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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 and Gillian F Bishop
Med J Aust 2000; 173 (5): 236-240.
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
- - 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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  2. Leape LL, Brennan TA, Laird N, et al. Nature of adverse events in hospitalised patients: results of the Harvard Medical Practice Study II. N Engl J Med 1991; 324: 377-384.
  3. Wilson R McL, Runciman WB, Gibberd RW, et al. The Quality in Australian Health Care Study. Med J Aust 1995; 163: 458-471.
  4. Schein RMH, Hazday N, Pena M, et al. Clinical antecedents to inhospital cardiopulmonary arrest. Chest 1990; 98: 1388-1392.
  5. Franklin C, Mathew J. Developing strategies to prevent inhospital cardiac arrest: analyzing responses of physicians and nurses in the hours before the event. Crit Care Med 1994; 22: 246-247.
  6. Lundberg JS, Perl TM, Wiblen T, et al. Septic shock: an analysis of outcomes for patients with onset on hospital wards versus intensive care units. Crit Care Med 1998; 26: 1020-1024.
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  8. McQuillan P, Pilkington S, Allan A, et al. Confidential inquiry into quality of care before admission to intensive care. BMJ 1998; 316: 1853-1858.
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  10. Hourihan F, Bishop G, Hillman KM, et al. The medical emergency team: a new strategy to identify and intervene in high risk patients. Clin Intensive Care 1995; 6: 269-272.
  11. Frank ED. A shock team in a general hospital. Anesth Analg 1967; 46: 740-745.
  12. Goldhill DR. Introducing the postoperative care team [editorial]. BMJ 1997; 314: 389.
  13. Microsoft Access [computer program]. Version 2.0. Redmond, Wa: Microsoft, 1994.
  14. SAS for Windows [computer program]. Version 6.12. Cary, NC: SAS Institute Inc, 1997.
  15. Stremple JF, Bross DS, Davis CL, McDonald GO. Comparison of postoperative mortality and morbidity in VA and nonfederal hospitals. J Surg Res 1994; 56: 405-416.
  16. Hosmer DW, Lemeshow S. Applied logistic regression. New York: John Wiley and Sons, 1989.
  17. Iezzoni LI. The risks of risk adjustment. JAMA 1997; 278: 1600-1607.
  18. Dubois RW, Rogers WH, Moxley JH, et al. Hospital inpatient mortality. Is it a predictor of quality? N Engl J Med 1987; 317: 1674-1680.
  19. Green J, Passman LJ, Wintfield N. Analyzing hospital mortality. The consequences of diversity in patient mix. JAMA 1991; 265: 1849-1853.
  20. Iezzoni LI, Shwartz M, Ash A, et al. Severity measurement methods and judging hospital death rates for pneumonia. Med Care 1996; 34: 11-28.
  21. Hillman KM, Bishop G, Lee A, et al. Identifying the general ward patient at high risk of cardiac arrest. Clin Int Care 1996; 7: 242-243.
  22. Daffurn KD, Lee A, Hillman KM, et al. Do nurses know when to summon emergency assistance? Intensive Crit Care Nurs 1994; 10: 115-120.
  23. Grufferman S. Complexity and the Hawthorne effect in community trials [editorial]. Epidemiology 1999; 10: 209-210.
  24. Garside P. Organisational context for quality: lessons from the fields of organisational development and change management. Qual Health Care 1998; 7 Suppl: S8-15.
  25. Koeck C. Time for organisational development in healthcare organisations [editorial]. BMJ 1998; 317: 1267-1268.
  26. Cerra FB. Healthcare reform: the role of coordinated critical care. Crit Care Med 1993; 21: 457-464.

(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


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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

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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)

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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
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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.
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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.
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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.
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Peter J Bristow
Ken M Hillman
Tien Chey
Kathy Daffurn
Theresa C Jacques
Sandra L Norman
Gillian F Bishop