Effect of Varying the Case Mix on the Standardized Mortality Ratio and W Statistic

Glance, Laurent G.; Osler, Turner M.; Shinozaki, Tamotsu

doi:10.1378/chest.117.4.1112

Cited by 47 publications

(37 citation statements)

References 21 publications

(17 reference statements)

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“…In all the original cohorts, this proportion was higher (30.9% for APACHE IV, 38.5% for SAPS 3 and 30.4% for MPM 0 -III). Even small differences in case mix have been shown to influence the score's calibration [24]. Another important difference is that we studied a predominantly low-risk population, a fact that may be related to the absence of intermediate care units in the 3 hospitals [25].…”

Section: Discussionmentioning

confidence: 99%

Caution when using prognostic models: A prospective comparison of 3 recent prognostic models

Nassar

Mocelin

Nunes

et al. 2012

Journal of Critical Care

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Section: Discussionmentioning

confidence: 99%

Caution when using prognostic models: A prospective comparison of 3 recent prognostic models

Nassar

Mocelin

Nunes

et al. 2012

Journal of Critical Care

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“…SAPS 3 demonstrated poor calibration, which may have been due to inappropriateness for assessing an ACS population or to different clinical presentation and treatment choices in this specific population. 25,26 Nevertheless, some studies have already suggested that SAPS 3 underestimates in-hospital mortality among general critical care patients, which may be due to the model itself. 27,28 On the other hand, APACHE IV and GRACE presented appropriate calibration and similar accuracy.…”

Section: Discussionmentioning

confidence: 99%

SAPS 3, APACHE IV or GRACE: which score to choose for acute coronary syndrome patients in intensive care units?

et al. 2013

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CONTEXT AND OBJECTIVE: Acute coronary syndromes (ACS) are a common cause of intensive care unit (ICU) admission. Specific prognostic scores have been developed and validated for ACS patients and, among them, GRACE (Global Registry of Acute Coronary Events) has had the best performance. However, intensive care clinicians generally use prognostic scores developed from heterogeneous populations of critically ill patients, such as APACHE IV (Acute Physiologic and Chronic Health Evaluation IV) and SAPS 3 (Simplified Acute Physiology Score 3). The aim of this study was to evaluate and compare the performance of these three scores in a non-selected population of ACS cases.

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“…The current practice for choosing a quality measure, however, is ad hoc, with no consideration of the relationship between the specification of risk-adjustment model and that of the QI. For example, the New York CABG report cards use the ratio QI (New York State Department of Health 2004); both indicators are used to measure intensive care unit performance (Glance et al 2000;Schuster 1992); and the difference specification is used to assess trauma center quality (Sacco et al 1994). Theoretically, these choices may affect conclusions about quality (Mukamel et al 2000).…”

Section: Specification Of the Quality Indicatormentioning

confidence: 99%

Misspecification issues in risk adjustment and construction of outcome-based quality indicators

Dick

Glance

et al. 2007

Health Serv Outcomes Res Method

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Hospital ''report cards'' reporting risk-adjusted health outcomes are increasingly used to benchmark quality of care. However, risk adjustment methods that do not fully account for the interrelationship between quality, risks and outcomes may lead to biased quality measures. This study aims to determine whether the current approach based on logistic regression and observed-to-expected outcome comparisons (O-E difference or O/E ratio) provides unbiased measures of quality. We first provided a conceptual framework to demonstrate that O-E difference or O/E ratio is inconsistently specified when estimates are based on logistic risk adjustment models. To examine the misspecification issue empirically, risk adjustment was performed based on coronary artery bypass graft (CABG) surgery data from New York's Cardiac Surgery Reporting System, and quality indicators (QI) of different specifications were calculated for hospital profiling. Computer simulations further explored the issue of misspecified QIs. Results showed that risk-adjusted mortality rates (RAMR) calculated from different QIs identified the same hospital outliers based on 95% confidence intervals, but generated different rank orders for hospitals in both high-quality and low-quality tails of the quality distributions. Simulation results further showed that, compared to O-E and O/E, logistically transformed QIs were superior regarding their abilities to identify hospitals of true extreme rankings, especially when the outcome was less prevalent or the number of patients per hospital was small. Based on our findings, we recommend that analysts consider the use of logistically transformed QI prior to publicly releasing quality rankings using measures based on O-E or O/E.

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Effect of Varying the Case Mix on the Standardized Mortality Ratio and W Statistic

Cited by 47 publications

References 21 publications

Caution when using prognostic models: A prospective comparison of 3 recent prognostic models

Caution when using prognostic models: A prospective comparison of 3 recent prognostic models

SAPS 3, APACHE IV or GRACE: which score to choose for acute coronary syndrome patients in intensive care units?

Misspecification issues in risk adjustment and construction of outcome-based quality indicators

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