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MPM II also consists of two scores: MPM0, the admission model, which contains 15 variables; and MPM24 the 24-hour model, which contains 5 of the admission variables and 8 additional variables and is designed for patients selleck who stay in the ICU for more than 24 hours. Unlike the APACHE and SAPS systems where variables are weighted, in MPM II each variable (except age, which is entered as the actual age in years), is designated as present or absent and given a score of 1 or 0 accordingly. A logistic regression equation is then used to provide a probability of hospital mortality. The authors also developed a Weighted Hospital Days scale (WHD-94) by subjectively assigning weights to days in the ICU and to hospital days after ICU discharge from the first ICU stay, and an equation to predict an ICU's mean WHD-94, thus providing an index of resource utilization [16].

MPM0 has Bleomycin recently been updated Bleomycin using a database of 124,885 patients from 135 ICUs in 98 hospitals (all in North America except for one in Brazil) collected in 2001 to 2004 [17]. MPM0-III uses Bleomycin 16 variables, including 3 physiological parameters, obtained within 1 hour of ICU admission to estimate mortality probability at hospital discharge; the MPM0 characterization is, therefore, based on patient condition largely before ICU care begins. The WHD-94 predictive equation has also been updated [18].DiscussionSeveral studies have compared the different outcome prediction scoring systems. For example, in a study of 10,393 patients from Scottish ICUs, Livingston and colleagues [19] compared the APACHE II and III, an APACHE II using United Kingdom-derived coefficients (UK APACHE II), SAPS II, and MPM0 and MPM24.

These authors reported that all models showed good discrimination, although observed mortality was significantly different from that predicted by all models. SAPS II had the best performance overall, but APACHE II had better calibration. In a retrospective study Bleomycin of 11,300 patients from 35 hospitals in California, Kuzniewicz and colleagues [20] recently used logistic regression to re-estimate the coefficients for the APACHE IV, MPM0-III and SAPS II scores and applied the new equations to assess risk-adjusted mortality rates. These authors noted that discrimination and calibration were adequate for all models, with discrimination of APACHE IV slightly better than that of the other two scores (area under the receiver operating characteristic curve 0.

892 for APACHE IV, 0.873 for SAPS II, and 0.809 for MPM0 III, P < 0.001).In addition to using a more geographically heterogeneous database for development, the SAPS 3 model attempted to address any Belinostat (PXD101) geographic variation by providing separate customized equations of different geographical regions. Nevertheless, local customization may still help improve the calibration of these scores in individual countries or regions as demonstrated for the APACHE III in Cleveland, Ohio [21], or more recently for the SAPS 3 score in Austria [22].