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In a logistic regression model adjusted for age and antimalarial treatment, with death as dependent variable and established predictors of severe malaria as independent variables, significant predictors in the final model (n = 482) were renal impairment (log BUN), acidosis (BE), parasitemia (log parasitemia; but an inverse correlation), and plasma PfHRP2 concentration (Table 4).
This increases the clinical face validity of the variables included as predictors in the final algorithms.
Inspection of the variance inflation factor did not suggest multicollinearity among predictors in the final model.
All four TPB variables emerged as significant independent predictors in the final regression equation.
Predictors in the final model were age, sex, race and CD4 count.
The Charlson Comorbidity Index remained a significant predictor in the final model (see Table 5).
Moreover, decade sum was a significant predictor in the final model of the regression analysis.
A significance level of P < 0.05 was used to include a predictor in the final model.
However, unit sum was included as significant predictor in the final model of the regression analysis on item RT, whereas the categorical predictor carry was not considered.
Different studies used differing prediction targets, candidate predictors, and populations, affecting the predictors included in the final predictive models.
41 43 of 53 assessed predictors remained in the final models.
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