Literature DB >> 10084494

Community-wide assessment of intensive care outcomes using a physiologically based prognostic measure: implications for critical care delivery from Cleveland Health Quality Choice.

C A Sirio1, L B Shepardson, A J Rotondi, G S Cooper, D C Angus, D L Harper, G E Rosenthal.   

Abstract

STUDY
OBJECTIVES: To examine the applicability of a previously developed intensive care prognostic measure to a community-based sample of hospitals, and assess variations in severity-adjusted mortality across a major metropolitan region.
DESIGN: Retrospective cohort study.
SETTING: Twenty-eight hospitals with 38 ICUs participating in a community-wide initiative to measure performance supported by the business community, hospitals, and physicians. PATIENTS: Included in the study were 116,340 consecutive eligible patients admitted to medical, surgical, neurologic, and mixed medical/surgical ICUs between March 1, 1991, and March 31, 1995. MAIN OUTCOME MEASURES: The risk of hospital mortality was assessed using a previous risk prediction equation that was developed in a national sample, and a reestimated logistic regression model fit to the current sample. The standardized mortality ratio (SMR) (actual/predicted mortality) was used to describe hospital performance.
RESULTS: Although discrimination of the previous national risk equation in the current sample was high (receiver operating characteristic [ROC] curve area = 0.90), the equation systematically overestimated the risk of death and was not as well calibrated (Hosmer-Lemeshow statistic, 2407.6, 8 df, p < 0.001). The locally derived equation had similar discrimination (ROC curve area = 0.91), but had improved calibration across all ranges of severity (Hosmer-Lemeshow statistic = 13.5, 8 df, p = 0.10). Hospital SMRs ranged from 0.85 to 1.21, and four hospitals had SMRs that were higher or lower (p < 0.01) than 1.0. Variation in SMRs tended to be greatest during the first year of data collection. SMRs also tended to decline over the 4 years (1.06, 1.02, 0.98, and 0.94 in years 1 to 4, respectively), as did mean hospital length of stay (13.0, 12.4, 11.6, and 11.1 days in years 1 to 4; p < 0.001). However, excluding the increasing (p < 0.001) number of patients discharged to skilled nursing facilities attenuated much of the decline in standardized mortality over time.
CONCLUSIONS: A previously validated physiologically based prognostic measure successfully stratified patients in a large community-based sample by their risk of death. However, such methods may require recalibration when applied to new samples and to reflect changes in practice over time. Moreover, although significant variations in hospital standardized mortality were observed, changing hospital discharge practices suggest that in-hospital mortality may no longer be an adequate measure of ICU performance. Community-wide efforts with broad-based support from business, hospitals, and physicians can be sustained over time to assess outcomes associated with ICU care. Such efforts may provide important information about variations in patient outcomes and changes in practice patterns over time. Future efforts should assess the impact of such community-wide initiatives on health-care purchasing and institutional quality improvement programs.

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Year:  1999        PMID: 10084494     DOI: 10.1378/chest.115.3.793

Source DB:  PubMed          Journal:  Chest        ISSN: 0012-3692            Impact factor:   9.410


  28 in total

Review 1.  [Medical emergency teams: current situation and perspectives of preventive in-hospital intensive care medicine].

Authors:  S G Russo; C Eich; M Roessler; B M Graf; M Quintel; A Timmermann
Journal:  Anaesthesist       Date:  2008-01       Impact factor: 1.041

2.  Comparison between SAPS II and SAPS 3 in predicting hospital mortality in a cohort of 103 Italian ICUs. Is new always better?

Authors:  Daniele Poole; Carlotta Rossi; Nicola Latronico; Giancarlo Rossi; Stefano Finazzi; Guido Bertolini
Journal:  Intensive Care Med       Date:  2012-05-15       Impact factor: 17.440

3.  Prediction of long-term mortality in ICU patients: model validation and assessing the effect of using in-hospital versus long-term mortality on benchmarking.

Authors:  Sylvia Brinkman; Ameen Abu-Hanna; Evert de Jonge; Nicolette F de Keizer
Journal:  Intensive Care Med       Date:  2013-08-07       Impact factor: 17.440

4.  Oxidative stress and multi-organ failure in hospitalized elderly people.

Authors:  James S Powers; L Jackson Roberts; Emily Tarvin; Nobuko Hongu; Leena Choi; Maciej Buchowski
Journal:  J Am Geriatr Soc       Date:  2008-06       Impact factor: 5.562

5.  External validation of the SAPS II, APACHE II and APACHE III prognostic models in South England: a multicentre study.

Authors:  Dieter H Beck; Gary B Smith; John V Pappachan; Brian Millar
Journal:  Intensive Care Med       Date:  2003-01-18       Impact factor: 17.440

Review 6.  Clinical review: scoring systems in the critically ill.

Authors:  Jean-Louis Vincent; Rui Moreno
Journal:  Crit Care       Date:  2010-03-26       Impact factor: 9.097

7.  A predictive model for the early identification of patients at risk for a prolonged intensive care unit length of stay.

Authors:  Andrew A Kramer; Jack E Zimmerman
Journal:  BMC Med Inform Decis Mak       Date:  2010-05-13       Impact factor: 2.796

8.  Use of the All Patient Refined-Diagnosis Related Group (APR-DRG) Risk of Mortality Score as a Severity Adjustor in the Medical ICU.

Authors:  Daniel Baram; Feroza Daroowalla; Ruel Garcia; Guangxiang Zhang; John J Chen; Erin Healy; Syed Ali Riaz; Paul Richman
Journal:  Clin Med Circ Respirat Pulm Med       Date:  2008-04-18

9.  Is there a July phenomenon? The effect of July admission on intensive care mortality and length of stay in teaching hospitals.

Authors:  William A Barry; Gary E Rosenthal
Journal:  J Gen Intern Med       Date:  2003-08       Impact factor: 5.128

10.  Obstetric intensive care unit admission: a 2-year nationwide population-based cohort study.

Authors:  Joost J Zwart; Just R O Dupuis; Annemiek Richters; Ferko Ory; Jos van Roosmalen
Journal:  Intensive Care Med       Date:  2009-11-10       Impact factor: 17.440

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