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Postgraduate Education Corner: CONTEMPORARY REVIEWS IN CRITICAL CARE MEDICINE |

Severity Scoring in the Critically IllSeverity Scoring in the Critically Ill: Part 2: Maximizing Value From Outcome Prediction Scoring Systems FREE TO VIEW

Michael J. Breslow, MD; Omar Badawi, PharmD
Author and Funding Information

From the Department of Research and Product Marketing (Drs Breslow and Badawi), Philips Healthcare; and the Department of Pharmacy Practice and Science (Dr Badawi), University of Maryland School of Pharmacy, Baltimore, MD.

Correspondence to: Michael J. Breslow, MD, Research and Product Marketing, Philips Healthcare, Ste 1900, 217 E Redwood St, Baltimore, MD 21202; e-mail: michael.breslow@philips.com


Reproduction of this article is prohibited without written permission from the American College of Chest Physicians (http://www.chestpubs.org/site/misc/reprints.xhtml).


© 2012 American College of Chest Physicians


Chest. 2012;141(2):518-527. doi:10.1378/chest.11-0331
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Part 2 of this review of ICU scoring systems examines how scoring system data should be used to assess ICU performance. There often are two different consumers of these data: lCU clinicians and quality leaders who seek to identify opportunities to improve quality of care and operational efficiency, and regulators, payors, and consumers who want to compare performance across facilities. The former need to know how to garner maximal insight into their care practices; this includes understanding how length of stay (LOS) relates to quality, analyzing the behavior of different subpopulations, and following trends over time. Segregating patients into low-, medium-, and high-risk populations is especially helpful, because care issues and outcomes may differ across this severity continuum. Also, LOS behaves paradoxically in high-risk patients (survivors often have longer LOS than nonsurvivors); failure to examine this subgroup separately can penalize ICUs with superior outcomes. Consumers of benchmarking data often focus on a single score, the standardized mortality ratio (SMR). However, simple SMRs are disproportionately affected by outcomes in high-risk patients, and differences in population composition, even when performance is otherwise identical, can result in different SMRs. Future benchmarking must incorporate strategies to adjust for differences in population composition and report performance separately for low-, medium- and high-acuity patients. Moreover, because many ICUs lack the resources to care for high-acuity patients (predicted mortality >50%), decisions about where patients should receive care must consider both ICU performance scores and their capacity to care for different types of patients.

Figures in this Article

As described in part 11 of this review, ICU scoring systems evolved to meet the desire of clinical and administrative leaders to assess the quality of care provided by their ICUs. Measuring ICU performance and using this information to guide quality improvement activities remains an important rationale for their use today. Yet ICU scoring systems differ from other quality metrics in several important ways. Unlike best practice compliance, in which 100% compliance is a logical goal, optimal ICU care will never result in all patients surviving their ICU stay and ICU length of stay (LOS) will never equal zero days. Many users simply aspire to a standardized mortality ratio (SMR) value <1.0 without really considering how they can derive additional value from ICU scoring system data. So how should users obtain maximal value from a relative performance metric like SMRs or actual-to-predicted LOS ratios? Another unusual characteristic of ICU scoring systems is their use across a wide variety of diagnoses and patient acuities. Although there is value in having a single tool that generates a single score for the entire ICU population, this aggregation can obscure important variability in performance. The goal of this review is to help ICU clinicians and quality personnel maximize the value derived from ICU scoring system data, better understand the performance of their ICU, and use this information to identify areas for focused quality improvement efforts. We also address several key issues in the use of ICU scoring system data for benchmarking. This latter issue deserves particular attention in light of recent efforts by several countries (and the State of California) to use ICU scoring systems to benchmark ICUs. Throughout the review we use data from the eICU Research Institute (eRI) to illustrate key points. The eRI contains aggregate deidentified data from >500 ICUs with remote ICU care programs2 and constitutes an important resource for understanding scoring system characteristics. As employees of Philips VISICU, and cognizant of our potential conflict of interest, we have tried in this review to focus on concepts and data of general applicability to all users of ICU scoring system data.

Hospitals generally use ICU scoring systems in order to better understand how well their ICUs are performing. As discussed in part 1, only APACHE (Acute Physiology and Chronic Health Evaluation) (Cerner Corp) provides both mortality and LOS predictions,3-5 although Vasilevskis et al5 recently developed LOS prediction algorithms from Mortality Probability Model data elements. Given the current focus on health-care costs and the preeminent role of LOS in determining ICU costs, LOS is becoming an increasingly essential ICU performance metric.6 APACHE is also the only scoring system that provides separate ICU and hospital predictions. Although most would agree that hospital mortality is the key mortality metric, inconsistencies between the two can identify quality gaps (eg, premature discharge, poor floor care). Similarly, threefold-higher ICU costs make this the key LOS metric.7 Here also, discordance between ICU and hospital LOS can indicate the presence of systemic problems (eg, lack of floor beds).

Historically, some ICUs performed one-time analyses of ICU performance as a form of “spot check.” Currently, most ICUs use scoring systems to track performance over time, with the goal of continuously improving quality and rapidly detecting problems that might reflect gaps in care.8 However, consumers of scoring system data must avoid too frequent measurement of actual-to-predicted ratios, because results from small numbers of patients (eg, monthly in most ICUs and quarterly in low-census ICUs) can result in less reliable data and wide CIs. Use of larger population samples (longer observation periods) and persistence of changes over time increase the likelihood that changes observed are real. Other tools such as run charts, which show results over multiple time periods, can help visualize longitudinal performance and reduce reliance on larger sample sizes.9

Another issue in the longitudinal tracking of performance is compensating for general trends in care. Several studies have reported on global reductions in hospital mortality over time.10,11 Although there are no ICU-specific data, the recalibration of the APACHE algorithms provides some helpful insights.3,4 The APACHE III algorithms, when applied to the APACHE IV reference population, generated a hospital SMR of 0.93, suggesting that mortality decreased by slightly less than 1% a year over the 10 years between the two calibrations.3 Our group performed a similar comparison for LOS performance using the eRI data set and observed negligible difference in the actual-to-predicted ratios, suggesting little change in LOS performance over the same interval. Although these data suggest that time-related changes in ICU performance have been small over the past decade, larger changes in aggregate performance may be seen in the future. Specifically, the current focus on quality improvement and cost reduction, the introduction of new therapeutic approaches to several high-impact diseases (eg, severe sepsis), and the implementation of new ICU care models may accelerate future improvements.12-14 In order to compensate for temporal trends, scoring systems need periodic recalibration; the CalHospitalCompare (California Intensive Care Outcomes) project is considering yearly updates of their algorithms (R. Adams Dudley, MD, oral communication, December 2010). Although regular recalibration is necessary to ensure that ICU outcomes are not compared with an old reference population that used therapies and practices that are different from those in current use, it is equally important to track ICU performance trends by referencing newly calibrated systems to their predecessors, and making this information available to consumers of public health information.

Mortality is a key ICU quality metric and reflects many aspects of ICU care, including use of best practices, accurate diagnosis, and effective and timely therapies. ICU scoring systems provide mortality predictions based on ICU admission status/severity of illness. Although these predictions provide little value in managing individual patients, aggregate predictions correlate very well with observed outcomes in the reference population. Because of this population-level accuracy, ICU scoring systems measure mortality performance by comparing the actual number of deaths in an ICU population with the sum of the individual mortality predictions of the group. This method is known as the indirect method of standardization, and the resulting measure is the SMR. The benefits of indirect standardization are its ease of calculation and its stability in small sample sizes. However, despite its widespread acceptance, the SMR has some intrinsic limitations. High-risk patients contribute disproportionately to the SMR, because more of these patients die. An ICU with better than average outcomes in its low-risk population can have an aggregate SMR >1.0 if outcomes are below average in the high-risk group, even though the high-risk group represents a smaller percentage of the total ICU population. (Tables 1 and 2 demonstrate this effect).

Table Graphic Jump Location
Table 1 —Comparison of Aggregate SMRs in Two Hypothetical ICUs With Similar Performance Within Risk Groups But Different Severity Distribution

SMR = standardized mortality ratio.

Table Graphic Jump Location
Table 2 —Subgroup SMR Data Showing How Aggregate SMR Data Can Obscure Poor Low-Risk Population Performance

See Table 1 legend for expansion of abbreviations.

Unlike the case with most quality metrics, in which hospitals set goals for how they rank relative to other institutions (eg, top 25%, top 10%), there are insufficient data in the public domain for similar calibration of ICU scoring system results. As a result, many ICUs simply target SMR/actual-to-predicted ratios <1.0. Kuzniewicz et al15 and Vasilevskis et al4 provided individual mortality and LOS scores for the 29 hospitals in their original California Intensive Care Outcomes publications; the 25th to 75th percentile scores were approximately 0.8 and 1.20 for mortality and 0.85 and 1.15 for ICU LOS. Whether these data reflect actual variance across ICUs in the United States is unknown. It is also worth noting that both APACHE IV and Mortality Probability Model III (from time of admission) were calibrated to patients cared for in ICUs that chose to measure ICU performance and invested resources for this purpose.3,4,16 The performance of these self-selected hospitals may not reflect that of the average US ICU.

ICU LOS is another important quality and financial metric (ICU LOS is the primary determinant of ICU cost).6 LOS is also an important measure of operating efficiency, because occupancy rates are high in many ICUs. Capacity constraints affect ED throughput, ambulance diversion status, elective surgical schedules, and acceptance of intrahospital transfers. LOS is affected by many factors, including quality of ICU care, end-of-life policies, discharge planning, and downstream bed availability. However, unlike mortality, where increasing severity of illness is linearly related to predicted mortality rate, LOS demonstrates a more complex relationship to admission acuity. As Figure 1 illustrates, predicted LOS increases with increasing acuity, and then decreases at the highest levels of severity. To better understand this behavior, we examined year 2006 data from 62,000 patients in the eRI database (153 ICUs), and analyzed survivors and nonsurvivors separately. Details of the ICUs contributing data to the eRI database are described elsewhere.2,17

Figure Jump LinkFigure 1. A, ICU mortality and LOS, non-coronary artery bypass graft (CABG) patients. B, Hospital mortality and LOS, non-CABG patients. Actual mortality and LOS data from the APACHE (Acute Physiology and Chronic Health Evaluation) III validation data set, shown as a function of the APACHE first ICU day acute physiology score (APS). Data are displayed by fifth percentiles. LOS = length of stay. (Reproduced with permission from the Cerner Corporation, Kansas City, MO).Grahic Jump Location

Figure 2 shows LOS data for survivors and nonsurvivors as a function of acuity/mortality risk. For surviving patients, LOS increased linearly as predicted mortality rose. In contrast, LOS was largely unrelated to acuity for nonsurvivors. It thus appears that the relationship between severity of illness and LOS in Figure 1 reflects the behavior of two distinct populations, survivors and nonsurvivors. It seems reasonable to attribute the linear relationship between severity and LOS in survivors to sicker patients requiring longer times to recover from their illness and be stable enough for ICU discharge. The explanation for the lack of relationship between LOS and acuity in nonsurvivors is unknown. We speculate that some patients who have low mortality risk at ICU admission (and short predicted LOS) develop complications and eventually succumb to these new problems, whereas some high-mortality-risk patients, who would have a long LOS if they lived, die within 1 or 2 days of admission despite maximal therapy. In some ICUs, care limitations may also contribute to this behavior.

Figure Jump LinkFigure 2. Average ICU LOS by predicted mortality. eICU Research Institute data for all patients (62,397) discharged from eICU Program ICUs in 2006 showing average ICU LOS data for surviving and nonsurviving patients as a function of APACHE-III-predicted hospital mortality. Patients are aggregated into deciles of predicted mortality. Mortality predictions were generated using the APACHE III first ICU day mortality prediction algorithm. ALOS = average length of stay; Pts = patients. See Figure 1 legend for expansion of other abbreviations.Grahic Jump Location

The divergent LOS behavior between survivors and nonsurvivors has potentially important consequences. The APACHE-predicted LOS for high-risk patients is a blend of the average survivor and nonsurvivor LOS (eg, if 8 days and 4 days, respectively, for patients with 75% predicted mortality, the predicted LOS would be 5 days). ICUs that have higher than predicted survival for these patients have more survivors than the reference population, and thus the APACHE-blended LOS prediction underestimates what their LOS should be. Using the example here, 50% mortality in the 75% predicted mortality group would translate into an expected LOS of 6 days. This phenomenon increases this ICU’s actual-to-predicted LOS ratio, and penalizes high-performing ICUs that have better-than-predicted high-risk patient mortality rates. The converse is also true: poor-performing ICUs that have excess mortality in this group benefit from the assumption of a fixed high-acuity population mortality rate. For this reason, we report actual-to-predicted LOS performance for low-, medium-, and high-risk patients separately, and provide aggregate actual-to-predicted LOS data both with and without patients with mortality predictions >50%.

The move toward benchmarking ICUs is driven primarily by regulator and consumer interest in identifying high- and low-performing institutions. As discussed in Part 1 of this review, several countries have mandatory ICU reporting; the State of California appears to be moving in this direction as well. In all of these regions, standard ICU scoring systems have been recalibrated against their patient population. Although calibration does not affect the ability to compare ICUs within the region, local calibration precludes comparisons with ICUs that use scoring systems calibrated against other populations. The adoption of regionally calibrated scoring systems makes it more difficult to determine whether certain countries have developed care models that achieve superior outcomes. Publication of regression models would enable such comparisons and would be desirable.

Despite the limited adoption of ICU scoring systems in the United States, other countries have mandated their use in all ICUs.18-20 There is a broad audience for quality data, and we can anticipate increased use of scoring systems for benchmarking ICU performance. Consumers of this information, however, must be cautious in how they use these data, because differences in the numbers of high- and low-risk patients can affect calculated SMR. Two ICUs with identical mortality rates for their low- and high-risk patients can have different aggregate SMRs if they have different numbers of low- and high-risk patients. This behavior reflects the disproportionate impact of high-risk patients on SMR, and ICUs with fewer such patients will have this effect diluted. Epidemiologists have long recognized this phenomenon and generally advocate against the practice of comparing simple SMRs.21Table 1 shows how population mix can affect the overall SMR ranking for two hypothetical ICUs.

This bias in SMR can be addressed through another simple technique referred to as the method of direct standardization.21,22 This can be done by assuming a distribution of high-, medium-, and low-risk patients equal to that in the reference population (or any population deemed appropriate for standardization). Unlike the SMR, this generates an adjusted mortality rate that adequately addresses the confounding introduced by different distributions of patient acuity between populations. Unfortunately, the adjusted mortality rate has no real meaning; we, therefore, recommend transforming the adjusted mortality rate of sample populations into a standardized rate ratio.22, This can be done by dividing the adjusted mortality rate by the mortality rate of the reference population, which generates a “population-adjusted SMR” that is more intuitive to most consumers of benchmarking data. Direct standardization and subsequent calculation of the population-adjusted SMR requires large samples with at least 30 total events (deaths) and multiple events within each stratum.22 Therefore, this may only be feasible over fairly long time horizons, especially for smaller ICUs (eg, yearly), or perhaps more frequently if ICU data are aggregated at the hospital level.

Population adjustment requires stratifying the population into multiple risk groups; more groups equate to more accurate adjustment but this also creates mathematical instability when events within strata are rare. We have used this technique on data from ICUs in the eRI and confirmed that population variability is present, but actual population variability is generally insufficient to induce large degrees of bias into the SMR. Figure 3 shows 105 eRI ICUs that cared for at least 1,000 patients in 2010, and displays the correlation between the standard SMR and the “population-adjusted SMR” generated using six risk groups. The SMR and “population-adjusted SMR” are highly correlated in this group. These data suggest that, although population-adjusted SMRs can measure performance more accurately when there are major differences in population distributions, the adjustment will not significantly impact most ICUs.

Figure Jump LinkFigure 3. Correlation between direct and indirect standardization. Simple SMR data compared with population-adjusted (direct standardization) mortality ratio data from 105 ICUs in the eICU Research Institute data set. Only ICUs with at least 1,000 patients with APACHE IV hospital mortality predictions were included. Data are from patients discharged from the hospital in 2010. SMR = standardized mortality ratio. See Figure 1 legend for expansion of other abbreviations.Grahic Jump Location

Use of population-adjusted SMRs addresses the confounding introduced by case mix, but it does not provide insight into heterogeneity in performance across risk groups. We believe that heterogeneous performance is a major quality concern, and have provided separate outcome data for low-, medium-, and high-acuity patients as part of the routine performance data set provided to all ICUs with electronic ICU care programs for the past 6 years. We initially segregated patients by acuity because we speculated that processes important for preventing complications in low-risk patients might be different from those that result in improved survival of high-acuity patients.23 To examine this hypothesis, we used 10% and 50% APACHE III-predicted hospital mortality cutoffs to differentiate low-, medium-, and high-risk populations, respectively. Although these cutoffs result in dissimilar-sized groups, the number of deaths in each group is similar, and this maximizes the reliability of the SMR calculations. This definition of low risk is also used by APACHE for their “low risk monitor” subset of patients.24 Across the electronic ICU program install base, approximately two-thirds of ICU patients are low risk on admission, which is similar to the APACHE IV validation cohort.1,2 Larger tertiary care hospital ICUs tend to have slightly fewer low-risk patients (although frequently >50%) and more patients with very high mortality risk. Figure 4 shows the lack of correlation between mortality performance in low- and high-acuity patients in the same 105 ICUs from Figure 3. These data demonstrate wide disparity within ICUs in their performance in the different acuity groups, and highlight the rationale for examining low-, medium-, and high-acuity groups separately.

Figure Jump LinkFigure 4. Lack of correlation between high-risk and low-risk SMRs. SMR data for low- and high-mortality-risk patients from 105 ICUs in the eICU Research Institute data set. Only ICUs with at least 1,000 patients with APACHE IV hospital mortality predictions were included. Data are from patients discharged from the hospital in 2010. Low- and high-risk populations had APACHE-IV-predicted hospital mortality below 10% and above 50%, respectively. There was little intra-ICU correlation between performance in the two populations. See Figure 1 and 3 legends for expansion of abbreviations.Grahic Jump Location

As stated previously, because of heterogeneity in performance across risk groups, we advocate independent assessment of outcomes in low-, medium-, and high-acuity patients for both ICU quality assessment and for benchmarking. Cutoffs of 10% and 50% predicted hospital mortality to define low-, medium-, and high-risk groups are easily understood, have some historical precedence, and improve the stability of the calculations. Of particular concern, aggregate SMRs can mask quality problems in the low-risk cohort of patients. The patients in this group, which represents more than one-half of all admissions in most ICUs, are often admitted because they are at risk of complications. Table 2 illustrates this problem in a hypothetical tertiary care ICU.

Despite having an SMR of 2.0 in the low-risk population, which represents 45% of the total patients, the aggregate SMR for this ICU is slightly below 1.0. This hypothetical example resembles actual observations that we presented to ICU leaders at an academic medical center. Although the cause of discrepant performances may vary, it is easy to see how excessive focus on high-acuity patients, who can require considerable attention when acutely unstable, can divert attention from more “stable” patients. Regardless of the basis for this problem, outcomes in this subpopulation in these ICUs improved markedly once the problem was recognized.

Subgroup analysis by acuity is also important for LOS. In a recent analysis of July to December 2007 data from four ICUs within a single large health system, 16% of patients had ICU stays in excess of 6 days. LOS outliers came from all three acuity groups (low, medium, and high), with the lowest incidence in the low-mortality-risk group and the highest incidence in the high-risk population. However, because two-thirds of the patients were in the low-risk group, this population made up 50% of the total LOS outliers. These low-risk LOS outliers accounted for 25% of the total ICU days. They also had fivefold higher mortality (actual to predicted) than low-risk patients with shorter LOS, suggesting that complications accounted for both longer stays and lower survival. LOS outliers are important because they have substantially higher costs, and ICU leaders should determine whether their long stays are attributable to high acuity on arrival to the ICU (mostly unavoidable LOS) or to potentially avoidable complications in low-risk patients. Examining low- and high-risk populations enables ready differentiation of these two different causes for long LOS, and has relevance to both quality and financial personnel. We have reported low-risk LOS outlier data as a component of our ICU quality metrics for the past 6 years. Over this time period, there was a 30% decrease in the number of low-risk LOS outliers (and a decrease in aggregate actual-to-predicted LOS ratios). We speculate that providing these data prompted organizational efforts to reduce avoidable complications in this population.

Another potential use for ICU scoring systems is to better understand the behavior of select populations of patients (eg, patients cared for by individual providers or patients with specific diagnoses). However, care must be taken to ensure adequate sample size when using scoring systems for this purpose. Simplified Acute Physiology Score (SAPS) researchers have presented subgroup performance by geographic region,25 and APACHE researchers have produced sepsis and coronary artery bypass graft-specific models to facilitate evaluating outcomes in these subgroups more effectively.26,27 Recent programs aimed at improving outcomes in patients with severe sepsis have created interest in using scoring system data to assess the impact of these efforts.12 Similar cautions regarding adequate sample size apply to this use case as well.

Clinical investigators use severity of illness scores and/or mortality and LOS predictions to compare outcomes across different experimental groups.28,29 Two different methodologies are generally used. In one, actual-to-predicted ratios of the experimental groups are compared directly with the control group in order to assess whether outcomes differ. Drawbacks to this approach include the added variability in patient predictions, the potential for bias, and the assumption that the prediction model, which was derived in a different population, optimally explains outcome differences in the study population. Certain scoring systems (eg, APACHE) have been customized for particular populations such as cardiac surgery by including predictors specific to that population.2 Although this may be useful in unique situations, in general, the usefulness of the SMR remains limited as an outcome for clinical research. Whenever possible, a customized model that adjusts for confounders specifically related to the exposure and outcome of interest should be used, which may not be adequately captured by the risk scoring system alone. For this reason, many investigators create customized regression models for their study population that include a severity of illness score as a single covariate.14

ICU scoring systems have been proposed to have value in ICU discharge and admission decisions. APACHE provides data on patients who were admitted to the ICU with a low predicted mortality and required no major intervention during the first ICU day, referred to as “low-risk monitor” patients. While intended to provide retrospective information about the appropriateness of ICU admission practices (because patients cannot be classified as low-risk monitor until after the first ICU day), Zimmerman and Kramer24 recently proposed that APACHE could help identify patients not requiring ICU interventions. It may also be reasonable to assume that severity of illness might correlate with risk of deterioration after ICU discharge. Investigators have developed predictive algorithms using patient and physiologic variables to differentiate patients who did and did not develop postdischarge problems (readmission or death).30,31 Although these models showed moderate discrimination, there are as yet no data establishing the usefulness of such algorithms in discharge decision making.

ICU scoring systems have the potential to provide useful information for both ICU quality personnel and the general public. Garnering maximal value from scoring system data requires in-depth knowledge of how these scoring systems behave in different populations, and how care changes over time. Sophisticated users who evaluate low-, medium-, and high-acuity subgroup performance independently can use these data to target quality issues and improve quality of care. The increasing use of ICU scoring for benchmarking has the potential to provide helpful comparative performance data. However, adoption of simple SMR reporting for this purpose is problematic, because it fails to adjust for differences in population composition and lacks information about performance of different acuity subpopulations. Because the audience for benchmarking data often knows little about these tools, quality leaders need to advocate for refinements in current reporting strategies. One potential value of benchmarking is the identification of superior systems of care. The current practice of calibrating scoring systems in each region (eg, Great Britain, the Netherlands) precludes comparison of international outcomes; it is hoped that broader adoption of scoring systems across multiple countries will lead to the use of internationally generated algorithms.

Financial/nonfinancial disclosures: The authors have reported to CHEST the following conflicts of interest: Drs Breslow and Badawi are employees of Philips Healthcare, which sells a tele-ICU solution.

Other contributions: We thank David Stone, MD, for his assistance in preparing this manuscript.

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Figures

Figure Jump LinkFigure 1. A, ICU mortality and LOS, non-coronary artery bypass graft (CABG) patients. B, Hospital mortality and LOS, non-CABG patients. Actual mortality and LOS data from the APACHE (Acute Physiology and Chronic Health Evaluation) III validation data set, shown as a function of the APACHE first ICU day acute physiology score (APS). Data are displayed by fifth percentiles. LOS = length of stay. (Reproduced with permission from the Cerner Corporation, Kansas City, MO).Grahic Jump Location
Figure Jump LinkFigure 2. Average ICU LOS by predicted mortality. eICU Research Institute data for all patients (62,397) discharged from eICU Program ICUs in 2006 showing average ICU LOS data for surviving and nonsurviving patients as a function of APACHE-III-predicted hospital mortality. Patients are aggregated into deciles of predicted mortality. Mortality predictions were generated using the APACHE III first ICU day mortality prediction algorithm. ALOS = average length of stay; Pts = patients. See Figure 1 legend for expansion of other abbreviations.Grahic Jump Location
Figure Jump LinkFigure 3. Correlation between direct and indirect standardization. Simple SMR data compared with population-adjusted (direct standardization) mortality ratio data from 105 ICUs in the eICU Research Institute data set. Only ICUs with at least 1,000 patients with APACHE IV hospital mortality predictions were included. Data are from patients discharged from the hospital in 2010. SMR = standardized mortality ratio. See Figure 1 legend for expansion of other abbreviations.Grahic Jump Location
Figure Jump LinkFigure 4. Lack of correlation between high-risk and low-risk SMRs. SMR data for low- and high-mortality-risk patients from 105 ICUs in the eICU Research Institute data set. Only ICUs with at least 1,000 patients with APACHE IV hospital mortality predictions were included. Data are from patients discharged from the hospital in 2010. Low- and high-risk populations had APACHE-IV-predicted hospital mortality below 10% and above 50%, respectively. There was little intra-ICU correlation between performance in the two populations. See Figure 1 and 3 legends for expansion of abbreviations.Grahic Jump Location

Tables

Table Graphic Jump Location
Table 1 —Comparison of Aggregate SMRs in Two Hypothetical ICUs With Similar Performance Within Risk Groups But Different Severity Distribution

SMR = standardized mortality ratio.

Table Graphic Jump Location
Table 2 —Subgroup SMR Data Showing How Aggregate SMR Data Can Obscure Poor Low-Risk Population Performance

See Table 1 legend for expansion of abbreviations.

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