Neither Single nor a Combination of Routine Laboratory Parameters can Discriminate between Gram-positive and XXXX .

@entity1 @entity2259
Adequate early empiric antibiotic therapy is pivotal for the outcome of @entity1 with bloodstream infections. In clinical practice the use of surrogate laboratory parameters is frequently proposed to predict underlying bacterial pathogens; however there is no clear evidence for this assumption. In this study, we investigated the discriminatory capacity of predictive models consisting of routinely available laboratory parameters to predict the presence of Gram-positive or @entity2259 . Major machine learning algorithms were screened for their capacity to maximize the area under the receiver operating characteristic curve (ROC-AUC) for discriminating between Gram-positive and Gram-negative cases. Data from 23,765 @entity1 with clinically suspected @entity2259 were screened and 1,180 bacteremic @entity1 were included in the study. A relative predominance of @entity2259 (54.0%), which was more pronounced in females (59.1%), was observed. The final model achieved 0.675 ROC-AUC resulting in 44.57% sensitivity and 79.75% specificity. Various parameters presented a significant difference between both genders. In gender-specific models, the discriminatory potency was slightly improved. The results of this study do not support the use of surrogate laboratory parameters for predicting classes of causative pathogens. In this @entity1 cohort, gender-specific differences in various laboratory parameters were observed, indicating differences in the host response between genders.
