Implementation of a real-time computerized sepsis alert in
nonintensive care unit patients*
Amber M. Sawyer, PharmD; Eli N. Deal, PharmD; Andrew J. Labelle, MD; Chad Witt, MD;
Steven W. Thiel, MD; Kevin Heard, BS; Richard M. Reichley, RPh; Scott T. Micek, PharmD;
Marin H. Kollef, MD
T
imely interventions are
essen-tial in the management of
pa-tients with sepsis rapidly
pro-gressing to severe sepsis and
septic shock (1). Early goal-directed
ther-apy involving fluid resuscitation and
appro-priate antibiotic administration has been
shown to improve patient outcomes,
in-cluding a significant decrease in mortality
(2– 4). Clinical evidence suggests that
pa-tients developing sepsis on general hospital
wards may experience delays in treatment,
including fluid resuscitation, vasopressors,
and antibiotic therapy as well as experience
delays in intensive care unit transfer
result-ing in adverse outcomes (5–7).
To improve early sepsis management,
a real-time, computerized prediction tool
(PT) using recursive partitioning
regres-sion tree analysis and an
informatics-based alert system was developed at
Barnes-Jewish Hospital (8) (Fig. 1). The
sepsis PT is an algorithm that includes
routine laboratory and hemodynamic
val-ues to provide a simple screening method
for the identification of impending sepsis
that can be used in an automated fashion
with an electronic medical record system.
These parameters were selected because
patients with sepsis usually present with
a constellation of abnormal vital signs
and laboratory findings (fever or
hypo-thermia, tachycardia, tachypnea,
abnor-mal white blood cell count, creatinine,
liver function studies) and progression
from a syndrome of abnormal vital signs
and laboratory values to organ
dysfunc-tion and shock (9, 10). The goal of this
study was to evaluate whether
implemen-tation of the sepsis screening and alert
system facilitated early appropriate
inter-ventions for patients identified to be at
risk for developing sepsis.
METHODS
Study Location.The study was conducted at Barnes-Jewish Hospital, a 1250-bed aca-demic medical center in St Louis, MO. Six adult medicine wards were assessed from Oc-tober 2008 through June 2009. Two of the medicine wards were assigned to be the inter-vention wards and the remaining four wards served as the control wards. The medicine wards are closed areas with patient care deliv-ered by dedicated housestaff physicians under the supervision of a board-certified attending physician. The study was approved by the Washington University School of Medicine Human Studies Committee.
Patients. Patients admitted to one of the control wards were included in the noninterven-tion group (NIG). The intervennoninterven-tion group (IG) included all patients identified by the PT on the intervention wards who had a real-time sepsis alert generated. Patients were excluded from the study if they were not committed to aggressive therapy, defined as those with a pre-existing “do not resuscitate order” at the time of the alert or a documented decision for hospice or comfort care measures before the alert.
*See also p. 588.
From the Department of Pharmacy (AMS, END, STM), Barnes-Jewish Hospital, St Louis, MO; the Pul-monary and Critical Care Division (AJL, CW, SWT, MHK), Washington University School of Medicine, St Louis, MO; and Medical Informatics (KH, RMR), Barnes-Jewish Hospital, St Louis, MO.
This study was funded in part by the Barnes-Jewish Hospital Foundation.
The authors have not disclosed any potential con-flicts of interest.
For information regarding this article, E-mail: [email protected]
Copyright © 2011 by the Society of Critical Care Medicine and Lippincott Williams & Wilkins
DOI: 10.1097/CCM.0b013e318205df85
Objective:
Early therapy of sepsis involving fluid resuscitation
and antibiotic administration has been shown to improve patient
outcomes. A proactive tool to identify patients at risk for
devel-oping sepsis may decrease time to interventions and improve
patient outcomes. The objective of this study was to evaluate
whether the implementation of an automated sepsis screening
and alert system facilitated early appropriate interventions.
Design:
Prospective, observational, pilot study.
Setting:
Six medicine wards in Barnes-Jewish Hospital, a
1250-bed academic medical center.
Patients:
Patients identified by the sepsis screen while
admit-ted to a medicine ward were included in the study. A total of 300
consecutive patients were identified comprising the
noninterven-tion group (n
ⴝ
200) and the intervention group (n
ⴝ
100).
Interventions:
A real-time sepsis alert was implemented for the
intervention group, which notified the charge nurse on the
pa-tient’s hospital ward by text page.
Measurements and Main Results:
Within 12 hrs of the sepsis
alert, interventions by the treating physicians were assessed,
includ-ing new or escalated antibiotics, intravenous fluid administration,
oxygen therapy, vasopressors, and diagnostic tests. After exclusion
of patients without commitment to aggressive management, 181
patients in the nonintervention group and 89 patients in the
inter-vention group were analyzed. Within 12 hrs of the sepsis alert, 70.8%
of patients in the intervention group had received
>
1 intervention vs.
55.8% in the nonintervention group (p
ⴝ
.018). Antibiotic escalation,
intravenous fluid administration, oxygen therapy, and diagnostic
tests were all increased in the intervention group. This was a
single-center, institution- and patient-specific algorithm.
Conclusions:
The sepsis alert developed at Barnes-Jewish
Hospital was shown to increase early therapeutic and diagnostic
interventions among nonintensive care unit patients at risk for
sepsis. (Crit Care Med 2011; 39:469 – 473)
Study Design and Data Collection. This was a prospective pilot study that incorporated real-time data collection. The sepsis alerts were generated from a medical informatics system (Clinical Desktop; BJC HealthCare, St Louis, MO) using the PT developed to identify nonintensive care unit patients at risk for sep-sis (8). The PT is an algorithm consep-sisting of common laboratory values and hemodynamic parameters routinely monitored in hospital-ized patients who were selected by the authors as a result of their availability and potential relevance to sepsis, including the shock index (heart rate divided by systolic blood pressure), mean arterial pressure, international normal-ized ratio, white blood cell count, hemoglobin, absolute neutrophil count, serum albumin, to-tal bilirubin, and sodium (Fig. 1). The process of generating a sepsis alert began with nurses’ entry of patient hemodynamic values into the electronic medical record along with auto-matic transfer of laboratory data. Once the patient-specific data became electronically available, the values were automatically screened by the PT and if a patient was iden-tified as a “case,” a sepsis alert was generated through the informatics system. The alert was then sent by secure e-mail to the primary investigator including patient name, location, and alerting values (Fig. 2).
Additional steps to the PT process were added to formulate the active sepsis alert for the IG. The alert was sent automatically for identified patients on the two intervention wards through a text page to the charge nurse on those wards within 10 mins of the PT’s identification (Fig. 2). A standardized educa-tion strategy was introduced to nurses and physicians on the intervention wards previous to implementing the active alert with the text pages. The investigators provided sepsis alert system education during staff meetings ex-plaining the rationale, importance, and objec-tives of the study. Charge nurses were encour-aged to respond immediately to the page, assess the patient, and inform the treating or on-call physician of the alert and the clinical status of the patient. It would then be at the physician’s discretion as to what, if any, inter-ventions were warranted.
The primary outcome was the active ad-ministration of therapeutic and diagnostic in-terventions assessed within 12 hrs of the alert, including antibiotic escalation, intravenous fluid administration, oxygen therapy, vaso-pressor initiation, and diagnostics (microbio-logic cultures and imaging). Secondary out-comes included rate of intensive care unit (ICU) transfer, rate of ICU transfer within 12 hrs of alert generation, inhospital mortality, hospital length of stay, and hospital length of stay after the alert.
Data and Definitions.Baseline character-istics were recorded for each patient and in-cluded age, sex, race, comorbid conditions, and Acute Physiology and Chronic Health
Evaluation scores (11). Other baseline charac-teristics recorded included temperature (⬎38°C or⬍36°C) and white blood cell count (ⱖ16,000 cells/mm3 or ⬍4000 cells/mm3)
ⱕ24 hrs before or after generation of the sep-sis alert; antibiotic therapy and diagnostics ⱕ48 hrs before generation of the sepsis alert; and use of intravenous maintenance fluids, oxygen therapy, and vasopressors at the time of the sepsis alert.
Interventions were defineda priorias oc-curring within 12 hrs after the sepsis alert and included: antibiotic escalation, defined as any new antibiotic received or optimization of the dose of current antibiotic(s) as previously de-scribed at our institution (12, 13); administra-tion of fluids, defined as any intravenous fluid ⱖ500 mL administered overⱕ30 mins or the
initiation of intravenous maintenance fluids at a rate ⱖ50 mL/hr for ⱖ3 hrs; oxygen therapy, defined as any form of supplemental oxygen or ventilation initiated or current requirements es-calated; and diagnostic tests, including microbi-ologic cultures (blood, urine, sputum, wound) and radiographic imaging. The intravenous fluid thresholds were selected because they represented obtainable thresholds from our database that were felt to be clinically relevant by the investigators. The definitions for sepsis, severe sepsis, and septic shock were taken from the Surviving Sepsis Campaign (4).
Statistical Analysis.Our sample size calcu-lation was based on our prior experience (3, 5) and assumed a postalert intervention rate of 60% in the NIG and a postalert intervention
Figure 1.For each branch to the left indicates that the patient meets the conditions and to the right
either the patient does not meet the condition or the data are missing.MAP, mean arterial pressure;
INR, international normalized ratio;WBC, white blood cell count.
Figure 2.Process of generating an automated sepsis alert.NIG, noninterventional group;IG,
rate of 75% in the IG. We calculated that approximately 304 patients would need to be enrolled to achieve statistical power of 80% with a two-sided significance level of .05. We planned on enrolling patients into the study in a ratio of one intervention patient to two non-intervention patients. Chi square and Fisher’s exact tests were performed for all dichoto-mous variables. Student’sttest was performed for continuous variables. All tests were two-tailed and ap value of⬍.05 was considered statistically significant. Analyses were per-formed using SPSS 16.0 software package (SPSS Inc, Chicago, IL).
RESULTS
Patients.
Baseline demographics were
similar in both groups and initial
treat-ment before the sepsis alert was evenly
distributed (Table 1). Of the 300 patients
identified by the alerts, 181 (90.5%) in
the NIG and 89 (89.0%) in the IG were
committed to aggressive medical
man-agement and constituted the study
pop-ulation. No nurses or physicians were
contacted as a result of the sepsis alerts
for any patient in the NIG. The number of
patients actually developing sepsis, severe
sepsis, and septic shock was similar in the
IG and the NIG (Table 2). All episodes of
sepsis, severe sepsis, and septic shock
oc-curred within 48 hrs of generating the
sepsis alert.
Outcomes.
Within 12 hrs of
generat-ing the sepsis alert, 70.8% of patients in
the IG had received
ⱖ
1 interventions vs.
55.8% in the NIG (
p
⫽
.018). Significant
increases were seen in antibiotic
escala-tion (36.0% vs. 23.8%;
p
⫽
.035),
intra-venous fluid administration (38.2% vs.
23.8%;
p
⫽
.013), and oxygen therapy
(20.2% vs. 8.3%;
p
⫽
.005) (Fig. 3).
Di-agnostics, including both microbiologic
cultures and radiographic imaging, were
also greater in the IG. Patients in both
groups had similar rates of ICU transfer;
however, patients in the IG were more
likely to be transferred to the ICU within
12 hrs of the sepsis alert (9.0% vs. 4.4%)
(Fig. 4). Hospital mortality, total hospital
length of stay, and length of stay after
generation of the sepsis alert were also
similar between groups (Table 2).
DISCUSSION
The results of this pilot study suggest
that an automated sepsis alert using a
validated PT could influence the
manage-ment of patients predicted to develop
sep-sis on general hospital wards. By
imple-menting an informatics-based sepsis
screening and alert tool, comprised of
routine laboratory and hemodynamic
pa-rameters, we were able to significantly
increase the rate of interventions within
12 hrs of identifying patients at risk for
the onset of sepsis. The potential
impor-tance of these findings is suggested by a
recent multicentered study performed by
the Surviving Sepsis Campaign, which
demonstrated that one-third of patients
with sepsis originate from hospital wards
and that these patients have a greater
overall mortality compared with patients
with sepsis originating in the emergency
department or the ICU (14).
Track and trigger scoring systems to
detect clinical deterioration of patients
on general hospital wards are used
glob-ally at many institutions for the triage of
acutely ill patients admitted to these
wards. Scoring systems such as the
mod-ified early warning system provide a tool
for bedside evaluation based on
physio-logical parameters (15). If the assessment
(tracking) results in a score that is
indic-ative of deterioration, escalation of care
may need to be initiated (trigger).
Rec-ommendations have been made by the
Department of Health in London for
es-tablishing critical care teams trained to
respond quickly to a deteriorating patient
on a hospital ward and to make
appropri-ate interventions (16, 17). The intention
was to extend critical care skills beyond
the ICU to all wards to ensure timely ICU
admission or support on the wards, to
Table 1.Baseline characteristics
Characteristic
Nonintervention Group (n⫽181)
Intervention Group
(n⫽89) p
Age, years (mean⫾SD) 52.6⫾17.4 50.4⫾17.7 .347
Male, no. (%) 92 (50.8) 49 (55.1) .513 White, no. (%) 104 (57.5) 46 (51.7) .369 Comorbidities, no. (%) Cardiovascular 86 (47.5) 49 (55.1) .244 Renal 38 (21.0) 24 (26.9) .273 Dialysis 17 (9.4) 8 (8.9) .914 Liver 67 (37.0) 35 (39.3) .713
Solid organ transplant 8 (4.4) 4 (4.5) .978
Underlying malignancy 28 (15.5) 11 (12.4) .494
HIV-positive 5 (2.8) 4 (4.5) .456
APACHE II score (mean⫾SD) 17.6⫾6.6 17.7⫾7.2 .572
ⱕ24 hrs before alert Temperature⬍36°C or⬎38.0°C and WBC ⬍4000 or⬎16,000 cells/mm3 29 (16.0) 13 (14.6) .763 ⱕ48 hrs before alert Antibiotics 94 (51.9) 42 (47.2) .464 Radiographs 112 (61.9) 51 (57.3) .470 Microbiology cultures 109 (60.2) 54 (60.6) .943 Time of alert Maintenance IV fluids 44 (24.3) 19 (21.3) .589 Oxygen 51 (28.2) 22 (24.7) .548 Vasopressors 1 (0.5) 1 (1.1) .607
APACHE, Acute Physiology and Chronic Health Evaluation; WBC, white blood cells; IV, intravenous.
Table 2. Secondary outcomes
Outcome
Nonintervention Group (n⫽181)
Intervention Group
(n⫽89) p
Hospital LOS, days, median (IQR) 7 (5–14) 9 (5–15) .805
Hospital LOS after alert days, median (IQR) 5 (3–10) 6 (3–12) .724
Hospital mortality, no. (%) 21 (11.6) 9 (10.1) .714
Septic shockawithin 12 hrs of alert, no. (%) 6 (3.3) 5 (5.6) .368
Sepsis, no. (%) 42 (23.2) 11 (12.4) .035
Severe sepsis,no. (%) 44 (24.3) 27 (30.3) .290
Septic shock,a
no. (%) 17 (9.4) 13 (14.6) .200
LOS, length of stay; IQR, interquartile range.
aRequirement for vasopressors to maintain mean arterial pressures⬎60 mm Hg despite
avert ICU admissions, and to enhance the
skills of nonspecialists treating acutely ill
patients (17).
There is limited evidence to suggest
that clinical response teams contribute to
improved patient outcomes. One
ward-randomized trial reported reduced
inhos-pital mortality after implementing a
crit-ical care outreach service on surgcrit-ical and
medical wards (18), whereas other
non-randomized trials have reported
reduc-tions in ICU admission and length of stay
(19, 20). However, conflicting evidence
was seen from the hospital-randomized
Medical Emergency Team
Implementa-tion trial performed in Australia, showing
no improvement in outcomes including
cardiac arrest, ICU admission, and
mor-tality (21). Presence of a dedicated
med-ical emergency team led to more
emer-gency team calls without substantially
affecting patient outcomes. Similarly, a
national survey of hospitals in England
found that critical care outreach services
that have been widely introduced
throughout England were associated with
no clear evidence of improvement
inpa-tient outcomes (22). For both of these
negative studies, a major potential
limi-tation was the inability to identify early
signs of patient deterioration when
inter-ventions might be most helpful. The
im-portance of this is noted in a recent
re-port suggesting that 2900 US hospitals
now have rapid response systems in place
without clear demonstration of their
ef-ficacy (23).
Although early warning scoring
sys-tems and critical care teams are
impor-tant for providing acute care to ward
pa-tients and making decisions to transfer
patients to a higher level of care, early
identification of patient deterioration still
needs improvement. Currently, scoring
systems are usually initiated and
calcu-lated by the ward staff and could either be
performed too late or not at all (15). In a
recent study by Donohue et al (24)
look-ing at track and trigger system failures in
a critical care outreach service, they
re-ported the track and trigger scoring
sys-tem was used most often to quantify the
patients’ clinical deterioration rather
than to identify the initial signs of
dete-rioration. The computerized system used
in our study provides a potential solution
to issues such as this by using an
auto-matic screening tool that prompts
fur-ther assessment by the generated alert
that is delivered real-time to the bedside
care provider.
The sepsis PT developed at our
insti-tution is essentially an early tracking
method that provides an algorithm that
can be used as an automated screening
tool for hospitalized patients with
im-pending sepsis (8). It provides a simple
method that can be used with an
infor-matics-based system to predict the onset
of sepsis before overt clinical signs and
symptoms are recognized. Our method of
implementation included a text page sent
directly to the nursing staff to prompt
patient evaluation and physician
notifica-tion, although other forms of
communi-cation could be used. The main limitation
of the PT is its low positive predictive
value. When the prediction tool was
de-veloped from a series of patients from
2005 and validated against cohorts from
2006 and 2007, the positive predictive
value of identifying a patient that
trans-ferred to the ICU secondary to severe
sepsis or septic shock was found to be
19.5% with a negative predictive value of
95.8% (8). However, refinements in the
accuracy of such PTs over time may
im-prove their diagnostic accuracy. Such
re-finements could include the use of health
information technology bundles with
re-mote physician coverage as was recently
shown to improve resource use and
out-comes among ICU patients in an
academ-ic-affiliated community hospital (25).
Limitations.
The main limitation of
our study is the relatively small number
Figure 3.New therapies and diagnostics obtained within 12 hrs of the sepsis alert in the intervention
group (black bars) and the nonintervention group (white bars).
Figure 4.Intensive care unit (ICU) transfer and hospital mortality for patients in the intervention
of patients making up the sepsis cases in
our model validation study (8) and the IG
in the present study. Our study is also
underpowered to detect a mortality
dif-ference given the observed mortality rate
of approximately 10% in this patient
pop-ulation. The small sample size may also
explain the diagnostic heterogeneity we
observed in the incidence of sepsis, severe
sepsis, and septic shock between the
study groups. Similarly, the low overall
predictive accuracy for the PT suggests
that this model may not be good enough
for routine clinical use (8). This is
sup-ported by the lack of demonstrable
clini-cal benefit resulting from
implementa-tion of the PT in the IG. The narrow
group of patients with sepsis evaluated in
these studies also suggests that the PT
may be too limited in terms of its scope
for identifying a broad group of “at-risk”
patients. To improve the predictive
accu-racy of the PT, we are in the process of
refining and recalibrating our PT using a
more general hospital ward population,
including surgical patients, and adding
real-time vital signs captured
continu-ously with a locally developed wireless
monitoring device.
Another important limitation of our
study is that we examined all
interven-tions equally in determining the
effec-tiveness of the PT. It is likely that certain
interventions such as fluid resuscitation
of shock and appropriate antibiotic
ther-apy are more important than others. A PT
that allows earlier use of such targeted
interventions may be more clinically
rel-evant than one that simply allows any
intervention to be applied. Additionally,
our study was not blinded, which may
have allowed unforeseen biases to
influ-ence the results. Finally, we cannot
ex-clude an education effect from having
occurred as a result of our training of the
nurses and physicians on the
interven-tion wards. This may have accounted, at
least in part, for the greater use of
spe-cific therapies such as antibiotic
escala-tion and intravenous fluids compared
with the nonintervention wards.
CONCLUSION
Implementation of a real-time
com-puterized sepsis alert on hospital medical
wards resulted in an increase in early
interventions, including antibiotic
esca-lation, intravenous fluids, oxygen
ther-apy, and diagnostics in patients identified
to be at risk for sepsis. However, there
was no improvement in patient outcomes
or lengths of stay with use of this
com-puterized alert. Based on this pilot study,
a larger, randomized controlled trial is
planned at our institution to determine
whether patient outcomes can be
im-proved with use of a sepsis alert.
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