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Clinical Investigations in Critical Care |

Prediction of Risk of Death Using 30-Day Outcome*: A Practical End Point for Quality Auditing in Intensive Care

Petra L. Graham; David A. Cook
Author and Funding Information

*From Statistics (Ms. Graham), School of Mathematical and Physical Sciences, University of Newcastle, Callaghan; and Intensive Care Unit (Dr. Cook), Princess Alexandra Hospital, Woolloongabba, Australia.

Correspondence to: Petra L. Graham, MS, Statistics, School of Mathematical and Physical Sciences, Building “V”, University of Newcastle, Callaghan, NSW 2308, Australia, e-mail: pgraham@maths.newcastle.edu.au



Chest. 2004;125(4):1458-1466. doi:10.1378/chest.125.4.1458
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Study objective: To validate the APACHE (acute physiology and chronic health evaluation) III unadjusted and similar hospital mortality estimate models on 30-day mortality, and to propose a simple approach to modeling local 30-day in-hospital mortality of critically ill hospitalized adults for quality management and risk-adjusted monitoring.

Design: Noninterventional, observational study.

Patients: A total of 5,278 consecutive eligible hospital admissions between January 1, 1995, and December 31, 1999.

Measurements: Prospective collection of demographic, diagnostic, physiologic, laboratory, and hospital admission and discharge data.

Results: The APACHE III mortality predictions exhibited excellent discrimination (receiver operating characteristic [ROC] curve area) for 30-day outcome (ROC area, 0.89) and hospital outcome (ROC area, 0.89). Calibration curves and Hosmer-Lemeshow statistics demonstrated good calibration of all models on 30-day outcome, except for the unadjusted APACHE III model. New, simplified risk adjustment models showed good discrimination and calibration on development and test data. ROC areas were 0.88 (developmental data) and 0.87 (test data), and the new model calibration was equivalent to the APACHE III model.

Conclusion: For quality audit, 30-day in-hospital mortality can be used as an alternative outcome to survival to hospital discharge. New logistic regression models provide evidence that local models, possessing good calibration and discrimination, may be built from a few explanatory variables.

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