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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

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.

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