Intensive Care Medicine

, 35:623

End-of-life practices in 282 intensive care units: data from the SAPS 3 database

Authors

    • Service de Réanimation Médicale, Hôpital Saint-Louis et Université Paris 7, Assistance Publique, Hôpitaux de Paris
  • Barbara Metnitz
    • Department of Medical StatisticsUniversity of Vienna
  • Charles L. Sprung
    • Department of Anesthesiology and Critical Care MedicineHadassah Hebrew University Medical Center
  • Jean-François Timsit
    • Medical ICUHôpital A. Michallon
    • Team 11, Outcome of Cancer and Critical Illnesses. UJF-INSERM U823Centre de Recherche Institut Albert Bonniot
  • François Lemaire
    • Assistance Publique Hop de Paris, Hopital H. MondorParis 12 University
  • Peter Bauer
    • Department of Medical StatisticsUniversity of Vienna
  • Benoît Schlemmer
    • Service de Réanimation Médicale, Hôpital Saint-Louis et Université Paris 7, Assistance Publique, Hôpitaux de Paris
  • Rui Moreno
    • Unidade de Cuidados Intensivos Polivalente, Hospital de St. António dos CapuchosCentro Hospitalar de Lisboa Central E.P.E
  • Philipp Metnitz
    • Department of Anesthesiology and General Intensive CareUniversity Hospital of Vienna
  • on behalf of the SAPS 3 investigators
Original

DOI: 10.1007/s00134-008-1310-6

Cite this article as:
Azoulay, É., Metnitz, B., Sprung, C.L. et al. Intensive Care Med (2009) 35: 623. doi:10.1007/s00134-008-1310-6

Abstract

Objective

To report incidence and characteristics of decisions to forgo life-sustaining therapies (DFLSTs) in the 282 ICUs who contributed to the SAPS3 database.

Methods

We reviewed data on DFLSTs in 14,488 patients. Independent predictors of DFLSTs have been identified by stepwise logistic regression.

Results

DFLSTs occurred in 1,239 (8.6%) patients [677 (54.6%) withholding and 562 (45.4%) withdrawal decisions]. Hospital mortality was 21% (3,050/14,488); 36.2% (1,105) deaths occurred after DFLSTs. Across the participating ICUs, hospital mortality in patients with DFLSTs ranged from 80.3 to 95.4% and time from admission to decisions ranged from 2 to 4 days. Independent predictors of decisions to forgo LSTs included 13 variables associated with increased incidence of DFLSTs and 7 variables associated with decrease incidence of DFLST. Among hospital and ICU-related variables, a higher number of nurses per bed was associated with increased incidence of DFLST, while availability of an emergency department in the same hospital, presence of a full time ICU-specialist and doctors presence during nights and week-ends were associated with a decreased incidence of DFLST.

Conclusion

This large study identifies structural variables that are associated with substantial variations in the incidence and the characteristics of decisions to forgo life-sustaining therapies.

Keywords

Intensive careEnd-of-lifeSAPS 3Treatment withholdingTreatment withdrawal

Introduction

The development of life-sustaining treatments over the last half century has resulted in some patients remaining dependent on life support until death [1]. In these patients, continued curative treatment is rarely the best option [2]. Prolonging non-beneficial treatments robs patients of their dignity and families of an opportunity to prepare for bereavement [3]. Intensivists have, therefore, limited the use of life-sustaining treatments in these situations. Presently, most deaths in the intensive care unit (ICU) occur after decisions to forgo life-sustaining treatment (DFLSTs) [46], and the incidence of decisions to forgo LSTs may be increasing [7]. Making DFLST, which may consist in withholding and/or withdrawing life support, marks a shift from curative care to comfort care. Patients with DFLSTs are closely monitored and given palliative care as needed to ensure optimal comfort.

DFLSTs must be ethically appropriate. Perceptions of what is ethical, however, may vary. Substantial variability in the decision making process has been documented in previous research. These variations concern the incidence of decisions to forgo LSTs, the characteristics of patients who receive these and the procedure that is followed for making decisions to forgo LSTs [4, 5, 812]. Variations were also identified in responses to ethical scenarios [6, 13]. There is widespread agreement that there is no ethical difference between withholding and withdrawal [6], although withdrawal has been described as more difficult for intensivists, and is not used in some countries [10]. A single large ICU study recorded practices in 37 ICUs from 17 European countries [10]. The results show considerable variability in decisions to forgo LSTs in Europe. However, no large study across widely disparate geographic areas has been reported to date. The objectives of this study were to collect data on decisions to forgo LSTs in 14,488 patients admitted to 282 ICUs in seven different regions, and to identify factors associated with decisions to forgo LSTs in ICUs.

Patients and methods

We used the prospective international cohort created for the SAPS 3 study [14, 15]. The organization of the project, data collection, and study cohort have been described in detail elsewhere [14, 15]. This is a pre-planed analysis of the SAPS 3 study. Participating countries can be seen from Table E10 of the ESM of the SAPS 3 cohort description [14, 15]. Definitions of major therapeutic limitation during ICU stay were collected at ICU discharge. The questions asked to researchers evaluates if major therapeutic limitations were used during the ICU stay. Only those limitations expected to have had a relevant impact on patient’s morbidity and/or mortality were registered. Date where withholding or withdrawing therapy was first used was registered.

Database

The SAPS 3 hospital outcome cohort comprises 16,784 patients from 303 ICUs. We excluded the 2,296 patients for whom no data were available regarding decisions to forgo LSTs. This left 14,488 (86.3%) patients for the study. Among them, 1,239 (8.6%) received decisions to forgo LSTs.

Data quality

The database was evaluated for completeness and reliability. Independent raters rescored the data, and kappa coefficients and intra-class correlation coefficients were computed, as appropriate [16]. Data quality proved excellent, as shown by the detailed results reported in the ESM file of the SAPS 3 primary report [14, 15].

Statistical analysis

Statistical analysis was performed using the SAS system, version 9.1 (SAS Institute Inc., Cary, NC). All P values smaller than 0.05 were considered significant. Unless otherwise specified, results are expressed as median and quartiles. The chi-square test was used for categorical data. For continuous variables, ANOVA was used. Univariate logistic regression analyses were performed to identify patient- and ICU-related factors that might predict decisions to forgo LSTs. Factors that were significant in the univariate analyses were entered into a multivariate stepwise logistic regression analysis. These have been added in the footnote of Table 3. If regions were introduced into the model, significant differences could have been found. However, since participating countries were not representative samples in each country, we did not introduced this variable in the model.

Results

Figure 1 shows the patient flow chart of 14,488 patients admitted to 282 ICUs in seven geographic areas. ICU organizational and managerial characteristics were available for 271 ICUs (Table 1). Overall the median (quartile) number of admissions per year was 441.5 (267–723) patients per ICU.
https://static-content.springer.com/image/art%3A10.1007%2Fs00134-008-1310-6/MediaObjects/134_2008_1310_Fig1_HTML.gif
Fig. 1

Patient flow chart showing incidence and outcome of decisions to forgo life-sustaining therapies in patients included in the SAPS3 database

Table 1

ICU characteristics

 

ICUs

Patients

Univariate logistic regression analysis of explanatory variables

 

n

%

n

%

OR

95% CI

P value

Patient’s geographic location

  South Europe and Mediterranean countries

139

51.3

5,533

38.2

0.88

0.78–0.99

0.04

  Central and Western Europe

51

18.83

3,982

27.5

   

  Central and South America

30

11.1

1,678

11.6

0.78

0.64–0.954

0.01

  Australasia

14

5.2

1,546

10.7

1.21

1.01–1.444

0.03

  East Europe

26

9.6

767

5.3

   

  North America

5

1.8

662

4.7

   

  North Europe

6

2.2

320

2.2

3.48

2.67–4.54

<0.001

ICU characteristics (data are available for 271 ICUs)

  University hospitals

128

47.2

  

1.03

0.91–1.16

0.62

  Availability of an emergency department in the same hospital

243

89.7

  

0.68

0.56–0.84

<0.001

  Multidisciplinary meetings

125

46.1

  

1.06

0.94–1.20

0.30

  Clinical rounds performed by nurses and doctors together

166

61.2

  

1.15

1.01–1.32

0.02

  Availability of doctors in the ICU during weekdays

216

79.7

  

1.03

0.91–1.18

0.62

  Availability of doctors in the ICU during nights and weekends

209

77.1

  

0.80

0.71–0.92

0.001

  Number of staffed ICU beds (median, Q1–Q3)

9 (7–12)

   

0.98

0.97–0.98

<0.001

  Number of physicians per bed (median, Q1–Q3)

0.8 (0.5–1.2)

   

1.09

0.98–1.22

0.09

  Patient to nurse ratio (median, Q1–Q3)

3.0 (2.3–3.9)

   

1.03

1.02–1.05

<0.001

  Full time specialist (median, Q1–Q3)

4 (4–7)

   

0.97

0.96–0.98

<0.001

As shown in Fig. 1, decisions to forgo LSTs were implemented in 1,239 (8.6%) patients, including 677 (54.6%) patients who received withholding decisions and 562 (45.4%) who received withdrawal decisions. Hospital mortality was 21% (3,050/14,488). Among the deaths, 1,105 (36.2%) occurred after decisions to forgo LSTs. Hospital mortality was 86.4% in patients with withholding decisions and 92.5% in patients with withdrawal decisions.

Table 1 shows that decisions to forgo LSTs were more common in hospitals without emergency departments, in smaller ICUs, and in ICUs with lower nurse-to-patient ratios and larger numbers of physicians per ICU bed. DFLSTs were also more common when intensivists were present only during weekdays (compared to ICUs where intensivists were present during weekdays and weekends), when multidisciplinary meetings were held, and when nurses and intensivists performed clinical rounds together. Conversely, DFLSTs were less common in ICUs that had at least one full time intensivist and in those with intensivists available at night and over weekends.

Among patients who died, the proportion with DFLSTs ranged from 26 to 63.5% according to the region where the patient was admitted. Moreover, the proportion of hospital survivors with withdrawal decisions ranged from 2.4 to 30.3% and the proportion with withholding decisions ranged from 4 to 40% according to the region. Table E1 and figure E1 describes significant differences across the participating regions. As shown in Table 2, overall patients with DFLSTs were older, and a larger proportion of them exhibited severe co-morbid conditions and immunosuppression. Admission from a ward and life-sustaining treatment before ICU admission were more common among patients with than without decisions to forgo LSTs. SAPS 3 and SOFA scores at ICU admission were 45 (36–57) and 3 (2–5) in patients without decisions to forgo LSTs compared to 67 (58–77) and 6 (4–9) in patients with decisions to forgo LSTs (< 0.0001 for both scores), respectively.
Table 2

Patient characteristics

 

No DFLST

DFLST

P value

(n = 13,249)

(n = 1,239)

n

%

n

%

Patient’s age (median, quartiles)

63 (48–73)

 

70 (58–78)

 

<0.0001

Comorbidities

 Chronic pulmonary failure

509

3.8

79

6.4

<0.0001

 COPD

1,652

12.5

184

14.9

0.01

 Class IV NYHA chronic heart failure

137

1

28

2.3

0.0001

 Cirrhosis

379

2.9

65

5.2

<0.0001

 Chronic renal failure

730

5.5

113

9.1

<0.0001

Hematological cancer

174

1.3

59

4.8

<0.0001

 Cancer

370

2.8

61

4.9

<0.0001

 Cancer therapy (chemotherapy, immunosupression radiotherapy, steroids)

    

<0.0001

Intra-hospital location before ICU admission

 Operative room

5,561

42

188

15.2

<0.0001

 Emergency room

3,613

27.3

360

29.1

0.17

 Ward

2,178

16.4

413

33.3

<0.0001

 Intermediate care unit/high dependency unit

326

2.5

78

6.3

<0.0001

 Other

311

2.3

32

2.6

0.60

 Other ICU

449

3.4

66

5.3

0.0004

Surgical status

 No surgical procedure, miss

6,080

45.8

845

68.2

<0.0001

 Scheduled surgery

4,986

37.6

130

10.5

<0.0001

 Emergency surgery

2,183

16.5

264

21.3

<0.0001

Patient’s case-mix

 Scheduled surgery

4,986

37.6

130

10.5

<0.0001

 Emergency surgery

2,183

16.5

264

21.3

<0.0001

 Unplanned ICU admission

8,126

61.3

1,067

86.1

<0.0001

Use of major therapeutic options before ICU admission

 CPR

536

4

181

14.6

<0.0001

 Mechanical ventilation

5,872

44.3

631

50.9

<0.0001

 Vasoactive drugs

2,239

16.9

395

31.9

<0.0001

 Acute infection at ICU admission

    

<0.0001

 No infection

10,623

80.2

725

58.5

<0.0001

 Clinically improbable/colonization

197

1.5

22

1.8

0.42

 Clinically probable/documented

1,673

12.6

331

26.7

<0.0001

 Microbiologically documented

747

5.6

161

13

<0.0001

 Missing

9

0.1

0

 

0.35

Reasons for ICU admission

 Basic monitoring

4,553

34.4

149

12

<0.0001

 Neurological

  Coma

610

4.6

97

7.8

0.01

  Focal neurological deficit

245

1.8

35

2.8

<0.0001

  Intracranial mass effect

266

2.0

53

4.3

<0.0001

 Hepatic

  Liver failure

146

1.1

48

3.9

<0.0001

 Renal

  Acute renal failure

527

4

163

13.1

<0.0001

 Respiratory

  Acute lung injury and ARDS

679

5

179

14.4

<0.0001

  Acute respiratory failure in COPD patients

874

6.6

139

11.2

<0.0001

  Acute respiratory failure (not ALI or ARDS)

1,222

9.2

152

12.3

0.0005

 Cardiovascular

  Septic shock

389

2.9

145

11.7

<0.0001

  Non septic shock

188

1.4

42

3.4

<0.0001

  Chest pain with ECG changes

811

6.1

25

2

<0.0001

  Hypovolemic or hemorragic shock

454

3.4

64

5.2

0.0016

  Anaphylactic, mixed and undefined shock

234

1.8

70

5.7

<0.0001

 Digestive

  Severe pancreatitis

86

0.6

21

1.7

<0.0001

 Hematological

  Severe hemolysis

8

0.1

6

0.5

<0.0001

 Metabolic

  Hypo and hyperthermia, Hypo and hyperglycemia (includes diabetic comas), Other

262

2

17

1.4

0.1381

 Other

  Severe trauma patient

654

4.9

40

3.2

0.0071

Acute medical disease

 Cardiovascular

  Myocardial infarction

825

6.2

98

7.9

0.02

  Rhythm disturbances

608

4.6

47

3.8

0.19

 Digestive

  Esophageal or gastric varices rupture

71

0.5

13

1

0.02

  Cholecystitis

71

0.5

4

0.3

0.31

  Other (includes esophageal, gastric varices, Other)

741

5.6

102

8.2

0.0001

 Trauma

  Isolated brain trauma

209

1.6

36

2.9

0.0005

 Neurological

  Cerebrovascular accident

694

5.2

169

13.6

<0.0001

  Post-anoxic coma

55

0.4

41

3.3

<0.0001

  Intracranial tumor

380

2.9

19

1.5

0.006

 Other

465

3.5

23

1.9

0.002

ICU mortality

1,228

9.3

989

79.8

<0.0001

Hospital mortality

1,945

14.7

1105

89.2

<0.0001

Length of ICU stay (median, quartiles)

2 (1–5)

 

5 (2–13)

 

<0.0001

SOFA score (median, Q1–Q3)

3 (2–5)

 

6 (4–9)

 

<0.0001

SAPS 3 score (median, Q1–Q3)

45 (36–57)

 

67 (58–77)

 

<0.0001

Destination at ICU Discharge

 Unplanned discharge

1,293

9.7

54

4.4

<0.0001

 Home

303

2.3

21

1.7

0.17

 Other hospital

729

5.5

20

1.6

<0.0001

 Same hospital (ward)

9,057

68.3

178

14.4

<0.0001

 Same hospital (high dependency unit or other ICU)

2,070

15.6

24

1.9

<0.0001

Table 3 reports independent predictors of DFLST implementation identified by stepwise logistic regression. The following variables were associated with increased incidence of DFLST: higher age, hospital location before ICU admission, unplanned ICU admission, documented infection at admission, non-surgical status or emergency surgery, higher SOFA score at ICU admission, comorbidities such as NYHA-IV chronic heart failure, hematological malignancies and solid tumors; ICU admission for shock, ICU admission for neurological cause such as cerebrovascular accident, intracranial tumor or post-anoxic coma; pancreatitis and other digestive causes (excluding cholecistitis). The need for vasoactive agents and longer length of ICU stay were determinants of DFLST. Among ICU-related variables, a higher number of nurses per patient was associated with increased incidence of DFLST [odds ratio of 1.03 (1.005–1.058)/nurse per bed].
Table 3

Results of multivariate stepwise logistic regression on decision to forgo life-sustaining therapies

Variables

Odds ratio

95% CI

P

Patients age (years)

1.030

1.024–1.035

<0.0001

Intrahospital location before ICU admission

 Emergency room

1.674

1.234–2.270

0.0009

 Other ICU

2.457

1.881–3.208

<0.0001

Unplanned ICU admission

1.653

1.245–2.195

0.0005

Documented infection at admission

1.337

1.104–1.618

0.0030

Surgical status

 No

1.858

1.375–2.511

<0.0001

 Emergency surgery

1.708

1.258–2.318

0.0006

Length of stay in the ICU

1.016

1.010–1.021

<0.0001

Mechanical ventilation at ICU admission

1.391

1.148–1.685

0.0008

SOFA Score at ICU admission

1.160

1.131–1.190

<0.0001

Co-morbidities

 Chronic heart failure (NYHA IV)

2.054

1.242–3.397

0.0050

 Hematological malignancy

2.053

1.327–3.175

0.0012

 Cancer

3.203

2.216–4.629

<0.0001

Reason(s) for ICU admission

 Cardiovascular

  Hypovolemic or hemorrhagic shock

1.508

1.054–2.157

0.0245

  Septic shock

1.949

1.458–2.605

<0.0001

  Anaphylactic or mixed and undefined shocks

2.198

1.533–3.153

<0.0001

 Severe pancreatitis

2.595

1.389–4.850

0.0028

 Diabetic complications

0.480

0.243–0.946

0.0339

 Acute lung injury

0.734

0.571–0.944

0.0158

Use of major therapeutic option before ICU admission

 Vasoactive drugs

1.327

1.095–1.609

0.0040

Acute medical disease

 Neurologic

   

  Cerebrovascular accident

3.007

2.313–3.910

<0.0001

  Intracranial tumor

2.349

1.213–4.548

0.0113

  Post-anoxic coma

2.757

1.568–4.848

0.0004

 Cardiovascular

  Rhythm disturbances

0.630

0.425–0.932

0.0209

 Digestive

  Cholecistitis

0.232

0.064–0.848

0.0272

  Other (includes esophageal, gastric varices, other)

1.480

1.103–1.987

0.0091

  Isolated brain trauma

2.100

1.272–3.468

0.0037

 Other

0.515

0.295–0.902

0.0202

ICU-related variables

 Full time specialist

0.967

0.947–0.988

0.0025

 Nurse per bed

1.031

1.005–1.058

0.0173

 Emergency department available in the same hospital

0.658

0.499–0.869

0.0031

 Doctors presence during nights and weekends

0.725

0.596–0.881

0.0012

The following variables were entered in the multivariate stepwise logistic regression: ICU-related variables (staffed beds, full time specialists, number of nurse per bed, availability of an emergency department in hospital, daily clinical rounds and presence of doctors during night and weekends), patient’s age, location before ICU admission, intrahospital location before ICU admission, unplanned ICU admission, documented infection at ICU admission, surgical status, length of ICU stay, mechanical ventilation on admission day, area, SOFA score on admission day, comorbidities, reasons for admission, use of mechanical ventilation, vasopressors or CPR before ICU admission and acute medical disease

Seven variables were independently associated with a decreased incidence of DLST, namely, ICU admission for diabetic complication, rhythm disturbances, acute lung injury, or cholecistitis. Among ICU and hospital-related variables, availability of an emergency department in the same hospital, presence of a full time ICU-specialist and doctors presence during nights and week-ends were also associated with a decreased incidence of DFLST.

Discussion

Decisions to forgo LSTs in adult ICU patients have been a focus of increasing research over the last two decades. Descriptive studies were performed at local, national, [4, 5, 810] and multinational levels [5, 8, 10]. Using the SAPS 3 database of 14,488 patients in 282 ICUs, we found that in addition to previously identified predictors (case-mix, severity, co-morbidities and nature of the acute medical disease), organizational variables were independently associated with the incidence of DFLSTs. Namely, the number of nurses, availability of an ED in the same hospital, the presence of full time intensivist including doctors who make the rounds during the week-end days were independently associated with incidence of DFLSTS.

We decided not to study the impact of geographic area on the incidence of DFLSTs. Indeed, center participation to the SAPS 3 database included criteria to minimize heterogeneity in terms of outcome in homogeneous groups of patients.

A major strength of this study is the large sample of patients. In addition, we collected information on ICU characteristics. Very little is known about the potential impact of ICU characteristics and critical-care organization on end-of-life practices. The status of the institution, e.g., private versus public and teaching versus non-teaching, has been reported to affect end-of-life practices [1719]. As expected, DFLSTs were made in the sickest ICU patients [4, 5, 810]. However, the impact of organizational factors on the incidence of DFLSTs suggests that these substantially influence the end-of-life decision-making procedure. Indeed, these results suggest that in ED-patients who were admitted from another hospital, DFLSTs were more likely to occur. Along this line, presence of a full time ICU-specialist and availability of doctors making rounds during weekend days is associated with decrease in incidence of DFLST. These findings must be integrated in a strategy to better understand factors that influence end-of-life care.

This finding of significant impact of organizational factors on DFLSTs invite qualitative studies into factors that determine the incidence, pattern, and outcomes of DFLSTs. Studies have shown variations in decisions to forgo LSTs with personal physician characteristics, experience [20], gender [21], specialty [19] or time working in ICUs [22]. Religious beliefs and cultural background play a role [6, 10, 21, 23]. The current study suggests that, in addition, ICU resources, case-mix, co-morbidities, patterns of ICU may also influence decisions to forgo LSTs. Along this line, the fact that the presence of full time intensivist was associated with lower risk of decisions to forgo LST may be ascribed to a less opened ICU admission policy when each single admission is discussed with the senior intensivist rather than commanded by the primary physician.

Our study has several limitations. First, the database used for the study was not designed for an investigation of decisions to forgo LSTs. Nevertheless, a sub-study on decisions to forgo LSTs was planned early in the designing of the SAPS 3 study, so that investigators were aware of the need to collect accurate data on treatment withholding and with withdrawal decisions. Second, we did study whether the country or the region were potential determinants of DFLSTs. Beyond the lack of representative sample of each country or region, we also may hypothesize that the variability across geographic areas demonstrated in our study may mask variability within each country and within each ICU, as previously reported [13]. Last, information on DFLSTs was missing for about 15% of the patients in the database. The patients did not differ from the rest of the cohort in terms of severity or mortality, suggesting that missing data did not indicate absence of decisions to forgo LSTs but instead reflected failure to record information on decisions to forgo LSTs.

In summary, this multicenter international study documents variables that influence significantly the procedure of end-of-life decisions. The finding that organizational factors may have significant impact on incidence of DFLST raises crucial questions about the determinants of DFLSTs and the definition of optimal DFLST practice.

In the future, guidelines may help spread excellence in end-of-life care. However, certain types of cultural variations are permissible and should not be perceived as incorrect practices. In addition, organizational factors should be recognized as factors potentially influencing ICU end-of-life care.

Acknowledgments

Statistical analysis was supported by a grant from the Fund of the Austrian National Bank, Project # 10995 ONB. We thank the participants from all over the world who dedicated a significant amount of their time and effort to this project, proving that it is still possible to conduct a worldwide academic study. A complete list of participants is found in the web-site of the project (http://www.saps3.org).

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