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Updated: Dec 23, 2025

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Published on: April 18, 2011
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.
Abstract:
Trauma patients are at risk of repeated hospital-acquired infections, however predictive scores aiming to identify susceptibility to such infections are lacking. The objective of this study was to investigate whether commonly employed disease-severity scores can successfully predict susceptibility to multiple independent infectious episodes (MIIEs) among trauma patients. A secondary analysis of data derived from the prospective, longitudinal study "Inflammation and the Host Response to Injury" ("Glue Grant") was performed. 1,665 trauma patients, older than 16, were included. Patients who died within seven days from the time of injury were excluded. Five commonly used disease-severity scores [Denver, Marshall, Acute Physiology and Chronic Health Evaluation II (APACHE II), Injury Severity Score (ISS), and New Injury Severity Score (NISS)] were examined as independent predictors of susceptibility to MIIEs. The latter was defined as two or more independent infectious episodes during the index hospital stay. Multivariable logistic regression was used for the statistical analysis. 22.58% of the population was found to be susceptible to MIIEs. Denver and Marshall scores were highly predictive of the MIIE status. For every 1-unit increase in the Denver or the Marshall score, there was a respective 15% (Odds Ratio:1.15; 95% CI: 1.07-1.24; p < 0.001) or 16% (Odds Ratio:1.16; 95% CI: 1.09-1.24; p < 0.001) increase in the odds of MIIE occurrence. APACHE II, ISS, and NISS were not independent predictors of susceptibility to MIIEs. In conclusion, the Denver and Marshall scores can reliably predict which trauma patients are prone to MIIEs, prior to any clinical sign of infection. Early identification of these individuals would potentially allow the implementation of rapid, personalized, preventative measures, thus improving patient outcomes and reducing healthcare costs.
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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