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Table 4 summarizes the final step in each of the two regression models predicting mortality.
This finding would be important in incorporating HOMA-IR into future models predicting mortality for clinical or epidemiological purposes, especially since HOMA-IR is readily available.
It is hoped that the recently published Diabetes Complications Severity Index (DCSI) will improve the ability to adjust for the severity of type 2 diabetes in future regression models predicting mortality (9).
The C-statistics for the two models predicting mortality based on the clinical variables were as follows: 0.788 (95% CI 0.775, 0.802) for the ICD-10 model versus c-statistic = 0.784 955% CI 0.780, 0.790) for the ICD-9-CM model.
We also compared two logistic regression models predicting mortality at one year based on the presence of clinical diagnoses present at baseline: (1) A model predicting 1-year mortality based on the 1995 2001 merged data, using ICD-9-CM coding, and (2) a model predicting 1-year mortality based on the 2002 2005 merged data using ICD-10 coding.
Results from unadjusted and adjusted proportional hazard models predicting mortality for all the variables of interest (per their SD increases) are shown in Table 2. Inverse and significant relationships of all the measures of physical function and SRH with mortality were found, even when models were adjusted for age and gender (all p values < 0.001).
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Cardiac surgical risk models predict mortality preoperatively, whereas intensive care unit (ICU) models predict mortality postoperatively.
After identifying variables correlated with ADL using these models, the addition of direct measures of ADL impairment to the final model predicting mortality resulted in improved predictive ability of the model as determined changes in the AIC, -2 log likelihood ratio and c-statistic similar to the primary analysis.
Good calibration was found with our model (predicted mortality: 8.3%) with a non significant Hosmer-Lemeshow test (x 2 6.14, p: 0.52).
APACHE III, a prognostic model, predicts mortality.
The higher the value, the better the model predicts mortality.
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