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  • Open Access

A meta-analysis to derive literature-based benchmarks for readmission and hospital mortality after patient discharge from intensive care

  • 1,
  • 1, 2,
  • 1,
  • 5,
  • 1, 6 and
  • 1, 3, 6Email author
Critical Care201418:715

https://doi.org/10.1186/s13054-014-0715-6

  • Received: 1 October 2014
  • Accepted: 10 December 2014
  • Published:

Abstract

Introduction

We sought to derive literature-based summary estimates of readmission to the ICU and hospital mortality among patients discharged alive from the ICU.

Methods

We searched MEDLINE, Embase, CINAHL and the Cochrane Central Register of Controlled Trials from inception to March 2013, as well as the reference lists in the publications of the included studies. We selected cohort studies of ICU discharge prognostic factors that in which readmission to the ICU or hospital mortality among patients discharged alive from the ICU was reported. Two reviewers independently abstracted the number of patients readmitted to the ICU and hospital deaths among patients discharged alive from the ICU. Fixed effects and random effects models were used to estimate the pooled cumulative incidence of ICU readmission and the pooled cumulative incidence of hospital mortality.

Results

The analysis included 58 studies (n = 2,073,170 patients). The majority of studies followed patients until hospital discharge (n = 46 studies) and reported readmission to the ICU (n = 46 studies) or hospital mortality (n = 49 studies). The cumulative incidence of ICU readmission was 4.0 readmissions (95% confidence interval (CI), 3.9 to 4.0) per 100 patient discharges using fixed effects pooling and 6.3 readmissions (95% CI, 5.6 to 6.9) per 100 patient discharges using random effects pooling. The cumulative incidence of hospital mortality was 3.3 deaths (95% CI, 3.3 to 3.3) per 100 patient discharges using fixed effects pooling and 6.8 deaths (95% CI, 6.1 to 7.6) per 100 patient discharges using random effects pooling. There was significant heterogeneity for the pooled estimates, which was partially explained by patient, institution and study methodological characteristics.

Conclusions

Using current literature estimates, for every 100 patients discharged alive from the ICU, between 4 and 6 patients on average will be readmitted to the ICU and between 3 and 7 patients on average will die prior to hospital discharge. These estimates can inform the selection of benchmarks for quality metrics of transitions of patient care between the ICU and the hospital ward.

Keywords

  • Intensive Care Unit
  • Hospital Mortality
  • Random Effect Model
  • Cumulative Incidence
  • Quality Metrics

Introduction

Transitions of patient care between providers are vulnerable periods in health care delivery that expose patients to preventable errors and adverse events [1]. The discharge of patients from the intensive care unit (ICU) to a hospital ward is one of the highest-risk transitions of care [1]. This has been attributed to the sickest patients in the hospital being transitioned from a resource-rich environment to one with fewer resources, the number of providers involved, a lack of standardized discharge procedures and the complexity of verbal and written communication between providers and patients and/or their families as well as between providers themselves [2]-[5].

Opportunities exist to improve the quality of care during ICU discharge, and measures of ICU readmission and hospital mortality following patient discharge from the ICU have been proposed as quality metrics [6]-[10]. However, the reported incidences of readmission and hospital mortality vary widely, and there are currently no established benchmarks to guide quality improvement efforts [11],[12].

Therefore, we performed a secondary meta-analysis of studies by conducting a systematic review of prognostic factors for readmission to the ICU and hospital mortality in patients discharged alive from the ICU to derive literature-based estimates of these outcomes.

Material and methods

We followed the recommendations set forth in the Preferred Reporting Items in Systematic Reviews and Meta-Analysis and Meta-Analysis of Observational Studies in Epidemiology statements [13],[14]. This study did not require research ethics approval, as all of the data are in the public domain. Similarly, no consent was required from patients, as all of the data were abstracted in aggregate and are available in the public domain.

Search strategy and data sources

We systematically searched the following four databases for articles published between the inception dates of the databases and March 2013: MEDLINE, Embase, CINAHL and Cochrane Central Register of Controlled Trials. Searches were completed using a combination of the following terms: “intensive care unit,” “patient discharge” and readmission/mortality/medical emergency team activation, with appropriate wildcards and variations in spelling. We identified additional articles by reviewing the reference lists of studies identified for inclusion.

Inclusion criteria

We selected all studies in which prognostic factors for ICU readmission and hospital mortality were reported. The following were the inclusion criteria: (1) study design was a cohort study, (2) study participants were adult patients (>16 years old) who were discharged alive from the ICU, (3) prognostic factors for ICU discharge were reported and (4) raw data were reported that allowed calculation of the cumulative incidence of ICU readmission or the cumulative incidence of hospital mortality for patients discharged alive from the ICU prior to hospital discharge. Because there is no widely accepted time period for measuring readmission and mortality after patient discharge from the ICU (for example, 24 hours), and because authors of previous reviews have reported the use of different time periods, we included all follow-up periods [15]. We excluded articles that described discharge from a high-dependency or step-down unit. Two reviewers independently and in duplicate reviewed the titles and abstracts of retrieved publications and subsequently the full text of relevant articles. Agreement between reviewers for inclusion of full-text articles was good (κ = 0.84, 95% confidence interval (CI), 0.67 to 1.00).

Data abstraction

Two reviewers independently and in duplicate abstracted data describing study purpose, design, setting (country, type of ICU), sample size, study population (age, length of follow-up, severity of illness), outcomes (readmission to the ICU and hospital mortality following patient discharge alive from the ICU) and study quality. Disagreements were resolved through consensus. Authors of the included studies were contacted to gather missing data.

Risk of bias assessment

Study quality was evaluated using 11 characteristics: ethical approval reported, eligibility criteria described, definition of cohort timing provided, demographics described, comorbidities reported, severity of illness score reported, study duration reported, completeness of follow-up, adjustment for potential confounders, sample size calculation reported and study limitations reported. Studies that satisfied six or more of the criteria were classified as being of high quality.

Analysis

In the primary analysis, we focused on describing the cumulative incidence of readmission to the ICU and the cumulative incidence of hospital mortality for patients discharged alive from the ICU. Readmissions to the ICU and hospital mortality were calculated using data from each article on raw events (total number of events) and study population (total number of patients discharged alive from the ICU). The cumulative incidence was pooled using both Mantel-Haenszel fixed effects (assumes a single common incidence across studies) and DerSimonian and Laird random effects models (does not assume a single common incidence across studies) [16],[17].

Statistical heterogeneity was examined by calculating I 2-statistics, wherein a P-value <0.05 and an I 2-value >50% indicated the presence of heterogeneity among the included studies [18]. Stratified analyses were performed to examine for potential sources of heterogeneity between studies using prespecified subgroups that included geographic region (North America, Europe, Australasia, other region), ICU type (medical-surgical, cardiovascular, other ICU), patient characteristics (age <60 years vs. ≥60 years, predicted mortality <10% vs. ≥10% according to illness severity score) and study characteristics (patients with do-not-resuscitate (DNR) goals of care included, adjustment for confounding factors, duration of follow up ≤21 days vs. >21 days, sample size <1,000 patients vs. ≥1,000 patients, number of ICUs 1 vs. >1 and a composite measure of study quality).

All data analysis was conducted using Stata version 11.0 software (StataCorp, College Station, TX, USA).

Results

We identified 58 studies that satisfied the inclusion criteria and that had data which allowed calculation of the cumulative incidence of readmission to the ICU (n = 46 studies) or the cumulative incidence of hospital mortality (n = 49 studies) for patients discharged alive from the ICU (Figure 1) [2],[4],[5],[8],[11],[12],[19]-[70]. The characteristics of the studies are summarized in Table 1. The studies were published between 1986 and 2013 and represented 18 countries, including the United States (n = 12), the United Kingdom (n = 8), Australia (n = 6), Canada (n = 6) and Germany (n = 4). The number of patients within the studies ranged from 86 to 704,963, with an aggregate total of 2,073,170 patients included in our meta-analysis. The majority of studies were conducted in mixed medical-surgical ICUs (n = 34), with fewer studies conducted in cardiac ICUs (n = 7) or exclusively medical ICUs (n = 4) or surgical ICUs (n = 3). The mean (standard deviation) age of patients was 59.7 (5.4) years among the 44 studies in which a mean age was reported. Patient illness severity in most studies was reported based on the Acute Physiology and Chronic Health Evaluation score (n = 31) or the Simplified Acute Physiology Score (n = 12). The majority of studies were single-centered (n = 32), included patients with DNR orders (n = 42) and used multivariable adjustment (n = 49) in their data analysis. Most studies followed patients until hospital discharge (n = 46). In three studies, the investigators reported readmission to the ICU and hospital mortality at fixed time periods following patient discharge from the ICU (48 hours [26], 7 days [44] and 2 weeks [36]).
Figure 1
Figure 1

Selection process for articles for review. CI, Confidence interval; ICU, Intensive care unit.

Table 1

Description of included studies a

Study

Year

Countries

Follow-up

Type of ICU

ICUs, n

Patients, n

Age, yr (mean)

Female (%)

SOI measure

SOI score (mean)

Readmission (%)

Mortality (%)

Strauss et al. [70]

1986

USA

Hospital discharge

Medical-surgical

1

912

50

N/A

APS

N/A

15

9.9

Rubins et al. [69]

1988

USA

Hospital discharge

Medical

1

229

59.9

2.2

APACHE II

10.6

13.1

3

Chen et al. [68]

1998

Canada

Hospital discharge

Medical-surgical

7

5,127

59.3

38.0

APACHE II

17.1

4.6

5.5

Cohn et al. [67]

1999

USA

Hospital discharge

Cardiovascular

38

2,228

65.3

32.4

N/A

N/A

5.7

1.0

Cooper et al. [8]

1999

USA

Hospital discharge

Variousc

28

103,968

63.5

48.0

APACHE III

44.3

6.1

N/A

Smith et al. [66]

1999

UK

N/A

Medical-surgical

1

283

66

45.6

APACHE II

17b

7.8

11

Goldfrad and Rowan [65]

2000

UK

Hospital discharge

Medical-surgical

62

12,748

58.2

N/A

APACHE II

14.7

8.3

17.1

Daly et al. [64]

2001

UK

Hospital discharge

Medical-surgical

1

5,475

N/A

30.5

APACHE II

13.7

2.6

3.7

Rosenberg et al. [5]

2001

USA

Hospital discharge

Medical

1

3,310

53

66.5

APACHE III

49

9.6

9.6

Moreno et al. [63]

2001

Netherlands

Hospital discharge

N/A

48

2,958

N/A

N/A

SAPS II

30.1

N/A

8.6

Calafiore et al. [61]

2002

Italy

Hospital discharge

Cardiovascular

1

1,194

N/A

18.5

N/A

N/A

1.3

0.3

Beck et al. [62]

2002

UK

Hospital discharge

Medical-surgical

1

1,654

57

38.3

APACHE II

18.3

7.6

12.6

Kogan et al. [58]

2003

Israel

Hospital discharge

N/A

1

1,613

63.5

N/A

N/A

N/A

3.3

0.4

Bardell et al. [59]

2003

Canada

Hospital discharge

Cardiovascular

1

2,117

65

30.0

N/A

N/A

3.5

2.8

Metnitz et al. [57]

2003

Austria

Hospital discharge

Medical-surgical

30

15,180

62.7

39.4

N/A

N/A

5.1

N/A

Uusaro et al. [56]

2003

Finland

Hospital discharge

N/A

18

20,636

N/A

N/A

SAPS II

34

N/A

10.1

Azoulay et al. [60]

2003

France

Hospital discharge

Variousd

7

1,385

65b

36.5

SAPS II

36b

N/A

10.8

Yoon et al. [53]

2004

Korea

Hospital discharge

Medical-surgical

34

1,929

55.5

35.8

APACHE III

N/A

4.1

17.3

Duke et al. [55]

2004

Australia

Hospital discharge

Medical-surgical

1

1,870

62b

N/A

APACHE II

18.5

5.1

4.9

Fortis et al. [54]

2004

Greece

Hospital discharge

Medical-surgical

1

86

63

43.0

APACHE II

14

N/A

15.1

Vohra et al. [52]

2005

UK

Hospital discharge

Cardiovascular

1

7,177

70.4

N/A

N/A

N/A

2.5

N/A

Azoulay et al. [2]

2005

Europe, Canada, Israel

Hospital discharge

Medical-surgical

28

1,872

60b

37.4

SAPS II

35b

N/A

10.4

Alban et al. [51]

2006

USA

Hospital discharge

Surgical

1

10,840

58.8

N/A

APACHE II

15.4

2.7

9.4

Mayr et al. [49]

2006

Austria

1 yr

Medical-surgical

1

3,347

59.2

28.6

SAPS II

37.6

3

4.3

Priestap and Martin [48]

2006

Canada

Hospital discharge

Medical-surgical

31

47,163

61.7

40.8

APACHE II

15.1

5.3

9.3

Tobin and Santamaria [47]

2006

Australia

Hospital discharge

Medical-surgical

1

10,963

64

35.0

APACHE II

13b

N/A

4.4

Fernandez et al. [50]

2006

Spain

Hospital discharge

Medical-surgical

1

1,159

60.2

N/A

APACHE II

20b

N/A

9.6

    

Medical-surgical

        

Pilcher et al. [46]

2007

Australia/New Zealand

Hospital discharge

Medical-surgical

41

76,690

59

N/A

APACHE III

46.3

5.3

5.8

Song et al. [45]

2007

Korea

54.4 mo

N/A

1

1,087

65

N/A

APACHE III

N/A

8.6

N/A

Ho et al. [42]

2008

Australia

Hospital discharge

Medical-surgical

1

603

53

N/A

APACHE II

15.7

2

4.3

Gajic et al. [44]

2008

USA, Netherlands

7 days

Medical

1

1,242

N/A

45.8

APACHE III

59.2

8.1

0.4

Campbell et al. [12]

2008

UK

Hospital discharge

Medical-surgical

1

4,376

63b

41.1

APACHE II

19b

8.8

11.2

Hanane et al. [43]

2008

USA

Hospital discharge

Medical-surgical

3

11,659

62.7

46.8

APACHE III

51.3

9.1

4.5

Kaben et al. [41]

2008

Germany

Hospital discharge

Surgical

1

2,852

62

35.9

SAPS I

33.5

13.3

4.8

Laupland et al. [40]

2008

Canada

Hospital discharge

Medical-surgical

4

17,864

63.7b

26.6

APACHE II

25.1

N/A

6.7

Sakr et al. [39]

2008

Europe

60 days

N/A

198

1,729

59.8

39.3

SAPS II

31.4

N/A

7.2

Chrusch et al. [38]

2009

Canada

7 days

Medical, Surgical

2

8,222

59.3

N/A

APACHE II

18.6

5.2

0.3

Litmathe et al. [37]

2009

Germany

Hospital discharge

Cardiovascular

1

3,374

74.3

30.3

N/A

N/A

5.9

2.1

Fernandez et al. [35]

2010

Spain

Hospital discharge

Medical-surgical

31

3587

61.5

33.6

N/A

N/A

4.6

5.9

Al-Subaie et al. [36]

2010

UK

14 days

Medical-surgical

1

1,185

60

45.1

APACHE II

16b

7

2.9

Utzolino et al. [33]

2010

Germany

Hospital discharge

Surgical

1

2,114

62.1

36.4

N/A

N/A

11.8

3.7

Silvestre et al. [34]

2010

Portugal

Hospital discharge

Medical-surgical

1

156

55

40.4

APACHE II

14.6

N/A

18.6

    

Medical-surgical

        

Renton et al. [29]

2011

Australia

Hospital discharge

Medical-surgical

97

247,103

59.9

N/A

APACHE III

47

5.5

5.3

Fernandez et al. [32]

2011

Spain

Hospital discharge

Medical-surgical

31

201

60.5

31

N/A

N/A

6

22

Kramer et al. [11]

2011

USA

Hospital discharge

Medical-surgical

38

229,961

N/A

44.0

N/A

N/A

6

N/A

Silva et al. [28]

2011

Brazil

Hospital discharge

Medical-surgical

4

600

60.7

43.3

SAPS II

25.5

9.1

N/A

Laupland et al. [31]

2011

France

Hospital discharge

Mixed

N/A

5992

62b

39

SAPS II

40b

N/A

5.9

Ouanes et al. [30]

2012

France

Hospital discharge

Medical-surgical

4

3,462

60.6

38.3

SAPS II

35.1

3.3

3.2

Badawi and Breslow [26]

2012

USA

48 hr/Hospital discharge

Mixed

402

704,963

62.1

45.9

APACHE IV

47

2.5

3.1

Reini et al. [21]

2012

Sweden

30 days

Medical-surgical

1

354

60.6

25.4

SAPS III

61b

3.7

8.2

Araújo et al. [27]

2012

Portugal

Hospital discharge

Medical-surgical

1

296

64.7

43.0

SAPS II

43.7

4.7

22.6

Brown et al. [25]

2012

USA

21 days

Medical-surgical

156

196,250

N/A

N/A

MPMO-III

10.9

5.4

N/A

Joskowiak et al. [24]

2012

Germany

Hospital discharge

Cardiovascular

1

7,105

69.1

30.7

euroSCORE

9

7.8

1.2

Timmers et al. [20]

2012

Netherlands

11 yr

Medical-surgical

1

1,682

58.6

33.3

APACHE II

11.1

8

N/A

Mahesh et al. [23]

2012

UK

Hospital discharge

Cardiovascular

1

6,101

N/A

27.8

euroSCORE

7.6

N/A

0.39

Ranzani et al. [22]

2012

Brazil

Hospital discharge

Medical

1

409

48.6

49

APACHE II

16

17.4

18.3

Kramer et al. [4]

2013

USA

Hospital discharge

Mixed

105

263,082

61.5

N/A

APACHE IV

41.3

6.3

N/A

Yip and Ho [19]

2013

Australia

34 mo

Medical-surgical

1

1,446

50.2

35.7

APACHE II

19b

7.3

12.3

aAPACHE, Acute Physiology and Chronic Health Evaluation; APS, Acute Physiology Score; ICU, Intensive care unit; MICU, Medical intensive care unit; MPMO-III, Mortality Probability Admission Model; N/A, Not available; NICU, Neurosurgical intensive care unit; SAPS, Simplified Acute Physiology Score; SICU, Surgical intensive care unit;. bMedian score. cMixed, MICU, SICU, NICU. dTwo Mixed, two SICUs and three MICUs.

The pooled cumulative incidence of readmission to the ICU and cumulative incidence of hospital mortality using both fixed effects models and random effects models are summarized in Figure 2 and Figure 3, respectively. In patients discharged alive from the ICU, the fixed effects pooled cumulative incidence of readmission to the ICU during the same hospitalization was 4.0 readmissions per 100 patient discharges (95% CI, 3.9 to 4.0), whereas the random effects pooled cumulative incidence was 6.3 readmissions per 100 patients (95% CI, 5.6 to 6.9). In patients discharged alive from the ICU, the fixed effects pooled hospital mortality cumulative incidence during the same hospitalization was 3.3 deaths per 100 patient discharges (95% CI, 3.3 to 3.3), whereas the random effects pooled cumulative incidence was 6.8 deaths per 100 patient discharges (95% CI, 6.1 to 7.6). Heterogeneity among these estimates was high, with I 2-values of 99.7% and P < 0.001 for all estimates.
Figure 2
Figure 2

Incidence of readmission to the intensive care unit (ICU) for patients discharged alive from the ICU. CI, Confidence interval.

Figure 3
Figure 3

Incidence of hospital mortality for patients discharged alive from the intensive care unit. CI, Confidence interval.

The stratified pooled cumulative incidence of readmission to the ICU and the stratified pooled cumulative incidence of hospital mortality for patients discharged alive from the ICU varied by geographic region, ICU type, patient characteristics and study characteristics (Table 2). Compared to medical-surgical ICUs, lower cumulative incidences of readmission (3.8 vs. 5.6 readmissions per 100 patient discharges) and hospital mortality (0.1 vs. 4.4 deaths per 100 patient discharges) were reported for cardiovascular ICUs. The cumulative incidence of ICU readmission and hospital mortality varied according to age, severity of illness and goals of care designations of the patients included in the studies. For example, studies that excluded patients with DNRs had lower cumulative incidences of readmission (3.5 vs. 5.5 readmissions per 100 patient discharges) and hospital mortality (2.2 vs. 3.5 deaths per 100 patient discharges) compared to studies that included DNR patients.
Table 2

Pooled cumulative incidence of ICU readmission and hospital mortality after patient discharge from ICU a

Variables

ICU readmission

Hospital mortality

 

Studies, n

Patients, n

Fixed effects pooled proportion (95% CI)

Random effects pooled proportion (95% CI)

Studies, n

Patients, n

Fixed effects pooled proportion (95% CI)

Random effects pooled proportion (95% CI)

Total pooled estimates

46

2,002,269

0.040 (0.039 - 0.040)

0.063 (0.056 - 0.069)

49

1,254,183

0.033 (0.033 - 0.033)

0.068 (0.061 - 0.076)

Geographic region

North America

16

1,591,273

0.037 (0.037 - 0.038)

0.064 (0.053 - 0.076)

13

815,876

0.030 (0.029 - 0.030)

0.050 (0.036 - 0.065)

Europe

20

77,646

0.048 (0.047 - 0.049)

0.062 (0.050 - 0.074)

27

95,681

0.025 (0.024 - 0.026)

0.081 (0.064 - 0.098)

Australia / New Zealand

5

327,712

0.054 (0.054 - 0.055)

0.051 (0.047 - 0.056)

6

338,675

0.054 (0.053 - 0.055)

0.057 (0.051 - 0.063)

Other regions

5

5,638

0.049 (0.043 - 0.054)

0.081 (0.050 - 0.111)

3

3,951

0.010 (0.007 - 0.013)

0.119 (0.000 - 0.256)

ICU type

Medical-surgical ICU

28

883,365

0.056 (0.055-0.056)

0.058 (0.054 - 0.061)

29

471,305

0.044 (0.044 - 0.045)

0.086 (0.073 - 0.099)

Cardiovascular ICU

6

23,195

0.038 (0.035-0.040)

0.044 (0.024 - 0.065)

6

22,119

0.007 (0.006 - 0.008)

0.012 (0.006 - 0.019)

Other ICU types

12

1,095,709

0.032 (0.032-0.033)

0.081 (0.065 - 0.096)

14

760,759

0.031 (0.031 - 0.032)

0.066 (0.049 - 0.082)

Patient characteristics

Age <60

16

376,251

0.054 (0.053 - 0.054)

0.065 (0.057 - 0.072)

18

378,326

0.041 (0.041 - 0.042)

0.092 (0.075 - 0.109)

Age >60

29

1,624,824

0.038 (0.037 - 0.038)

0.062 (0.053 - 0.070)

28

865,604

0.033 (0.032 - 0.033)

0.060 (0.049 - 0.070)

SOI predicted <10% mortality

3

3,369

0.086 (0.077 - 0.095)

0.086 (0.077 - 0.095)

2

9,059

0.005 (0.003 - 0.006)

0.044 (0.000 - 0.125)

SOI predicted >10% mortality

31

1,534,181

0.036 (0.036 - 0.037)

0.064 (0.056 - 0.072)

39

1,228,973

0.035 (0.035 - 0.036)

0.076 (0.067 - 0.084)

Study characteristics

DNR patients excluded

13

1,372,056

0.035 (0.035 - 0.035)

0.068 (0.056 - 0.080)

14

1,132,425

0.022 (0.021 - 0.023)

0.057 (0.045 - 0.070)

DNR patients included

33

630,213

0.055 (0.055 - 0.056)

0.059 (0.054 - 0.064)

35

121,758

0.035 (0.035 - 0.035)

0.076 (0.064 - 0.089)

High study quality

36

1,643,624

0.037 (0.037 -0.037)

0.066 (0.058 - 0.073)

40

1,215,780

0.033 (0.033 - 0.033)

0.071 (0.063 - 0.079)

Low study quality

10

358,645

0.058 (0.057-0.059)

0.052 (0.043 - 0.061)

9

38,403

0.034 (0.032 - 0.036)

0.062 (0.033 - 0.091)

Adjusted for confounding factors

41

1,618,703

0.037 (0.036 - 0.037)

0.060 (0.054 - 0.067)

43

1,231,324

0.034 (0.034 - 0.035)

0.065 (0.057 - 0.072)

Not adjusted for confounding factors

5

383,566

0.063 (0.062 - 0.064)

0.076 (0.069 - 0.082)

6

22,859

0.013 (0.011 - 0.014)

0.110 (0.034 – 0.186)

Follow-up >21 days

41

1,090,407

0.057 (0.057 - 0.058)

0.061 (0.057 - 0.065)

45

538,571

0.045 (0.044 - 0.045)

0.076 (0.066 - 0.086)

Follow-up <21 days

5

911,862

0.029 (0.029 - 0.029)

0.056 (0.037 - 0.074)

4

715,612

0.028 (0.027 - 0.028)

0.016 (0.000 - 0.036)

Patient number >1000

37

1,998,382

0.040 (0.039 - 0.040)

0.060 (0.053 - 0.067)

39

1,250,654

0.033 (0.033 - 0.033)

0.060 (0.052 - 0.068)

Patient number <1000

9

3,887

0.058 (0.051 - 0.065)

0.086 (0.046 - 0.126)

10

3,529

0.085 (0.076 - 0.094)

0.129 (0.089 - 0.168)

Multiple ICU study

19

1,934,123

0.040 (0.039 - 0.040)

0.051 (0.035 - 0.066)

20

1,177,518

0.035 (0.035 – 0.036)

0.076 (0.064 - 0.087)

Single ICU study

27

68,146

0.041 (0.040 - 0.043)

0.063 (0.059 - 0.067)

29

76,665

0.017 (0.016 - 0.018)

0.064 (0.053 - 0.075)

aCI, Confidence interval; DNR, Do-not-resuscitate order; ICU, Intensive care unit; SOI, Severity of illness.

Discussion

In this meta-analysis, we report the first pooled estimates of readmission to the ICU and hospital mortality for patients discharged alive from the ICU. These estimates suggest that, on average, for every 100 patients discharged alive from the ICU, between 4 and 6 patients will be readmitted to the ICU and between 3 and 7 patients will die prior to hospital discharge. Important variations in the incidence of readmission and mortality were observed according to geographic regions and patient-related, institutional and study methodological characteristics.

Our study underscores important opportunities and challenges in improving the quality of care provided to patients discharged from intensive care. We identified estimates of readmission and death for patients discharged alive from the ICU that are similar in magnitude to the estimates of adverse events reported in an Institute of Medicine report, To Err Is Human, that prompted major efforts to improve the safety and quality of care [71],[72]. Although readmission to the ICU and hospital mortality after ICU discharge do not equate to medical errors or adverse events and are not necessarily preventable [12], our data highlight that patient discharge from the ICU is a high-risk transition of care. There are opportunities to reduce the risks pertaining to patients (for example, relapsing and/or remitting comorbid illness), providers (for example, differential continuity of care), institutions (for example, availability of transition resources) and health systems (for example, ICU capacity) [73]. Our analysis reinforces the importance of measuring performance and considering internal (that is, monitoring performance over time) and external (that is, monitoring performance across institutions) benchmarking to guide quality improvement activities. For example, deviations from anticipated performance could be used to trigger audits of patient care to identify potentially preventable events and their root causes and thereby implement locally tailored interventions.

Our results can be used to inform quality metrics designed to measure the incidence of readmission to the ICU and the incidence of hospital mortality after patient discharge from the ICU. Currently, there is no consensus on ICU benchmarks for readmission and post-ICU mortality. ICU readmission was initially identified by Cooper et al. as an important indicator that captured complementary aspects of hospital-related performance [8]. Rosenberg et al. identified a readmission incidence of 7% and suggested its use as a quality-of-care indicator [5]. More recently, professional societies [6], provider groups [74] and accreditation organizations [75] across multiple countries [76] have proposed ICU readmission as a quality indicator, but they have not specified benchmark values. Measures of ICU and hospital mortality have similarly been proposed [10],[76]. Systematic reviews and meta-analyses have been used to derive quality improvement benchmarks [77], and our present study provides literature-based estimates of readmission to the ICU and hospital mortality that could be used by institutions to select potential benchmark values.

So, which literature-based estimates should be considered? Our analyses provide two sets of pooled estimates for both ICU readmission and hospital mortality that offer a range of potential benchmarks. The fixed effects model assumes that ICU readmission incidence is the same from study to study and provides a weighted average that gives large studies greater weight [78]. The random effects model does not assume that the ICU readmission incidence is the same from study to study (that is, that it may vary from study to study) and provides a weighted average that gives studies of different sizes similar weights [79]. Although the random effects model does better justice to the full range of data available, it does potentially allow a larger weight to be given to smaller studies that may have been selected for publication on the basis of their higher event rates [18]. Therefore, one approach would be to consider ICU readmission incidence (6 patients per 100 patient discharges) and hospital mortality incidence (7 patients per 100 patient discharges) above the random effects estimates to represent suboptimal quality of care. To represent adequate quality of care accurately, it may be necessary to consider ICU readmission incidence (4 to 6 patients per 100 patient discharges) and hospital mortality incidence (3 to 7 patients per 100 patient discharges) using both the fixed effects and random effects models. It may also be necessary to consider ICU readmission incidence (4 patients per 100 patient discharges) and hospital mortality incidence (3 patients per 100 patient discharges) below the fixed effects estimates as high-quality care and benchmark targets. The stratified analyses can be used to further refine benchmark selection to more closely represent different organizations’ patient and institutional characteristics. As an important caveat, the data highlight the complexity of identifying appropriate benchmarks, reinforce the importance of a cautious approach to adopting benchmarks and suggest potential value in employing benchmark ranges as opposed to individual values in quality improvement initiatives.

Our data also highlight that hospital mortality is common among patients discharged from the ICU. This reinforces observations that the utilization of intensive care resources by patients with life-limiting illnesses is steadily rising and that end-of-life care is increasingly initiated in the ICU [80],[81]. Whereas many of these patients will die during their ICU stay, others will be discharged from the ICU before dying. This suggests that consideration needs to be given to ensure that end-of-life care is effectively delivered during transitions of care. Incorporating joint metrics for goals of care reconciliation at the time of patient discharge from the ICU, as well as both ICU readmission and hospital mortality following patient discharge from the ICU, may help in the evaluation and monitoring of the care provided to patients discharged from the ICU who are at the end of life [82].

There are caveats to our study findings. First, the studies included in this analysis were identified by conducting a literature search targeted for studies in which associations between prognostic factors and the risk of readmission to ICU and hospital mortality for patients discharged alive from the ICU were examined. Nevertheless, it is unlikely that the incidence in other studies reporting readmission and death after patient discharge would be different from ours. Second, we identified heterogeneity that is not fully explained. This is an expected finding, given the diversity of geographic locations (for example, health systems, available resources), institutions (for example, procedures for discharge and post-ICU care), providers (for example, discharge practices) and patient populations (for example, severity of illness, patient and family care preferences) in the included studies. We have discussed the relative merits and limitations of using fixed effects models and random effects models to interpret benchmarks. Against this backdrop of heterogeneity, our meta-analysis summarizes what other institutions are reporting. Third, in the majority of studies, patients were followed to hospital discharge and data at fixed time periods following patient discharge from the ICU were not reported. Although measuring readmission to the ICU and hospital mortality during the remainder of a patient’s hospital stay provides valuable information, the implications of these events likely vary by time period (that is, implication of patient readmission within 24 hours is likely different from readmission within 7 days [15]) and may introduce bias into external benchmarking activities if the hospitals being compared employ different discharge practices (for example, timing of discharge or disposition to home, to rehabilitation, to long-term care [83]). Establishing consensus time periods for measuring quality metrics of transitions of patient care between the ICU and hospital ward would facilitate future research and quality improvement initiatives.

Conclusions

On the basis of our analysis of the literature, for every 100 patients discharged alive from the ICU, on average, between 4 and 6 patients will be readmitted to the ICU and between 3 and 7 patients will die prior to hospital discharge. Opportunities exist to improve the quality of care provided to patients discharged from intensive care. The literature-based estimates derived from this systematic review and meta-analysis can be used to inform the selection of benchmarks for quality metrics of transitions of patient care between the ICU and the hospital ward.

Key messages

  • The discharge of patients from the ICU to a hospital ward is a vulnerable period in health care delivery.

  • Estimates suggest that for every 100 patients discharged alive from the ICU, on average, between 4 and 6 patients will be readmitted to the ICU and between 3 and 7 patients will die prior to hospital discharge.

  • The literature-based estimates derived from this systematic review and meta-analysis can be used to inform the selection of benchmarks for quality metrics of transitions of patient care between the ICU and the hospital ward.

Abbreviations

APACHE: 

Acute Physiology and Chronic Health Evaluation

APS: 

Acute Physiology Score

CI: 

Confidence interval

DNR: 

Do not resuscitate

ICU: 

Intensive care unit

MICU: 

Medical intensive care unit

MPMO-III: 

Mortality Probability Admission Model

NICU: 

Neurosurgical intensive care unit

SAPS: 

Simplified Acute Physiology Score

SICU: 

Surgical intensive care unit

SOI: 

Severity of illness

Declarations

Acknowledgements

The project was supported by an Establishment Grant (20100368) from Alberta Innovates Health Solutions. WAG and HTS are supported by Population Health Investigator Awards from Alberta Innovates Health Solutions. HTS is supported by a New Investigator Award from the Canadian Institutes of Health Research. DR is supported by an Alberta – Innovates Health Solutions Clinician Fellowship Award, a Knowledge Translation Canada Strategic Training in Health Research Fellowship and funding from the Canadian Institutes of Health Research. The funding sources had no role in the design, conduct or reporting of this study. FSH, DR and HTS had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. We would like to thank Simon Berthelot for aiding with the literature search and reviewing abstracts and Nik Bobrovitz for aiding in the selection and critical appraisal of publications.

Authors’ Affiliations

(1)
Department of Community Health Sciences, University of Calgary, 3280 Hospital Drive NW, Calgary, Canada
(2)
Department of Surgery, University of Calgary, 3280 Hospital Drive NW, Calgary, T2N 4Z6, AB, Canada
(3)
Department of Critical Care Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, T2N 4Z6, AB, Canada
(4)
Department of Critical Care Medicine, Alberta Health Services, 11220-83 Ave, Edmonton, T6G 2B7, AB, Canada
(5)
Division of Critical Care, University of Alberta, 11220-83 Ave, Edmonton, T6G 2B7, AB, Canada
(6)
Department of Medicine, University of Calgary, 3280 Hospital Drive NW, Calgary, AB, Canada

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