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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Crit Care Med. Author manuscript; available in PMC 2010 May 1.
Published in final edited form as:
PMCID: PMC2782597

Intensive Care Unit Occupancy and Patient Outcomes



Although ICUs with higher overall patient volume may achieve better outcomes than lower volume ICUs, there are few data on the effects of increasing patient loads on patients within the ICU.


To examine the association of ICU occupancy on the patient outcomes within the same ICU.


We examined 200,499 patients in 108 ICUs using the Acute Physiology and Chronic Health Evaluation IV database in 2002 - 2005. Daily census on the day of admission was determined for each patient and defined in relation to the mean census. We used conditional logistic regression to compare inpatient outcomes of patients admitted on high census days to those admitted in the same ICU on low census days. We controlled for severity of illness at the patient level using data on clinical, demographic and physiologic variables on admission to the ICU.

Measurements and Main Results

Patients admitted on high census days had the same odds of inpatient mortality or transfer to another hospital as patients admitted on average or on low census days. These findings were robust to multiple alternative definitions of day of admission census and were confirmed in several subgroup analyses.


The ICUs in this data set are able to function as high-reliability organizations. They are able to scale up their operations to meet the needs of a wide-range of operating conditions while maintaining consistent patient mortality outcomes.

Keywords: Intensive care, mortality, volume, high-reliability organizations


There is growing interest in concentrating critically ill patients into a smaller number of intensive care units (ICUs). This interest arises due to the increasing demand for critical care by a growing population at risk, (1) due to hospital closures, (2) as the result of payer-initiatives to achieve economies of scale, (3) and because of possible policy decisions to implement a regionalized system of care. (4, 5)

Several studies demonstrate that high volume ICUs provide improved outcomes for a range of serious conditions. (6-13) However, these cross-sectional studies do not address the effect of increasing patient volume within a hospital on outcomes; that is, they demonstrate average effects rather than marginal effects, although the marginal effects are quite relevant from a policy perspective. Results of studies of the effect of changes in patient volume on outcomes have been mixed. (14-16) We are unaware of any multi-center studies focusing on the relationship between day-to-day patient volume and ICU outcomes in the U.S.

In this study, we examine the association of daily ICU occupancy with the outcomes of patients admitted to that ICU on that day. We study a range of critical illnesses within the Acute Physiology and Chronic Health Evaluation (APACHE) IV database, containing detailed clinical and physiologic information on patients admitted to 108 ICUs. Day-of-admission census is our primary exposure variable because of the importance of rapid initial treatment for outcomes in many critical illnesses. (17-19) A fixed effects model is used at the ICU level to compare patients to others admitted within the same ICU, but on a different day.


Study Population and Data

Data came from patients admitted to ICUs participating in the APACHE clinical information system from January 2002 through June 2005. These units were diverse in size, geographic region, and teaching status. The APACHE program prospectively collects physiologic, clinical, demographic, and admission source data. Data are entered by teams who undergo intensive training and receive regular quality reviews. These data support several risk-adjustment models of ICU outcomes. (20-22)

All patients admitted to APACHE ICUs were eligible for the study. Patients undergoing coronary artery bypass grafting (CABG) were excluded as they have different risk-adjustment profiles than other critically ill patients. (21, 22) We also excluded ICUs caring for fewer than 100 patients in the data and the first 100 patients at a site to insure that our census measures were stable. Only a patient's first admission to the ICU during any given hospitalization was analyzed.

This manuscript was considered exempt from review by the University of Pennsylvania Institutional Review Board.


Our exposure of interest is the census of each ICU on the day of ICU admission. Census is defined as the total number of patients who spent at least 2 hours in each ICU on the calendar day on which a given patient was admitted. The mean census of each ICU across the study period was computed. In order to take into account differences between ICUs in their size and inherent uncertainty in determining the total capacity in each ICU, ICU census is analyzed as the ratio of the day-of-admission census to the mean census, divided into deciles. As sensitivity analyses, models were re-estimated using other parameterizations of our key exposure variable. We avoided using “mean census during the patient's ICU stay” or some similar construct as ICU census after admission for a patient is endogenous to our outcomes.

The primary outcomes were in-hospital mortality and discharge to another hospital. As a secondary outcome we examined length of stay in the ICU, which is recorded directly in the database; for these analyses we excluded 16,400 patients in 8 ICU's whose precise entrance and exit times within a given day are not in the dataset.

Risk Adjustment

Risk adjustment was performed using the APACHE IV risk adjustment formulae. The risk equations include the day one acute physiology score, age, select chronic health items, primary diagnosis, hospital admission source, pre-ICU length of stay, whether or not a sedated patient could have their Glasgow coma score assessed, whether or not a patient was receiving invasive mechanical ventilation, and whether or not the patient had received emergency surgery, as described elsewhere. (21, 22) Separate risk adjustment formulae are available for inpatient mortality and ICU length of stay.

For regressions examining the association with discharge to another hospital, we have adjusted for APACHE IV-predicted risk of death as a marker of severity of illness, as we are unaware of a validated risk-adjustment model for that precise outcome.

Statistical Analysis

In key analyses, the relationship between census and outcome was examined using multivariable conditional logistic and linear regression, adjusting for APACHE risk of death. All regression models were parameterized with an ICU-level fixed effect in order to fully control for all shared, time-invariant characteristics of the ICU (including the nominal capacity of the ICU – “how many beds the unit has”), without having to measure those characteristics. (23) Individual-level risk-adjusted predicted outcome was included in all regression models with linear, quadratic and cubic terms to insure flexibility. The regression results can be interpreted as the effect of the day-of-admission census comparing each patient to other patients admitted to the same ICU. An adjusted R2 measure is reported in the Appendix for each regression, re-scaled as maximum R2 is less than one for a dichotomous outcome. (24) Analyses were carried out in Stata 9.2 and SAS 9.0-9.2.


We examined 200,499 patients admitted to 108 ICUs in 46 hospitals. Patient characteristics are in Table 1. The mean age was 61.5 years; the median Acute Physiology Score was 34. 63.1% of patients were discharged home, 13.3% died during their hospitalization. Characteristics of the ICUs are in Table 2. The average daily census was 12.8 across ICUs, with a median of 11 and an interquartile range of 9 to 15.

Table 1
Patient Characteristics (n = 200,499)
Table 2
ICU characteristics (n=108)

There was wide variability in the day-of-admission census. The lowest decile of patients were admitted to ICU's with a census at 65% of their mean daily census; the highest decile of patients were admitted to ICU's operating at 147% of their mean daily census.

Response to Unusually High Daily Census

Severity of illness as measured by Acute Physiology Scores (APS) of patients did not markedly change with increasing occupancy of the ICU. (Figure 1) In a fixed effects model comparing patients to other patients in the same ICU, there was a small decline in mean APS with increasing patient occupancy. (Appendix Table, A.I.) Patients admitted on the highest census days had an APS 2.57 (+/- 0.26 SE, p < 0.0001) lower than those on the lowest census days (comparing deciles 1 and 10).

Figure 1
Unadjusted Acute Physiology Score

There was little difference in mortality with increasing census on day of admission. As shown in Figure 2 without adjustment, patients admitted on the highest census days were slightly less likely to die as an inpatient; there was no increase in rates of transfers of patients to other hospitals with increasing census. As shown in Figure 3, there was no significant change in mortality with increasing census when a fixed effects regression is used to compare patients to others within the same ICU, and after adjustment for differences in predicted inpatient mortality using APACHE IV. (Appendix Table A.II; the joint test for the occupancy variables was insignificant at p=0.149.) Moreover, the estimates are quite precise, ruling out large associations – patients in the highest decile have an odds ratio for inpatient mortality of 0.98 (95% CI 0.91, 1.06) relative to those in decile 6 (the mean census). (Figure 3) Fixed effects models confirmed that there was no significant increase in rates of transfer to other hospitals with increasing census. (Appendix Table A.III.)

Figure 2
Unadjusted Mortality, Transfer Rates and ICU LOS
Figure 3Figure 3
Conditional Logistic Regression for Mortality

Unadjusted length of stay in the ICU decreased modestly with increasing volume. This apparent effect disappeared when APACHE IV predicted length of stay was included as a covariate in the fixed effects regression. (Appendix Table A.IV.)

Sustained (14 Day) High Census

A very similar pattern was seen when examining the effects of the census over the 14 days prior to and including the day of admission. This was parameterized as a ratio of the 14-day moving average census to the mean census across the study period. There was no clinically significant change in the odds of death with increasing 14-day census in unadjusted or the fixed effects regression models. (See Figure 3 and Appendix Table A.V.) In the regression, the joint test for the occupancy variables was insignificant (p=0.21) Of note, there was an effect of occupancy on rates of transfers to other hospitals (joint test, p=0.0032), however, transfers were more common on days of the lowest occupancy. (Table A.VI)

Sensitivity Analyses

We conducted several sensitivity tests in order to confirm our mortality results, as shown in Table 3. In no case was there evidence of increased mortality with increased patient load.

Table 3
Sensitivity Analyses


Our results demonstrate that unusually high census on day-of-admission is not associated with clinically meaningful negative outcomes among critically ill patients across a range of conditions. This result was robust to alternative specifications of day-of-admission census, and was true in important subgroups, including the subset of highest acuity patients. This results is consistent with some US and UK past work (14, 15) although the results contradict a single-center study in a UK ICU. (16) While individual practitioners may suffer from the effects of increased workload, (25) the existing organizational structure in the ICUs in our data appear to be able to buffer patients from any mortal adverse effects of that increased workloads.

Why Might Increasing Census Worsen Patient Outcomes?

In economics, the finding of so-called “declining marginal productivity” is common. Beyond a certain point, a worker can not manufacture an item as quickly as the previous one. In the healthcare, this effect has been robustly studied in the Emergency Department (ED) crowding literature. Patients seen during busy periods in the ED have longer inpatient lengths of stay and poorer care. (26-28) Australian data suggests that ED crowding may even be associated with increased all-cause mortality. (29, 30) These results dovetail with the literature demonstrating improved outcome for patients with lower nursing ratios. (16, 25, 31)

Given these prior results suggesting that mortality of ICU patients would be increasing with day-of-admission census, our findings are reassuring. We find no evidence of a meaningful increase in mortality across a broad-range of observed census ratios. Our analysis has intentionally focused at the organizational level of the ICU as a whole. We look at the total number of patients cared for by an ICU in a day, as that may be under control of ICU managers and policy directors. This complements other research that has taken a more micro-level, looking at the workloads of a particular practitioner. At the organizational level, diverse compensating mechanisms exist to support individual practitioners. While studying the effectiveness of individual-level approaches (e.g. reducing nursing workloads) is valuable, there are also policy implications from studying the organizational aggregate effect.

Our data suggest that these ICUs are able to function as high-reliability organizations. They are able to safely scale up their operations as needed to meet the demands of a wide-range of operating conditions while maintaining consistent patient mortality outcomes. (32, 33) This is true when increased demand is acute – measured at a single-day level – or more chronic, measured across 2 weeks of sustained activity. Given the pessimism about the reliability of health care organizations, this finding is encouraging and suggests an area for detailed process studies. (34, 35) Our data do not allow us to investigate the particular processes that generate this aggregate mortality result, nor do they guarantee that the results are present for other measures of quality. But our data have important implications for regionalization of critical care, disaster planning, and selection of high quality critical care.


Regionalization is generally understood as a process of centralizing the care of patients of some type in designated centers of excellence, as in trauma and neonatal care. (36) Trauma networks have been associated with remarkable improvements in outcomes. (37-41) Leading critical care organizations are engaged in a discussion of regionalization of non-trauma critical care. (5, 12)

Analyses of the potential value of regionalization have emphasized the difference between average outcomes for patients cared for in low volume versus high volume hospital. Thus Krumholz and colleagues suggest that nearly 10,000 AMI patients might be saved each year were they to receive the same quality of care provided by the best hospitals.(42) Similar results have been found for non-post-operative mechanical ventilation. (43) These analyses assume, without data, that the average effectiveness of the ICU is the same as the marginal effect of the ICU. That is, they assume that ICUs will be able to provide the same quality of care for the patients during high occupancy as they have for the average of the preceding patients. The present study supports such an assumption, at least in the short-term.

In particular, the present study suggests that when assigning patients to providers to maximize the quality of care, across the observed range of variability, the highest quality providers are able to maintain their quality even at workloads much above their mean census. This implies, but does not prove, the viability of regionalization strategies, and related approaches such as concentrating high-risk procedures (3, 44) and when designing evacuations during disasters. Our results suggest that these ICUs maintain high quality despite high census – if the unit will accept the patient, it may be safe to send them.


Our results have several limitations. First, they may not be generalize to all ICUs. The APACHE hospitals invested in information technology, and may not be representative of ICUs in the U.S. – or of the more constrained ICU resources typical of other developed countries. (45) Second, in any observational study an unobserved confounder might be present. Such a confounder would need to be associated with improved survival in the ICU and more common on high census days to explain our results. Third, given the importance of early response for several key critical illnesses, we have chosen to focus on census on day-of-admission. For some conditions, particularly safety-related complications such as catheter-related blood stream infections, the workload throughout the entire ICU stay may be more important. Fourth, limitations of our data require that we use inpatient mortality and inpatient discharge destination as key outcomes. We hope that replications of this work will use unambiguous 30 day outcomes. Fifth, our data do not address the outcomes of patients who could not be admitted to the ICU due to high census, so we cannot speak to population health effects—high census days may affect outcomes on the hospital ward or ED. Finally, we have chosen to use a minimally parametric fixed effects estimator. As such, our standard errors may be somewhat less precise than a model that made more restrictive assumptions; however, our point estimates suggest only very small effects, if any.


A diverse set of ICUs seems able to maintain consistent mortality outcomes across a range of daily censuses. Some ICUs display a hallmark of high reliability organizations: consistent outcomes despite wide range of operating conditions. Further, this implies, but does not yet prove, that patients may be concentrated in high volume ICUs without overwhelming those ICUs, and without thereby losing the potential benefits of concentration.


The authors would like to thank David A. Asch for his insightful comments.

Role of the Funding Source: This work was funded by NIH / NHLBI 5T32HL007891 and K08-HL091249 to TJI. The funder had no role in the specific project.

Appendix Table: Fixed Effects Regression Results

All regressions control for APACHE IV predicted mortality except as indicated.

Table A.I

OutcomeAPS*No Control for APACHE prediction*
Obs Used183774
Beta95%CI LL95%CI UL
Lower CensusDecile 1reference
Decile 2-0.299-0.8110.212
Decile 3-0.539-1.053-0.026
Decile 4-0.570-1.093-0.048
Ratio to Mean CensusDecile 5-0.801-1.317-0.285
Decile 6-0.641-1.171-0.111
Decile 7-0.942-1.457-0.427
Decile 8-1.349-1.869-0.830
Higher CensusDecile 9-1.781-2.295-1.267
Decile 10-2.570-3.085-2.055
F-Test for all Deciles16.399 d.f., p=<.0001

Table A.II

OutcomeIn-Hospital Death
Obs Used196877
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.010.9391.086
Decile 20.9520.8861.023
Decile 30.9720.9051.044
Decile 40.9860.9171.059
Ratio to Mean CensusDecile 50.9340.8681.005
Decile 6reference
Decile 70.9850.9161.06
Decile 81.0510.9771.13
Higher CensusDecile 90.9950.9251.071
Decile 100.9830.9111.062
Wald Test for All Deciles13.31629 d.f., p=0.1488
Rescaled R-Squared0.3943

Table A.III

OutcomeTransferred to Another Hospital
Obs Used196877
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.0740.981.176
Decile 21.0240.9341.123
Decile 31.0560.9631.158
Decile 40.990.8991.09
Ratio to Mean CensusDecile 51.0160.9221.119
Decile 6reference
Decile 70.9460.861.041
Decile 80.9570.8691.054
Higher CensusDecile 91.0290.9371.129
Decile 100.950.8641.045
Wald Test for All Deciles16.46759 d.f., p=0.0577
Rescaled R-Squared0.0042

Table A.IV

OutcomeICU LOS* Controls for APACHE-predicted LOS *
Obs Used178657
SubgroupFull ICU LOS Data
Beta95%CI LL95%CI UL
Lower CensusDecile 10.1430.0180.268
Decile 20.056-0.0670.179
Decile 30.025-0.0970.148
Decile 40.088-0.0340.211
Ratio to Mean CensusDecile 50.117-0.0070.242
Decile 6reference
Decile 7-0.052-0.1770.073
Decile 80.017-0.1070.141
Higher CensusDecile 90.031-0.0940.156
Decile 100.048-0.0810.177
F-Test for all Deciles1.779 d.f., p=0.0689

Table A.V

OutcomeIn-Hospital Death
Obs Used196877
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.0730.9971.155
Decile 21.0280.9571.104
Decile 31.030.961.106
14 Day Ratio to Mean CensusDecile 41.020.9511.095
Decile 51.0030.9351.076
Decile 6reference
Decile 71.0520.9811.128
Decile 81.0390.9681.115
Higher CensusDecile 91.1021.0271.183
Decile 101.020.9461.1
Wald Test for All Deciles12.14749 d.f., p=0.2051
Rescaled R-Squared0.3943

Table A.VI

OutcomeTransferred to Another Hospital
Obs Used196877
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.0290.9351.133
Decile 21.1221.0261.227
Decile 31.0850.9911.187
14 Day Ratio to Mean CensusDecile 41.1191.0221.225
Decile 51.0230.9331.121
Decile 6reference
Decile 71.0050.9161.103
Decile 80.9530.8681.046
Higher CensusDecile 91.010.9211.108
Decile 100.9970.9061.097
Wald Test for All Deciles24.78819 d.f., p=0.0032
Rescaled R-Squared0.0043

Table A.VII

OutcomeIn-Hospital Death
Obs Used60454
SubgroupPost-Operative Patients
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.1760.9541.449
Decile 21.0260.8371.258
Decile 31.0210.8361.247
Decile 40.9660.7881.185
Ratio to Mean CensusDecile 50.950.7681.176
Decile 6reference
Decile 70.9610.7761.19
Decile 81.040.8521.27
Higher CensusDecile 90.9630.7831.185
Decile 100.990.7971.23
Wald Test for All Deciles6.07759 d.f., p=0.7321
Rescaled R-Squared0.1961

Table A.VIII

OutcomeIn-Hospital Death
Obs Used136423
SubgroupNon-Post-Operative Patients
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 10.9730.8971.054
Decile 20.9360.8641.014
Decile 30.9610.8871.04
Decile 40.9890.9141.071
Ratio to Mean CensusDecile 50.9340.8611.012
Decile 6reference
Decile 70.9860.9091.069
Decile 81.0540.9711.144
Higher CensusDecile 91.0120.9331.098
Decile 100.990.9091.079
Wald Test for All Deciles13.80719 d.f., p=0.1294
Rescaled R-Squared0.3994

Table A.IX

OutcomeIn-Hospital Death
Obs Used15464
SubgroupHigh Predicted Risk of Death
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.0230.8671.207
Decile 20.8760.7471.029
Decile 30.9950.851.166
Decile 40.9360.7981.098
Ratio to Mean CensusDecile 50.9430.8031.107
Decile 6reference
Decile 70.9620.8191.13
Decile 81.1680.9861.384
Higher CensusDecile 90.9970.8441.179
Decile 100.9280.781.104
Wald Test for All Deciles14.36089 d.f., p=0.1101
Rescaled R-Squared0.1138

Table A.X

OutcomeIn-Hospital Death
Obs Used152133
SubgroupWeekday Admissions
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.0130.9281.105
Decile 20.9240.8481.007
Decile 30.9680.8901.052
Decile 40.9770.8991.062
Ratio to Mean CensusDecile 50.9340.8581.016
Decile 6reference
Decile 70.9790.9011.064
Decile 81.0430.9601.133
Higher CensusDecile 90.9710.8941.054
Decile 100.9690.8891.056
Wald Test for All Deciles11.9539 d.f., p=0.216
Rescaled R-Squared0.390

Table A.XI

OutcomeIn-Hospital Death
Obs Used44744
SubgroupWeekend Admissions
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 10.9990.8721.145
Decile 20.9990.8711.145
Decile 30.9740.8481.120
Decile 40.9940.8611.147
Ratio to Mean CensusDecile 50.9270.7991.076
Decile 6reference
Decile 71.0030.8591.170
Decile 81.0940.9371.278
Higher CensusDecile 91.1170.9481.316
Decile 101.0600.8871.268
Wald Test for All Deciles8.19669 d.f., p=0.5145
Rescaled R-Squared0.405

Table A.XII

OutcomeIn-Hospital Death
Obs Used67163
SubgroupNon-Teaching Hospital
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 10.9740.8521.113
Decile 20.9030.791.033
Decile 30.9520.8281.095
Decile 40.9270.81.075
Ratio to Mean CensusDecile 50.9190.7991.056
Decile 6reference
Decile 70.9680.8391.118
Decile 81.0190.8921.164
Higher CensusDecile 90.9470.8251.088
Decile 100.9930.8651.14
Wald Test for All Deciles6.62089 d.f., p=0.6765
Rescaled R-Squared0.4025

Table A.XIII

OutcomeIn-Hospital Death
Obs Used62958
SubgroupSmall Teaching Hospital
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 10.9410.8281.07
Decile 20.9040.7931.031
Decile 30.9870.871.119
Decile 40.910.8011.035
Ratio to Mean CensusDecile 50.8810.771.008
Decile 6reference
Decile 70.920.8091.046
Decile 80.9790.851.126
Higher CensusDecile 90.9730.8541.109
Decile 100.910.7981.038
Wald Test for All Deciles7.82239 d.f., p=0.5522
Rescaled R-Squared0.382

Table A.XIV

OutcomeIn-Hospital Death
Obs Used66756
SubgroupMember of Council of Teaching Hospitals (COTH)
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.1080.9781.254
Decile 21.0290.9181.155
Decile 30.9490.8481.061
Decile 41.0770.9671.198
Ratio to Mean CensusDecile 50.9730.8671.093
Decile 6reference
Decile 71.0440.931.172
Decile 81.1241.0021.261
Higher CensusDecile 91.0430.9271.174
Decile 101.0110.8751.167
Wald Test for All Deciles12.8899 d.f., p=0.1677
Rescaled R-Squared0.3981

Table A.XV

OutcomeIn-Hospital Death
Obs Used196877
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 10.9580.8901.030
Decile 20.9580.8921.030
Decile 30.9470.8821.016
Absolute Difference from Mean CensusDecile 40.9430.8771.014
Decile 50.9280.8620.999
Decile 6reference
Decile 70.930.8641.001
Decile 80.9950.9251.069
Higher CensusDecile 90.9990.9281.075
Decile 100.9460.8751.023
Wald Test for All Deciles10.3359 d.f., p=0.324
Rescaled R-Squared0.3942

Table A.XVI

OutcomeIn-Hospital Death
Obs Used178775
SubgroupOnly first visit of first hospitalization
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.0110.9371.092
Decile 20.9530.8831.028
Decile 30.9670.8961.042
Decile 40.9870.9151.065
Ratio to Mean CensusDecile 50.9310.8621.007
Decile 6reference
Decile 70.9950.9211.076
Decile 81.0360.9591.119
Higher CensusDecile 90.9940.9201.074
Decile 100.9860.9091.070
Wald Test for All Deciles10.66219 d.f., p=0.2996
Rescaled R-Squared0.4013

Table A.XVII

OutcomeAdmitted after Cardiac Arrest
Obs Used196877
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.0410.8931.213
Decile 21.0650.9151.241
Decile 30.9740.8351.137
Decile 40.9530.8161.113
Ratio to Mean CensusDecile 51.0720.9171.253
Decile 6reference
Decile 71.0420.8931.217
Decile 81.0160.8661.191
Higher CensusDecile 90.9590.8211.121
Decile 100.9190.7841.078
Wald Test for All Deciles8.46099 d.f., p=0.4885
Rescaled R-Squared0.0003


OutcomeReadmission to ICU within 7 Days of Discharge
Obs Used196877
Odds Ratio95%CI LL95%CI UL
Lower CensusDecile 11.0010.8911.125
Decile 20.9860.881.105
Decile 31.020.9111.143
Decile 41.0780.9631.206
Ratio to Mean CensusDecile 50.9440.8391.062
Decile 6reference
Decile 71.0070.8951.132
Decile 81.0040.8961.126
Higher CensusDecile 91.0340.9221.16
Decile 101.0020.8891.129
Wald Test for All Deciles5.93969 d.f., p=0.7459
Rescaled R-Squared0.0105

Table A.XIX

OutcomeICU LOS* Controls for APACHE-predicted LOS *
Obs Used178657
SubgroupFull ICU LOS Data
Beta95%CI LL95%CI UL
Lower CensusDecile 1-0.007-0.1310.117
Decile 20.004-0.1150.124
Decile 30.016-0.1020.135
14 Day Ratio to Mean CensusDecile 4-0.041-0.1590.077
Decile 5-0.022-0.1400.095
Decile 6reference
Decile 70.034-0.0830.151
Decile 8-0.051-0.1700.067
Higher CensusDecile 9-0.005-0.1270.117
Decile 100.069-0.0570.196
F-Test for all Deciles0.619 d.f., p=0.7886


Dr. Kramer is employed by Cerner Corporation.

Dr. Kramer has stock ownership and stock options in Cerner Corporation.

All other authors have no potential conflicts of interest to disclose.

TJI has no potential or real conflicts of interest with this manuscript. AAK has no conflicts of interest; however, AAK an employee of the Cerner Corporation and own stock in such, which provides the APACHE data systems used as a basis of the analysis. JMK has no potential or real conflicts of interest with this manuscript.


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