Denver and Marshall scores successfully predict susceptibility to multiple independent infections in trauma patients

Marianna Almpani1,2, Amy Tsurumi1,2,3, Thomas Peponis1

  • 1Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America.

Plos One
|April 30, 2020
PubMed

Insights

Denver and Marshall scores effectively predict multiple hospital infections in trauma patients. Early identification of at-risk individuals can improve outcomes and reduce healthcare costs.

Area of Science:

  • Trauma care
  • Infectious disease epidemiology
  • Clinical risk prediction

Background:

  • Trauma patients face a high risk of hospital-acquired infections.
  • Predictive scores for identifying susceptibility to multiple independent infectious episodes (MIIEs) are currently lacking.
  • This study addresses the need for early identification of MIIE-prone trauma patients.

Purpose of the Study:

  • To evaluate the predictive capability of commonly used disease-severity scores for MIIEs in trauma patients.
  • To determine if scores like Denver, Marshall, APACHE II, ISS, and NISS can identify patients susceptible to recurrent infections.
  • To inform the development of targeted preventative strategies.

Main Methods:

  • Secondary analysis of data from the prospective 'Glue Grant' study.
  • Inclusion of 1,665 trauma patients (age >16, excluding those deceased within 7 days).
  • Multivariable logistic regression analysis examining five disease-severity scores (Denver, Marshall, APACHE II, ISS, NISS) as predictors of MIIEs.

Main Results:

  • 22.58% of trauma patients experienced MIIEs.
  • Denver and Marshall scores demonstrated high predictive value for MIIEs.
  • Each 1-unit increase in Denver score (OR: 1.15) or Marshall score (OR: 1.16) significantly increased MIIE odds.
  • APACHE II, ISS, and NISS were not independent predictors of MIIEs.

Conclusions:

  • The Denver and Marshall scores reliably predict trauma patients at risk for MIIEs before clinical signs appear.
  • Early identification enables personalized preventative measures, potentially improving patient outcomes.
  • Utilizing these scores can lead to reduced healthcare costs through proactive infection management.

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