Research
Rates of in-hospital arrests, deaths and intensive care admissions:
the effect of a medical emergency team
Peter J Bristow, Ken M Hillman, Tien Chey, Kathy Daffurn, Theresa C
Jacques, Sandra L Norman, Gillian F Bishop and E Grant Simmons
MJA 2000; 173: 236-240
Abstract -
Methods -
Results -
Discussion -
Conclusion -
Acknowledgements
Authors' details
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More articles on Emergency medicine
Abstract |
Objectives: To evaluate the effectiveness of a
medical emergency team (MET) in reducing the rates of selected
adverse events. Design: Cohort comparison study after casemix
adjustment. Patients and setting: All adult (≥ 14 years) patients
admitted to three Australian public hospitals from 8 July to 31
December 1996. Intervention studied: At Hospital 1, a medical emergency
team (MET) could be called for abnormal physiological parameters or
staff concern. Hospitals 2 and 3 had conventional cardiac arrest
teams. Main outcome measures: Casemix-adjusted rates of
cardiac arrest, unanticipated admission to intensive care unit
(ICU), death, and the subgroup of deaths where there was no
pre-existing "do not resuscitate" (DNR) order documented. Results: There were 1510 adverse events identified among
50 942 admissions. The rate of unanticipated ICU admissions was less
at the intervention hospital in total (casemix-adjusted odds
ratios: Hospital 1, 1.00; Hospital 2, 1.59 [95% CI, 1.24-2.04];
Hospital 3, 1.73 [95% CI, 1.37-2.16]). There was no significant
difference in the rates of cardiac arrest or total deaths between the
three hospitals. However, one of the hospitals with a conventional
cardiac arrest team had a higher death rate among patients without a
DNR order. Conclusions: The MET hospital had fewer unanticipated
ICU/HDU admissions, with no increase in in-hospital arrest rate or
total death rate. The non-DNR deaths were lower compared with one of
the other hospitals; however, we did not adjust for DNR practices. We
suggest that the MET concept is worthy of further study.
|
| |
Certain in-hospital deaths may be preventable.1-3 Nearly 85% of
hospital inpatients who suffer a cardiorespiratory arrest have
documented observations of deterioration in the eight hours before
the arrest.4,5 Recent studies have
demonstrated suboptimal care of hospitalised patients before their
admission to the intensive care unit (ICU), and that these patients
have a higher mortality.6-8 The authors of these
studies urge earlier intervention.
One approach to providing an early response to at-risk inpatients
throughout the hospital is the medical emergency team
(MET),9,10 which replaces the
conventional cardiac arrest team. The MET responds to specific
clinical criteria (such as bradycardia, tachycardia, hypotension
and threatened airway) in order to prevent further deterioration.
Others have advocated similar approaches to reduce unexpected
hospital deaths and morbidity.11,12
In our study, selected outcomes in a hospital with a MET were compared
with outcomes in two hospitals with conventional cardiac arrest
teams. These outcomes were rates of cardiorespiratory arrest,
unanticipated admission to the ICU or high dependency unit (HDU),
death, and deaths where there was no prexisting "do not resuscitate"
(DNR) order.
|
| Methods |
This study was a prospective cohort comparison of three hospitals,
testing whether an early intervention team, the MET, was associated
with fewer adverse events among inpatients, after adjusting for
casemix differences.
The study was approved by the ethics committees of each participating
hospital and the University of New South Wales.
|
Setting |
The hospitals were similarly sized Australian public hospitals,
with bed capacities in the range 380-530.
At Hospital 1, the cardiac arrest team was replaced by a MET, which any
staff member could call for immediate assistance. Staff could summon
the MET if concerned about a patient's condition or if the patient's
vital signs exceeded certain levels (Box 1).10 An education program
explained the MET's role to all new staff. However, calling the MET
when criteria were met was not compulsory. The MET consisted of the ICU
registrar and senior nurse, and medical registrar.
At Hospitals 2 and 3, the arrest team was paged by nursing or medical
staff for cardiorespiratory arrest. The arrest team consisted of the
ICU registrar, medical registrar, and ICU or coronary care nurse.
|
Data collection | |
We identified all cardiorespiratory arrest calls, deaths, and
ICU/HDU admissions at the three hospitals among patients 14 years and
over in hospital during the period from 8 July to 31 December 1996.
These were designated "events".
Soon after an event, the patient's medical record was reviewed for
demographic information. In addition, for cardiac arrests and
deaths, documentation of a DNR order before arrest or death was
recorded. Each ICU/HDU admission was classified as to whether the
patient was admitted to ICU/HDU for the same reason he or she was
admitted to hospital. If not, the ICU/HDU admission was defined as
unanticipated. For example, a patient admitted to ICU with
respiratory distress after a cholecystectomy would be
unanticipated.
Data were collected by three critical care nurses (one at each
hospital) trained in the use of a specifically designed form,
which was piloted for two weeks before data collection. The nurses
were familiar with the medical record at the three hospitals. Where
the information contained in the history was unclear, the attending
staff were asked for clarification.
Data were entered into a database.13 Data cleaning was
performed and any anomalies checked by reference to the datasheet or
medical record. Data were then exported to SAS14 for analysis.
|
Outcome measures | |
The primary endpoints were the casemix-adjusted rates of ICU/HDU
unanticipated admission, cardiac arrest, death, and deaths without
a prior DNR order. These were called "total event rates".
For any patient, one event could result in additional events (eg,
cardiac arrest followed by unanticipated ICU admission and then
death). The "index event" was defined as the event the data collectors
considered the first in a series of events. The casemix-adjusted
rates of index events were compared between the three hospitals as
secondary endpoints.
To ensure that any decline in the rate of unanticipated admissions was
not caused by excess anticipated admissions, the casemix-adjusted
rate of all ICU/HDU admissions was calculated as a control measure.
|
Casemix adjustment | |
Demographic and diagnostic data on the patient population (aged
≥ 14 years) admitted to the hospitals for the period were
obtained. The study data were merged by medical record number and date
of admission with the complete inpatient statistical data to create a
dataset with 50 942 records. This enabled us to identify the
admissions for which an event occurred and analyse the data at the
patient level.
Using simple and multiple logistic regression, we modelled the
probability of an event occurring during hospitalisation, adjusted
for patient demographics and diagnostic characteristics. Models
were derived independently for each total event and for each index
event. Parameters were added to the model in a stepwise fashion.
To prevent overparameterising the models (where minor,
non-significant differences cumulatively hide true differences),
when C (equivalent to the area under the receiver operator
characteristics curve) reached 0.85 no further parameters were
added to the model. This always occurred with fewer than six
parameters used. Demographic and casemix independent variables
that were tested for use in the models are detailed in Box 2.
The models developed used groups of diagnostic categories based on
ICD-9-CM codes using the principal diagnosis and the stay diagnosis
only.15 The ICD-9-CM code
groupings used are available from the principal author (PJB). The
performance of the models was assessed by Hosmer-Lemeshow
goodness-of-fit tests.16
The risk of an event occurring in a hospital compared with the MET
hospital was presented as an adjusted odds ratio with 95% confidence
intervals. A level of significance of 5% was used in all statistical
tests.
|
| Results |
|
Hospital demographics | |
Characteristics of all patients (aged ≥ 14 years) admitted to the
three hospitals during the study period are shown in Box 3. Hospital 2
had fewer admissions than the other hospitals. Hospital 1 had a higher
proportion of male patients admitted, and a lower proportion of
admissions from the emergency department (ED). This hospital also
had a younger patient population, which is reflected in differences
in casemix: Hospital 1 had lower proportions of patients with stroke,
severe acute heart disease, gastrointestinal disease, and
musculoskeletal and connective tissue diseases, but higher
proportions with severe trauma and follow-up care without acute
diagnosis (eg, dialysis).
The rates of DNR orders in dying patients were 77% in Hospital 1, and 64%
and 70% in Hospitals 2 and 3, respectively (P = 0.006).
|
Prevalence and characteristics of events | |
A total of 1510 adverse events (unanticipated ICU/HDU admissions,
arrest calls, and deaths) were recorded during the study period for
the three hospitals. There were 1100 index events. The prevalence and
characteristics of events are summarised in Box 4 for total event
rates and Box 5 for index rates.
There was a significantly reduced rate of unanticipated ICU/HDU
admissions at the MET intervention hospital after casemix
adjustment (for both the total event rate and the index rate). After
adjustment, Hospital 2 had 49 (95% CI, 20-87) more unanticipated
ICU/HDU admissions over a six-month period, and Hospital 3 had 92 (95%
CI, 47-146) more, compared with Hospital 1. The rate of all ICU/HDU
admissions was lower at Hospital 1 than at one control hospital, and
trended to lower than at the other.
There was no statistically significant difference in cardiac arrest
rate or death rate after casemix adjustment. The casemix-adjusted
death rate in patients where there was no documentation found of a DNR
order was significantly higher at Hospital 2, translating to 27 (95%
CI, 7-53) extra non-DNR deaths.
|
Model performance | |
Box 6 presents an example of the odds ratios after addition of the most
significant variables in the multiple logistic regression models
derived from the data for the total arrest data. It shows the C
statistic as each variable was added to the cardiac arrest model.
In the cardiac arrest models, the variables that were adjusted for
were emergency admissions, age over 74, heart disease, lung disease
and infectious disease as diagnoses.
In the total death models, the terms adjusted for were emergency
admissions, age over 74, single day stay emergency admissions, and
cancer and infectious disease. The same demographic variables were
used in the index death model, although in the total non-DNR death
model both age ≥ 75 and age 65-74 were used in the model.
In the unanticipated ICU/HDU models, the variables adjusted for were
single day admission, emergency admission, and cancer and
gastrointestinal disease diagnoses. The total unanticipated
ICU/HDU model also included the variables stroke and infectious
disease as diagnoses.
All models satisfied the Hosmer- Lemeshow test.16 |
| Discussion |
|
Rationale for our methods | |
In this study, we attempted to determine if the MET system was
associated with a reduced rate of adverse events among inpatients. To
do this, we compared the rates of adverse events between three
hospitals after casemix adjustment.17-19 This method was chosen
as we decided that randomisation at the patient level was
impractical. A random pattern of response to calls would probably
have dissuaded staff caring for patients from calling the MET.
Randomisation by ward would have risked contamination bias and
engendered problems of casemix, as wards differ in the nature of their
patients. Historical comparison at Hospital 1 between a period
before and a period after introduction of the MET team was
impractical, as the team had been trialled and evolved for six years
before the study.
The models we used appear to adequately fit the data, according to the
Hosmer-Lemeshow goodness-of-fit tests, and with good model
performance measured by C statistics. However, multiple methods of
casemix adjustment are possible, and these may give divergent
results. This is a limitation of casemix adjustment
methodology.20
To avoid concealing real differences by excessive modelling,
parameters were added stepwise by multivariate analysis until the
models reasonably represented the data. The terms which appeared in
the final models were usually those that could be expected to
influence the outcomes. Thus, advanced age and emergency admissions
were factors in the death and cardiac arrest models. The cardiac
arrest models also included the terms for heart disease, lung and
infectious disease. Infectious disease was an unexpected variable
and was also significant in the total death model.
Other differences (such as levels of hospital funding, ICU/HDU
capacity, the number and seniority of medical and nursing staff, and
the level of out-of-hours cover) may also have contributed to the
results. However, to adjust for these would have been more difficult
than for the variables studied, which relate directly to the patients
at risk and are easily and reliably obtained.
|
Explanation of findings | |
After casemix adjustment, we found reduced rates of both total and
index unanticipated ICU/HDU admissions at the MET intervention
hospital. There were no differences in the rates of cardiac arrests or
deaths. However, at one hospital without the MET, there was a higher
rate of non-DNR deaths, the subset of deaths most likely potentially
preventable by a MET.
The reduction in unanticipated ICU/HDU admissions that was seen in
the MET intervention hospital could result from many factors. One
possible explanation is that the MET was effective and able to
intervene on the wards and prevent further deterioration. Another
possible reason may relate to differences in referral practices:
perhaps the presence of MET backup engendered a feeling that ICU/HDU
referral was not needed. Misclassification of ICU/HDU admissions as
anticipated rather than unanticipated was excluded as an
explanation of the difference by the finding that the rate of all
ICU/HDU admissions was lower at the intervention hospital than one
control hospital and trended to lower at the other.
The lack of efficacy of the MET to prevent cardiorespiratory arrest
and modify death rate may be related to lack of sensitivity of calling
criteria, or because pathophysiological processes (eg, shock)
become irreversible. Another possible explanation for the lack of
effect of the MET on event rates is underutilisation. Based on a
previous study,21 up to 706 MET calls could
have been expected, and yet only 150 were made. Frequent education is
probably also required to ensure the appropriate calling of a
MET.22 No special efforts
regarding staff education in the study period were made. The clinical
staff of the hospital were unaware of the study, to negate any possible
Hawthorne effect.23
Finally, organisational changes such as introduction of a MET are
difficult to implement in hospitals.24,25 Our results probably
reflect the effectiveness of the implementation of the MET system as
much as the concept of early intervention.
|
Future directions | |
Our study cannot answer definitively if the MET was the cause of the
benefit we observed; it does show that the MET concept is worthy of
further study. The study could be likened to a Phase II trial of a drug
comparing three hospitals at one point in time. Further studies of the
MET system's efficacy are needed, such as a before-after comparison
in several hospitals, or a comparison of a larger sample size of
intervention and control hospitals. Such studies should be repeated
some time after the intervention.
Is the benefit observed useful and worth pursuing? If it is possible to
reduce unanticipated ICU admissions without increased mortality
this may result in cost saving. It has been estimated that the US spends
about 1% of its gross national product on intensive care
facilities.26 However, any savings in
intensive care would be offset by the cost of establishing and
maintaining a MET. The MET may also have unexpected costs and benefits
on processes such as staff satisfaction with care provided. Again,
these need to be quantified.
|
| Conclusion |
In this study, we found that fewer patients were unexpectedly
admitted to ICU or HDU at a hospital with the MET system, and this
hospital had fewer non-DNR deaths than one of the other hospitals.
There was no significant change in the casemix-adjusted rate of
arrests or total deaths. This may be an advantage of an early response
team, which could have important implications for patient care in
hospitals.
We believe that the MET concept should be studied further in a larger
sample of institutions.
|
Acknowledgements | |
Funding for the study was provided by a Commonwealth Department of
Health and Family Services Research and Development Grant (HS338).
Associate Professor Robert Gibberd assisted with the statistical
analysis.
|
References | |
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Hillman KM, Bishop G, Lee A, et al. Identifying the general ward
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(Received 15 Feb, accepted 10 Jul, 2000)
|
Authors' details | |
Liverpool Hospital, Sydney, NSW.
Peter J Bristow, MB BS, FRACP, Staff Specialist, Department
of Intensive Care; Ken M Hillman, MB BS, FFICANZCA,
Professor, University of New South Wales Clinical School; Kathy
Daffurn, RN, MAppSc, Co-Director, Division of Critical Care;
Sandra L Norman, MN, BAppSc, Clinical Nurse Specialist,
Department of Intensive Care; Gillian F Bishop, MB ChB,
FFICANZCA, Director, Department of Intensive Care; Tien Chey,
BSc, MAppStat, Statistician, Epidemiology Unit.
Department of Intensive Care, St George Hospital, Sydney, NSW.
Theresa C Jacques, MB BS, FFICANZCA, Director.
Department of Intensive Care, Illawarra Regional Hospital,
Wollongong, NSW.
E Grant Simmons, MB BS, FFICANZCA, Director.
Reprints will not be available from the authors. Correspondence: Dr P
J Bristow, Intensive Care Offices, Alfred Hospital, Commercial
Road, Prahran, VIC 3181. p.bristowATalfred.org.au
Make a
comment
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| |
1: Criteria for calling the medical emergency team10
Cardiorespiratory arrest
Threatened airway
Respiratory rate
≤5 breaths per minute
≥36 breaths per minute
Pulse rate
≤40 beats per minute
≥140 beats per minute
Systolic blood pressure ≤90mmHg
Repeated or prolonged seizures
Fall in Glasgow Coma Score >2 points
Concern about patient status not detailed above
|
|
| | Back to text | | | |
2: Variables available for calculation of the various models
Sex (binary)
Seven age categories (14-24, 25-34, 35-44, 45-54, 55-64, 65-74, ≥75)
Same-day admission (binary) (ie, admission and discharge occurred on the same calendar day)
Referral from emergency department (binary)
Australian born (binary)
Casemix categories (16 indicator variables, available from author)
Hospital (three indicator variables)
|
| | Back to text | | | |
3: Characteristics of admissions
at the three study hospitals from 8 July to 31 December 1996 |
|
Hospital*
|
|
|
|
|
Characteristic |
1
|
2
|
3
|
Test of Independence
|
|
Number of admissions |
18338
|
13059
|
19545
|
|
Male admissions |
44.9%
|
42.9%
|
42.8%
|
χ2=21.06
(2 df)
|
Same-day admissions |
47.7%
|
47.0%
|
46.7%
|
χ2=4.35
(2 df)
|
Admission via emergency department |
29.6%
|
36.0%
|
35.1%
|
χ2=186.53
(2 df)
|
Australian born
Country of birth not stated |
49.3%
6.8%
|
67.2%
0.5%
|
50.2%
23.2%
|
Not tested
|
Age distribution
14-24
25-34
35-44
45-54
55-64
65-74
≥75
|
9.7%
14.9%
14.3%
12.4%
18.1%
20.5%
10.0%
|
8.6%
15.2%
9.6%
9.8%
18.5%
22.2%
16.0%
|
7.8%
13.1%
11.1%
10.4%
14.4%
22.1%
21.1%
|
χ2=1146
(12 df)
|
Diagnostic category
1. Cancer
2. Stroke
3. Severe acute heart disease
4. Metabolic and electrolyte disorders
5. Pulmonary disease
6. Ophthalmologic disease
7. Low risk heart disease
8. Gastrointestinal disease
9. Urologic disease
10. Musculoskeletal, connective tissue disease
11. Infectious diseases
12. Symptoms and ill-defined conditions
13. Severe trauma
14. Follow-up care without acute diagnosis
15. Pregnancy, childbirth, puerperium
16. Others |
4.4%
1.4%
2.6%
1.3%
3.3%
2.0%
3.5%
6.4%
1.9%
1.8%
1.0%
3.2%
2.9%
34.0%
10.8%
19.5%
|
4.1%
1.8%
3.0%
1.6%
2.9%
1.2%
2.9%
8.9%
1.7%
3.4%
0.7%
3.0%
2.1%
30.1%
14.1%
18.5%
|
5.3%
1.6%
3.2%
1.1%
4.2%
0.5%
4.6%
10.2%
1.9%
3.2%
1.0%
6.7%
1.8%
23.4%
11.0%
20.5%
|
χ2=1562
(50 df)
|
|
*Hospital 1 had the medical emergency team.
Test for any difference between the three hospitals. P |
| | Back to text | | | |
4: Comparisons
of total event rates by hospitals |
Event |
n |
Crude rates/10000 |
Unadjusted ORs |
Adjusted ORs* |
|
Cardiac arrest |
Hospital 1
Hospital 2
Hospital 3 |
69
66
99 |
38
51
51 |
1.00
1.34 (0.96-1.89)
1.35 (0.99-1.83) |
1.00
1.14 (0.81-1.61)
1.00 (0.73-1.37) |
Death |
Hospital 1
Hospital 2
Hospital 3 |
243
240
295 |
133
184
151 |
1.00
1.39 (1.16-1.67)
1.14 (0.96-1.35) |
1.00
1.08 (0.89-1.30)
0.83 (0.70-1.00) |
Non-DNR death |
Hospital 1
Hospital 2
Hospital 3 |
55
86
88 |
30
66
45 |
1.00
2.20 (1.57-3.09)
1.50 (1.07-2.11) |
1.00
1.68 (1.19-2.36)
0.94 (0.67-1.33) |
Unanticipated ICU/HDU admission |
Hospital 1
Hospital 2
Hospital 3 |
118
146
234 |
64
112
120 |
1.00
1.73 (1.36-2.21)
1.86 (1.49-2.32) |
1.00
1.59 (1.24-2.04)
1.73 (1.37-2.16) |
|
*Odds ratios (ORs) adjusted for patient characteristics
and diagnostic categories. Hospital 1 (which has the medical emergency team)
is the reference for the ORs. For shaded ORs, 95% CIs do not cross 1.0.
DNR="do not resuscitate" order documented. ICU=intensive care unit. HDU=high
dependency unit. |
| | Back to text | | | |
5: Comparisons of index event
rates by hospitals |
Event |
n |
Crude rates/10000 |
Unadjusted ORs |
Adjusted ORs* |
|
Cardiac arrest |
Hospital 1
Hospital 2
Hospital 3 |
60
63
84 |
33
48
43 |
1.00
1.48 (1.04-2.10)
1.31 (0.94-1.83) |
1.00
1.24 (0.87-1.78)
0.96 (0.69-1.35) |
Death |
Hospital 1
Hospital 2
Hospital 3 |
119
139
191 |
65
106
98 |
1.00
1.65 (1.29-2.11)
1.51 (1.20-1.90) |
1.00
1.24 (0.97-1.60)
1.05 (0.82-1.33) |
Unanticipated ICU/HDU admission |
Hospital 1
Hospital 2
Hospital 3 |
82
140
222 |
45
107
114 |
1.00
2.41 (1.83-3.17)
2.56 (1.98-3.30) |
1.00
2.17 (1.65-2.87)
2.35 (1.82-3.04) |
|
*Odds ratios (ORs) adjusted for
patient characteristics and diagnostic categories. Hospital 1 (which has
the medical emergency team) is the reference for the ORs. For shaded ORs,
95% CIs do not cross 1.0. ICU=intensive care unit. HDU=high dependency unit.
|
| | Back to text | | | |
6: An example of how the odds
ratios and C statistic changed as variables were added stepwise to the model
for total cardiac arrests |
|
Odds ratio* (95% CI) |
|
|
|
|
|
Hospital 2 |
Hospital 3 |
C statistic |
|
Crude odds ratio
Admission via emergency department
Age ≥75
years Severe acute heart disease
Low risk heart disease
Infectious disease
Pulmonary disease
|
1.34 (0.96-1.89)
1.16 (0.83-1.63)
1.06 (0.75-1.49)
1.06 (0.75-1.49)
1.07 (0.76-1.50)
1.09 (0.77-1.53)
1.14 (0.81-1.61) |
1.35 (0.99-1.83)
1.19 (0.87-1.62)
0.98 (0.72-1.34)
0.99 (0.72-1.35)
0.99 (0.72-1.35)
1.00 (0.73-1.37)
1.00 (0.73-1.37) |
0.533
0.755
0.798
0.809
0.826
0.833
0.850 |
|
*Hospital 1 is the
reference for the odds ratios. |
| | Back to text |
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