Predicting in-hospital mortality in pneumonia-associated septic shock patients using a classification and regression tree: a nested cohort study.

ABSTRACT:

Background

Pneumonia complicated by septic shock is associated with significant morbidity and mortality. Classification and regression tree methodology is an intuitive method for predicting clinical outcomes using binary splits. We aimed to improve the prediction of in-hospital mortality in patients with pneumonia and septic shock using decision tree analysis.

Methods

Classification and regression tree models were applied to all patients with pneumonia-associated septic shock in the international, multicenter Cooperative Antimicrobial Therapy of Septic Shock database between 1996 and 2015. The association between clinical factors (time to appropriate antimicrobial therapy, severity of illness) and in-hospital mortality was evaluated. Accuracy in predicting clinical outcomes,

SUBMITTER: Speiser JL 

PROVIDER: S-EPMC6186142 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature