Study or Evidence-Based Practice (EBP) Project : Prediction of Sepsis in the Burn ICU Patient

Abstract

Purpose: The purpose of this study was twofold: 1) determine the ability of the SIRS and ABA criteria to predict sepsis in the burn patient; and 2) develop a model representing the best combination of clinical predictors associated with sepsis in the same population. Design: A retrospective, case-controlled, within-patient comparison of burn patients admitted to a single intensive care unit from January 2005 to September 2010. Methods: Blood culture results were paired with clinical condition: "positive-sick"; "negative-sick", and "screening-not sick". Data for predictors were collected for the 72 hours prior to blood culture. Sample: Fifty-nine adult, thermally-injured burn subjects were included in the study, representing 177 culture periods. Analysis: Significant dichotomized predictor variables were evaluated using logistic regression, Generalized Estimating Equations and ROC AUC analyses to assess model predictive ability. Bootstrapping methods evaluated potential model over-fitting. Findings: SIRS criteria were not associated with culture type, with an average of 98% of subjects meeting criteria in the 3 days prior. ABA sepsis criteria were significantly different among culture type only on the day prior (p = 0.004). The model variables identified included: heart rate>130, mean blood pressure<60 mmHg, base deficit<-6 mEq/L, temperature<36 deg C, use of vasoactive medications, and glucose>150 mg/dl. The model was significant in predicting "positive culture-sick" and sepsis state ("sick"), with AUC of 0.775 (p < 0.001) and 0.714 (p < .001), respectively; comparatively, the ABA criteria AUC was 0.619 (p = 0.028) and 0.597 (p = .035), respectively. Implications for Military Nursing: ABA criteria performed well, but only for the day prior to positive blood culture results.

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Document Details

Document Type
Technical Report
Publication Date
May 15, 2012
Accession Number
ADA617031

Entities

People

  • Elizabeth A. Mann

Organizations

  • University of Texas Health Science Center at Houston

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Blood
  • Blood Cells
  • Burns
  • Cardiovascular Physiological Phenomena
  • Cell Count
  • Computers
  • Detection
  • Education
  • Health Services
  • Heart Rate
  • Infection
  • Intensive Care Units
  • Knowledge Management
  • Leukocytes
  • Medical Personnel
  • Systemic Inflammatory Response Syndrome

Fields of Study

  • Medicine

Readers

  • Regression Analysis.
  • Trauma or Military Medicine