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All variables that were associated (P < 0.1) with complicated hospitalization were included in the initial multivariate prediction rule.
All variables that were found to be associated (P<0.1) with 90-day mortality in the univariate analysis (Table 5); were included in the initial multivariate prediction rule.
All variables that were found to be associated (P < 0.1) with 90-day mortality in the univariate analysis were included in the initial multivariate prediction rule.
Studies were included if they met all of the following criteria: (1) Study population were patients with chest pain suggestive of stable angina pectoris, (2) study reported a new multivariate prediction rule that included clinical variables, (3) outcome was the presence of significant CAD defined as ≥50% stenosis in at least one vessel.
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After ROC analysis and multivariate logistic regression analysis a prediction rule for risk was developed.
The multivariate meta-analysis will result in a clinical prediction rule with optimal diagnostic accuracy characteristics.
The final multivariate regression model was used to create a clinical prediction rule.
We used the literature and bivariate analyses to identify candidate predictors, and multivariate logistic regression was used to develop a clinical prediction rule for CWS.
Univariate, multivariate and decision tree procedures were used to deduce a prediction rule from over 186 variables; retrospectively collated from clinical data for 213 patients.
Therefore, on the basis of multivariate analysis of each risk factor, we derived equations for each prediction rule (Table 3).
Variables and their respective threshold values which were found during multivariate analysis and test data of classification analysis were assigned a score to create a prediction rule which was then applied to the derivation and validation cohort for all applicable cases.
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