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Clinical Investigations in Critical Care |

The Hospital Mortality of Patients Admitted to the ICU on Weekends* FREE TO VIEW

S. Allen Ensminger, MD; Ian J. Morales, MD; Steve G. Peters, MD, FCCP; Mark T. Keegan, MB, MRCPI; Javier D. Finkielman, MD; James F. Lymp, PhD; Bekele Afessa, MD, FCCP
Author and Funding Information

*From the Department of Medicine (Dr. Ensminger), Division of Pulmonary and Critical Care Medicine, Department of Medicine (Drs. Morales, Peters, Finkielman, and Afessa), Division of Critical Care, Department of Anesthesia (Dr. Keegan), Division of Biostatistics, and Department of Health Science Research (Dr. Lymp), Mayo Clinic College of Medicine, Rochester, MN.

Correspondence to: Bekele Afessa, MD, FCCP, Division of Pulmonary and Critical Care Medicine, Mayo Clinic and Foundation, 200 First St SW, Rochester, MN 55905; e-mail: Afessa.Bekele@mayo.edu



Chest. 2004;126(4):1292-1298. doi:10.1378/chest.126.4.1292
Text Size: A A A
Published online

Study objectives: Previous studies have suggested that patients are more likely to die in the hospital if they are admitted on a weekend than on a weekday. This study was conducted to determine whether weekend admission to the ICU increases the risk of dying in the hospital.

Design: Retrospective cohort study.

Setting: ICU of a single tertiary care medical center.

Patients: A total of 29,084 patients admitted to medical, surgical, and multispecialty ICUs from October 1994 through September 2002.

Interventions: None.

Measurements and results: The weekend ICU admissions comprised 27.9% of all ICU admissions (8,108 ICU admissions). The overall hospital mortality rate was 8.2% (2,385 deaths). Weekend ICU admission was associated with a higher unadjusted hospital mortality rate than that for weekday ICU admission (11.3% vs 7.0%, respectively; odds ratio [OR], 1.70; 95% confidence interval [CI], 1.55 to 1.85). In multivariable analyses controlling for the factors associated with mortality such as APACHE (acute physiology and chronic health evaluation) III predicted mortality rate, ICU admission source, and intensity of treatment, no statistically significant difference in hospital mortality was found between weekend and weekday admissions in the overall study population (OR, 1.06; 95% CI, 0.95 to 1.17). For weekend ICU admissions, the observed hospital mortality rates of the medical, multispecialty, and surgical ICUs were 15.2%, 17.2%, and 6.4%, respectively, and for weekday ICU admissions the rates were 16.3%, 10.1%, and 3.5%, respectively. Subgroup analyses showed that weekend ICU admission was associated with higher adjusted hospital mortality rates than was weekday ICU admission in the surgical ICU (OR, 1.23; 95% CI, 1.03 to 1.48), but not in the medical or multispecialty ICUs.

Conclusions: The overall adjusted hospital mortality rate of patients admitted to the ICU on a weekend was not higher than that of patients admitted on a weekday. However, weekend ICU admission to the surgical ICU was associated with an increased hospital mortality rate.

Previous studies have shown that patients admitted to hospitals on a weekend experience worse outcomes than those admitted on a weekday.13 The reasons for these variations have not been well-defined.4Since critically ill patients are at high risk for adverse outcomes, necessary staffing, diagnostic studies, and therapeutic procedures ideally should be available at all times. However, cost pressures and market realities may lead to decreased staffing during the weekend and may contribute to poor outcomes in patients admitted to the ICU.5

There is a paucity of data addressing the relationship of weekend ICU admission to mortality. In one study6 of 156,136 patients admitted to 38 ICUs in 28 hospitals, weekend ICU admission was associated with a higher risk of death. That study included a heterogeneous group of ICUs with differences in staffing and in available diagnostic and therapeutic modalities.6 The objective of the current study was to determine the relationship between weekend ICU admission and the adjusted hospital mortality rate of critically ill patients in a tertiary care academic medical center, with relatively uniform staffing and availability of diagnostic and therapeutic options.

In this cohort study, we reviewed the prospectively collected APACHE (acute physiology and chronic health evaluation) III database of patients admitted to some of the ICUs of the Mayo Clinic (Rochester, MN) from October 1994 through September 2002. The Mayo Foundation Institutional Review Board approved the study.

Subjects and Setting

We identified patients who had been admitted to the adult ICUs from the APACHE III database. The Mayo Medical Center includes two hospitals with approximately 1,900 beds. One multispecialty ICU, one medical ICU, and two surgical ICUs were included in this study. Only the first ICU admission of each patient was included. Patients admitted to neurologic and neurosurgical ICUs, the cardiovascular surgery ICU, and the coronary care ICU were not included in the study because they were not part of the APACHE III database. Patients who did not authorize their medical records to be reviewed for research were excluded from the study.

Critical care service teams, consisting of attending intensivists, critical care fellows, residents of various specialties, medical students, critical care pharmacists, critical care nurses, and respiratory therapists provided care in all ICUs. The attending intensivists, all of whom were board-certified or board-eligible, made bedside patient rounds at least twice a day, supervised invasive procedures during the day, and guided all staff in patient care. The intensivists did not stay in-house at night, but were available to guide patient care by phone and to come to the ICU as needed. Critical care fellows and residents of various specialties staffed the ICUs 24 h per day and every day of the week. The nurse/patient ratio was 1:1 or 1:2. This ratio was maintained irrespective of the time of day or day of the week.

Measurements

Data were abstracted from the APACHE III database using appropriate software (Cerner Corporation; Kansas City, MO). These data had been collected prospectively. The APACHE III prognostic system calculates the probability of hospital death based on operative status, chronic health condition, admission diagnosis, age, pulse rate, mean arterial BP, temperature, respiratory rate, arterial oxygen tension, hematocrit, WBC count, creatinine level, urine output, levels of BUN, sodium, albumin, bilirubin, and glucose, acid-base status, and neurologic status. The data collected included age, ethnicity, gender, ICU admission date and time, ICU admission source, ICU admission type (postoperative or nonoperative), intensity of treatment, ICU admission diagnosis group, APACHE III score and predicted hospital mortality, and hospital discharge status. The admission sources were categorized as operating room/recovery room, emergency department/direct admission from outpatient clinic, transfer from the same hospital, and transfer from other institutions. The intensity of treatment was divided into the following three categories: “active treatment,” if a patient received ≥ 1 of 33 items of the therapeutic intervention scoring system78 that were defined as ICU-specific therapy on the first ICU day; “high-risk monitor,” if a patient who had not received active treatment on the first ICU day had a > 10% probability of receiving active treatment during the ICU stay; and “low-risk monitor,” if a patient who had not received active treatment on the first ICU day had a < 10% probability of receiving active treatment during the ICU stay.9 Examples of the 33 active treatment items include controlled ventilation, tracheal intubation, cardiac pacing, intraaortic balloon assist, vasoactive drug infusion, hemodialysis, emergency endoscopy, ventriculostomy, and induced hypothermia.910 The ICU admission diagnosis groups included cardiovascular, genitourinary, GI, hematologic, metabolic/endocrine, musculoskeletal/skin, neurologic, respiratory, transplant, and trauma. APACHE III scores and predicted hospital mortality rates were calculated as described by Knaus et al.11 The type of ICU that each patient was admitted to was recorded.

The time of admission was designated as the time of the patient’s admission to the ICU. The weekend was defined as the period from 5:00 pm Friday to 7:00 am Monday. All other times were considered to be weekdays.

Statistical Analysis

We used means, SDs, medians, and interquartile ranges (IQRs) to summarize continuous data. Bivariate relationships between the ICU admission day (weekend or weekday) or hospital mortality and continuous variables were tested using the t test or the rank-sum test, as appropriate. χ2 tests were used for the comparison of categoric variables.

Multiple logistic regression was used to compare hospital mortality rates among patients who were admitted to the ICU on a weekend vs those admitted on a weekday. We created the logistic regression model by entering variables associated with hospital mortality with p values of < 0.05 by univariate analysis. The model was adjusted for predicted mortality, intensity of treatment, and ICU admission source. Age, APACHE III score, and ICU admission diagnosis were not included in the model because they were used in calculating the predicted mortality rate as a measure of overall severity.11 Gender and ethnicity did not contribute to model fit and so also were not included. Differences in mortality rates were expressed as odds ratios (ORs) for death with their 95% confidence intervals (CIs) and the corresponding p values. Standardized mortality ratio (SMR) was calculated as the ratio of observed to predicted mortality. We used the Hosmer-Lemeshow statistic and the area under the curve (AUC) of the receiver operating characteristic to determine the calibration and discrimination, respectively, of the logistic regression model.

Subgroup analyses were performed for each type of ICU. Patients with missing data were excluded from analysis involving the missing data. We considered p values of < 0.05 to be statistically significant. All reported p values are two-tailed. A statistical software package (StatView, version 5.0; SAS Institute; Cary, NC) was used for most of the statistical analyses. We used another software package (SPSS, version 11.5; SPSS Inc; Chicago, IL) to perform the Hosmer-Lemeshow analysis and to calculate the AUC.

There were 41,197 ICU admissions during the study period, of which 30,234 were first admissions. Excluding 1,150 patients who did not authorize their medical records to be reviewed for research, 29,084 patients were included in the study. Patients were predominantly white (95.5%). The weekend ICU admissions comprised 27.9% of the study population (8,108 patients). Of the 15,705 ICU admissions through 1998, 4,019 patients (25.6%) had been admitted on weekends, compared to 4,089 of 13,379 patients admitted during the later periods (30.6%; p < 0.0001).

Differences in baseline characteristics between the weekday and weekend ICU admission are shown in Table 1 . Patients admitted to the ICU on the weekend were sicker, as measured by the APACHE III score and predicted mortality rate (Table 1). The mean APACHE III score of the weekday ICU admissions was 43.1 (SD, 22.6), and the predicted mortality rate was 8.8% (SD, 15.1%). The mean APACHE III score of the weekend ICU admissions was 47.6 (SD, 27.4), and the predicted mortality rate was 12.9% (SD, 19.2%). The median APACHE III scores of the weekday and weekend ICU admissions were 39 (IQR, 28 to 53) and 43 (IQR, 29 to 61), respectively. The median predicted mortality rates of the weekday and weekend ICU admissions were 3.1% (IQR, 1.4 to 8.3%) and 4.7% (IQR, 1.6 to 15.0%), respectively.

Compared to weekday ICU admissions, weekend ICU admissions were more likely to occur directly from the emergency department or as a transfer from within the same hospital or from another hospital (Table 1). Patients admitted to the ICU on the weekend were less likely to be admitted from recovery rooms or operating rooms, or to have a postoperative diagnosis. They were also more likely to require high-risk monitoring or active treatment. The three most common ICU admission diagnosis groups, on both weekdays and weekends, were cardiovascular, respiratory, and GI. Forty-two percent of the medical ICU admissions occurred on the weekend compared to 22% of the surgical ICU admissions and 28% of the multispecialty ICU admissions (p < 0.0001). The ICU admission sources were the recovery room or operating room in 90 of the medical ICU admissions (1%).

The overall hospital mortality rate during the study period was 8.2% (2,385 deaths; 95% CI, 7.9 to 8.5%), with an SMR of 0.89 (95% CI, 0.86 to 0.93). Bivariate analyses showed statistically significant differences in age, ICU admission source, first day APACHE III score and predicted mortality, and intensity of treatment between survivors and nonsurvivors. For patients with predicted mortality rates of < 25%, between 25% and 50%, between 50% and 75%, and > 75%, the observed mortality rates were 4.1%, 27.9%, 50.0%, and 76.4%, respectively. There were no statistically significant differences in gender and ethnicity between survivors and nonsurvivors. The hospital mortality rate for postoperative patients was 3.1% (526 of 16,952 patients) compared to 15.3% of nonsurgical patients (1,859 of 12,132 patients; p < 0.0001).

The observed mortality rate of patients admitted to the ICU on a weekend was 11.3% (917 of 8,108 patients) compared to 7.0% of patients admitted to the ICU on a weekday (1,468 of 20,976 patients; OR, 1.7; 95% CI, 1.6 to 1.8; p < 0.0001). The SMR was 0.80 (95% CI, 0.76 to 0.84) for weekday ICU admission and 0.88 (95% CI, 0.82 to 0.94) for weekend ICU admission. When adjusted for the confounding variables, weekend ICU admission was not found to be independently associated with increased hospital mortality (Table 2 ). Although univariate analysis showed the type of admission (ie, nonoperative vs postoperative) to be associated with hospital outcome, it was not included in the multiple logistic regression models because of its collinearity with the ICU admission source. There was no statistically significant interaction between the effects of the predicted mortality rate and weekend ICU admission. The Hosmer-Lemeshow statistic of the regression model in Table 2 was 103 with a p value of < 0.001. The AUC of the model was 0.867 (95% CI, 0.859 to 0.875).

The observed mortality rates of patients admitted to medical, multispecialty, and surgical ICUs were 15.8%, 12.1%, and 4.2%, respectively, and their SMRs were 0.97 (95% CI, 0.91 to 1.03), 0.85 (95% CI, 0.78 to 0.92), and 0.67 (95% CI, 0.62 to 0.72), respectively. For weekend ICU admissions, the observed hospital mortality rates of medical, multispecialty, and surgical ICUs were 15.2%, 17.2%, and 6.4%, respectively, and for weekday ICU admissions the mortality rates were 16.3%, 10.1%, and 3.5%, respectively. Compared to weekday ICU admissions, the adjusted mortality rate for weekend ICU admissions was higher in the surgical ICU but not in the medical or multispecialty ICUs (Tables 345 ).

This study examined whether weekend ICU admission is an independent risk factor for mortality in > 29,000 patients who had been admitted to an academic medical center. Using the APACHE III prognostic system for measuring severity of illness and adjusting for variables that may influence hospital mortality, our results showed that weekend ICU admission was not associated with an overall increased risk of hospital death. However, weekend ICU admission was associated with increased hospital mortality in the subgroup of patients admitted to the surgical ICU. We also found that weekend ICU admissions are more likely to be patients who are younger and sicker, and who have received a nonoperative diagnosis.

To our knowledge, there is only one published study addressing the relationship of weekend ICU admission with mortality.6 Barnett and colleagues6 found that approximately 20% of ICU admissions occurred on the weekend, and that patients admitted on a weekend were younger, sicker, and more likely to have received a nonsurgical diagnosis. In our study, 28% of the ICU admissions occurred during the weekend, and patient characteristics were similar to those reported by Barnett et al.6

A non-ICU Canadian study1 of about 3.8 million hospital admissions has shown the association of increased mortality with weekend hospital admission for some serious medical conditions. In the study by Barnett et al6 of ICU patients, weekend ICU admission was associated with a modest increase in mortality. In contrast, our study did not show a statistically significant association between the overall adjusted mortality rate and weekend ICU admission. Many factors, including number of beds, staffing, location and teaching status of hospitals, the severity of disease, and the presence of coexisting illness, could lead to variations in hospital death rates.4,1213 The staffing of ICUs by intensivists has been reported to reduce mortality,1415 and decreased nurse staffing also has been associated with poor patient outcome.1621 One critical care study22 has shown the importance of early resuscitation in improving outcomes in critically ill patients. Prompt clinical responses are more likely if medical staff and support systems are available for patients in the ICU. Critical care fellows and residents staff our ICUs 24 h per day and 7 days per week. Patient care is guided by critical care fellowship-trained and board-certified intensivists who are available in-house every day during the daytime and when needed at night. We have also been able to maintain adequate nurse staffing despite the national shortage. Critical care pharmacists and respiratory therapists are available in each ICU. Our institution provides diagnostic and therapeutic modalities, as well as specialty consult services, throughout the week. Differences in the structure of our critical care practice, compared to some of the ICUs included in the study by Barnett et al,6 may be responsible for the absence of an overall increase in the mortality of patients admitted to the ICU on weekends in our study.

Despite application of sophisticated prognostication tools such as APACHE III, unmeasured differences in severity of illness may still exist. The current study showed that the intensity of treatment and the ICU admission source, as well as the APACHE III predicted mortality rate, were independently associated with increased hospital mortality. If we had not made an adjustment for the intensity of treatment and the ICU admission source, our results would have shown increased mortality associated with weekend ICU admission. In the study by Barnett et al,6 multiple logistic regression was used to adjust for age, gender, APACHE III acute physiology score, ICU admission source and diagnosis, and individual ICU to determine the independent relationship between ICU admission day and in-hospital mortality. Since age, APACHE III acute physiology score, and disease diagnosis were included in calculating the predicted mortality rate of each ICU admission, we did not include them in our multiple logistic regression model.11 We also did not include the ICU admission type (ie, nonoperative or postoperative) in our model because it is strongly related to the ICU admission source.

Differences in the definition of weekend, in addition to the logistic regression model used, may partly explain the differences between the results of the study by Barnett et al6 and our results. We used our current definition of weekend because, in many hospitals, the weekend work ethic with decreased resources starts at 5:00 pm on Friday and lasts until 7:00 am on Monday. In the study by Barnett et al,,6 the primary analysis involved a comparison of the term weekend, which was defined as the time between 12:00 am. Saturday and 12:00 am. Monday, with midweek ICU admissions, and they found higher mortality rates not only for patients admitted to the ICU on the weekend but also on Mondays and Fridays. However, their finding of increased mortality on Mondays and Fridays may have resulted from a spillover of weekend ICU admissions after 5:00 pm on Fridays and before 7:00 am on Mondays.

Although the overall risk of hospital death was not significantly increased with weekend ICU admission, patients admitted to a surgical ICU during the weekend had a higher adjusted mortality rate in the present study. Our methodology does not allow us to provide a good explanation for the increased risk of death associated with weekend ICU admission in the surgical ICU. However, there are a number of possible explanations for the increased mortality rate of patients admitted to the surgical ICU on the weekend. Even when patients originate from the recovery or operating room, the proportion of emergency surgical cases increases on the weekend, in the absence of electively scheduled procedures. Unmeasured confounding variables, such as a delay in seeking and delivering care, may have a more pronounced impact on the outcomes of surgical patients on the weekend. Despite our attempts to adjust for confounding variables, the APACHE III data may not account for all of the differences in the severity of illness, especially for certain groups of trauma and surgical patients. APACHE II and simplified acute physiology score II do not perform well in predicting mortality in surgical ICU patients.23Since APACHE II and simplified acute physiology score II mortality prediction models were developed primarily in medical ICUs, their poor performance in surgical ICUs is not surprising.2426 Although a large number of surgical patients were included at the development phase of the APACHE III system, it has also been shown not to perform well in predicting mortality in surgical patients.27

This study has several potential limitations. Our results cannot be extrapolated to other institutions because we included admissions to only one tertiary medical center with a unique structure of health-care delivery, and the study population lacks racial diversity. A significant number of critically ill patients are treated in the ICUs of nonteaching community hospitals without intensivists and critical care fellows to guide their care throughout the week. The adverse effects of weekend care due to inadequate ICU staffing and support systems are likely to be more pronounced in such community hospitals than in tertiary ICUs. As previously mentioned, mortality rates predicted by APACHE III may not be as accurate in surgical patients. Our analysis did not take holidays into account, which may be similar to weekend days in regard to levels of staffing and test availability. However, our institution recognizes only six national holidays, and this is unlikely to significantly affect our results. Despite adjustments for prognostically important variables, our model had poor calibration as indicated by the high Hosmer-Lemeshow statistic. Although calibration could have been improved significantly (χ2 = 5.30; p = 0.73) by replacing predicted hospital mortality with a dichotomization at the median, the estimated OR and inference for the weekend variable are not affected by this transformation.

The current study shows that weekend ICU admission is not significantly associated with overall increased adjusted hospital mortality. As long as adequate staffing is maintained, and necessary diagnostic and therapeutic modalities are available, weekend ICU admission need not be associated with poor patient outcome. However, with the anticipated shortage of critical care physicians, registered nurses, and other health-care providers, and with the pressure on medical centers to cut costs, patient care could be compromised unintentionally.2829 In academic centers, new regulations regarding resident work hours are intended to provide rested and alert physicians, but could lead to inadequate physician coverage, especially on the weekend.3031 Since critically ill patients in the ICU are particularly vulnerable, a health-care system should ensure adequate care regardless of the day and time.

Abbreviations: APACHE = acute physiology and chronic health evaluation; AUC = area under the curve; CI = confidence interval; IQR = interquartile range; OR = odds ratio; SMR = standardized mortality ratio

This research was supported by the Anesthesia Clinical Research Unit and by the Pulmonary and Critical Care Division Research Fund, Mayo Clinic and Foundation.

Table Graphic Jump Location
Table 1. Baseline Characteristics of 29,084 Patients Admitted to the ICU*
* 

RR/OR = recovery room/operating room; ED = emergency department.

 

Values given as mean (SD).

 

Values for 12 patients were missing from the database.

§ 

Values given as No. (%).

 

Values for 244 patients were missing from the database.

 

Values given as median (IQR).

# 

Values for 4 patients were missing from the database.

Table Graphic Jump Location
Table 2. Multivariate Logistic Regression Analysis Showing the Association of Variables With Hospital Mortality of 29,080 Patients Admitted to the ICU
* 

Four patients were excluded from the analysis for missing data. See Table 1 for abbreviations not used in the text.

Table Graphic Jump Location
Table 3. Multivariate Logistic Regression Analysis Showing the Association of Variables With Hospital Mortality of 6,776 Patients Admitted to the Medical ICU*
* 

Two patients were excluded from the analysis for missing data. See Table 1 for abbreviations not used in the text.

Table Graphic Jump Location
Table 4. Multivariate Logistic Regression Analysis Showing the Association of Variables With Hospital Mortality of 4,841 Patients Admitted to the Multispecialty ICU*
* 

See Table 1 for abbreviations not used in the text.

Table Graphic Jump Location
Table 5. Multivariate Logistic Regression Analysis Showing the Association of Variables With Hospital Mortality of 17,463 Patients Admitted to the Surgical ICU*
* 

Two patients were excluded from the analysis for missing data. See Table 1 for abbreviations not used in the text.

Bell, CM, Redelmeier, DA (2001) Mortality among patients admitted to hospitals on weekends as compared with weekdays.N Engl J Med345,663-668. [CrossRef] [PubMed]
 
Mangold, WD Neonatal mortality by the day of the week in the 1974–75 Arkansas live birth cohort.Am J Public Health1981;71,601-605. [CrossRef] [PubMed]
 
Hendry, RA The weekend: a dangerous time to be born?Br J Obstet Gynaecol1981;88,1200-1203. [CrossRef] [PubMed]
 
Halm, EA, Chassin, MR Why do hospital death rates vary?N Engl J Med2001;345,692-694. [CrossRef] [PubMed]
 
Tanio, C Weekend work: balancing competing interests.J Gen Intern Med1999;14,66-67. [CrossRef] [PubMed]
 
Barnett, MJ, Kaboli, PJ, Sirio, CA, et al Day of the week of intensive care admission and patient outcomes: a multisite regional evaluation.Med Care2002;40,530-539. [CrossRef] [PubMed]
 
Cullen, DJ, Civetta, JM, Briggs, BA, et al Therapeutic intervention scoring system: a method for quantitative comparison of patient care.Crit Care Med1974;2,57-60. [CrossRef] [PubMed]
 
Keene, AR, Cullen, DJ Therapeutic Intervention Scoring System: update 1983.Crit Care Med1983;11,1-3. [CrossRef] [PubMed]
 
Zimmerman, JE, Wagner, DP, Knaus, WA, et al The use of risk predictions to identify candidates for intermediate care units: implications for intensive care utilization and cost.Chest1995;108,490-499. [CrossRef] [PubMed]
 
Knaus, WA, Wagner, DP, Draper, EA, et al The range of intensive care services today.JAMA1981;246,2711-2716. [CrossRef] [PubMed]
 
Knaus, WA, Wagner, DP, Draper, EA, et al The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults.Chest1991;100,1619-1636. [CrossRef] [PubMed]
 
Park, RE, Brook, RH, Kosecoff, J, et al Explaining variations in hospital death rates: randomness, severity of illness, quality of care.JAMA1990;264,484-490. [CrossRef] [PubMed]
 
Carmel, S, Rowan, K Variation in intensive care unit outcomes: a search for the evidence on organizational factors.Curr Opin Crit Care2001;7,284-296. [CrossRef] [PubMed]
 
Young, MP, Birkmeyer, JD Potential reduction in mortality rates using an intensivist model to manage intensive care units.Eff Clin Pract2000;3,284-289. [PubMed]
 
Pronovost, PJ, Angus, DC, Dorman, T, et al Physician staffing patterns and clinical outcomes in critically ill patients: a systematic review.JAMA2002;288,2151-2162. [CrossRef] [PubMed]
 
Needleman, J, Buerhaus, P, Mattke, S, et al Nurse-staffing levels and the quality of care in hospitals.N Engl J Med2002;346,1715-1722. [CrossRef] [PubMed]
 
Tarnow-Mordi, WO, Hau, C, Warden, A, et al Hospital mortality in relation to staff workload: a 4-year study in an adult intensive-care unit.Lancet2000;356,185-189. [CrossRef] [PubMed]
 
Dang, D, Johantgen, ME, Pronovost, PJ, et al Postoperative complications: does intensive care unit staff nursing make a difference?Heart Lung2002;31,219-228. [CrossRef] [PubMed]
 
Dimick, JB, Swoboda, SM, Pronovost, PJ, et al Effect of nurse-to-patient ratio in the intensive care unit on pulmonary complications and resource use after hepatectomy.Am J Crit Care2001;10,376-382. [PubMed]
 
Dimick, JB, Pronovost, PJ, Heitmiller, RF, et al Intensive care unit physician staffing is associated with decreased length of stay, hospital cost, and complications after esophageal resection.Crit Care Med2001;29,753-758. [CrossRef] [PubMed]
 
Beckmann, U, Baldwin, I, Durie, M, et al Problems associated with nursing staff shortage: an analysis of the first 3600 incident reports submitted to the Australian Incident Monitoring Study (AIMS-ICU).Anaesth Intensive Care1998;26,396-400. [PubMed]
 
Rivers, E, Nguyen, B, Havstad, S, et al Early goal-directed therapy in the treatment of severe sepsis and septic shock.N Engl J Med2001;345,1368-1377. [CrossRef] [PubMed]
 
McNelis, J, Marini, C, Kalimi, R, et al A comparison of predictive outcomes of APACHE II and SAPS II in a surgical intensive care unit.Am J Med Qual2001;16,161-165. [CrossRef] [PubMed]
 
Osler, TM, Rogers, FB, Glance, LG, et al Predicting survival, length of stay, and cost in the surgical intensive care unit: APACHE II versus ICISS.J Trauma1998;45,234-237. [CrossRef] [PubMed]
 
Turner, JS, Morgan, CJ, Thakrar, B, et al Difficulties in predicting outcome in cardiac surgery patients.Crit Care Med1995;23,1843-1850. [CrossRef] [PubMed]
 
Jones, DR, Copeland, GP, de Cossart, L Comparison of POSSUM with APACHE II for prediction of outcome from a surgical high-dependency unit.Br J Surg1992;79,1293-1296. [CrossRef] [PubMed]
 
Barie, PS, Hydo, LJ, Fischer, E Comparison of APACHE II and III scoring systems for mortality prediction in critical surgical illness.Arch Surg1995;130,77-82. [CrossRef] [PubMed]
 
Angus, DC, Kelley, MA, Schmitz, RJ, et al Caring for the critically ill patient. Current and projected workforce requirements for care of the critically ill and patients with pulmonary disease: can we meet the requirements of an aging population?JAMA2000;284,2762-2770. [CrossRef] [PubMed]
 
Stechmiller, JK The nursing shortage in acute and critical care settings.AACN Clin Issues2002;13,577-584. [CrossRef] [PubMed]
 
Steinbrook, R The debate over residents’ work hours.N Engl J Med2002;347,1296-1302. [CrossRef] [PubMed]
 
Philibert, I, Friedmann, P, Williams, WT New requirements for resident duty hours.JAMA2002;288,1112-1114. [CrossRef] [PubMed]
 

Figures

Tables

Table Graphic Jump Location
Table 1. Baseline Characteristics of 29,084 Patients Admitted to the ICU*
* 

RR/OR = recovery room/operating room; ED = emergency department.

 

Values given as mean (SD).

 

Values for 12 patients were missing from the database.

§ 

Values given as No. (%).

 

Values for 244 patients were missing from the database.

 

Values given as median (IQR).

# 

Values for 4 patients were missing from the database.

Table Graphic Jump Location
Table 2. Multivariate Logistic Regression Analysis Showing the Association of Variables With Hospital Mortality of 29,080 Patients Admitted to the ICU
* 

Four patients were excluded from the analysis for missing data. See Table 1 for abbreviations not used in the text.

Table Graphic Jump Location
Table 3. Multivariate Logistic Regression Analysis Showing the Association of Variables With Hospital Mortality of 6,776 Patients Admitted to the Medical ICU*
* 

Two patients were excluded from the analysis for missing data. See Table 1 for abbreviations not used in the text.

Table Graphic Jump Location
Table 4. Multivariate Logistic Regression Analysis Showing the Association of Variables With Hospital Mortality of 4,841 Patients Admitted to the Multispecialty ICU*
* 

See Table 1 for abbreviations not used in the text.

Table Graphic Jump Location
Table 5. Multivariate Logistic Regression Analysis Showing the Association of Variables With Hospital Mortality of 17,463 Patients Admitted to the Surgical ICU*
* 

Two patients were excluded from the analysis for missing data. See Table 1 for abbreviations not used in the text.

References

Bell, CM, Redelmeier, DA (2001) Mortality among patients admitted to hospitals on weekends as compared with weekdays.N Engl J Med345,663-668. [CrossRef] [PubMed]
 
Mangold, WD Neonatal mortality by the day of the week in the 1974–75 Arkansas live birth cohort.Am J Public Health1981;71,601-605. [CrossRef] [PubMed]
 
Hendry, RA The weekend: a dangerous time to be born?Br J Obstet Gynaecol1981;88,1200-1203. [CrossRef] [PubMed]
 
Halm, EA, Chassin, MR Why do hospital death rates vary?N Engl J Med2001;345,692-694. [CrossRef] [PubMed]
 
Tanio, C Weekend work: balancing competing interests.J Gen Intern Med1999;14,66-67. [CrossRef] [PubMed]
 
Barnett, MJ, Kaboli, PJ, Sirio, CA, et al Day of the week of intensive care admission and patient outcomes: a multisite regional evaluation.Med Care2002;40,530-539. [CrossRef] [PubMed]
 
Cullen, DJ, Civetta, JM, Briggs, BA, et al Therapeutic intervention scoring system: a method for quantitative comparison of patient care.Crit Care Med1974;2,57-60. [CrossRef] [PubMed]
 
Keene, AR, Cullen, DJ Therapeutic Intervention Scoring System: update 1983.Crit Care Med1983;11,1-3. [CrossRef] [PubMed]
 
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    Print ISSN: 0012-3692
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