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Caring for the Critically Ill Patient |

Physician Staffing Patterns and Clinical Outcomes in Critically Ill Patients:  A Systematic Review FREE

Peter J. Pronovost, MD, PhD; Derek C. Angus, MB, ChB, MPH; Todd Dorman, MD; Karen A. Robinson, MSc; Tony T. Dremsizov, MBA; Tammy L. Young
[+] Author Affiliations

Author Affiliations: Departments of Anesthesiology and Critical Care Medicine (Drs Pronovost and Dorman), Surgery (Drs Pronovost and Dorman), Medicine (Dr Dorman), Health Policy and Management (Dr Pronovost), and Epidemiology, Bloomberg School of Public Health (Ms Robinson), Johns Hopkins University, Baltimore, Md; and the Clinical Research, Investigation, and Systems Modeling of Acute Illness (CRISMA) Laboratory, Department of Critical Care Medicine (Dr Angus, Mr Dremsizov, and Ms Young), and Department of Health Policy and Management, Graduate School of Public Health (Dr Angus), University of Pittsburgh, Pittsburgh, Pa.


Caring for the Critically Ill Patient Section Editor: Deborah J. Cook, MD, Consulting Editor, JAMA.


JAMA. 2002;288(17):2151-2162. doi:10.1001/jama.288.17.2151.
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Published online

Context Intensive care unit (ICU) physician staffing varies widely, and its association with patient outcomes remains unclear.

Objective To evaluate the association between ICU physician staffing and patient outcomes.

Data Sources We searched MEDLINE (January 1, 1965, through September 30, 2001) for the following medical subject heading (MeSH) terms: intensive care units, ICU, health resources/utilization, hospitalization, medical staff, hospital organization and administration, personnel staffing and scheduling, length of stay, and LOS. We also used the following text words: staffing, intensivist, critical, care, and specialist. To identify observational studies, we added the MeSH terms case-control study and retrospective study. Although we searched for non–English-language citations, we reviewed only English-language articles. We also searched EMBASE, HealthStar (Health Services, Technology, Administration, and Research), and HSRPROJ (Health Services Research Projects in Progress) via Internet Grateful Med and The Cochrane Library and hand searched abstract proceedings from intensive care national scientific meetings (January 1, 1994, through December 31, 2001).

Study Selection We selected randomized and observational controlled trials of critically ill adults or children. Studies examined ICU attending physician staffing strategies and the outcomes of hospital and ICU mortality and length of stay (LOS). Studies were selected and critiqued by 2 reviewers. We reviewed 2590 abstracts and identified 26 relevant observational studies (of which 1 included 2 comparisons), resulting in 27 comparisons of alternative staffing strategies. Twenty studies focused on a single ICU.

Data Synthesis We grouped ICU physician staffing into low-intensity (no intensivist or elective intensivist consultation) or high-intensity (mandatory intensivist consultation or closed ICU [all care directed by intensivist]) groups. High-intensity staffing was associated with lower hospital mortality in 16 of 17 studies (94%) and with a pooled estimate of the relative risk for hospital mortality of 0.71 (95% confidence interval [CI], 0.62-0.82). High-intensity staffing was associated with a lower ICU mortality in 14 of 15 studies (93%) and with a pooled estimate of the relative risk for ICU mortality of 0.61 (95% CI, 0.50-0.75). High-intensity staffing reduced hospital LOS in 10 of 13 studies and reduced ICU LOS in 14 of 18 studies without case-mix adjustment. High-intensity staffing was associated with reduced hospital LOS in 2 of 4 studies and ICU LOS in both studies that adjusted for case mix. No study found increased LOS with high-intensity staffing after case-mix adjustment.

Conclusions High-intensity vs low-intensity ICU physician staffing is associated with reduced hospital and ICU mortality and hospital and ICU LOS.

Figures in this Article

Approximately 1% of the US gross domestic product is consumed in the care of intensive care unit (ICU) patients.1 Despite this considerable investment of resources, there is wide variation in ICU organization,2,3 and studies have suggested that differences in ICU organization may affect patient outcome. For example, staffing ICUs with critical care physicians (intensivists) may improve clinical outcomes.4 A conceptual model that explains this finding is that physicians who have the skills to treat critically ill patients and who are immediately available to detect and treat problems may prevent or attenuate morbidity and mortality.2 Staffing ICUs with intensivists may also decrease resource use because these physicians may be better at reducing inappropriate ICU admissions, preventing complications that prolong length of stay (LOS), and recognizing opportunities for prompt discharge.2

Intensive care unit staffing is typical of an organizational issue in health care in that, despite its potential importance in clinical and economic outcomes, it is not studied by using randomized trials. For example, the widely held belief that outcomes are better after surgery performed by experienced surgeons or hospitals is based solely on observational data.5 Practical and ethical reasons exist to explain why such organizational characteristics are not subjected to randomized trials. Yet, as changes occur in the way health care is organized, financed, and delivered, it will be important to understand the impact of organizational characteristics, such as ICU physician and nurse staffing, on patient outcomes through systematic reviews.6 To inform health policy, we will need to synthesize evidence that is predominantly observational. Accordingly, the goal of this systematic review was to examine the effect of ICU physician staffing on hospital and ICU mortality and LOS.

Study Selection Criteria

We sought to identify and review all studies that met the following criteria: randomized or observational controlled trials of critically ill adults or children, ICU physician staffing strategies, hospital and ICU mortality, and LOS.

Citation Search Strategy

To identify literature in electronic databases, we searched MEDLINE from January 1, 1965, through September 30, 2001, by using the following medical subject heading (MeSH) terms: intensive care units, ICU, health resources/utilization, hospitalization, medical staff, hospital organization and administration, personnel staffing and scheduling, length of stay, and LOS. We used the following text words: staffing, intensivist, critical, care, and specialist. We used the search strategy for retrieval of controlled clinical trials proposed by Robinson and Dickersin.7 To identify observational studies, we added the MeSH terms case-control study and retrospective study.

We also searched EMBASE, HealthStar (Health Services, Technology, Administration, and Research), and HSRPROJ (Health Services Research Projects in Progress) via Internet Grateful Med and The Cochrane Library (1998, issue 3), which contains the CENTRAL Database of Controlled Trials, the Database of Abstracts of Review Effectiveness, and the Cochrane Database of Systematic Reviews.

In addition, we used the related articles feature of PubMed, which identifies related articles by using a hierarchical search engine that is not solely based on MeSH headings. This search was completed with articles selected by 2 of the authors (P.J.P. and D.C.A.).812 Although we searched for non–English-language citations, subsequent article review involved only English-language publications. To identify studies published in abstract form only, we hand-searched the abstract proceedings from the annual scientific assemblies of the Society of Critical Care Medicine, the American College of Chest Physicians, and the American Thoracic Society from January 1, 1994, through December 31, 2001.

Study Selection

After all citations based on our search strategy were identified, 2 of the authors (P.J.P. and D.C.A.) independently reviewed each abstract to confirm eligibility. If an abstract was selected as eligible, the same authors independently reviewed the respective article, if available, to confirm that it met inclusion criteria. Abstracts from meeting proceedings were included if the data were not published as peer-reviewed articles. To resolve discrepancies, the 2 reviewers either had to reach consensus, or use a third reviewer (T.D.).

Data Extraction

Using a data collection form, we extracted data from the studies to describe patient characteristics, study methods, and study findings. We also abstracted quantitative data regarding the intervention, cointerventions, study design and duration, unit of analysis, risk adjustment, degree of follow-up, adjustment of historical trends, and type of ICU. All data were abstracted independently by each of the 2 primary reviewers and verified for accuracy by the third reviewer, again with discussion used to resolve differences among reviewers. All reviewers were intensivists with formal training in clinical epidemiology and biostatistics. We did not mask the reviewers to author, institution, or journal because such masking reportedly makes little difference to the results of a systematic review.13

Data Synthesis and Analysis

We measured the percentage of agreement before discussion among reviewers in study selection, study design, and data abstraction. For data synthesis, we constructed evidence tables to present data separately for the 4 main outcome variables: hospital mortality, ICU mortality, hospital LOS, and ICU LOS. Because of wide variation in the methods used to evaluate hospital costs, we did not include cost as an outcome.

We classified the study design as a randomized clinical trial, cohort study (prospective, retrospective, or historical control), case-control study, or outcomes study (cross-sectional). We classified the method of risk adjustment as follows: validated physiologic method (discrimination and calibration of the model previously reported), selected clinical data (discrimination and calibration of the model not reported), and no risk adjustment.

Because ICU physician staffing varied widely among studies in the control and intervention groups, we initially classified ICU physician staffing as follows: (1) closed ICU (the intensivist is the patient's primary attending physician), (2) mandatory critical care consultation (the intensivist is not the patient's primary attending physician, but every patient admitted to the ICU receives a critical care consultation), (3) elective critical care consultation (the intensivist is involved in the care of the patient only when the attending physician requests a consultation), and (4) no critical care physician (intensivists were unavailable). Because it is difficult to distinguish between a closed ICU and a mandatory critical care consultation, and because in several studies we were not able to do so, we further grouped ICU physician staffing into high intensity (mandatory intensivist consultation or closed ICU) or low intensity (no intensivist or elective intensivist consultation).

Evaluation of Study Quality

We elected to evaluate study quality as the risk of bias caused by temporal trends, confounding, and incomplete follow-up. We classified the risk of bias caused by temporal trends as low if the study duration was shorter than 2 years, medium if 2 through 4 years, and high if longer than 4 years. We classified the risk of bias from confounding as low if the authors used a validated physiologic method of risk adjustment, medium if the authors used selected clinical data, and high if the authors used no risk adjustment. We classified the risk of bias from incomplete follow-up as low if it was 90% to 100% complete; medium for 80% to 89% complete; and high for less than 80% complete.

Data Analysis

Because the studies varied markedly in design, risk adjustment method, and ICU physician staffing in the control and intervention groups, we performed a qualitative and quantitative assessment of heterogeneity among trials. Because we considered the qualitative heterogeneity among studies to be significant, we were reluctant to perform a quantitative synthesis of study results.14 Nevertheless, we used the test for quantitative heterogeneity.15,16 We present a random-effects, summary relative risk (RR) by using the methods of DerSimonian.17 When the data were available, we summarized mortality data from each study with RRs, odds ratios (ORs), and estimated 95% confidence intervals (CIs) for the ORs by using Woolf's method.18 We summarized LOS data as a relative reduction. We evaluated for publication bias with a funnel plot. All statistical calculations were performed with STATA 7.0 statistical software (STATA Corp, College Station, Tex). When possible, we reported unadjusted and adjusted outcomes for baseline severity of illness. When absolute rates of hospital mortality were unavailable, we reported the observed-expected mortality rate, and when the SD of LOS data were unavailable, we assumed it to be equal to the mean.2 We used mean rather than median LOS because few studies reported medians. Results were considered significant at P<.05.

Study Selection and Characteristics

We identified 3544 citations from the electronic search, of which 660 were duplicates and 294 were unavailable in English and were excluded. We also identified 13 citations from hand searching meeting proceedings. Of the 2590 abstracts reviewed, we rejected 2556 (99%) because the intervention was not ICU physician staffing or because the published abstract was superseded by the subsequent article. We rejected an additional 8 abstracts after reviewing and discussing the corresponding article because the intervention was not ICU physician staffing or because the reviewers were not able to determine the type of ICU physician staffing.1926 Twenty-six studies2,812,2746 met selection criteria (19 articles and 7 published abstracts). The reviewers had 99% crude agreement in the selection of eligible abstracts and 96% crude agreement in the selection of eligible articles (Table 1a). Figure 147 presents the study search strategy (QUOROM: Quality of Reporting of Meta-analyses).

Table Graphic Jump LocationTable 1a. Characteristics of Reviewed Studies Concerning ICU Physician Staffing and Outcomes*
Figure 1. Study Flow Diagram
Graphic Jump Location
ICU indicates intensive care unit; LOS, length of stay. The asterisk indicates that the article by Multz et al11 had 2 comparisons (retrospective and prospective).

Twenty studies (77%) were from North America,2,8,11,12,2735,3742,46 3 (12%) were from Europe,9,44,45 and 3 (12%) were from Asia.10,36,43 Eleven (42%) were from academic medical centers,810,12,28,29,31,34,43,45,46 6 (23%) were from community teaching hospitals,11,27,32,33,36,41 4 (15%) were from nonteaching community hospitals,30,35,38,44 and 5 (19%) included a variety of hospitals2,37,39,40,42 (3 studies included all hospitals in Maryland2,39,40). One article included a prospective and retrospective control arm.11 Because our goal was to describe the available literature, we treated this article as 2 studies and thus had 27 studies for qualitative synthesis (Table 1).

Table 1 summarizes important aspects of these 27 studies, which included ICU patients treated between 1979 and 2000. Study populations included medical patients in 11 studies (41%),11,12,27,28,32,36,38,42,44,45 surgical patients in 9 (33%),2,10,29,31,33,3941,46 mixed medical and surgical patients in 4 (15%),8,9,30,35 and pediatric patients in 3 (11%).34,37,43 Sample sizes varied from 177 to 5415 patients, with a mean sample size of 1001 patients (SD, 1190) and a median sample size of 551 patients (25%-75% interquartile range, 277-1213).

Study Design

All of the studies used an observational design (Table 1). Twenty-two were cohort studies, with 19 using historical controls (before-and-after design),812,2836,38,41,4345 2 using concurrent controls,11,46 and 1 using both.27 Five studies were cross-sectional with concurrent controls.2,37,39,40,42 In one study, the ICU physician staffing in the intervention group was via remote videoconferencing.41 Twenty of the studies evaluated a single ICU,812,2836,38,41,4346 2 evaluated 2 ICUs,11,27 1 evaluated 16 ICUs,37 1 evaluated 35 ICUs,39 1 evaluated 39 ICUs,2 1 evaluated 42 ICUs,42 and 1 did not report the number of ICUs evaluated.40

ICU Physician Staffing

Twenty-five studies compared high with low-intensity ICU physician staffing. Of the remaining 2, one compared a closed ICU with a mandatory consultation28 and the other compared elective consultation with no intensivist involved.32 Of the 25 studies comparing high with low-intensity staffing, 9 compared a closed ICU (intervention group) with elective consultation (control group),9,11,27,29,33,4244 3 compared mandatory consultation (intervention) with no intensivist (control),34,37,46 5 compared mandatory consultation (intervention) with elective consultation (control group),2,3841 and 5 compared closed ICU (intervention) with no intensivist (control).8,30,35,36,45 In 2 studies, we could not differentiate between a closed ICU and a mandatory consultation,10,12 and in 2 studies10,31 we could not differentiate between an elective consultation and no intensivist.

Quality Characteristics

The quality characteristics of the studies are listed in Table 2. Fifteen of the 24 studies that reported the study period had low risk of bias from temporal trends, whereas 8 studies had medium risk and 1 had high risk. All 27 studies had complete follow-up and thus a low risk of bias from incomplete follow-up. No study followed up patients after hospital discharge.

Table Graphic Jump LocationTable 2. Quality Characteristics of Reviewed Studies*

Twenty-one of 27 studies had low risk of bias from confounding, whereas 6 studies had medium risk. All studies reported some form of risk adjustment. Twenty-one studies used a validated physiologic method (15 used the Acute Physiology and Chronic Health Evaluation Score [APACHE] only,48,49 2 used the Mortality Prediction Model,50 2 used the Pediatric Risk of Mortality Score,51,52 1 used the Physiologic Severity Index [PSI],53 and 1 reported both APACHE II and the Glasgow Coma Scale54). Six studies used selected clinical data (the first used nursing hours per patient,35 a second used age, reason for admission, and mental status,30 a third used a customized case-mix index and patient acuity measured by percentage of patients requiring mechanical ventilatory support,38 and 3 others used discharge data in a regression model to adjust for patient demographics, severity of illness, comorbid disease, and hospital and surgeon volume2,39,40) (Table 1).

Eleven studies reported differences in severity of illness between the high- and low-intensity groups. In 4 studies,28,31,45,46 the high-intensity group compared with the low-intensity group had significantly higher APACHE scores, suggesting higher baseline severity of illness. Three studies reported higher severity in the low-intensity group by using different severity instruments.4244 Two studies reported higher baseline severity in the high-intensity group by using the distribution of the PSI score34 and APACHE II score.10 Another study reported higher ICU nursing hours per day and suggested that this represented higher severity in the high-intensity physician staffing group.35 The author of the study,38 which used patient acuity and case-mix index, also suggested greater severity in the arm with the high-intensity physician staffing. There was no evidence of publication bias on a funnel plot of hospital mortality (Figure 2).

Figure 2. Funnel Plot of Hospital Mortality
Graphic Jump Location
The funnel plot provides an estimate of publication bias. In the absence of bias, the studies should be symmetrically distributed along the funnel. If small studies with negative results are unpublished, the plot will appear asymmetrical. Our plot suggests no evidence of publication bias. Log OR indicates log odds ratio.
Impact of High vs Low-Intensity ICU Physician Staffing

Hospital Mortality. Seventeen studies (63%) reported hospital mortality according to ICU physician staffing as a primary outcome measure (Table 3). The hospital mortality rate ranged from 6% to 74% in the low-intensity staffing group and from 1% to 57% in the high-intensity staffing group (Table 3). Overall, 16 (94%) of the 17 studies showed a decrease in hospital mortality rate for ICU patients with high-intensity physician staffing; in the one study that showed increased mortality with high-intensity physician staffing, the increase was not statistically significant.28 In 10 (67%) of 15 studies2,8,9,12,32,3942,44 that reported unadjusted mortality and 9 (64%) of 14 studies2,8,12,30,32,37,40,41,44 that reported adjusted mortality, the decrease was statistically significant (Table 3). No study reported a statistically significant increase in hospital mortality with high-intensity ICU physician staffing. The random-effects pooled estimate of the unadjusted RR for high-intensity vs low-intensity staffing is 0.71 (95% CI, 0.62-0.82) (Figure 3A).

Table Graphic Jump LocationTable 3. Hospital and ICU Mortality With Low- and High-Intensity ICU Physician Staffing*
Figure 3. Unadjusted Hospital and ICU Mortality With Low- and High-Intensity ICU Physician Staffing
Graphic Jump Location
Data from studies demonstrate the relative risk (RR) with 95% confidence intervals (CI) of hospital and intensive care unit (ICU) mortality with high intensity vs low intensity ICU physician staffing. The RRs less than 1 suggest reduced mortality with high intensity staffing while RRs greater than 1 suggest increased mortality with high intensity staffing. The size of the data markers corresponds to the weight of the studies. Larger markers imply less uncertainty from the results of the individual study, and carry more weight in calculating the random effects pooled estimate from the systematic review.

ICU Mortality. Fifteen studies (56%) evaluated the impact of ICU physician staffing on ICU mortality, with 12 studies (80%) reporting ICU mortality adjusted for severity of illness (Table 3). Overall, 14 (93%) of these 15 studies810,27,29,3136,38,41,43 showed a decrease in ICU mortality rate for ICU patients with high-intensity physician staffing. Nine (69%) of the 13 studies810,29,32,35,38,41,43 that reported unadjusted ICU mortality rates found a statistically significant reduction with high-intensity physician staffing in the ICU (Figure 3B and Table 3). In 9 (75%) of the 12 studies810,29,32,34,35,41,43 that adjusted for severity of illness, ICU mortality significantly decreased as well with high-intensity physician staffing. The random-effects, pooled estimate of the unadjusted RR for high-intensity vs low-intensity staffing is 0.61 (95% CI, 0.50-0.75).

Hospital LOS. Thirteen studies (48%) evaluated the impact of ICU physician staffing on hospital LOS (Table 4). The hospital LOS ranged from 8 to 33 days in the low-intensity group and 7 to 24 days in the high-intensity group. Ten (77%) of 13 studies reported a reduction in hospital LOS with high-intensity staffing (range of relative reduction, 5%-42%).2,11,28,32,36,39,40,44,46 In 6 of these studies, the reduction was statistically significant (Figure 4A).2,11,32,39,46 Only 1 study (8%) reported a statistically significant increase in hospital LOS with high-intensity physician staffing, but this study compared patients admitted to a neurosurgical ICU with patients admitted to a general ICU, and the results were not adjusted for baseline severity of illness.42 Only 4 studies adjusted hospital LOS for baseline severity of illness.2,3941 Two of these studies2,39 showed a statistically significant decrease in hospital LOS with high-intensity physician staffing in the ICU, with the remaining 2 studies40,41 showing no significant difference in hospital LOS.39

Table Graphic Jump LocationTable 4. Hospital and ICU Length of Stay with Low- and High-Intensity ICU Physician Staffing*
Figure 4. Unadjusted Hospital and Intensive Care Unit (ICU) Length of Stay (LOS) With Low- and High-Intensity ICU Physician Staffing
Graphic Jump Location
Data from studies are plotted with the high-intensity mean LOS as a y-coordinate and the low-intensity mean LOS as an x-coordinate with the 95% confidence intervals (error bars) calculated by the authors of the systemic review. A discrepancy exists between the plotting of the error bars for study 10 in panel B (error bar crosses the line of equivalency) and P<.001 (as reported by Carson et al). The diagonal line represents the line of equivalency. Data points below the line of equivalency suggest shorter LOS in the high-intensity group, and those above the line suggest shorter LOS in the low-intensity group. Numbers refer to references (r indicates retrospective; p, prospective). Asterisks indicate SD, assumed to be equal to the mean LOS.

Intensive Care Unit LOS. Eighteen studies (67%) evaluated the impact of ICU physician staffing on ICU LOS (Table 4). The ICU LOS ranged from 2 to 13 days in the low-intensity group and 2 to 10 days in the high-intensity group. Fourteen (78%) of 18 studies reported that ICU LOS decreased with high-intensity physician staffing (Figure 4B).2,10,11,29,30,32,33,36,38,41,43,44,46 In 11 of these studies, this decrease was statistically significant.2,10,11,32,33,36,38,41,43,46 The study that compared a closed neurosurgical ICU to a general ICU was the only one to report a statistically significant increase in ICU LOS with high-intensity ICU physician staffing in the neurosurgical ICU.42 Three of 18 studies reported higher severity in the high-intensity group,28,38,46 2 reported higher severity in the low-intensity group,43,44 and the remaining 13 reported no difference between the 2 groups.2,1012,29,30,3234,36,41,42 Only 2 studies adjusted ICU LOS for baseline severity of illness2,42; ICU LOS in both studies favored high-intensity physician staffing.

We found that greater use of intensivists in the ICU led to significant reductions in ICU and hospital mortality and LOS. These findings were consistent across a variety of populations and hospital settings and have potentially important implications for patient care. Given the variation in ICU physician staffing and the potential for reduced mortality implied by these studies, a more rigorous evaluation of the optimal ICU organization is essential.

Intensive care is one of the largest and most expensive aspects of US health care. There are approximately 6000 ICUs in the United States,55 caring for approximately 55 000 patients daily,55 with an annual budget of approximately $180 billion.1 The proportion of ICUs with high-intensity ICU physician staffing is unclear, but appears to be relatively small. In 1992, Groeger et al3 suggested that only 10% of ICUs in the United States require an intensivist to act as the patients' primary physician. In 1999, Schmitz et al55 estimated that one third of all ICU patients in the United States were treated by intensivists acting as either primary physicians or consultants. Since most ICU patients are cared for with low-intensity physician staffing and high-intensity staffing appears to be associated with improved outcomes, mandatory ICU physician staffing may improve ICU process and outcomes.

The general lack of intensivist staffing in the United States contrasts with the usual closed ICU approach in Europe and Australia. A survey56 by the Audit Commission for Local Authorities and the National Health Service in England and Wales found that closed systems are common and intensivists initiate care in 80% of all ICUs. The average 6-bed general ICU in the United Kingdom has 3 consultants with fixed commitments to the unit and 3 more taking part in the on-call rota.56 According to Cole et al,57 all ICUs in Victoria, the second most populous state in Australia, have been following the closed model for more than a decade. In 1997, a task force of the European Society of Intensive Care Medicine58 issued recommendations on minimal requirements for intensive care departments (ICDs). Although the recommendations were not evidence based, the task force emphasized that the director of an ICD should be an intensivist and that it is essential that a qualified intensivist provide 24-hour coverage in level II and III (moderate- and high-intensity care) ICDs.58 The task force also recommended 24-hour coverage by an intensivist for level I ICDs.58

Our review identified several issues that may be important for researchers studying health care organizational characteristics. Our initial search, based on MeSH terms and text words, yielded a large number of citations, yet failed to identify several relevant articles that we had previously identified.8,9,11,12,28,30,32,34 Although each shared intensive care unit as a MeSH term, the assignment of other MeSH terms was inconsistent. By incorporating the related articles feature, we were able to identify additional relevant articles. The configuration of MeSH terms is not ideal for a comprehensive review of health care organizational characteristics, and investigators and library scientists should improve this indexing situation.

There are a number of potential limitations to consider regarding this literature. First, there is a risk of selection bias. Mark59 describes 3 areas of possible selection bias in critical appraisal: selection of representative subjects (generalizability), selection of subjects to exposure (confounding variables), and selection of subjects at outcome (distorted samples). We believe the findings are generalizable because there was a consistent benefit associated with high-intensity staffing in studies of medical and surgical patients, studies from academic and community hospitals, and studies from inside and outside the United States. Because the studies are not randomized, the risk of confounding variables is considerable. However, an important strength of this literature was the consistent use of risk-adjustment methods. Critical care medicine has developed sophisticated, well-validated, risk-adjustment methods that use multiple clinical and physiologic variables to predict the risk of in-hospital death.4852 In our analysis, 22 (81%) of 27 studies used such methods to minimize bias from confounding variables. Finally, all 27 studies had complete follow-up, and there was therefore no risk of bias from distorted samples.

A second potential limitation is publication bias. However, the funnel plot suggested that risk for publication bias was not significant (Figure 2). There was no quantitative heterogeneity among studies, and the results were consistent across studies, increasing our confidence in the validity of our conclusions. Moreover, from our discussions with staff of critical care societies (American Thoracic Society, American College of Chest Physicians, and Society of Critical Care Medicine at their annual meetings during 1999-2001), we found no evidence of any relevant negative unpublished studies.

A third potential limitation is risk for temporal trends in mortality to bias study results. Temporal trends in any before-and-after study design could affect the results of this review and reduce the strength of our inferences. We believe this source of bias is small for several reasons. First, evidence for the effectiveness of therapies in reducing mortality in critically ill patients occurred only at the end of the study periods.6062 Second, there were no trends for reduced mortality in critically ill patients during the study periods. Third, most of the studies were conducted during a short period, and thus the effect of any temporal trends is likely small.

A fourth potential limitation is the use of ICU mortality and LOS as outcome measures. Because no study described explicit criteria for discharge from the ICU, differences in discharge practices between the treatment and control groups may have influenced the results. For example, early ICU discharge may have artificially reduced ICU mortality without decreasing hospital mortality. However, the improvement in mortality and LOS observed with high-intensity ICU physician staffing was observed at ICU and hospital discharge.

There are also limitations in the way we conducted our review. First, 3 of the authors (P.J.P., D.C.A., and T.D.) are intensivists and potentially biased. The high degree of agreement among reviewers may be due to similar clinical and research interests and may have encoded systematic error. Second, we included only articles published in English, although we are not aware of relevant non–English-language publications. The exclusion of non–English-language articles should not significantly affect the study results.63 Third, we did not perform a formal evaluation of study quality, because the particular scale chosen may influence the results.64 Rather, we identified relevant methodologic aspects of the study (a priori) and assessed these individually.

Our systematic review was rigorously conducted and transparently reported, following recommendations outlined by the Meta-analysis of Observational Studies in Epidemiology Group.14 Because it is unclear how to proceed when there is qualitative but not quantitative heterogeneity among studies, we present pooled estimates by using the random-effects model and recommend cautious interpretation of these results.

We should attempt to identify the characteristics of high-intensity ICU staffing that improved outcome. We found previously that daily rounds by an ICU physician were associated with improved outcomes in patients who underwent abdominal aortic surgery. Yet how daily rounds translate into improved outcomes remains unclear.2 For example, were the improved outcomes due to specific critical care training and expertise or to increased availability, perhaps with reduced response time, of a team of physicians whose sole responsibility was to provide care in the ICU? Some of the improvements may be possible through alternative staffing models, such as telemedicine.41 Finally, other ICU characteristics, such as nurse-to-patient ratios, also affect patient outcomes.65 Determining how to best organize ICU staffing from a multidisciplinary standpoint to optimize patient outcomes is a high research priority. Meanwhile, our findings provide evidence to support the recommendations by the Leapfrog Group66,67 and Society of Critical Medicine for ICU physician staffing.68 We believe this systematic review summarizes and clarifies the available literature, helps guide public policy, and provides a basis for future research.

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Stroup DF, Berlin JA, Morton SC.  et al. for the Meta-analysis of Observational Studies in Epidemiology (MOOSE) Group.  Meta-analysis of observational studies in epidemiology: a proposal for reporting.  JAMA.2000;283:2008-2012.
L'Abbe KA, Detsky AS, O'Rourke K. Meta-analysis in clinical research.  Ann Intern Med.1987;107:224-233.
Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias.  Biometrics.1994;50:1088-1101.
DerSimonian R. Meta-analysis in clinical trials.  Control Clin Trials.1986;7:177-188.
Woolf B. On estimating the relation between blood group and disease.  Ann Hum Genet.1955;19:251-253.
Eagle KA, Mulley AG, Field TS.  et al.  Variation in intensive care unit practices in two community hospitals.  Med Care.1991;29:1237-1245.
Mirski MA, Chang CW, Cowan R. Impact of a neuroscience intensive care unit on neurosurgical patient outcomes and cost of care.  J Neurosurg Anesthesiol.2001;13:83-92.
Park CA, McGwin GJ, Smith DR.  et al.  Trauma-specific intensive care units can be cost effective and contribute to reduced hospital length of stay.  Am Surg.2001;67:665-670.
Knaus WA, Draper EA, Wagner DP, Zimmerman JE. An evaluation of outcome from intensive care in major medical centers.  Ann Intern Med.1986;104:410-418.
Teres D, Brown RB, Lemeshow S, Parsells JL. A comparison of mortality and charges in two differently staffed intensive care units.  Inquiry.1983;20:282-289.
Lima C, Levy MM. The impact of an on-site intensivist on patient charges and length of stay in the medical intensive care unit [abstract].  Crit Care Med.1995;23:A238.
Cowen J, Matchett S, Kaufman J, Baker K, Wasser T, Ray D. Progressive reduction in severity-adjusted mortality after implementation of a critical care program [abstract].  Crit Care Med.1999;27:A35.
Pollack MM, Patel KM, Ruttimann E. Pediatric critical care training programs have a positive effect on pediatric intensive care mortality.  Crit Care Med.1997;25:1637-1642.
Al-Asadi L, Dellinger RP, Deutch J, Nathan SS. Clinical impact of closed versus open provider care in a medical intensive care unit [abstract].  Am J Respir Crit Care Med.1996;153:A360.
Carson SS, Stocking C, Podsadecki T.  et al.  Effects of organizational change in the medical intensive care unit of a teaching hospital.  JAMA.1996;276:322-328.
Ghorra S, Reinert SE, Cioffi W, Buczko G, Simms HH. Analysis of the effect of conversion from open to closed surgical intensive care unit.  Ann Surg.1999;229:163-171.
Li TC, Phillips MC, Shaw L, Cook EF, Natanson C, Goldman L. On-site physician staffing in a community hospital intensive care unit.  JAMA.1984;252:2023-2027.
Jacobs MC, Hussain E, Hanna A.  et al.  Improving the outcome and efficiency of surgical intensive care: the impact of full time medical intensivists [abstract].  Chest.1998;114:276S-277S.
Manthous CA, Amoateng-Adjepong Y, al-Kharrat T.  et al.  Effects of a medical intensivist on patient care in a community teaching hospital.  Mayo Clin Proc.1997;72:391-399.
Marini CP, Nathan IM, Ritter G, Rivera L, Jurkiewicz A, Cohen JR. The impact of full-time surgical intensivists on ICU utilization and mortality [abstract].  Crit Care Med.1995;23:A235.
Pollack MM, Katz RW, Ruttimann UE, Getson PR. Improving the outcome and efficiency of intensive care: the impact of an intensivist.  Crit Care Med.1988;16:11-17.
Reich HS, Buhler L, David M, Whitmer G. Saving lives in the community: impact of intensive care leadership [abstract].  Crit Care Med.1998;25:A44.
Tai DYH, Goh SK, Eng PCT, Wang YT. Impact on quality of patient care and procedure use in the medical intensive care unit (MICU) following reorganisation.  Ann Acad Med Singapore.1998;27:309-313.
Pollack MM, Cuerdon TT, Patel KM, Ruttimann UE, Getson PR, Levetown M. Impact of quality-of-care factors on pediatric intensive care unit mortality.  JAMA.1994;272:941-946.
DiCosmo BF. Addition of an intensivist improves ICU outcomes in a non-teaching community hospital [abstract].  Chest.1999;116:238S.
Dimick JB, Pronovost PJ, Heitmiller RF, Lipsett PA. Intensive care unit physician staffing is associated with decreased length of stay, hospital cost, and complications after esophageal resection.  Crit Care Med.2001;29:753-758.
Dimick JB, Pronovost PJ, Lipsett PA. The effect of ICU physician staffing and hospital volume on outcomes after hepatic resection [abstract].  Crit Care Med.2000;28:A77.
Rosenfeld BA, Dorman T, Breslow MJ.  et al.  Intensive care unit telemedicine: alternate paradigm for providing continuous intensivist care.  Crit Care Med.2000;28:3925-3931.
Diringer MN, Edwards DF. Admission to a neurologic/neurosurgical intensive care unit is associated with reduced mortality rate after intracerebral hemorrhage.  Crit Care Med.2001;29:635-640.
Goh AYT, Lum LCS, Abdel-Latif MEA. Impact of 24 hour critical care physician staffing on case-mix adjusted mortality in paediatric intensive care.  Lancet.2001;357:445-446.
Blunt MC, Burchett KR. Out-of-hours consultant cover and case-mix-adjusted mortality in intensive care.  Lancet.2000;356:735-736.
Topeli A. Effect of changing organization of intensive care unit from "open policy without critical care specialist" to "closed policy with critical care specialist" [abstract].  Am J Respir Crit Care Med.2000;161:A397.
Hanson CW, Deutschman CS, Anderson HL.  et al.  Effects of an organized critical care service on outcomes and resource utilization: a cohort study.  Crit Care Med.1999;27:270-274.
Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, Stroup DF. Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement.  Lancet.1999;354:1896-1900.
Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system.  Crit Care Med.1985;13:818-829.
Knaus WA, Wagner DP, Draper EA.  et al.  The APACHE III prognostic system.  Chest.1991;100:1619-1636.
Lemeshow S, Teres D, Klar J, Avrunin JS, Gehlbach SH, Rapoport J. Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients.  JAMA.1993;270:2478-2486.
Pollack MM, Ruttimann UE, Getson PR. Pediatric risk of mortality (PRISM) score.  Crit Care Med.1988;16:1110-1116.
Pollack MM, Patel KM, Ruttimann UE. PRISM III: an updated pediatric risk of mortality score.  Crit Care Med.1996;24:743-752.
Yeh TS, Pollack MM, Ruttimann UE, Holbrook PR, Fields AI. Validation of a physiologic stability index for use in critically ill infants and children.  Pediatr Res.1984;18:445-451.
Teasdale GM, Jennett B. Assessment of coma and impaired consciousness.  Lancet.1974;2:81-84.
Schmitz R, Lantin M, White A. Future Workforce Needs in Pulmonary and Critical Care Medicine. Cambridge, Mass: Abt Associates; 1999.
Audit Commission.  Critical to Success: The Place of Efficient and Effective Critical Care Services Within the Acute Hospital. London, England: Audit Commission; 1999.
Cole L, Bellomo R, Silvester W, Reeves JH.for the Victorian Severe Acute Renal Failure Study Group.  A prospective, multicenter study of the epidemiology, management, and outcome of severe acute renal failure in a "closed" ICU system.  Am J Respir Crit Care Med.2000;162:191-196.
Ferdinande P. Recommendations on minimal requirements for intensive care departments.  Intensive Care Med.1997;23:226-232.
Mark DH. Interpreting the term selection bias in medical research.  Fam Med.1997;29:132-136.
Bernard GR, Vincent JL, Laterre PF.  et al.  Efficacy and safety of recombinant human activated protein C for severe sepsis.  N Engl J Med.2001;344:699-709.
The ARDS Network Authors for the ARDS Network.  Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome.  N Engl J Med.2000;342:1301-1308.
Van den Berghe G, Wouters P, Weekers F.  et al.  Intensive insulin therapy in critically ill patients.  N Engl J Med.2001;345:1359-1367.
Moher D, Pham B, Klassen TP.  et al.  Does the language of publication of reports of randomized trials influence the estimates of intervention effectiveness reported in meta-analyses [Thomas C. Chalmers Centre for Systematic Reviews Web site]? Available at: http://www.cheori.org/tcc/viewpoints/language.htm. Accessibility verified September 16, 2002.
Juni P, Witschi A, Bloch R, Egger M. The hazards of scoring the quality of clinical trials for meta-analysis.  JAMA.1999;282:1054-1060.
Pronovost PJ, Dang D, Dorman T.  et al.  Intensive care unit nurse staffing and the risk for complications after abdominal aortic surgery.  Eff Clin Pract.2001;4:199-206.
Milstein A, Galvin RS, Delbanco SF, Salber P, Buck Jr CR. Improving the safety of health care: the leapfrog initiative.  Eff Clin Pract.2000;3:313-316.
Birkmeyer JD, Birkmeyer CM, Wennberg DE, Young M. Leapfrog safety standards: the potential benefits of universal adoption [Leapfrog Patient Safety Standards Web site]. Available at: http://leapfroggroup.org/PressEvent/birkmeyer.pdf. Accessibility verified September 16, 2002.
Brilli RJS. Critical care delivery in the intensive care unit.  Crit Care Med.2001;29:2007-2019.

Figures

Figure 1. Study Flow Diagram
Graphic Jump Location
ICU indicates intensive care unit; LOS, length of stay. The asterisk indicates that the article by Multz et al11 had 2 comparisons (retrospective and prospective).
Figure 2. Funnel Plot of Hospital Mortality
Graphic Jump Location
The funnel plot provides an estimate of publication bias. In the absence of bias, the studies should be symmetrically distributed along the funnel. If small studies with negative results are unpublished, the plot will appear asymmetrical. Our plot suggests no evidence of publication bias. Log OR indicates log odds ratio.
Figure 3. Unadjusted Hospital and ICU Mortality With Low- and High-Intensity ICU Physician Staffing
Graphic Jump Location
Data from studies demonstrate the relative risk (RR) with 95% confidence intervals (CI) of hospital and intensive care unit (ICU) mortality with high intensity vs low intensity ICU physician staffing. The RRs less than 1 suggest reduced mortality with high intensity staffing while RRs greater than 1 suggest increased mortality with high intensity staffing. The size of the data markers corresponds to the weight of the studies. Larger markers imply less uncertainty from the results of the individual study, and carry more weight in calculating the random effects pooled estimate from the systematic review.
Figure 4. Unadjusted Hospital and Intensive Care Unit (ICU) Length of Stay (LOS) With Low- and High-Intensity ICU Physician Staffing
Graphic Jump Location
Data from studies are plotted with the high-intensity mean LOS as a y-coordinate and the low-intensity mean LOS as an x-coordinate with the 95% confidence intervals (error bars) calculated by the authors of the systemic review. A discrepancy exists between the plotting of the error bars for study 10 in panel B (error bar crosses the line of equivalency) and P<.001 (as reported by Carson et al). The diagonal line represents the line of equivalency. Data points below the line of equivalency suggest shorter LOS in the high-intensity group, and those above the line suggest shorter LOS in the low-intensity group. Numbers refer to references (r indicates retrospective; p, prospective). Asterisks indicate SD, assumed to be equal to the mean LOS.

Tables

Table Graphic Jump LocationTable 1a. Characteristics of Reviewed Studies Concerning ICU Physician Staffing and Outcomes*
Table Graphic Jump LocationTable 2. Quality Characteristics of Reviewed Studies*
Table Graphic Jump LocationTable 3. Hospital and ICU Mortality With Low- and High-Intensity ICU Physician Staffing*
Table Graphic Jump LocationTable 4. Hospital and ICU Length of Stay with Low- and High-Intensity ICU Physician Staffing*

References

Halpern NA, Bettes L, Greenstein R. Federal and nationwide intensive care units and healthcare costs: 1986-1992.  Crit Care Med.1994;22:2001-2007.
Pronovost PJ, Jencks M, Dorman T.  et al.  Organizational characteristics of intensive care units related to outcomes of abdominal aortic surgery.  JAMA.1999;281:1310-1312.
Groeger JS, Strosberg MA, Halpern NA.  et al.  Descriptive analysis of critical care units in the United States.  Crit Care Med.1992;20:846-863.
Vincent JL. Need for intensivists in intensive-care units [editorial].  Lancet.2000;356:695-696.
Dudley RA, Johansen KL, Brand R, Rennie DJ, Milstein A. Selective referral to high-volume hospitals.  JAMA.2000;283:1159-1166.
Cook DJ, Mulrow CD, Haynes RB. Systematic reviews: synthesis of best evidence for clinical decisions.  Ann Intern Med.1997;126:376-380.
Robinson KA, Dickersin K. Development of a highly sensitive search strategy for the retrieval of reports of controlled trials using PubMed.  Int J Epidemiol.2002;31:150-153.
Brown JJ, Sullivan G. Effect on ICU mortality of a full-time critical care specialist.  Chest.1989;96:127-129.
Baldock G, Foley P, Brett S. The impact of organisational change on outcome in an intensive care unit in the United Kingdom.  Intensive Care Med.2001;27:865-872.
Kuo HS, Tang GJ, Chuang JH.  et al.  Changing ICU mortality in a decade: effect of full-time intensivist.  Crit Care Shock.2000;3:57-61.
Multz AS, Chalfin DB, Samson IM.  et al.  A "closed" medical intensive care unit (MICU) improves resource utilization when compared with an "open" MICU.  Am J Respir Crit Care Med.1998;157:1468-1473.
Reynolds HN, Haupt MT, Thill-Baharozian MC, Carlson RW. Impact of critical care physician staffing on patients with septic shock in a university hospital medical intensive care unit.  JAMA.1988;260:3446-3450.
Berlin JA. Randomized Trial Comparing Masked/Unmasked Meta-analysesRockville, Md: Agency for Health Care Policy and Research; 1996. Report AHCPR-97-20.
Stroup DF, Berlin JA, Morton SC.  et al. for the Meta-analysis of Observational Studies in Epidemiology (MOOSE) Group.  Meta-analysis of observational studies in epidemiology: a proposal for reporting.  JAMA.2000;283:2008-2012.
L'Abbe KA, Detsky AS, O'Rourke K. Meta-analysis in clinical research.  Ann Intern Med.1987;107:224-233.
Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias.  Biometrics.1994;50:1088-1101.
DerSimonian R. Meta-analysis in clinical trials.  Control Clin Trials.1986;7:177-188.
Woolf B. On estimating the relation between blood group and disease.  Ann Hum Genet.1955;19:251-253.
Eagle KA, Mulley AG, Field TS.  et al.  Variation in intensive care unit practices in two community hospitals.  Med Care.1991;29:1237-1245.
Mirski MA, Chang CW, Cowan R. Impact of a neuroscience intensive care unit on neurosurgical patient outcomes and cost of care.  J Neurosurg Anesthesiol.2001;13:83-92.
Park CA, McGwin GJ, Smith DR.  et al.  Trauma-specific intensive care units can be cost effective and contribute to reduced hospital length of stay.  Am Surg.2001;67:665-670.
Knaus WA, Draper EA, Wagner DP, Zimmerman JE. An evaluation of outcome from intensive care in major medical centers.  Ann Intern Med.1986;104:410-418.
Teres D, Brown RB, Lemeshow S, Parsells JL. A comparison of mortality and charges in two differently staffed intensive care units.  Inquiry.1983;20:282-289.
Lima C, Levy MM. The impact of an on-site intensivist on patient charges and length of stay in the medical intensive care unit [abstract].  Crit Care Med.1995;23:A238.
Cowen J, Matchett S, Kaufman J, Baker K, Wasser T, Ray D. Progressive reduction in severity-adjusted mortality after implementation of a critical care program [abstract].  Crit Care Med.1999;27:A35.
Pollack MM, Patel KM, Ruttimann E. Pediatric critical care training programs have a positive effect on pediatric intensive care mortality.  Crit Care Med.1997;25:1637-1642.
Al-Asadi L, Dellinger RP, Deutch J, Nathan SS. Clinical impact of closed versus open provider care in a medical intensive care unit [abstract].  Am J Respir Crit Care Med.1996;153:A360.
Carson SS, Stocking C, Podsadecki T.  et al.  Effects of organizational change in the medical intensive care unit of a teaching hospital.  JAMA.1996;276:322-328.
Ghorra S, Reinert SE, Cioffi W, Buczko G, Simms HH. Analysis of the effect of conversion from open to closed surgical intensive care unit.  Ann Surg.1999;229:163-171.
Li TC, Phillips MC, Shaw L, Cook EF, Natanson C, Goldman L. On-site physician staffing in a community hospital intensive care unit.  JAMA.1984;252:2023-2027.
Jacobs MC, Hussain E, Hanna A.  et al.  Improving the outcome and efficiency of surgical intensive care: the impact of full time medical intensivists [abstract].  Chest.1998;114:276S-277S.
Manthous CA, Amoateng-Adjepong Y, al-Kharrat T.  et al.  Effects of a medical intensivist on patient care in a community teaching hospital.  Mayo Clin Proc.1997;72:391-399.
Marini CP, Nathan IM, Ritter G, Rivera L, Jurkiewicz A, Cohen JR. The impact of full-time surgical intensivists on ICU utilization and mortality [abstract].  Crit Care Med.1995;23:A235.
Pollack MM, Katz RW, Ruttimann UE, Getson PR. Improving the outcome and efficiency of intensive care: the impact of an intensivist.  Crit Care Med.1988;16:11-17.
Reich HS, Buhler L, David M, Whitmer G. Saving lives in the community: impact of intensive care leadership [abstract].  Crit Care Med.1998;25:A44.
Tai DYH, Goh SK, Eng PCT, Wang YT. Impact on quality of patient care and procedure use in the medical intensive care unit (MICU) following reorganisation.  Ann Acad Med Singapore.1998;27:309-313.
Pollack MM, Cuerdon TT, Patel KM, Ruttimann UE, Getson PR, Levetown M. Impact of quality-of-care factors on pediatric intensive care unit mortality.  JAMA.1994;272:941-946.
DiCosmo BF. Addition of an intensivist improves ICU outcomes in a non-teaching community hospital [abstract].  Chest.1999;116:238S.
Dimick JB, Pronovost PJ, Heitmiller RF, Lipsett PA. Intensive care unit physician staffing is associated with decreased length of stay, hospital cost, and complications after esophageal resection.  Crit Care Med.2001;29:753-758.
Dimick JB, Pronovost PJ, Lipsett PA. The effect of ICU physician staffing and hospital volume on outcomes after hepatic resection [abstract].  Crit Care Med.2000;28:A77.
Rosenfeld BA, Dorman T, Breslow MJ.  et al.  Intensive care unit telemedicine: alternate paradigm for providing continuous intensivist care.  Crit Care Med.2000;28:3925-3931.
Diringer MN, Edwards DF. Admission to a neurologic/neurosurgical intensive care unit is associated with reduced mortality rate after intracerebral hemorrhage.  Crit Care Med.2001;29:635-640.
Goh AYT, Lum LCS, Abdel-Latif MEA. Impact of 24 hour critical care physician staffing on case-mix adjusted mortality in paediatric intensive care.  Lancet.2001;357:445-446.
Blunt MC, Burchett KR. Out-of-hours consultant cover and case-mix-adjusted mortality in intensive care.  Lancet.2000;356:735-736.
Topeli A. Effect of changing organization of intensive care unit from "open policy without critical care specialist" to "closed policy with critical care specialist" [abstract].  Am J Respir Crit Care Med.2000;161:A397.
Hanson CW, Deutschman CS, Anderson HL.  et al.  Effects of an organized critical care service on outcomes and resource utilization: a cohort study.  Crit Care Med.1999;27:270-274.
Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, Stroup DF. Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement.  Lancet.1999;354:1896-1900.
Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system.  Crit Care Med.1985;13:818-829.
Knaus WA, Wagner DP, Draper EA.  et al.  The APACHE III prognostic system.  Chest.1991;100:1619-1636.
Lemeshow S, Teres D, Klar J, Avrunin JS, Gehlbach SH, Rapoport J. Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients.  JAMA.1993;270:2478-2486.
Pollack MM, Ruttimann UE, Getson PR. Pediatric risk of mortality (PRISM) score.  Crit Care Med.1988;16:1110-1116.
Pollack MM, Patel KM, Ruttimann UE. PRISM III: an updated pediatric risk of mortality score.  Crit Care Med.1996;24:743-752.
Yeh TS, Pollack MM, Ruttimann UE, Holbrook PR, Fields AI. Validation of a physiologic stability index for use in critically ill infants and children.  Pediatr Res.1984;18:445-451.
Teasdale GM, Jennett B. Assessment of coma and impaired consciousness.  Lancet.1974;2:81-84.
Schmitz R, Lantin M, White A. Future Workforce Needs in Pulmonary and Critical Care Medicine. Cambridge, Mass: Abt Associates; 1999.
Audit Commission.  Critical to Success: The Place of Efficient and Effective Critical Care Services Within the Acute Hospital. London, England: Audit Commission; 1999.
Cole L, Bellomo R, Silvester W, Reeves JH.for the Victorian Severe Acute Renal Failure Study Group.  A prospective, multicenter study of the epidemiology, management, and outcome of severe acute renal failure in a "closed" ICU system.  Am J Respir Crit Care Med.2000;162:191-196.
Ferdinande P. Recommendations on minimal requirements for intensive care departments.  Intensive Care Med.1997;23:226-232.
Mark DH. Interpreting the term selection bias in medical research.  Fam Med.1997;29:132-136.
Bernard GR, Vincent JL, Laterre PF.  et al.  Efficacy and safety of recombinant human activated protein C for severe sepsis.  N Engl J Med.2001;344:699-709.
The ARDS Network Authors for the ARDS Network.  Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome.  N Engl J Med.2000;342:1301-1308.
Van den Berghe G, Wouters P, Weekers F.  et al.  Intensive insulin therapy in critically ill patients.  N Engl J Med.2001;345:1359-1367.
Moher D, Pham B, Klassen TP.  et al.  Does the language of publication of reports of randomized trials influence the estimates of intervention effectiveness reported in meta-analyses [Thomas C. Chalmers Centre for Systematic Reviews Web site]? Available at: http://www.cheori.org/tcc/viewpoints/language.htm. Accessibility verified September 16, 2002.
Juni P, Witschi A, Bloch R, Egger M. The hazards of scoring the quality of clinical trials for meta-analysis.  JAMA.1999;282:1054-1060.
Pronovost PJ, Dang D, Dorman T.  et al.  Intensive care unit nurse staffing and the risk for complications after abdominal aortic surgery.  Eff Clin Pract.2001;4:199-206.
Milstein A, Galvin RS, Delbanco SF, Salber P, Buck Jr CR. Improving the safety of health care: the leapfrog initiative.  Eff Clin Pract.2000;3:313-316.
Birkmeyer JD, Birkmeyer CM, Wennberg DE, Young M. Leapfrog safety standards: the potential benefits of universal adoption [Leapfrog Patient Safety Standards Web site]. Available at: http://leapfroggroup.org/PressEvent/birkmeyer.pdf. Accessibility verified September 16, 2002.
Brilli RJS. Critical care delivery in the intensive care unit.  Crit Care Med.2001;29:2007-2019.
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