and alert
system facilitated
early appropriate
Prospective pilot
study with an
intervention
6 medical wards, 1250-bed
academic centre,
n= 270 (non-intervention
plus intervention)
Surviving Sepsis
Campaign
definition
NA Microbiologic
cultures and
radiographic
images
ICU transfer
ICU transfer
<12h after alert
Sepsis related
intervention <12h alert
(0.018),
ATB escalation
(0.035),
Fluids (0.013),
O2 therapy (0.005).
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ICU, Intensive Care Unit; NA, Not applicable; NR, Not reported; ATB, Antibiotics; ICD, International Classification of Diseases; SE, Sensitivity; SP, Specificity; PPV,
Positive Predictive Value; NPV, Negative Predictive Value; pts, patients; h, hours
a
Test reproduced using the negative alert patients (n=419) that did not develop sepsis (confirmed by author email communication)
b
Test reproduced combining 2006 and 2007 cohorts without arterial blood gas values for prediction
c
Test reproduced considering the sepsis discharge diagnosis instead of the composite variables reported by authors
interventions Mortality
Hospital length
of stay
Thiel et al,
2010,
32
USA
To identify early
predictors of septic
shock
Retrospective
cohort analysis
Medical, non-ICU units,
1200-bed academic centre,
n= 27 674 (derivation plus
validation)
ICD9 codes for
acute infection
matched to codes
for acute organ
dysfunction and the
need for
vasopressors within
24 hours of ICU
transfer
Se
b
= 0.17
Sp
b
= 0.97
PPV
b
= 0.20
NPV
b
= 0.96
NA NA
Umscheid et al,
2015,
30
USA
To describe the
development,
implementation and
impact of an early
warning and
response system for
sepsis
Pre and post study Non-ICU acute inpatient
units, 3 urban academic
hospitals of over 1500 beds,
n= 31 069
(pre and post
implementation)
Sepsis discharge
diagnosis
Pre
Se
c
= 0.22
Sp
c
= 0.97
PPV
c
= 0.39
NPV
c
= 0.94
Post
Se
c
= 0.23
Sp
c
= 0.98
PPV
c
= 0.45
NPV
c
= 0.94
Hospital and
ICU length of
stay
Vasopressors
Mortality
ICU transfer
<6h after alert
Fluids, ATB, lactic
acid and blood culture
orders <3h after alert
(0.01)
Transfusion order <6h
after alert (0.01)
Chest radiograph,
cardiac monitoring <6h
after alert (0.02)
Discharge home (0.04)
Sepsis discharge
diagnosis (0.02)
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Table II. Screening tool variables
Study General variables Inflammatory Hemodynamic Organ dysfunction Tissue perfusion Other
Gyang et al,
2015
27
Temperature >38°C,
<36°C
HR >90 beats/min
RR >20 breaths/min
Change mental status
WBC >12 000
or <4000 or
more than 10%
bands
SBP <90mmHg,
>40 mmHg decrease
in SBP from patient’s
baseline
MAP <65mmHg
UO <0.5ml/kg/hr for 2 hs (or
<30ml/h for 2hs)
Increase O
2
to maintain SpO
2
>90%
Absence bowel sounds (except
recent post-surgery)
Platelet count <100 000 µL–1
Serum creatinine increased by
0.3gm/dl in past 48hs
INR >1.5 or PTT >60seconds
Total bilirubin >4mg/d
Capillary refill >3
seconds
Lactate >2.0
mmol/L
PCO
2
<32 mmHg
Question of possible
sources
MacQueen
et al, 2015
29
Temperature >38°C
or <36°C
HR >90 beats/min
RR >20 breaths/min
- SBP <90 mmHg,
or MAP <65 mmHg
- - --
Manaktala et
al, 2016
31
Vital signs
a
- - - - Demographics
Medication
Laboratory values
a
Documentation elements
Medical problems
Symptoms of infection
Sawyer et al,
2011
28
- WBC
15.7x10
3
/mcl
MAP <68 mmHg INR 1.5,
INR 1.6,
Bilirubin <0.4 mg/dL,
Bilirubin 2.5 mg/dL
- Albumin 3.2 g/dl,
Albumin <2.6 mg/dL
Hemoglobin <10.9g/dL,
Hemoglobin 11.7 g/dl
Sodium 146 mmol/L
Neutrophils
15.9x10
3
/mcl,
Shock index 1.2
Thiel et al,
2010
32
- WBC
15.6x10
3
/ul
MAP <68 mmHg
INR 1.5,
INR 1.6
Bilirubin <0.4 mg/dL,
- Albumin 3.2 g/dl,
Albumin <2.5 mg/dL
Hemoglobin <11g/dL,
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Bilirubin 2.5 mg/dL
Hemoglobin 12 g/dl
Sodium 146 mmol/L
Shock index 1.2
Neutrophils 16x10
3
uL
Umscheid et
al, 2015
30
Temperature <36°C
or >38°C
HR >90 beats/min,
RR >20 breaths/min
WBC <4000 or
>12 000 or
>10% bands
SBP <100 mmHg - Lactate >2.2
mmol/L
PaCO
2
<32 mmHg
HR, Heart Rate; RR, Respiratory Rate; WBC, White Blood Cells; SBP, Systolic Blood Pressure; MAP, Main Arterial Pressure; INR, International normalised ratio; UO,
Urinary Output; O
2
, Oxygen; SpO
2
, Pulse Oximeter Oxygen Saturation; PTT, Partial Thromboplastin Time; PaCO
2
, partial pressure of carbon dioxide
a
No cut off values of variables were provided
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Table III. Screening process and response
Study Screening tool
name
Review periods for variables to
screen
Frequency of
screening
Screening
mechanism
Alert mechanism Response
Gyang et
al, 2015
27
Severe-Sepsis
Screening
Tool
Within the previous 8h of the
time of assessment
At the beginning of
every nursing shift,
Nurse driven,
paper based
Nurse to call primary
team
Nurse to initiate guideline
Primary team to order diagnostic tests,
administration of broad spectrum ATB
and fluids, ICU consultation/ transfer
MacQueen
et al,
2015
29
Sepsis Until
Proven
Otherwise
NR NR Nurse based Nurse to call a provider Provider to prescribe antibiotics and
intravenous fluid boluses as
recommended by the protocol
Manaktala
et al,
2016
31
Electronic
sepsis
surveillance
and alerting
system
NR Real time
surveillance
Electronic Alert sent to nurses on
mobile and desktop
computer
4 types of alerts could
be activated:
informational,
diagnosis, advice and
reminders
Nurses accepted or override the alert,
they were directed to contact physicians
about all diagnosis alert
Sawyer et
al, 2011
28
Prediction tool Immediately after registered in
electronic medical record
Continuously Electronic Automatic alert sent to
the nurse
Nurse assess the patient, and referred the
patient to a physician,
Physician to prescribe antibiotic,
escalation, administration of fluids and
oxygen, diagnostic tests
Thiel et al,
2010
32
Prediction tool 24 to 2h previous ICU
admission (cases)
48h controls
NA NA NA NA
Umscheid
et al,
2015
30
Early warning
and response
system
Vital signs 24h
Laboratory values 48h
Continuously Electronic Alert sent to bedside
nurse, RRC and
covering provider
Bedside nurse, RRC and covering
provider to evaluate the patient within
30min and enact changes in management
NR, Not reported; NA, Not applicable; ICU, intensive care unit; RRC, Rapid Response Coordinator; ATB, antibiotics; h, hours
... Although studies have shown that these screening tools generally improve process measures for sepsis care (eg, timely antibiotic administration), mortality benefits have not been clearly demonstrated. 13 Furthermore, these screens have highly variable sensitivity and specificity for actual sepsis, often with low positive predictive value (ie, 10% in one study). 13,14 This situation has raised the question of whether screening for sepsis and promoting overrecognition and treatment can contribute to unnecessary antibiotic use. ...
... 13 Furthermore, these screens have highly variable sensitivity and specificity for actual sepsis, often with low positive predictive value (ie, 10% in one study). 13,14 This situation has raised the question of whether screening for sepsis and promoting overrecognition and treatment can contribute to unnecessary antibiotic use. In this study, we characterized the rate and etiology of antibiotic escalation in response to the inpatient sepsis screen at our institution. ...
Article
Full-text available
Objective To determine the frequency and predictors of antibiotic escalation in response to the inpatient sepsis screen at our institution. Design Retrospective cohort study. Setting Two affiliated academic medical centers in Los Angeles, California. Patients Hospitalized patients aged 18 years and older who had their first positive sepsis screen between January 1, 2019, and December 31, 2019, on acute-care wards. Methods We described the rate and etiology of antibiotic escalation, and we conducted multivariable regression analyses of predictors of antibiotic escalation. Results Of the 576 cases with a positive sepsis screen, antibiotic escalation occurred in 131 cases (22.7%). New infection was the most documented etiology of escalation, with 76 cases (13.2%), followed by known pre-existing infection, with 26 cases (4.5%). Antibiotics were continued past 3 days in 17 cases (3.0%) in which new or existing infection was not apparent. Abnormal temperature (adjusted odds ratio [aOR], 3.00; 95% confidence interval [CI], 1.91–4.70) and abnormal lactate (aOR, 2.04; 95% CI, 1.28–3.27) were significant predictors of antibiotic escalation. The patient already being on antibiotics (aOR, 0.54; 95% CI, 0.34–0.89) and the positive screen occurred during a nursing shift change (aOR, 0.36; 95% CI, 0.22–0.57) were negative predictors. Pneumonia was the most documented new infection, but only 19 (50%) of 38 pneumonia cases met full clinical diagnostic criteria. Conclusions Inpatient sepsis screening led to a new infectious diagnosis in 13.2% of all positive sepsis screens, and the risk of prolonged antibiotic exposure without a clear infectious source was low. Pneumonia diagnostics and lactate testing are potential targets for future stewardship efforts.
... On the other hand, some data questioned the benefit of sepsis screening among hospitalized patients. A systematic review of six studies of electronic and paperbased sepsis screening suggested improvement in processes of care including the use of diagnostics [11]. However, the effect on treatment (fluid resuscitation and antibiotic administration) and outcome measures was less consistent [11]. ...
... A systematic review of six studies of electronic and paperbased sepsis screening suggested improvement in processes of care including the use of diagnostics [11]. However, the effect on treatment (fluid resuscitation and antibiotic administration) and outcome measures was less consistent [11]. Most existing studies are pre-post intervention studies [6,[12][13][14][15][16][17][18][19][20], and showed large reductions in mortality even with modest improvement in the compliance with the bundle. ...
Article
Full-text available
Background: To evaluate the effect of screening for sepsis using an electronic sepsis alert vs. no alert in hospitalized ward patients on 90-day in-hospital mortality. Methods: The SCREEN trial is designed as a stepped-wedge cluster randomized controlled trial. Hospital wards (total of 45 wards, constituting clusters in this design) are randomized to have active alert vs. masked alert, 5 wards at a time, with each 5 wards constituting a sequence. The study consists of ten 2-month periods with a phased introduction of the intervention. In the first period, all wards have a masked alert for 2 months. Afterwards the intervention (alert system) is implemented in a new sequence every 2-month period until the intervention is implemented in all sequences. The intervention includes the implementation of an electronic alert system developed in the hospital electronic medical records based on the quick sequential organ failure assessment (qSOFA). The alert system sends notifications of "possible sepsis alert" to the bedside nurse, charge nurse, and primary medical team and requires an acknowledgment in the health information system from the bedside nurse and physician. The calculated sample size is 65,250. The primary endpoint is in-hospital mortality by 90 days. Discussion: The trial started on October 1, 2019, and is expected to complete patient follow-up by the end of October 2021. Trial registration: ClinicalTrials.gov NCT04078594 . Registered on September 6, 2019.
... Systematic reviews evaluating the usefulness of automated alerting systems in sepsis have been reported in the literature. However, most of these studies evaluated reporting the diagnostic accuracy of the alerting system in predicting sepsis 12,[16][17][18] , and a few evaluated the effectiveness in terms of clinically relevant outcomes, such as mortality and length of stay (LOS). For instance, Hwang and colleagues analyzed studies published between 2009 and 2018 and found that algorithmbased methods had high accuracy in predicting sepsis. ...
Article
Full-text available
There is a large body of evidence showing that delayed initiation of sepsis bundle is associated with adverse clinical outcomes in patients with sepsis. However, it is controversial whether electronic automated alerts can help improve clinical outcomes of sepsis. Electronic databases are searched from inception to December 2021 for comparative effectiveness studies comparing automated alerts versus usual care for the management of sepsis. A total of 36 studies are eligible for analysis, including 6 randomized controlled trials and 30 non-randomized studies. There is significant heterogeneity in these studies concerning the study setting, design, and alerting methods. The Bayesian meta-analysis by using pooled effects of non-randomized studies as priors shows a beneficial effect of the alerting system (relative risk [RR]: 0.71; 95% credible interval: 0.62 to 0.81) in reducing mortality. The automated alerting system shows less beneficial effects in the intensive care unit (RR: 0.90; 95% CI: 0.73–1.11) than that in the emergency department (RR: 0.68; 95% CI: 0.51–0.90) and ward (RR: 0.71; 95% CI: 0.61–0.82). Furthermore, machine learning-based prediction methods can reduce mortality by a larger magnitude (RR: 0.56; 95% CI: 0.39–0.80) than rule-based methods (RR: 0.73; 95% CI: 0.63–0.85). The study shows a statistically significant beneficial effect of using the automated alerting system in the management of sepsis. Interestingly, machine learning monitoring systems coupled with better early interventions show promise, especially for patients outside of the intensive care unit.
... More so, action items, such as implementation of care paths in the form of standardized orders or through physician and nursing led education protocols, may be prompted by accurate risk assessment. Similar risk assessment tools, particularly for sepsis screening, derive risk from medical record data, and have been effective in prompting diagnostic testing and interventions, with some studies demonstrating a mortality benefit [37]. Given the prevalence of these 6 operations, encompassing approximately 80% of emergency general surgery cases, accurate assessment of risk factors and practices to reduce the risk of PRF and its consequences are relevant to all hospital types and warrant further study [16]. ...
Article
Background: Emergency general surgery (EGS) operations are associated with substantial risk of morbidity including postoperative respiratory failure (PRF). While existing risk models are not widely utilized and rely on traditional statistical methods, application of machine learning (ML) in prediction of PRF following EGS remains unexplored. Objective: The present study aimed to develop ML-based prediction models for respiratory failure following EGS and compare their performance to traditional regression models using a nationally-representative cohort. Methods: Non-elective hospitalizations for EGS (appendectomy, cholecystectomy, repair of perforated ulcer, large or small bowel resection, lysis of adhesions) were identified in the 2016-18 Nationwide Readmissions Database. Factors associated with PRF were identified using ML techniques and logistic regression. The performance of XGBoost and logistic regression was evaluated using the receiver operating characteristic curve and coefficient of determination (R2). The impact of PRF on mortality, length of stay (LOS) and hospitalization costs was secondarily assessed using generalized linear models. Results: Of 1,003,703 hospitalizations, 8.8% developed PRF. The XGBoost model exhibited slightly superior discrimination compared to logistic regression (0.900, 95% CI 0.899-0.901 vs 0.894, 95% CI 0.862-0.896). Compared to logistic regression, XGBoost demonstrated excellent calibration across all risk levels (R2: 0.998 vs 0.962). Congestive heart failure, neurologic disorders, and coagulopathy were significantly associated with increased risk of PRF. After risk-adjustment, PRF was associated with 10-fold greater odds (95% confidence interval (CI) 9.8-11.1) of mortality and incremental increases in LOS by 3.1 days (95% CI 3.0-3.2) and $11,900 (95% CI 11,600-12,300) in costs. Conclusions: Logistic regression and XGBoost perform similarly in overall classification of PRF risk. However, due to superior calibration at extremes of risk, ML-based models may prove more useful in the clinical setting, where probabilities rather than classifications are desired.
... La identificación temprana y el manejo apropiado en las primeras horas después del desarrollo de la sepsis mejoran los resultados 5 . Con el objetivo de promover la identificación temprana de la sepsis se han utilizado variables clínicas y herramientas de cribado, como los signos vitales, los signos de infección y las escalas qSO-FA y NEWS) 6 , todas ellas con una amplia variación en Tabla 1. Descripción demográfica y clínica de la población la precisión diagnóstica 7 . Parte de esta variabilidad diagnóstica radica en lo subjetivo de la qSOFA, ya que los tres valores que mide son dependientes del operador, y en el mayor tiempo que requiere la escala NEWS2, pues consta de ocho parámetros de evaluación sistémica. ...
... Best practice statement 28. For adults with sepsis or septic shock, we recommend prompt removal of intravascular access devices that are a possible source of sepsis or septic shock after other vascular access has been established. ...
... Best practice statement 28. For adults with sepsis or septic shock, we recommend prompt removal of intravascular access devices that are a possible source of sepsis or septic shock after other vascular access has been established. ...
Article
Objective To improve the timely diagnosis and treatment of sepsis many institutions implemented automated sepsis alerts. Poor specificity, time delays, and a lack of actionable information lead to limited adoption by bedside clinicians and no change in practice or clinical outcomes. We aimed to compare sepsis care compliance before and after a multi-year implementation of a sepsis surveillance coupled with decision support in a tertiary care center. Design Single center before and after study. Setting Large academic Medical Intensive Care Unit (MICU) and Emergency Department (ED). Population Patients 18 years of age or older admitted to *** Hospital MICU and ED from 09/4/2011 to 05/01/2018 with severe sepsis or septic shock. Interventions Electronic medical record-based sepsis surveillance system augmented by clinical decision support and completion feedback. Measurements and main results There were 1950 patients admitted to the MICU with the diagnosis of severe sepsis or septic shock during the study period. The baseline characteristics were similar before (N = 854) and after (N = 1096) implementation of sepsis surveillance. The performance of the alert was modest with a sensitivity of 79.9%, specificity of 76.9%, positive predictive value (PPV) 27.9%, and negative predictive value (NPV) 97.2%. There were 3424 unique alerts and 1131 confirmed sepsis patients after the sniffer implementation. During the study period average care bundle compliance was higher; however after taking into account improvements in compliance leading up to the intervention, there was no association between intervention and improved care bundle compliance (Odds ratio: 1.16; 95% CI: 0.71 to 1.89; p-value 0.554). Similarly, the intervention was not associated with improvement in hospital mortality (Odds ratio: 1.55; 95% CI: 0.95 to 2.52; p-value: 0.078). Conclusions A sepsis surveillance system incorporating decision support or completion feedback was not associated with improved sepsis care and patient outcomes.
Article
Aim To determine intensive care nurses’ awareness of identification of early sepsis findings. Background The incidence of sepsis is increasing in intensive care units, and if not identified early, it increases morbidity, mortality and cost of care. Intervention within one hour after the diagnosis of sepsis increases survival. Nurses’ ability to identify early findings of sepsis affects the time of diagnosis of sepsis. Design The study used a cross-sectional design. Methods The sample of the study consisted of 544 nurses working in adult intensive care units of hospitals in Turkey. The study data were collected online between 11 January–8 April 2021 using the snowball method. Data were statically analysed. All procedures of the study adhered to the STROBE guidelines. Results The nurses who had been working for 11 years or more, had worked with a patient diagnosed with sepsis in the last month and used a measurement tool in the diagnosis thought that it was significantly easier to determine the early warning findings of sepsis. In the study, the majority of nurses correctly identified the early findings of sepsis, but the rates of the correct responses to the variables of lactate >2 mM, leucopenia and hypothermia were low. Female gender, having a graduate degree, unit type, total work experience, having received training on sepsis and working with a patient diagnosed with sepsis in the last month made a significant difference in determining the early warning findings of sepsis accurately. Conclusions Nurses had a good rate of identifying early sepsis findings. Yet, they could not distinguish between early sepsis and late sepsis findings. Relevance to clinical practice The results of the study can support nursing practices in the diagnostic process by considering the factors affecting nurses’ ability to distinguish early sepsis findings from late sepsis findings and to identify them correctly.
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Objective: We created a system using a triad of change management, electronic surveillance, and algorithms to detect sepsis and deliver highly sensitive and specific decision support to the point of care using a mobile application. The investigators hypothesized that this system would result in a reduction in sepsis mortality. Methods: A before-and-after model was used to study the impact of the interventions on sepsis-related mortality. All patients admitted to the study units were screened per the Institute for Healthcare Improvement Surviving Sepsis Guidelines using real-time electronic surveillance. Sepsis surveillance algorithms that adjusted clinical parameters based on comorbid medical conditions were deployed for improved sensitivity and specificity. Nurses received mobile alerts for all positive sepsis screenings as well as severe sepsis and shock alerts over a period of 10 months. Advice was given for early goal-directed therapy. Sepsis mortality during a control period from January 1, 2011 to September 30, 2013 was used as baseline for comparison. Results: The primary outcome, sepsis mortality, decreased by 53% (P = 0.03; 95% CI, 1.06-5.25). The 30-day readmission rate reduced from 19.08% during the control period to 13.21% during the study period (P = 0.05; 95% CI, 0.97-2.52). No significant change in length of hospital stay was noted. The system had observed sensitivity of 95% and specificity of 82% for detecting sepsis compared to gold-standard physician chart review. Conclusion: A program consisting of change management and electronic surveillance with highly sensitive and specific decision support delivered to the point of care resulted in significant reduction in deaths from sepsis.
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Background Healthcare professionals may have difficulty in recognising the dying patient in acute care settings, and yet, this is essential if timely end-of-life care is to be provided. While approximately one-third of patients who pass away in-hospital are reviewed by the rapid response team (RRT), there is limited available research on other factors associated with mortality within the hospital setting. AimsTo describe the epidemiology of in-hospital mortality within a tertiary-level hospital, particularly in the context of RRT activation. Methods We utilised the database extraction of demographic, admission and RRT activation data on acute patients discharged from an Australian acute tertiary hospital between 1 January 2009 and 31 December 2013. Analyses included simple descriptors, Chi-squared and non-parametric Kruskal-Wallis tests as appropriate. ResultsOf the 44297 patients discharged from our hospital, 1603 died during admission. The general medical, haematology/oncology and intensive care teams provided care for the majority of the patients who died. A small number of diagnoses had in-patient mortality rates of greater than 25%. These included respiratory failure, alcoholic liver disease, vascular disorders of the intestine, sepsis and aspiration pneumonia. Over 75% of patients who received a RRT call survived to hospital discharge; however, patients who received four or more RRT calls during admission had an in-hospital mortality rate of over 40%. Conclusion Acute in-patient mortality is unequally distributed throughout the hospital, and a small number of diagnoses has large associated in-patient mortality rates. Repeated involvement of the RRT is associated with in-patient mortality.
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Policy makers in low-income and lower-middle-income countries (LMICs) are increasingly looking to develop ‘evidence-based’ frameworks for identifying priority health interventions. This paper synthesises and appraises the literature on methodological frameworks – which incorporate economic evaluation evidence – for the purpose of setting healthcare priorities in LMICs. A systematic search of Embase, MEDLINE, Econlit and PubMed identified 3968 articles with a further 21 articles identified through manual searching. A total of 36 papers were eligible for inclusion. These covered a wide range of health interventions with only two studies including health systems strengthening interventions related to financing, governance and human resources. A little under half of the studies (39%) included multiple criteria for priority setting, most commonly equity, feasibility and disease severity. Most studies (91%) specified a measure of ‘efficiency’ defined as cost per disability-adjusted life year averted. Ranking of health interventions using multi-criteria decision analysis and generalised cost-effectiveness were the most common frameworks for identifying priority health interventions. Approximately a third of studies discussed the affordability of priority interventions. Only one study identified priority areas for the release or redeployment of resources. The paper concludes by highlighting the need for local capacity to conduct evaluations (including economic analysis) and empowerment of local decision-makers to act on this evidence.
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Background: Sepsis is a leading cause of death, but evidence suggests that early recognition and prompt intervention can save lives. In 2005 Houston Methodist Hospital prioritized sepsis detection and management in its ICU. In late 2007, because of marginal effects on sepsis death rates, the focus shifted to designing a program that would be readily used by nurses and ensure early recognition of patients showing signs suspicious for sepsis, as well as the institution of prompt, evidence-based interventions to diagnose and treat it. Methods: The intervention had four components: organizational commitment and data-based leadership; development and integration of an early sepsis screening tool into the electronic health record; creation of screening and response protocols; and education and training of nurses. Twice-daily screening of patients on targeted units was conducted by bedside nurses; nurse practitioners initiated definitive treatment as indicated. Evaluation focused on extent of implementation, trends in inpatient mortality, and, for Medicare beneficiaries, a before-after (2008-2011) comparison of outcomes and costs. A federal grant in 2012 enabled expansion of the program. Results: By year 3 (2011) 33% of inpatients were screened (56,190 screens in 9,718 unique patients), up from 10% in year 1 (2009). Inpatient sepsis-associated death rates decreased from 29.7% in the preimplementation period (2006-2008) to 21.1% after implementation (2009-2014). Death rates and hospital costs for Medicare beneficiaries decreased from preimplementation levels without a compensatory increase in discharges to postacute care. Conclusion: This program has been associated with lower inpatient death rates and costs. Further testing of the robustness and exportability of the program is under way.
Article
IMPORTANCE: The Third International Consensus Definitions Task Force defined sepsis as “life-threatening organ dysfunction due to a dysregulated host response to infection.” The performance of clinical criteria for this sepsis definition is unknown. OBJECTIVE: To evaluate the validity of clinical criteria to identify patients with suspected infection who are at risk of sepsis. DESIGN, SETTINGS AND POPULATION: Among 1.3 million electronic health record encounters from January 1, 2010, to December 31, 2012, at 12 hospitals in southwestern Pennsylvania, we identified those with suspected infection in whom to compare criteria. Confirmatory analyses were performed in 4 data sets of 706 399 out-of-hospital and hospital encounters at 165 US and non-US hospitals ranging from January 1, 2008, until December 31, 2013. EXPOSURES: Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score, systemic inflammatory response syndrome (SIRS) criteria, Logistic Organ Dysfunction System (LODS) score, and a new model derived using multivariable logistic regression in a split sample, the quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) score (range, 0-3 points, with 1 point each for systolic hypotension [≤100 mm Hg], tachypnea [≥22/min], or altered mentation). MAIN OUTCOMES AND MEASURES: For construct validity, pairwise agreement was assessed. For predictive validity, the discrimination for outcomes (primary: in-hospital mortality; secondary: in-hospital mortality or intensive care unit [ICU] length of stay ≥3 days) more common in sepsis than uncomplicated infection was determined. Results were expressed as the fold change in outcome over deciles of baseline risk of death and area under the receiver operating characteristic curve (AUROC). RESULTS: In the primary cohort, 148 907 encounters had suspected infection (n = 74 453 derivation; n = 74 454 validation), of whom 6347 (4%) died. Among ICU encounters in the validation cohort (n = 7932 with suspected infection, of whom 1289 [16%] died), the predictive validity for in-hospital mortality was lower for SIRS (AUROC = 0.64; 95% CI, 0.62-0.66) and qSOFA (AUROC = 0.66; 95% CI, 0.64-0.68) vs SOFA (AUROC = 0.74; 95% CI, 0.73-0.76; P < .001 for both) or LODS (AUROC = 0.75; 95% CI, 0.73-0.76; P < .001 for both). Among non-ICU encounters in the validation cohort (n = 66 522 with suspected infection, of whom 1886 [3%] died), qSOFA had predictive validity (AUROC = 0.81; 95% CI, 0.80-0.82) that was greater than SOFA (AUROC = 0.79; 95% CI, 0.78-0.80; P < .001) and SIRS (AUROC = 0.76; 95% CI, 0.75-0.77; P < .001). Relative to qSOFA scores lower than 2, encounters with qSOFA scores of 2 or higher had a 3- to 14-fold increase in hospital mortality across baseline risk deciles. Findings were similar in external data sets and for the secondary outcome. CONCLUSIONS AND RELEVANCE: Among ICU encounters with suspected infection, the predictive validity for in-hospital mortality of SOFA was not significantly different than the more complex LODS but was statistically greater than SIRS and qSOFA, supporting its use in clinical criteria for sepsis. Among encounters with suspected infection outside of the ICU, the predictive validity for in-hospital mortality of qSOFA was statistically greater than SOFA and SIRS, supporting its use as a prompt to consider possible sepsis.
Article
Without a complete published description of interventions, clinicians and patients cannot reliably implement interventions that are shown to be useful, and other researchers cannot replicate or build on research findings. The quality of description of interventions in publications, however, is remarkably poor. To improve the completeness of reporting, and ultimately the replicability, of interventions, an international group of experts and stakeholders developed the Template for Intervention Description and Replication (TIDieR) checklist and guide. The process involved a literature review for relevant checklists and research, a Delphi survey of an international panel of experts to guide item selection, and a face-to-face panel meeting. The resultant 12-item TIDieR checklist (brief name, why, what (materials), what (procedure), who intervened, how, where, when and how much, tailoring, modifications, how well (planned), how well (actually carried out)) is an extension of the CONSORT 2010 statement (item 5) and the SPIRIT 2013 statement (item 11). While the emphasis of the checklist is on trials, the guidance is intended to apply across all evaluative study designs. This paper presents the TIDieR checklist and guide, with a detailed explanation of each item, and examples of good reporting. The TIDieR checklist and guide should improve the reporting of interventions and make it easier for authors to structure the accounts of their interventions, reviewers and editors to assess the descriptions, and readers to use the information.
Article
Importance The Third International Consensus Definitions Task Force defined sepsis as “life-threatening organ dysfunction due to a dysregulated host response to infection.” The performance of clinical criteria for this sepsis definition is unknown.Objective To evaluate the validity of clinical criteria to identify patients with suspected infection who are at risk of sepsis.Design, Settings, and Population Among 1.3 million electronic health record encounters from January 1, 2010, to December 31, 2012, at 12 hospitals in southwestern Pennsylvania, we identified those with suspected infection in whom to compare criteria. Confirmatory analyses were performed in 4 data sets of 706 399 out-of-hospital and hospital encounters at 165 US and non-US hospitals ranging from January 1, 2008, until December 31, 2013.Exposures Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score, systemic inflammatory response syndrome (SIRS) criteria, Logistic Organ Dysfunction System (LODS) score, and a new model derived using multivariable logistic regression in a split sample, the quick Sequential [Sepsis-related] Organ Failure Assessment (qSOFA) score (range, 0-3 points, with 1 point each for systolic hypotension [≤100 mm Hg], tachypnea [≥22/min], or altered mentation).Main Outcomes and Measures For construct validity, pairwise agreement was assessed. For predictive validity, the discrimination for outcomes (primary: in-hospital mortality; secondary: in-hospital mortality or intensive care unit [ICU] length of stay ≥3 days) more common in sepsis than uncomplicated infection was determined. Results were expressed as the fold change in outcome over deciles of baseline risk of death and area under the receiver operating characteristic curve (AUROC).Results In the primary cohort, 148 907 encounters had suspected infection (n = 74 453 derivation; n = 74 454 validation), of whom 6347 (4%) died. Among ICU encounters in the validation cohort (n = 7932 with suspected infection, of whom 1289 [16%] died), the predictive validity for in-hospital mortality was lower for SIRS (AUROC = 0.64; 95% CI, 0.62-0.66) and qSOFA (AUROC = 0.66; 95% CI, 0.64-0.68) vs SOFA (AUROC = 0.74; 95% CI, 0.73-0.76; P < .001 for both) or LODS (AUROC = 0.75; 95% CI, 0.73-0.76; P < .001 for both). Among non-ICU encounters in the validation cohort (n = 66 522 with suspected infection, of whom 1886 [3%] died), qSOFA had predictive validity (AUROC = 0.81; 95% CI, 0.80-0.82) that was greater than SOFA (AUROC = 0.79; 95% CI, 0.78-0.80; P < .001) and SIRS (AUROC = 0.76; 95% CI, 0.75-0.77; P < .001). Relative to qSOFA scores lower than 2, encounters with qSOFA scores of 2 or higher had a 3- to 14-fold increase in hospital mortality across baseline risk deciles. Findings were similar in external data sets and for the secondary outcome.Conclusions and Relevance Among ICU encounters with suspected infection, the predictive validity for in-hospital mortality of SOFA was not significantly different than the more complex LODS but was statistically greater than SIRS and qSOFA, supporting its use in clinical criteria for sepsis. Among encounters with suspected infection outside of the ICU, the predictive validity for in-hospital mortality of qSOFA was statistically greater than SOFA and SIRS, supporting its use as a prompt to consider possible sepsis.
Article
Importance Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination.Objective To evaluate and, as needed, update definitions for sepsis and septic shock.Process A task force (n = 19) with expertise in sepsis pathobiology, clinical trials, and epidemiology was convened by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine. Definitions and clinical criteria were generated through meetings, Delphi processes, analysis of electronic health record databases, and voting, followed by circulation to international professional societies, requesting peer review and endorsement (by 31 societies listed in the Acknowledgment).Key Findings From Evidence Synthesis Limitations of previous definitions included an excessive focus on inflammation, the misleading model that sepsis follows a continuum through severe sepsis to shock, and inadequate specificity and sensitivity of the systemic inflammatory response syndrome (SIRS) criteria. Multiple definitions and terminologies are currently in use for sepsis, septic shock, and organ dysfunction, leading to discrepancies in reported incidence and observed mortality. The task force concluded the term severe sepsis was redundant.Recommendations Sepsis should be defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. For clinical operationalization, organ dysfunction can be represented by an increase in the Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score of 2 points or more, which is associated with an in-hospital mortality greater than 10%. Septic shock should be defined as a subset of sepsis in which particularly profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone. Patients with septic shock can be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mm Hg or greater and serum lactate level greater than 2 mmol/L (>18 mg/dL) in the absence of hypovolemia. This combination is associated with hospital mortality rates greater than 40%. In out-of-hospital, emergency department, or general hospital ward settings, adult patients with suspected infection can be rapidly identified as being more likely to have poor outcomes typical of sepsis if they have at least 2 of the following clinical criteria that together constitute a new bedside clinical score termed quickSOFA (qSOFA): respiratory rate of 22/min or greater, altered mentation, or systolic blood pressure of 100 mm Hg or less.Conclusions and Relevance These updated definitions and clinical criteria should replace previous definitions, offer greater consistency for epidemiologic studies and clinical trials, and facilitate earlier recognition and more timely management of patients with sepsis or at risk of developing sepsis.
Article
Introduction: The optimal resuscitation strategy for patients with severe sepsis in resource-limited settings is unknown. Therefore, we determined the association between intravenous fluids, changes in vital signs and lactate after the first 6 hours of resuscitation from severe sepsis, and in-hospital mortality at a hospital in Uganda. Materials and methods: We enrolled patients admitted with severe sepsis to Mbarara Regional Referral Hospital and obtained vital signs and point-of-care blood lactate concentration at admission and after 6 hours of resuscitation. We used logistic regression to determine predictors of in-hospital mortality. Results: We enrolled 218 patients and had 6 hour postresuscitation data for 202 patients. The median (interquartile range) age was 35 (26-50) years, 49% of patients were female, and 57% were HIV infected. The in-hospital mortality was 32% and was associated with admission Glasgow Coma Score (adjusted odds ratio [aOR], 0.749; 95% confidence interval [CI], 0.642-0.875; P < .001), mid-upper arm circumference (aOR, 0.876; 95% CI, 0.797-0.964; P = .007), and 6-hour systolic blood pressure (aOR, 0.979; 95% CI, 0.963-0.995; P = .009) but not lactate clearance of 10% or greater (aOR, 1.2; 95% CI, 0.46-3.10; P = .73). Conclusions: In patients with severe sepsis in Uganda, obtundation and wasting were more closely associated with in-hospital mortality than lactate clearance of 10% or greater.