Predicting the risk of death in patients in intensive care unit.
BACKGROUND
The ability to identify critically ill patients who will not survive until hospital discharge may yield substantial cost savings. The aim of this study was to validate the mortality prediction model II (MPM II) in in-hospital mortality of critically ill patients for quality management and risk-adjusted monitoring.


METHODS
The data were collected prospectively from consecutive admissions to the Intensive Care Unit of Imam Hossein Medical Center in Tehran. A total of 274 admissions were analyzed using tests of discrimination and calibration of the logistic regression equation for mortality prediction model II at admission (MPM0 II) and at 24th hour (MPM24 II).


RESULTS
The mortality prediction model II exhibited excellent discrimination (receiver operating characteristic curve area). Calibration curves and Hosmer-Lemeshow statistics demonstrated good calibration of both models on outcome.


CONCLUSIONS
We recommend using mortality prediction model II in Iranian ICUs for routine audit requirements. Mortality prediction model II is not affected by the standards of treatment after admission to ICU. The information needed to calculate mortality prediction model II is easy to collect, and the model is applicable to all ICU admitted patients.
Saadat-Niaki
Asadolah
A
Department of Anesthesiology, Shaheed Beheshti University of Medical Sciences, Tehran 16179, Iran. saadatniaki@yahoo.com
Abtahi
Dariush
D
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015331:Cohort Studies; D016638:Critical Illness / Q000401:mortality*; D005260:Female; D006305:Health Status Indicators; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D007492:Iran; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D011237:Predictive Value of Tests; D012372:ROC Curve; D012189:Retrospective Studies