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closed-model ICU was provided by a rotating team comprising a board-certified intensivist, an ICU fellow, and a group of

5 house officers who took overnight call on rotation. Formal rounds were made twice daily by this team. Rotations were

4 weeks long for the ICU fellow and house officers and 2 weeks long for the intensivists. The intensivist took calls from

home at night. The nurse to patient ratio averaged 1:2. Decisions to transfer patients out of the ICU were made by the

intensivist and fellow; no set policies regarding these decisions were in place.

Patients were included if they survived their stay in the ICU. The date and time were recorded for (1) hospital admission,

(2) ICU admission, (3) the request to transfer the patient out of the ICU, (4) transfer out of the ICU, and (5) hospital

discharge. The pre-ICU length of stay (PreLOS) was the time from hospital admission to ICU admission. The time from

ICU admission until the transfer request is denoted as ICULOSdesired. ICU discharge delay was defined as the time

elapsed from when a request for transfer to a bed in a general care area was received in the hospital's admitting office to

the time that the patient left the ICU. Time intervals were measured in fractional hours.

To avoid erroneous mortality rates due to patients with multiple ICU admissions, we included only each patient's first ICU

admission during the study period. We also excluded patients discharged from our hospital directly to another acute

care hospital and patients with ICU discharge delays exceeding 96 hours. We determined in-hospital mortality from the

hospital's computerized information system. We used the 2002 to 2006 Ohio death registry to assess posthospital

mortality. We compared the 2 data sources against each other and against the US Social Security Death Index;[7]a

comparison of dates of death for 200 randomly chosen patients showed more than 95% agreement for the 3 sources.

Our primary analysis used multivariable logistic regression to evaluate the impact of ICU discharge delay on mortality to

30 days after ICU admission. We used 30-day mortality instead of the more commonly reported in-hospital mortality

because the latter is highly sensitive to interhospital and posthospital transfer patterns, which differ between hospitals

and can change over time;[8,9]a fixed time point is much less influenced by such artifacts. Existing information indicates

that in-hospital mortality and 30-day mortality are influenced by similar factors. [9,10]Similar models were constructed for

hospital mortality and mortality to 60 days after ICU admission. These models included slightly different numbers of

individuals because the Ohio death data available for this project ended December 31, 2006.

Models included adjustments for patients' demographics, comorbid conditions, type and severity of acute illness,

PreLOS, source of ICU admission, whether ICU admission occurred at night (8 PM-8 AM) or on a weekend (Saturday or

Sunday), existence of care limitation orders in ICU, ICULOSdesired, and whether the transfer request was initiated at

night or on a weekend.

Demographics were age, sex, and race dichoto - mized into white versus nonwhite. Comorbid illness was quantified as

the presence of 31 specific preexisting conditions,[11]as recorded in the hospital's administrative and billing database.

We subdivided the 31 conditions into 2 groups. Group 1 comprises conditions that are predictive of poorer outcomes.

Group 2 comprises comorbid conditions that have previously been associated with lower mortality; [11]this association

most likely represents the effect of coding bias, wherein milder chronic conditions are less likely to be coded for more

severely ill patients. The 2 comorbid illness variables included in our models were the number of conditions present

within each of these groups.

Acute diagnostic groupings were coded as the organ system responsible for ICU admission (respiratory, cardiovascular,

gastrointestinal, neurologic, miscellaneous medical conditions, or surgical conditions/trauma). Severity of acute illness

was measured by using the worst value in the initial ICU day for the acute physiology score (APS) from the Acute

Physiology and Chronic Health Evaluation II, and the need for invasive mechanical ventilation. The source of ICU

admission was coded as the emergency department, other care areas in the hospital, other ICUs, an outside hospital, or

other sources. Care limitations were represented by 2 mutually exclusive variables representing the presence, at any

time in the ICU, of orders to (1) provide only comfort care, or terminally withdraw any form of life-supporting therapies that

had been initiated, with the expectation of imminent death, or (2) withhold life-supporting therapies within the context of

otherwise providing all indicated care.

When preliminary analysis indicated that all members of a category had the same outcome, it was combined with the

most closely related category. We tested for nonlinear relationships between the dependent variable and continuous

covariates by using restricted cubic splines;[12]variables found to have such relationships were included in the model as

splines. The best-fitting logistic regression models were determined as those with the lowest value of Akaike's

information criterion.[13]

To explore the causes of ICU transfer delay, we similarly constructed a quantile regression model for the 90th percentile


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of ICU discharge delay.[12]This model additionally included post-ICU location, hospital survival status, and the number of

samesex patients contemporaneously awaiting transfer out of this ICU.

Discrimination of logistic regression models was evaluated with the c statistic. Model calibration was addressed via the

Hosmer-Lemeshow Hstatistic, for which a nonsignificant Pvalue can be taken as indicating adequate calibration. [14]

Goodness of fit for quantile regression was assessed as the pseudo R2.[15]Standard errors in logistic regression were

calculated with the Huber-White robust sandwich estimator,[13]whereas for quantile regression, we used bootstrapped

standard errors with 1000 repetitions.[16]

Continuous data are presented as mean (SD). Categorical data are presented as proportions. Pvalues less than .05 are

considered significant. Statistical analysis was done with Stata 10.0 software. This study was approved by the hospital's

institutional review board, which waived informed consent.

Results

Of 2624 eligible patients, 30-day and 60-day mortality information was unavailable for 223 and 285 patients, respectively.

Our primary analysis included the 2401 unique ICU survivors with known 30-day mortality (). Their mean delay in being

transferred out of ICU was 9.6 (SD, 11.7) hours (range, 093 hours). The fractions having ICU discharge delay of 0 to 12,

12.01 to 24, 24.01 to 48, 48.01 to 72, and 72.01 to 96 hours were 78.9%, 11.2%, 7.7%, 1.8%, and 0.5%, respectively;

23.7 hours represents the unconditional 90th percentile of this variable. Most patients (85%) were transferred from ICU togeneral care areas; 12% left the hospital directly from the ICU. Six percent died before hospital discharge; mortality at

30 days after ICU admission was 10.1%. also shows the characteristics of the entire cohort of 2624 unique ICU

survivors.

Table 1. Characteristics and outcomes of ICU survivorsa

CharacteristicPatients with 30-day vital

status data

Patients with vital status data to

hospital discharge

Number 2401 2624

Age, mean (SD), y 55.6 (18.0) 55.6 (18.0)

Male sex, No. (%) of patients 1269 (52.9) 1384 (52.7)

Nonwhite race, No. (%) of patients 887 (36.9) 975 (37.2)

No. of comorbid illnesses,bmean (SD)

Group 1

Group 2

1.9 (1.5)

1.2 (0.9)

1.9 (1.5)

1.2 (0.9)

Admitted to ICU, No. (%) of patients

At night (8 PM-8 AM)

On weekend (Saturday or Sunday)

1089 (45.4)

608 (25.3)

1209 (46.1)

676 (25.8)

ICU admission source, No. (%) of patients Emergency department

General care area

Other ICU

Outside hospital

Others/miscellaneous

1816 (75)

467 (20)

42 (2)

21 (1)

55 (2)

1992 (76)

508 (19)

45 (2)

21 (1)

58 (2)

Acute diagnostic grouping, No. (%) of patients

Respiratory

Cardiovascular

Neurologic

Gastrointestinal Miscellaneous

Surgical or trauma

658 (27)

433 (18)

433 (18)

344 (14)519 (22)

24 (1)

708 (27)

475 (18)

477 (18)

356 (14)584 (22)

24 (1)

APACHE II acute physiology score, mean (SD) 13.6 (7.6) 13.6 (7.6)


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APACHE II score (total), mean (SD) 17.5 (8.5) 17.5 (8.5)

Glasgow Coma Scale score, mean (SD) 12.2 (3.9) 12.2 (3.9)

Intubated, No. (%) of patients 601 (25.0) 655 (25.0)

Care limitation orders in ICU, No. (%) of patients

Comfort care or withdrawal of life support

Withholding of life support

96 (4.0)

275 (11.5)

106 (4.0)

297 (11.3)

Request for transfer out of ICU, No. (%) ofpatients



246 (10.3)

600 (25.0)

276 (10.5)

656 (25.0)

No. of patients simultaneously awaiting transfer

from ICU, mean (SD)1.3 (1.4) 1.2 (1.4)

ICU discharge location, No. (%) of patients

General care areas

Other ICU in our hospital

Left hospital directly from ICU

2043 (85.1)

70 (2.9)

288 (12.0)

2229 (84.9)

73 (2.8)

322 (12.3)

ICU discharge delay, h

Mean (SD)

Median (interquartile range)

9.6 (11.7)

6.2 (3.4, 10.9)

9.6 (11.7)

6.2 (3.4, 10.9)

ICU length of stay, actual, mean (SD), h 71.5 (85.3) 72.1 (88.0)

Hospital length of stay, mean (SD), h

Before ICU

After ICU

180.7 (172.7)

15.4 (66.4)

93.8 (111.4)

180.3 (173.2)

15.2 (66.1)

93.0 (110.4)

Hospital discharge status, No. (%) of patients

Died

Alive, discharged home Alive, discharged to other than home

136 (5.7)

1471 (61.3)794 (33.0)

147 (5.6)

1614 (61.5)863 (32.9)

30-day mortality, No. (%) of patients (out of

2401)243 (10.1)

Abbreviations: APACHE, Acute Physiology and Chronic Health Evaluation; ICU, intensive care unit.aThe patients with vital status data to 30 days after ICU admission comprised the cohort for the primary analysis.bGroup 1 comprises conditions that are predictive of poorer outcomes; group 2 comprises those comorbid conditions

previously associated with better outcomes.11

Table 1. Characteristics and outcomes of ICU survivorsa

CharacteristicPatients with 30-day vital

status data

Patients with vital status data to

hospital discharge

Number 2401 2624

Age, mean (SD), y 55.6 (18.0) 55.6 (18.0)

Male sex, No. (%) of patients 1269 (52.9) 1384 (52.7)

Nonwhite race, No. (%) of patients 887 (36.9) 975 (37.2)

No. of comorbid illnesses,bmean (SD)

Group 1

Group 2

1.9 (1.5)

1.2 (0.9)

1.9 (1.5)

1.2 (0.9)

Admitted to ICU, No. (%) of patients



1089 (45.4)

608 (25.3)

1209 (46.1)

676 (25.8)


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ICU admission source, No. (%) of patients

Emergency department

General care area

Other ICU

Outside hospital

Others/miscellaneous

1816 (75)

467 (20)

42 (2)

21 (1)

55 (2)

1992 (76)

508 (19)

45 (2)

21 (1)

58 (2)

Acute diagnostic grouping, No. (%) of patients

Respiratory

Cardiovascular

Neurologic

Gastrointestinal

Miscellaneous

Surgical or trauma

658 (27)

433 (18)

433 (18)

344 (14)

519 (22)

24 (1)

708 (27)

475 (18)

477 (18)

356 (14)

584 (22)

24 (1)

APACHE II acute physiology score, mean (SD) 13.6 (7.6) 13.6 (7.6)

APACHE II score (total), mean (SD) 17.5 (8.5) 17.5 (8.5)

Glasgow Coma Scale score, mean (SD) 12.2 (3.9) 12.2 (3.9)

Intubated, No. (%) of patients 601 (25.0) 655 (25.0)

Care limitation orders in ICU, No. (%) of patients

Comfort care or withdrawal of life support

Withholding of life support

96 (4.0)

275 (11.5)

106 (4.0)

297 (11.3)

Request for transfer out of ICU, No. (%) of

patients



246 (10.3)

600 (25.0)

276 (10.5)

656 (25.0)

No. of patients simultaneously awaiting transfer

from ICU, mean (SD)1.3 (1.4) 1.2 (1.4)

ICU discharge location, No. (%) of patients

General care areas

Other ICU in our hospital

Left hospital directly from ICU

2043 (85.1)

70 (2.9)

288 (12.0)

2229 (84.9)

73 (2.8)

322 (12.3)

ICU discharge delay, h

Mean (SD)

Median (interquartile range)

9.6 (11.7)

6.2 (3.4, 10.9)

9.6 (11.7)

6.2 (3.4, 10.9)

ICU length of stay, actual, mean (SD), h 71.5 (85.3) 72.1 (88.0)

Hospital length of stay, mean (SD), h

Before ICU After ICU

180.7 (172.7)

15.4 (66.4)93.8 (111.4)

180.3 (173.2)

15.2 (66.1)93.0 (110.4)

Hospital discharge status, No. (%) of patients

Died

Alive, discharged home

Alive, discharged to other than home

136 (5.7)

1471 (61.3)

794 (33.0)

147 (5.6)

1614 (61.5)

863 (32.9)

30-day mortality, No. (%) of patients (out of

2401)243 (10.1)

Abbreviations: APACHE, Acute Physiology and Chronic Health Evaluation; ICU, intensive care unit.a

The patients with vital status data to 30 days after ICU admission comprised the cohort for the primary analysis.bGroup 1 comprises conditions that are predictive of poorer outcomes; group 2 comprises those comorbid conditions

previously associated with better outcomes.11

Noting that 90% of transfer delays were less than 24 hours, we used all 2624 ICU survivors to construct a quantile

regression model for long delays, taken as the 90th conditional percentile of ICU discharge delay.[12]The only variables


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On weekend Binary 1.05 (0.71, 1.56) .78

Request for transfer out of ICU

At night

On weekend

Binary

Binary

1.04 (0.62, 1.75)

0.68 (0.44, 1.05)

.86

.09

End-of-life orders

Withhold life support

Withdrawal of life support

Binary

Binary3.13 (2.08, 4.71)

27.8 (14.4, 53.6)


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Third, we expected that needing mechanical ventilation reflects a higher severity of illness and would be associated with

higher 30-day mortality, but our results indicated the reverse (). The likely explanation lies in the interplay between

mechanical ventilation, neurologic function, and end-of-life wishes in this cohort of ICU survivors. Additional exploration of

the data demonstrated that mechanical ventilation was, as expected, associated with higher 30-day mortality among

patients with preserved neurologic function, as identified by a high score on the Glasgow Coma Scale (GCS) at ICU

admission. For those with poor neurologic function, however, mechanical ventilation was associated with lower mortality.

The key additional observation is that most patients with low GCS scores who did not receive mechanical ventilation had

orders to withhold life support and died soon after leaving the ICU. Thus, the apparent paradoxical protective effect of

mechanical ventilation reflects high mortality among those patients with low GCS scores for whom mechanicalventilation was not applied. Because they did not materially influence the observed relationship between mortality and

ICU discharge delay, we chose not to include GCS scores and these interaction terms in our models.

Table 3. Logistic regression model of mortality to 30 days after ICU admission among 2401 ICU survivorsa

Independent variable Form in the model Odds ratio (95% CI) P Direction of the effectb

ICU discharge delay, h Cubic splines Multiple terms .002 U-shapedc

Age, y Cubic splines Multiple terms


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discharge delay; such a study would be difficult to perform.

Conclusion

We observed that there is an optimal timing for patients to leave the ICU, with an increasing risk of subsequent death if

patients leave the ICU either too early or too late. Our findings further imply that clinical judgment is not reliable for

determining that optimal time window. Indeed, 2 professional task forces have recognized that intensivists have little

except subjective clinical judgment to guide them in determining when patients should be discharged from the ICU.[1,2]

Although further work is needed to confirm and clarify our findings, they indicate that the importance of determining whenpatients should be transferred from the ICU is not limited to the economic consideration of improving ICU bed utilization.

Future research is needed to discover objective, evidence-based, practical methods of determining when patients should

be transferred out of the ICU.

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