Literature DB >> 25459214

A predictive rule for mortality of inpatients with Staphylococcus aureus bacteraemia: A classification and regression tree analysis.

Daiki Kobayashi1, Kyoko Yokota2, Osamu Takahashi3, Hiroko Arioka4, Tsuguya Fukui5.   

Abstract

OBJECTIVE: To create a predictive rule to identify risk factors for mortality among patients with Staphylococcus aureus bacteraemia (SAB). DESIGN, SETTING AND PATIENTS: This was a retrospective cohort study of all adult patients with SAB at a large community hospital in Tokyo, Japan, from April 1, 2004 to March 31, 2011. Baseline data and clinically relevant factors were collected from electronic charts. The primary outcome was in-hospital mortality. All candidate predictors were included in a classification and regression tree (CART) analysis. A receiver operating characteristic (ROC) curve was drawn, and the area under the curve (AUC) was obtained. A cross-validation analysis was performed.
MEASUREMENTS AND MAIN RESULTS: A total of 340 patients had SAB during the study period. Of these, 118 (34.7%) patients died in hospital. Among 41 potential variables, the CART analysis revealed that underlying malignancy, serum blood glucose level, methicillin resistance, and low serum albumin were predictors of mortality. The AUC was 0.73 (95% CI: 0.67-0.79). For validation, the estimated risk was 0.26 (± SE: 0.02) in the resubstitution analysis and 0.33 (± SE: 0.03) in the cross-validation analysis.
CONCLUSION: We propose a predictive model for the mortality of patients with SAB consisting of four predictors: underlying malignancy, low serum albumin, high glucose, and methicillin resistance. This model may facilitate appropriate preventative management for patients with SAB who are at high risk of mortality.
Copyright © 2014 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Bacteraemia; CART analysis; Mortality; Predictive rule; Sepsis; Staphylococcus aureus

Mesh:

Substances:

Year:  2014        PMID: 25459214     DOI: 10.1016/j.ejim.2014.10.003

Source DB:  PubMed          Journal:  Eur J Intern Med        ISSN: 0953-6205            Impact factor:   4.487


  9 in total

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9.  Detecting the Hidden Properties of Immunological Data and Predicting the Mortality Risks of Infectious Syndromes.

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  9 in total

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