Effects of rapid response systems on clinical outcomes: Systematic review and meta-analysis

Article (PDF Available)inJournal of Hospital Medicine 2(6):422-32 · November 2007with68 Reads
DOI: 10.1002/jhm.238 · Source: PubMed
A rapid response system (RRS) consists of providers who immediately assess and treat unstable hospitalized patients. Examples include medical emergency teams and rapid response teams. Early reports of major improvements in patient outcomes led to widespread utilization of RRSs, despite the negative results of a subsequent cluster-randomized trial. To evaluate the effects of RRSs on clinical outcomes through a systematic literature review. MEDLINE, BIOSIS, and CINAHL searches through August 2006, review of conference proceedings and article bibliographies. Randomized and nonrandomized controlled trials, interrupted time series, and before-after studies reporting effects of an RRS on inpatient mortality, cardiopulmonary arrests, or unscheduled ICU admissions. Two authors independently determined study eligibility, abstracted data, and classified study quality. Thirteen studies met inclusion criteria: 1 cluster-randomized controlled trial (RCT), 1 interrupted time series, and 11 before-after studies. The RCT showed no effects on any clinical outcome. Before-after studies showed reductions in inpatient mortality (RR = 0.82, 95% CI: 0.74-0.91) and cardiac arrest (RR = 0.73, 95% CI: 0.65-0.83). However, these studies were of poor methodological quality, and control hospitals in the RCT reported reductions in mortality and cardiac arrest rates comparable to those in the before-after studies. Published studies of RRSs have not found consistent improvement in clinical outcomes and have been of poor methodological quality. The positive results of before-after trials likely reflects secular trends and biased outcome ascertainment, as the improved outcomes they reported were of similar magnitude to those of the control group in the RCT. The effectiveness of the RRS concept remains unproven.
Effects of Rapid Response Systems on Clinical
Outcomes: Systematic Review and Meta-Analysis
Sumant R. Ranji,
Andrew D. Auerbach,
Caroline J. Hurd,
Keith O’Rourke,
Kaveh G. Shojania,
Department of Medicine, University of California
San Francisco, San Francisco, California
Department of Medicine, University of Washing-
ton, Seattle, Washington
Department of Medicine, University of Ottawa
and Ottawa Health Research Institute, Ottawa, On-
tario, Canada
The authors thank Emmanuel King, MD, for gra-
ciously providing a copy of his manuscript prior to
publication and Alexis Meredith, MD, for providing
additional information regarding his study. Dr.
Shojania holds a Government of Canada Research
Chair in Patient Safety and Quality Improvement.
BACKGROUND: A rapid response system (RRS) consists of providers who immedi-
ately assess and treat unstable hospitalized patients. Examples include medical
emergency teams and rapid response teams. Early reports of major improvements
in patient outcomes led to widespread utilization of RRSs, despite the negative
results of a subsequent cluster-randomized trial.
PURPOSE: To evaluate the effects of RRSs on clinical outcomes through a system-
atic literature review.
DATA SOURCES: MEDLINE, BIOSIS, and CINAHL searches through August 2006,
review of conference proceedings and article bibliographies.
STUDY SELECTION: Randomized and nonrandomized controlled trials, interrupted
time series, and before-after studies reporting effects of an RRS on inpatient
mortality, cardiopulmonary arrests, or unscheduled ICU admissions.
DATA EXTRACTION: Two authors independently determined study eligibility, ab-
stracted data, and classified study quality.
DATA SYNTHESIS: Thirteen studies met inclusion criteria: 1 cluster-randomized
controlled trial (RCT), 1 interrupted time series, and 11 before-after studies. The
RCT showed no effects on any clinical outcome. Before-after studies showed
reductions in inpatient mortality (RR 0.82, 95% CI: 0.74-0.91) and cardiac arrest
(RR 0.73, 95% CI: 0.65-0.83). However, these studies were of poor methodological
quality, and control hospitals in the RCT reported reductions in mortality and
cardiac arrest rates comparable to those in the before-after studies.
CONCLUSIONS: Published studies of RRSs have not found consistent improvement in
clinical outcomes and have been of poor methodological quality. The positive results
of before-after trials likely reflects secular trends and biased outcome ascertainment,
as the improved outcomes they reported were of similar magnitude to those of the
control group in the RCT. The effectiveness of the RRS concept remains unproven.
Journal of Hospital Medicine 2007;2:422–432. © 2007 Society of Hospital Medicine.
KEYWORDS: systematic review, rapid response systems.
medical emergency team
is a group of clinicians trained to
quickly assess and treat hospitalized patients showing acute signs
of clinical deterioration. Equivalent terms used are rapid response
critical care outreach team,
and patient-at-risk team.
consensus panel
recently endorsed use of the term rapid response
system (RRS) to denote any system that uses a standard set of clinical
criteria to summon caregivers to the bedside of a patient who is
deemed unstable but not in cardiopulmonary arrest (in which case a
standard resuscitation team would be summoned). Such teams pri-
marily evaluate patients on general hospital wards.
RRSs have been developed in response to data indicating that
patients frequently demonstrate premonitory signs or receive in-
adequate care prior to unanticipated intensive care unit (ICU)
© 2007 Society of Hospital Medicine
DOI 10.1002/jhm.238
Published online in Wiley InterScience (www.interscience.wiley.com).
admission, cardiopulmonary arrest, or death out-
side the ICU.
Earlier identification and treat
ment of such patients could prevent adverse clini-
cal outcomes. The structure of RRSs varies but
generally includes a physician and nurse and may
also include other staff such as respiratory thera-
Teams are summoned by hospital staff to
assess patients meeting specific clinical criteria (see
box) about whom the bedside staff has significant
Initial studies of RRSs, performed primarily in
Australia and the United Kingdom, showed prom-
ising reductions in unanticipated ICU admissions,
cardiac arrests, and even overall inpatient mortali-
The considerable enthusiasm generated by
these studies
resulted in the Institute for
Healthcare Improvement (IHI) incorporating RRSs
into its “100,000 Lives” campaign,
and RRSs are
now being implemented in the more than 3000 U.S.
hospitals that joined the campaign. However, a re-
cent commentary on rapid response teams
and a
systematic review of critical care outreach teams
have raised concerns that this widespread imple-
mentation may not be justified by the available
evidence. We performed a systematic review of
studies of all variations of RRSs in order to deter-
mine their effect on patient outcomes and to char-
acterize variations in their organization and imple-
Literature Search and Inclusion and Exclusion Criteria
We systematically searched MEDLINE, CINAHL,
and BIOSIS through August 2006 for relevant stud-
ies using the various terms for RRSs (eg, “medical
emergency team,” “rapid response team,” “critical
care outreach”) and medical subject headings rele-
vant to inpatient care and critical illness (eg, “pa-
tient care team” and “resuscitation”; the full search
strategy is given in the Appendix). We also reviewed
the abstract lists from the 2004 and 2005 American
Thoracic Society and Society of Critical Care Med-
icine annual meetings and scanned reference lists
from key articles.
We screened the abstracts of the articles iden-
tified by the search, and 2 independent reviewers
abstracted potentially relevant articles using a stan-
dardized data abstraction form. Disagreements be-
tween the reviewers were resolved by consensus
and, if necessary, discussion with a third reviewer.
We included randomized controlled trials (RCTs),
controlled before-after studies, and interrupted
time series, including simple before-after studies
with no contemporaneous control group, though
we planned to separately analyze data from con-
trolled studies if possible. We included only En-
glish-language articles.
On the basis of RRS features in widely cited arti-
and the recommendations of a recent con
sensus statement,
we defined an RRS as having the
following characteristics: (1) its primary responsibility
is to intervene whenever hospitalized patients be-
come unstable before cardiopulmonary arrest occurs;
(2) it must primarily provide care outside the ICU and
emergency department; (3) specific clinical criteria
must be in place that define instability and trigger a
call to the team; and (4) it must be expected to re-
spond within a specified time. We defined these cri-
teria in order to distinguish studies of RRSs from
studies of cardiac arrest (“code blue”) teams or tradi-
tional consulting services.
To be included in the analysis, articles had to
report the effects of a rapid response system on at
least 1 of these outcomes: inpatient mortality, in-
patient cardiac arrest, or unscheduled ICU transfer.
We used the definitions of cardiac arrest and un-
scheduled ICU transfer given in the primary stud-
ies. In addition to these outcomes, we abstracted
information on the number of admissions and the
number of RRS calls during the study period. To
maximize the comparability of study outcomes, we
calculated the rates of mortality, cardiac arrest, un-
scheduled ICU transfer, and RRS calls per 1000
admissions for studies that did not supply data in
this fashion.
Assessment of Study Quality
Quality scoring instruments for studies included in
systematic reviews generally focus on randomized
Example of Rapid Response System Calling Criteria for Adult Patients
Any staff member may call the team if 1 of the following criteria is met:
Heart rate 140/min or 40/min
Respiratory rate 28/min or 8/min
Systolic blood pressure 180 mmHg or 90 mm Hg
Oxygen saturation 90% despite supplementation
Acute change in mental status
Urine output 50 cc over 4 hours
Staff member has significant concern about patient’s condition
Additional criteria used at some institutions:
Chest pain unrelieved by nitroglycerin
Threatened airway
Uncontrolled pain
Systematic Review of Rapid Response Systems / Ranji et al. 423
controlled trials, which we anticipated would ac-
count for a minority of included studies. On the
basis of recommendations for the assessment of
methodology for nonrandomized study de-
we identified and abstracted 4 important
determinants of internal validity (Table 1). The con-
sensus statement
recommends monitoring the ef
fectiveness of RRSs by measuring the rate of un-
scheduled ICU admissions (defined as an
unplanned admission to the ICU from a general
) and cardiac arrests of patients who were
not listed as “do not resuscitate” (DNR). As the
definition of unscheduled ICU admission allows
room for subjectivity, we considered the blinding of
assessment of this outcome to study group assign-
ment to be important, especially for retrospective
studies. Measurement of cardiac arrests should be
less susceptible to blinding issues, but one of the
functions of an RRS can be to initiate discussions
that result in changes in the goals of care and code
Thus, excluding patients made DNR by the
team from cardiac arrest calculations could falsely
lower the cardiac arrest rate.
We also abstracted 3 separate elements of study
quality pertaining to the external validity or gener-
alizability of included studies (Table 1). These ele-
ments were defined a priori by consensus reached
by discussion among the reviewers. These elements
were intended to provide a framework for interpret-
ing the included studies and to guide subgroup
analyses. They were not used to form a composite
quality score.
Statistical Analysis
We performed a random-effects meta-analysis to
calculate summary risk ratios with 95% confidence
intervals for the effects of RRSs on inpatient mor-
tality, cardiopulmonary arrest, and unscheduled
ICU admission. Included in the meta-analysis were
the studies that reported total number of admis-
sions and incidence of each outcome before and
after institution of the RRS. For randomized trials
that reported pre- and postintervention data, we
treated the intervention and control groups as sep-
arate trials in order to be able to compare their
effects with the before-after trials. For studies that
reported results adjusted for clustering (ie, by hos-
pital), we back-calculated the unadjusted results by
multiplying the standard error by the square of the
design factor coefficient.
We calculated the I
statistic to assess heterogeneity.
All analyses were
performed using Stata version 8.2 (Stata Corpora-
tion, College Station, TX).
The database searches identified 861 citations, and
1 additional prepublication study was supplied by
the study’s first author
; 89 articles underwent full-
text review (Fig. 1). Most studies excluded during
the full-text review did not meet our criteria for a
study of an RRS or were observational studies or
review articles. For instance, Sebat et al.
lished a study of a “shock team” at a community
hospital that intervened when any patient suffered
nontraumatic shock; the study did not meet our
inclusion criteria as all patients were admitted to
the ICU, most directly from the emergency depart-
ment. Another frequently cited study, by Bristow et
was excluded as it was a case-control study.
Thirteen studies,
—11 full-length stud
ies and 2 abstracts—met all criteria for inclusion.
Characteristics of Included Trials
The characteristics of included studies are outlined
in Table 2. Five studies were performed in Australia,
4 in the United States, and 4 in the United King-
dom. All were conducted in academic teaching hos-
pitals. Two studies
focused on pediatric inpa
tients, and the remainder involved hospitalized
adults. The RRS intervened for all hospitalized pa-
tients in all but 2 studies (1 of which focused on
surgical inpatients
and the other in which the RRS
Study Quality Criteria
Quality measures
A. Internal validity
1. Did the study have a contemporaneous control group?
2. If there was no contemporaneous control group, did the study report data
for more than 1 time point before and after the intervention?
3. Were nonobjective primary outcomes (eg, unplanned ICU transfer)
measured in a blinded fashion?
4. Were patients made DNR by the RRS included in calculations of the cardiac
arrest and mortality rates?
B. Generalizability
5. Was the intervention performed independent of other quality improvement
interventions targeting the care of critically ill patients?
6. Did the study report the number of admissions and RRS calls during the
study period?
7. Did the study report the availability of intensivists before and after the
Elements affecting study internal validity and translatability. These elements were chosen based on the
methods of the Cochrane collaboration.
These criteria were not used to determine article inclusion
or exclusion.
424 Journal of Hospital Medicine Vol2/No6/Nov/Dec 2007
evaluated only patients discharged from the ICU
In 2 studies,
the RRS was available to evaluate
outpatients, such as hospital visitors, in addition to
RRS Structure, Calling Criteria, and Responsibilities
Seven studies
that described the
team composition used variants of the medical
emergency team model, a physician-led team (Ta-
ble 2). In 6 of these 7 studies, the team included a
critical care physician (attending or fellow) and an
ICU nurse; in the sole RCT (the MERIT study
), the
team structure varied between hospitals, consisting
of a nurse and physician from either the emergency
department or ICU. Hospitalists, who are involved
in RRS responses at many U.S. hospitals, were pri-
mary team leaders of the RRS in only 1 study.
In 2
the RRS also responded to code blue
calls, and in 4 studies
the RRS and the code
blue team had separate personnel; the remaining
studies did not define the distinction between RRS
and code blue team.
In 4 studies the RRSs were led by nurses. One
study published in abstract form
used the rapid
response team model, consisting of a critical care
nurse and a respiratory therapist, with assistance as
needed from the primary medical staff and a critical
care physician. Three studies
from UK hospi
tals used the critical care outreach (CCO) model, in
which ICU-trained nurses respond initially with as-
sistance from intensivists. The CCO model also in-
volves follow-up on patients discharged from the
ICU and proactive rounding on unstable ward pa-
The hospitals used broadly similar approaches
to determining when to summon the RRS , relying
on combinations of objective clinical criteria (eg,
vital sign abnormalities) and subjective criteria (eg,
acute mental status change, staff member con-
cerned about patient’s condition). Three stud-
used a formal clinical score (the Patient-
At-Risk score or the Modified Early Warning score)
to trigger calls to the RRS. Three studies, 2 of them
from the same institution,
reported the fre
quency of specific triggers for RRS activation. Con-
cern by bedside staff and respiratory distress were
the most frequent activators of the RRS.
Study Internal Validity and Generalizability
One study,
the MERIT trial, conducted in Austra
lia, was a cluster-randomized RCT (randomized by
hospital) that adhered to recommended elements
of design and reporting for studies of this type.
this study, hospitals in the control group received
an educational intervention on caring for deterio-
rating patients only; hospitals in the intervention
group received the educational module and started
an RRS. An additional study
identified itself as a
randomized trial, but randomization occurred at
the hospital ward level, with introduction of the
intervention (critical care outreach) staggered so that
at different points an individual ward could have been
in either the control or intervention group; therefore,
Stage 1: title & abstract review by two
independent reviewers
N = 89
R1: 34 R3: 2
R2: 39
Stage 2: full text review by two
independent reviewers
Included studies
862 titles identified
(806 citations identified from MEDLINE
search, 14 citations identified from BIOSIS
search, 41 citations identified from CINAHL
search, 1 citation supplied by author)
2 Exclusions
Stage 3
: full text review and data
abstraction by two
endent reviewers
R1: 760
R2: 13
FIGURE 1. Article identification and triage trial flow diagram, as recom-
mended by the QUOROM statement for improving the methodological quality of
systematic reviews and meta-analyses.
Reasons for exclusion were: R1, not
a study of a rapid response system; R2, ineligible study design (not simple
before-after study, controlled before-after study, interrupted time series, or
randomized, controlled trial); R3, no eligible outcomes (did not report effect of
RRS on in-hospital cardiac arrest, unscheduled ICU admission, or inpatient
mortality); R4, overlapping publication. Data from 1 article
were pooled with
an included article,
and the other
was excluded because it contained
longer-term follow-up data from another included study.
Systematic Review of Rapid Response Systems / Ranji et al. 425
Studies Included in Meta-analysis
Study Location Hospital type Patient population RRS composition
Definition of
unscheduled ICU
Definition of
cardiac arrest
Buist et al., 2002
Australia Tertiary-care academic
Adult inpatients ICU registrar
Medical registrar
ICU nurse
DNR status
Cardiac arrest
Unscheduled ICU
Not supplied Any code blue
team activation
Bellomo et al., 2003
Australia Tertiary-care academic
Adult inpatients ICU nurse and ICU fellow
attended all calls; ICU
attending (8
AM-8 PM)
and medical registrar
attended if requested
Cardiac arrest
DNR status
NA Unresponsive, with
no pulse or
blood pressure,
and basic life
support initiated
Pittard et al., 2003
United Kingdom Tertiary-care academic
Adult inpatients on
surgical wards
Senior critical care nurses
and medical staff
Unscheduled ICU
Any patient not admitted
directly from
operating theater after
elective surgery
Bellomo et al., 2004
Australia Tertiary-care academic
Adult postoperative
ICU attending
ICU fellow
ICU registered nurse
Medical fellow
Unscheduled ICU
ICU length of
DNR status
Postoperative admission
to ICU because of a
clinical complication
DeVita et al., 2004
United States Tertiary-care academic
Adult inpatients ICU physician
2 other physicians
2 ICU nurses
Floor nurse
Respiratory therapist
Cardiac arrest NA Any code blue
team activation
Garcea et al., 2004
United Kingdom Teaching hospital Adult patients discharged
from ICU
2 ICU nurses
1 ICU nurse specialist
ICU consultant
physician (if needed)
Unscheduled ICU
Emergency readmissions
to ICU
Kenward et al.,
United Kingdom Teaching hospital Adult inpatients Not stated Mortality
Cardiac arrest
NA Loss of
Priestley et al., 2004
United Kingdom Teaching hospital Adult inpatients ICU nurse consultant
ICU physician available
if needed
Overall length of
Hillman et al., 2005
Australia 23 hospitals (17
academic, 6
Adult inpatients Varied between study
hospitals; required to
have at least 1 MD and
1 RN from ED or ICU
Cardiac arrest
Unscheduled ICU
DNR status
Any unscheduled
admission to ICU from
general ward
No palpable pulse
Hunt et al., 2005
United States Pediatric tertiary-care
academic hospital
Pediatric inpatients Not provided Cardiac arrest NA Not provided
Meredith et al., 2005
United States Tertiary-care academic
Adult inpatients,
outpatients, and
ICU registered nurse
Respiratory therapist
Mortality NA Not provided
Tibballs et al., 2005
Australia Pediatric tertiary-care
academic hospital
Pediatric inpatients ICU physician
Medical registrar
ED physician
ICU nurse
Cardiac arrest
Unscheduled ICU
Not supplied Not provided
King et al., 2006
United States Tertiary-care academic
Adult inpatients,
outpatients, and
Internal medicine
ICU nurse
Ward nurse
Cardiac arrest NA Not provided
RRS, rapid response system; ICU, intensive care unit; RN, registered nurse; ED, emergency department; DNR, do not resuscitate; NA, not applicable.
426 Journal of Hospital Medicine Vol2/No6/Nov/Dec 2007
this study was considered an interrupted time series.
All other trials included were before-after studies with
no contemporaneous control group
Most studies did not meet criteria for internal
validity or generalizability (Table 2). Two studies
did not report the number of RRS calls during the
study period. One study
omitted patients whose
resuscitation status was changed after RRS evalua-
tion from the calculation of inpatient mortality;
thus, the patients who had been made “do not
resuscitate” by the RRS did not contribute to the
calculated mortality rate. The disposition of these
patients was unclear in another study.
All studies
measured clinical outcomes retrospectively, and no
studies reported blinding of outcomes assessors for
nonobjective outcomes (eg, unplanned ICU admis-
sion). Studies generally did not report on the avail-
ability of intensivists or if other quality improve-
ment interventions targeting critically ill patients
were implemented along with the RRS.
RRS Usage and Effects on Patient Outcomes
Seven studies
reported enough informa
tion to calculate the RRS calling rate (4 stud-
reported the total number of calls but
not the number of admissions, and 2 studies
not report either). In these 7 studies, the calling rate
varied from 4.5 to 25.8 calls per 1000 admissions.
Three studies documented the calling rate before
and after the intervention: a study at a hospital with
a preexisting RRS
reported that the calling rate
increased from 13.7 to 25.8 calls per 1000 admis-
sions after an intensive education and publicity
program; in a pediatric trial,
the overall emer
gency calling rate (for cardiac arrests and medical
emergencies) was reported to increase from 6.6 to
10.4 per 1000 admissions; and in the MERIT trial,
calls increased from 3.1 to 8.7 per 1000 admissions.
Effects of RRS on Clinical Outcomes
Nine studies
reported the effect
of an RRS on inpatient mortality, 9 stud-
reported its effect on cardio
pulmonary arrests, and 6 studies
ported its effect on unscheduled ICU admissions.
Of these, 7 trials that reported mortality and car-
diopulmonary arrests and 6 studies that reported
unscheduled ICU admissions supplied sufficient
data for meta-analysis.
Observational studies demonstrated improve-
ment in inpatient mortality, with a summary risk
ratio of 0.82 (95% CI: 0.74-0.91, heterogeneity I
Factors Affecting Internal Validity and Generalizability of Studies Included in Meta-analysis
control group
Data reported at more
than 1 time before/
after intervention
RRS calling
rate reported
Outcomes analysis
included patients
made DNR by team
Blind measurement of
nonobjective outcomes
Intensivist always
Other QI efforts
during study
Buist et al., 2002
No No Yes No (mortality) No NR NR
Bellomo et al., 2003
No No Yes Yes (mortality) NA Yes (ICU fellow) No
Pittard et al., 2003
No No Yes NA No NR NR
Bellomo et al., 2004
No No Yes Yes (mortality) No Yes (ICU fellow) No
DeVita et al., 2004
No Yes Yes NA No Yes (critical care
Garcea et al., 2004
No No No Unclear No NR NR
Kenward et al., 2004
No No Yes Unclear No NR NR
Priestley et al., 2004
No (interrupted time
Yes No NA No NR NR
Hillman et al., 2005
Yes No Yes Unclear Yes NR No
Hunt et al., 2005
Meredith et al., 2005
No No Yes No NA No No
Tibballs et al., 2005
No No Yes Unclear No NR Yes (educational workshops/
more training in APLS)
King et al., 2006
No Yes Yes NA No Yes No
SBA, simple before-after (quasi-experimental) study; ITS, interrupted time series; RCT, randomized controlled trial; NA, not applicable; NR, not reported; APLS, advanced pediatric life support
Systematic Review of Rapid Response Systems / Ranji et al. 427
62.1%; Fig. 2). However, the magnitude of these
improvements was very similar to that seen in the
control group of the MERIT trial (RR 0.73, 95% CI:
0.53-1.02). The intervention group of the MERIT
trial also demonstrated a reduction in mortality
that was not significantly different from that of the
control group (RR 0.65, 95% CI: 0.48-0.87). We
found a similar pattern in studies reporting RRS
effects on cardiopulmonary arrests (Fig. 3). The ob-
servational studies did not show any effect on the
risk of unscheduled ICU admissions (summary RR
1.08, 95% CI: 0.96-1.22, heterogeneity I
79.1%) nor
did the MERIT trial (Fig. 4).
Despite the strong face validity of the RRS concept,
the current literature on medical emergency teams,
rapid response teams, and critical care outreach
suffers from substantial flaws that make it difficult
to determine the effect of an RRS on patient out-
comes. These flaws include the use of suboptimal
study designs, failure to report important cointer-
ventions, the methods in which outcomes were de-
fined, and lack of verification of the validity of the
outcomes measured. As a result, very little empiric
data are available to define the effectiveness of
RRSs or to provide guidance for hospitals planning
to implement an RRS.
Though early studies reported that RRSs ap-
peared to reduce mortality and cardiac arrest rates,
the sole randomized trial of an RRS (the MERIT
) showed no differences between intervention
and control hospitals for any clinical outcome. Both
inpatient mortality and cardiac arrest rates de-
clined in the intervention and control groups of the
MERIT trial, and the reductions in these outcomes
in observational trials were similar to those seen in
the MERIT control group. This strongly implies that
other factors besides the RRS were responsible for
the results of previous before-after studies. These
studies, which have been widely cited by propo-
nents of the RRS, suffer from methodological limi-
tations intrinsic to the study design and issues with
outcome measurement that may have introduced
systematic bias; these factors likely explain the con-
trast between the generally positive results of the
before-after studies and the negative results of the
MERIT trial.
Most early RRS trials used an uncontrolled be-
fore-after study design, as is common in quality
improvement studies.
This study design cannot
account for secular trends or other factors, includ-
Relativ e Risk
Reduc ed Inc r eased
.1 .5 1 5 10
Relativ e Risk
(95% CI)
MERIT, 2005 (intervention)
0.65 ( 0.48, 0.88)
MERIT, 2005 (control)
0.73 ( 0.53, 1.02)
0.69 ( 0.55, 0.86)
admits at
Adult 8.7 1.65
Adult 3.1 1.60
Adult NR NR
Adult 4.2
Adult 5.3 15.1
Pediatric 5.1 0.12
Randomized trials
Observational trials
Garcea, 2004 0.52 ( 0.35, 0.77)
Bellomo, 2004
0.59 ( 0.41, 0.86)
Bellomo, 2003
0.83 ( 0.68, 1.00)
Buist, 2002
0.90 ( 0.79, 1.04)
Tibballs, 2005
0.45 ( 0.10, 1.99)
0.82 ( 0.74, 0.91)
FIGURE 2. Effect of RRS on inpatient mortality The forest plot compares the relative risk of mortality after implementation of RRS with that before RRS
implementation. For the MERIT trial, we treated the 2 study arms (intervention and control) as separate before-after trials in order to compare with the observational
studies. The study by Garcea et al.
evaluated the effect of RRS on readmission to the ICU. The supplied outcomes are for in-hospital mortality of patients readmitted
to the ICU only; thus, the baseline mortality rate is not reported. The study by Bellomo et al. (2004)
evaluated the effect of RRS on postoperative patients only.
The other study performed at the same institution and published in 2003
reported outcomes of all inpatients. Therefore, we subtracted the results of the 2004
study from those reported in the 2003 study to avoid counting the same outcomes twice (RR, relative risk; NR, not reported; NA, not applicable).
428 Journal of Hospital Medicine Vol2/No6/Nov/Dec 2007
ing other QI interventions, that could influence the
effect of an intervention.
The statistically signifi
cant reduction in impatient mortality in the control
arm of the MERIT trial is an instructive example;
this decline could have been a result of the educa-
tional intervention on caring for deteriorating pa-
tients, other ongoing QI projects at the individual
hospitals, or simply random variation during the
relatively short (6-month) follow-up period. Such
factors could also entirely account for the impres-
sive results seen in the initial uncontrolled RRS
studies. Nearly all the studies we reviewed also did
not discuss any aspects of the hospital context that
could influence outcomes for critically ill patients,
such as the nurse-staffing ratio,
ICU bed availabil
overall hospital census,
or availability of
Relativ e Ris k
Reduced Increased
.1 .5 1 5 10
Relativ e Ris k
(95% CI)
MERIT, 2005 (intervention)
0.81 ( 0.60, 1.10)
MERIT, 2005 (control)
0.63 ( 0.48, 0.82)
0.70 ( 0.58, 0.86)
obs ervational==1
DeVita, 2004
0.81 ( 0.71, 0.92)
Bellomo, 2004
0.35 ( 0.18, 0.71)
Bellomo, 2003
0.36 ( 0.18, 0.71)
Buist, 2002
0.57 ( 0.39, 0.82)
Tibballs , 2005
0.58 ( 0.20, 1.70)
Hunt, 2005
0.46 ( 0.19, 1.13)
0.73 ( 0.65, 0.83)
admits at
Adult 8.7 1.60
Adult 3.1 2.60
Adult 25.8
4.2 2.48
Adult 5.6 3.68
Pediatric 5.1 0.19
Pediatric 10.4 2.42
Randomized trials
Observational trials
FIGURE 3. Effect of RRS on cardiopulmonary arrests The forest plot shows the relative risk of cardiopulmonary arrest after implementation of RRS. As in Figure
1, the MERIT trial intervention and control groups were treated as separate before-after trials.
Relative Risk
Reduced Increased
.5 1 5
Relative Risk
(95% CI)
MERIT, 2005 (intervention)
0.90 ( 0.56, 1.43)
MERIT, 2005 (control)
0.94 ( 0.58, 1.53)
0.92 ( 0.65, 1.29)
admits at
Adult 8.7 4.68
Adult 3.1 5.29
Adult NR NA
Adult 5.6 2.72
Adult 5.3 1.35
Pediatric 5.1 7.72
Randomized trials
Garcea, 2004
1.15 ( 0.80, 1.66)
Bellomo, 2004
Observational trials
0.58 ( 0.33, 1.01)
Buist, 2002
2.01 ( 1.35, 3.01)
Tibballs , 2005
1.04 ( 0.91, 1.19)
1.08 ( 0.96, 1.22)
FIGURE 4. Effect of RRS on unscheduled ICU admissions The forest plot shows the relative risk of an unscheduled ICU admission after implementation of RRS.
As shown in Figures 1 and 2, the MERIT trial intervention and control groups were treated as separate before-after trials. The study by Garcea et al.
the effect of RRS on readmissions to ICU. The supplied outcomes are for unscheduled readmissions to ICU; thus, the baseline unscheduled ICU admission rate is
not reported.
Systematic Review of Rapid Response Systems / Ranji et al. 429
or hospitalists.
Failure to control
for—or at least report important aspects of—the
environment in which the intervention was per-
formed is akin to failing to report baseline patient
comorbidities or concurrent therapies in a study of
a drug’s effectiveness.
Our review also suggests how bias in the mea-
surement of clinical outcomes may have contrib-
uted to the apparent effect of RRSs. In 1 before-
after study, patients for whom RRS activation
resulted in a change code status to do not resus-
citate (DNR) were excluded from calculations of
resulting in underreporting of mor
tality after RRS implementation. Disposition of
such patients was unclear in 3 other studies.
Some studies
defined cardiopulmonary arrest
as any activation of the code blue team, regard-
less of whether the patient was actually in cardiac
arrest. This almost inevitably would result in
fewer arrests after implementation of the RRS, as
the indications for calling the code blue team
would be narrower. Finally, nearly all studies
used trends in nonobjective primary outcomes
(eg, unplanned ICU transfer) to support RRS ef-
fects but did not validate any of these outcomes
(eg, how often did reviewers agree an ICU trans-
fer was “preventable”), and none of the assessors
of these outcomes were blinded.
Some have attributed the MERIT trial not
finding the RRS beneficial to inadequate imple-
mentation, as the RRS calling rate of 8.7 calls per
1000 admissions was less than the 15 calls per
1000 admissions cited as optimal in a “mature”
However, published studies generally re
ported a calling rate of 4-5 calls per 1000 admis-
with only 1 trial reporting a higher
calling rate.
A recent commentary
and a systematic review
of critical care outreach teams
both addressed the
effectiveness of RRSs. We sought to examine the ef-
fects of all RRS subtypes and using quantitative anal-
ysis and analysis of methodological quality, to deter-
mine the overall effect of RRSs. The results of our
analysis (which included data from several newer
) support and extend the conclusion of
prior reviews that RRSs, although a potentially prom-
ising intervention, do not unequivocally benefit pa-
tients and are not worthy of more widespread use
until more evidence becomes available. Our analysis
also demonstrates that many studies widely cited as
supporting wide implementation of RRSs are flawed
and probably not generalizable.
Despite these caveats, RRSs remain an intu-
itively attractive concept and may be of benefit at
some hospitals. Further studies in this area should
focus on identifying which patient populations are
at high risk for clinical decompensation, identifying
the role of clinical structures of care (eg, nurse-
staffing ratio, presence of hospitalists) in prevent-
ing adverse outcomes and determining which spe-
cific RRS model is most effective. As well, more
information is needed about educating bedside
staff and RRS team members, as this is likely critical
to success of the team. Unfortunately, only the ar-
ticle by King et al.
provided sufficient detail about
the implementation process to assist hospitals in
planning an RRS. The remaining articles had only
scant details about the intervention and its imple-
mentation, a common problem noted in the quality
improvement literature.
Our analysis had several limitations. We at-
tempted to identify as many RRS trials as possible
by searching multiple databases and reviewing ab-
stract proceedings, but as the RRS literature is in its
infancy, we may not have located other unpub-
lished studies or “gray literature.” There is no vali-
dated system for evaluating the methodological
strength of nonrandomized studies; therefore, we
assessed study quality on the basis of prespecified
criteria for internal and external validity. Finally, we
found significant statistical heterogeneity in our
quantitative analyses, indicating that the variability
between individual studies in treatment effects was
greater than that expected by chance. As the pri-
mary reasons we conducted a meta-analysis was to
compare the results of before-after trials with those
of the randomized MERIT trial, we did not further
explore the reasons for this heterogeneity, although
variation in patient populations and RRS structure
likely accounts for a significant proportion of the
Although there is a theoretical basis for imple-
menting a rapid response system, the published
literature shows inconsistent benefits to patients
and suffers from serious methodological flaws. Fu-
ture studies of RRSs should attempt to define which
patient populations are at risk, the essential char-
acteristics of RRSs, effective implementation strat-
egies, and—most important—whether any RRS im-
proves clinical outcomes. Until such evidence is
available, hospitals should not be mandated to es-
tablish an RRS and should consider prioritizing
quality improvement resources for interventions
with a stronger evidence base.
430 Journal of Hospital Medicine Vol2/No6/Nov/Dec 2007
Address for correspondence and reprint requests: Sumant R. Ranji, MD,
Hospitalist Group, Department of Medicine, University of California San Fran-
cisco, 533 Parnassus Avenue, Box 0131, San Francisco, CA 94143-0131; Fax
(415) 514-2094; E-mail: sumantr@medicine.ucsf.edu
Received 27 November 2006; revision received 23 February 2007; accepted 29
April 2007.
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432 Journal of Hospital Medicine Vol2/No6/Nov/Dec 2007
    • Effectiveness of the education program was measured using three types of outcome: learner outcomes, patient outcomes and system outcomes. Nineteen studies (Buckley and Gordon, 2011; Cooper et al., 2013; Crofts et al., 2006; 2007; Featherstone et al., 2005; Gordon and Buckley, 2009; Hart et al., 2014; Harvey et al., 2014; Kelly et al., 2013; Kinsman et al., 2012; Lewis, 2011; Liaw et al., 2011 Liaw et al., , 2013 Lindsey and Jenkins, 2013; Ludikhuize et al., 2011; Sittner et al., 2009; Smith and Poplett, 2004; Straka et al., 2012; Wehbe-Janek et al., 2012) measured the intervention's impact on perceived or real knowledge or performance , nine (Cooper et al., 2013; Featherstone et al., 2005; Gordon and Buckley, 2009; Hart et al., 2014; Harvey et al., 2014; Kelly et al., 2013; Lewis, 2011; Liaw et al., 2011; Wehbe-Janek et al., 2012 ) measured human factors or non-technical skills such as confidence, teamwork , leadership and communication, while one study measured the Table 1Included quantitative study details. Author, year and setting Title Design Focused Research Question (FRQ a ) (Y/N) Aim Intervention (I) Comparison (C)
    [Show abstract] [Hide abstract] ABSTRACT: Survival from in-hospital cardiac arrest is poor. Clinical features, including abnormal vital signs, often indicate patient deterioration prior to severe adverse events. Early warning systems and rapid response teams are commonly used to assist the health profession in the identification and management of the deteriorating patient. Education programs are widely used in the implementation of these systems. The effectiveness of the education is unknown.
    Article · Sep 2016
    • One method for improving the recognition of these patients, is the implementation of TTSs. Although the conclusions on effectiveness of TTSs in reducing clinical endpoints are still not uniform, when properly followed they are effective in identifying deteriorating patients [33, 34]. The effectiveness depends on appropriate implementation, compliance and an effective clinical response [35] .
    [Show abstract] [Hide abstract] ABSTRACT: Background: An unplanned ICU admission of an inpatient is a serious adverse event (SAE). So far, no in depth-study has been performed to systematically analyse the root causes of unplanned ICU-admissions. The primary aim of this study was to identify the healthcare worker-, organisational-, technical,- disease- and patient- related causes that contribute to acute unplanned ICU admissions from general wards using a Root-Cause Analysis Tool called PRISMA-medical. Although a Track and Trigger System (MEWS) was introduced in our hospital a few years ago, it was implemented without a clear protocol. Therefore, the secondary aim was to assess the adherence to a Track and Trigger system to identify deterioration on general hospital wards in patients eventually transferred to the ICU. Methods: Retrospective observational study in 49 consecutive adult patients acutely admitted to the Intensive Care Unit from a general nursing ward. 1. PRISMA-analysis on root causes of unplanned ICU admissions 2. Assessment of protocol adherence to the early warning score system. Results: Out of 49 cases, 156 root causes were identified. The most frequent root causes were healthcare worker related (46%), which were mainly failures in monitoring the patient. They were followed by disease-related (45%), patient-related causes (7, 5%), and organisational root causes (3%). In only 40% of the patients vital parameters were monitored as was instructed by the doctor. 477 vital parameter sets were found in the 48 hours before ICU admission, in only 1% a correct MEWS was explicitly documented in the record. Conclusions: This in-depth analysis demonstrates that almost half of the unplanned ICU admissions from the general ward had healthcare worker related root causes, mostly due to monitoring failures in clinically deteriorating patients. In order to reduce unplanned ICU admissions, improving the monitoring of patients is therefore warranted.
    Full-text · Article · Aug 2016
    • Defining patient deterioration through ACU and ICU nurses' perspectives patient outcomes were found to improve after their introduction, evidence regarding their impact remains scant (Esmonde et al., 2006; Ranji et al., 2007; Chan et al., 2010). These tools and resources are used infrequently by nurses on ACU (Odell et al., 2009; Donohue and Endacott, 2010) and the issue of patient deterioration not being recognized or acted upon remains.
    [Show abstract] [Hide abstract] ABSTRACT: AimTo explore the variations between acute care and intensive care nurses' understanding of patient deterioration according to their use of this term in published literature.Background Evidence suggests that nurses on wards do not always recognize and act upon patient deterioration appropriately. Even if resources exist to call for intensive care nurses' help, acute care nurses use them infrequently and the problem of unattended patient deterioration remains.DesignDimensional analysis was used as a framework to analyze papers retrieved in a nursing-focused database.MethodA thematic analysis of 34 papers (2002–2012) depicting acute care and intensive care unit nurses' perspectives on patient deterioration was conducted.FindingsNo explicit definition of patient deterioration was retrieved in the papers. There are variations between acute care and intensive care unit nurses' accounts of this concept, particularly regarding the validity of patient deterioration indicators. Contextual factors, processes and consequences are also explored.Conclusions From the perspectives of acute care and intensive care nurses, patient deterioration can be defined as an evolving, predictable and symptomatic process of worsening physiology towards critical illness. Contextual factors relating to acute care units (ACU) appear as barriers to optimal care of the deteriorating patient. This work can be considered as a first effort in modelling the concept of patient deterioration, which could be specific to ACU.Relevance to clinical practiceThe findings suggest that it might be relevant to include subjective indicators of patient deterioration in track and trigger systems and educational efforts. Contextual factors impacting care for the deteriorating patient could be addressed in further attempts to deal with this issue.
    Full-text · Article · Sep 2014
    • Before-and-after studies from a number of institutions support the premise that a MET reduces mortality [13-15], although the only randomised multicentre trial looking at the effect of MET on mortality failed to show a benefit [16], and two recent meta-analyses questioned their effect on hospital mortality [17,18]. Using routinely collected hospital administrative data we have previously reported that the introduction of a MET at our institution was associated with a reduction in all-cause hospital mortality over a number of years [19].
    [Show abstract] [Hide abstract] ABSTRACT: Introduction Medical emergency teams (MET) are implemented to ensure prompt clinical review of patients with deteriorating physiology with the intention of averting further deterioration, cardiac arrest and death. We sought to determine if MET implementation has led to reductions in hospital mortality across a large metropolitan health network utilising routine administrative data submitted by hospitals to the Department of Health Victoria. Methods The Victorian admissions episodes data set (VAED) contains data on all individual hospital separations in the State of Victoria, Australia. After gaining institutional ethics approval, we extracted data on all acute admissions to metropolitan hospitals for which we had information on the presence and timing of a MET system. Using logistic regression we determined whether there was an effect of MET implementation on mortality controlling for age, gender, Charlson comorbidity diagnostic groupings, emergency admission, same day admission, ICU admission, mechanical ventilation, year, indigenous ethnicity, liaison nurse service and hospital designation. Results 5911533 individual admissions and 73,599 associated deaths from July 1999 to June 2010 were included in the analysis. 52.2% were male and median age was 57(42-72 IQR). Mortality rates for MET and non-MET periods were 3.92 (3.88-3.95 95%CI) and 4.56 (4.51-4.61 95%CI) deaths per 1000 patient days with a rate ratio after adjustment for year of 0.88 (0.86-0.89 95%CI) P < 0.001. In a multivariable logistic regression, mortality was associated with a MET team being active in the hospital for more than 2 years. The odds ratio for mortality in hospitals where a MET system had been in place for greater than 4 years duration was 0.90 (0.88-0.92). Mortality during the first 2 years of a MET system being in place was not statistically different from pre-MET periods. Conclusions Utilising routinely collected administrative data we demonstrated that the presence of a hospital MET system for greater than 2 years was associated with an independent reduction in hospital mortality across a major metropolitan health network. Mortality benefits after the introduction of a MET system take time to become apparent.
    Full-text · Article · Oct 2012
    • However, great heterogeneity of systems exists concerning the used track and trigger method, the composition of the rapid response team, the rapid response team escalation protocol, and rapid response team interventions. Furthermore, although the usefulness of an RRS appears to be self-evident, research into its effectiveness has yielded equivocal results67891011. Despite the presence of an RRS, late rapid response team activation regularly occurs12131415 , suggesting suboptimal adherence of the ward staff with the RRS system.
    [Show abstract] [Hide abstract] ABSTRACT: Rapid response systems (RRSs) are considered an important tool for improving patient safety. We studied the effect of an RRS on the incidence of cardiac arrests and unexpected deaths. Retrospective before- after study in a university medical centre. We included 1376 surgical patients before (period 1) and 2410 patients after introduction of the RRS (period 2). Outcome measures were corrected for the baseline covariates age, gender and ASA. The number of patients who experienced a cardiac arrest and/or who died unexpectedly decreased non significantly from 0.50% (7/1376) in period 1 to 0.25% (6/2410) in period 2 (odds ratio (OR) 0.43, CI 0.14-1.30). The individual number of cardiac arrests decreased non-significantly from 0.29% (4/1367) to 0.12% (3/2410) (OR 0.38, CI 0.09-1.73) and the number of unexpected deaths decreased non-significantly from 0.36% (5/1376) to 0.17% (4/2410) (OR 0.42, CI 0.11-1.59). In contrast, the number of unplanned ICU admissions increased from 2.47% (34/1376) in period 1 to 4.15% (100/2400) in period 2 (OR 1.66, CI 1.07-2.55). Median APACHE ll score at unplanned ICU admissions was 16 in period 1 versus 16 in period 2 (NS). Adherence to RRS procedures. Observed abnormal early warning scores ≤72 h preceding a cardiac arrest, unexpected death or an unplanned ICU admission increased from 65% (24/37 events) in period 1 to 91% (91/101 events) in period 2 (p < 0.001). Related ward physician interventions increased from 38% (9/24 events) to 89% (81/91 events) (p < 0.001). In period 2, ward physicians activated the medical emergency team in 65% of the events (59/91), although in 16% (15/91 events) activation was delayed for one or two days. The overall medical emergency team dose was 56/1000 admissions. Introduction of an RRS resulted in a 50% reduction in cardiac arrest rates and/or unexpected death. However, this decrease was not statistically significant partly due to the low base-line incidence. Moreover, delayed activation due to the two-tiered medical emergency team activation procedure and suboptimal adherence of the ward staff to the RRS procedures may have further abated the positive results.
    Full-text · Article · Jun 2012
    • As a process of care, the MET provides a means of making available ICU level care for unstable patients on all units throughout the hospital. The benefits of the MET derive from early recognition of patient deterioration, rapid response to changing inpatient status, and aggressive intervention to stabilize and rescue patients in order to prevent cardiopulmonary arrest (DeVita, et al., 2004; DeVita et al., 2006; Bellomo et al., 2004; Schmid, et al., 2007; Peberdy et al., 2007; Winters et al., 2007; Sebat et al., 2007; Ranji, et al., 2007; Galhotra, et al., 2007). unexpected deaths, and unplanned ICU admissions (Peberdy et al., 2007; Hillman et al., 2005; Winters, et al., 2006; Subbe, Williams, Fligelstone & Gemmell, 2005; Buist, Harrison, Abaloz & Van Dyke, 2007; Jolley, Bendyk, Holaday, Lombardozzi, & Harmon, 2007; Chan, Renuks, Brahmajee, Berg, & Sasson, 2008).
    [Show abstract] [Hide abstract] ABSTRACT: Purpose: The purpose of this pilot study is to describe characteristics of hospitalized patients who experience a Medical Emergency Team activation in the Radiology Department (RD-MET) and their outcomes post RD-MET intervention. In addition, the study will compare the incidence of RD-MET to the incidence of MET activations occurring on general in-patient units of the same facility for the same time period.Theoretical Rationale: Failure-to-Rescue (FTR) or death of hospitalized patients following a complication is one hospital quality measure. Patient, nursing and organizational characteristics contribute to FTR. Patient instability always precedes FTR. Medical Emergency Teams (MET) activation can deploy critical-care providers to the unstable patient's bedside, but nurses' ability to detect instability and call the MET early improves outcomes. Particularly at risk for undetected instability are patients who require transport to the Radiology Department (RD). Little is known about the characteristics of patients who require MET activation while in the RD (RD-MET), factors associated with their nursing surveillance, and outcomes. Once known, modifiable characteristics could undergo intervention to improve outcomes.Methods: A descriptive comparative survey design pilot study will be conducted. We will 1) describe RD-MET patients in regard to their a) non-modifiable patient characteristics, b) modifiable patient characteristics, and c) modifiable surveillance characteristics, 2) determine differences in the characteristic profiles of patients who have poor outcomes post RD-MET (FTR [death before discharge], require higher care level post-MET) and patients with good outcomes (survive to discharge, return to same care level post-MET) and 3) compare the incidence of RD-MET to MET incidence on in-patient units in the same interval. Data will be obtained from the electronic medical record, Medical Emergency Database, and RD documentation. Analyses: The detailed descriptive analyses of the data, using standard descriptive summaries (e.g., means, standard deviation, percentiles, ranges and frequencies) and graphical techniques (e.g., histograms, scatter plots) will be used to describe the non-modifiable patient characteristics, modifiable patient characteristics and modifiable surveillance characteristics.The Student t-test, nonparametric test and/or chi-square test will be used as appropriate to compare the patient characteristics profile between two groups (patients with good outcome and patients with bad outcome). Logistic regression will be performed to identify predictors of patient at risk for poor outcomes post MET activation in the RD. The ratio of occurrence in the RD will be measured by the number of MET activations in the RD during the study period per 1000 RD in-patient admissions (procedures) during the study period. The ratio of occurrence in the general hospital population will be measured as the number of MET activations per 1000 hospital admissions. The chi-square test will be used to compare the difference of incidence rate between RD and general in-patient unit.Implications: Prospective identification of risk for instability and poor RD-MET outcomes will lead to development of targeted surveillance and educational interventions to improve safety and decrease FTR in the RD.
    Article · Aug 2011 · Annals of Intensive Care
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September 2009 · Joint Commission journal on quality and patient safety / Joint Commission Resources
December 2006 · Acta Anaesthesiologica Scandinavica · Impact Factor: 2.32
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