Tiempo de Egreso de UCI
-
Upload
giseladelarosa2006 -
Category
Documents
-
view
218 -
download
0
Transcript of Tiempo de Egreso de UCI
-
8/14/2019 Tiempo de Egreso de UCI
1/11
-
8/14/2019 Tiempo de Egreso de UCI
2/11
09/10/13 www.medscape.com/viewarticle/811246_print
www.medscape.com/viewarticle/811246_print 2/11
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
-
8/14/2019 Tiempo de Egreso de UCI
3/11
09/10/13 www.medscape.com/viewarticle/811246_print
www.medscape.com/viewarticle/811246_print 3/11
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)
-
8/14/2019 Tiempo de Egreso de UCI
4/11
09/10/13 www.medscape.com/viewarticle/811246_print
www.medscape.com/viewarticle/811246_print 4/11
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
At night (8 PM-8 AM)
On weekend (Saturday or Sunday)
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
At night (8 PM-8 AM)
On weekend (Saturday or Sunday)
1089 (45.4)
608 (25.3)
1209 (46.1)
676 (25.8)
-
8/14/2019 Tiempo de Egreso de UCI
5/11
09/10/13 www.medscape.com/viewarticle/811246_print
www.medscape.com/viewarticle/811246_print 5/11
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
At night (8 PM-8 AM)
On weekend (Saturday or Sunday)
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
-
8/14/2019 Tiempo de Egreso de UCI
6/11
-
8/14/2019 Tiempo de Egreso de UCI
7/11
09/10/13 www.medscape.com/viewarticle/811246_print
www.medscape.com/viewarticle/811246_print 7/11
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)
-
8/14/2019 Tiempo de Egreso de UCI
8/11
-
8/14/2019 Tiempo de Egreso de UCI
9/11
09/10/13 www.medscape.com/viewarticle/811246_print
www.medscape.com/viewarticle/811246_print 9/11
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
-
8/14/2019 Tiempo de Egreso de UCI
10/11
09/10/13 www.medscape.com/viewarticle/811246_print
www.medscape.com/viewarticle/811246_print 10/11
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.
Sidebar
eLetters
Now that you've read the article, create or contribute to an online discussion on this topic. Visit www.ajcconline.organd
click "Responses" in the second column of either the full-text or PDF view of the article.
References
1. Task Force of the American College of Critical Care Medicine, Society of Critical Care Medicine. Guidelines for
intensive care unit admission, discharge, and triage. Crit CareMed.1999;27:633638.
2. Truog RD, Brock DW, Cook DJ, et al. Rationing in the intensive care unit. Crit Care Med.2006;34:958963.
3. Garland A, Shaman Z, Baron J, Connors AF Jr. Physician-attributable differences in intensive care unit costs: a
single-center study.Am J Respir Crit Care Med.2006;174:12061210.
4. Garland A, Connors AF Jr. Physicians' influence over decisions to forgo life support. J Palliat Med.2007;10:1298
1305.
5. Daly K, Beale R, Chang RWS. Reduction in mortality after inappropriate early discharge from intensive care unit:
logistic regression triage model. BMJ.2001;322:12741276.
6. Goldfrad C, Rowan K. Consequences of discharges from intensive care at night. Lancet.2000;355:11381142.
7. Social Security Death Index (SSDI). http://search.ancestry.com/search/db.aspx?dbid=3693. Accessed June 21,
2013.
8. Baker DW, Einstadter D, Thomas CL, Husak SS, Gordon NH, Cebul RD. Mortality trends during a program that
publicly reported hospital performance. Med Care.2002;40:879890.
9. Vasilevskis EE, Kuzniewicz MW, Dean ML, et al. Relationship between discharge practices and intensive care
unit in-hospital mortality performance evidence of a discharge bias. Med Care.2009;47:803812.
10. Christensen S, Johansen M, Christiansen C, Jensen R, Lemeshow S. Comparison of Charlson comorbidity index
with SAPS and APACHE scores for prediction of mortality following intensive care. Clin Epidemiol.2011;3:203
211.
11. Elixhauser A, Steiner C, Harris DR, Coffey RM. Comorbidity measures for use with administrative data. Med
Care.1998;36:827.
12. Marrie RA, Dawson NV, Garland A. Quantile regression and restricted cubic splines are useful for exploring
relationships between continuous variables. J Clin Epidemiol.2009;62:510516.
13. Harrell FE Jr. Regression Modeling Strategies.New York, NY: Springer; 2001.
14. Hosmer DW, Lemeshow S.Applied Logistic Regression.New York, NY: Wiley; 1989.
http://www.ajcconline.org/ -
8/14/2019 Tiempo de Egreso de UCI
11/11