title: Do models incorporating comorbidities outperform those incorporating vital signs and injury pattern for predicting mortality in geriatric trauma?
abstract: BACKGROUND
  Geriatric trauma is becoming a significant public health concern. The most commonly used prediction models for mortality benchmarking are based on vital signs and injury pattern, including the Trauma and Injury Severity Score (TRISS), which is less accurate in the elderly. The ICD-9-based prediction models incorporating injuries and comorbidities, such as the University Health System Consortium Expected Mortality (UHC-EM), may be more accurate for the elderly.
  
  
  METHODS
  We retrospectively studied all trauma admissions from January 2005 to June 2012 at an academic level I adult trauma center. This was an observational study comparing expected to actual in-hospital mortality for both geriatric (age ≥65 years) and nongeriatric populations. Predictive ability for TRISS and UHC-EM was determined by the area under the receiver operator characteristic curve (AUC).
  
  
  RESULTS
  Geriatric patients had higher median TRISS predicted mortality (8.4% [interquartile range (IQR) 4.8%, 27.4%] vs 2.8% [IQR 1.1%, 30.2%], p < 0.001). Geriatric patients had a median UHC-EM 5 times higher than nongeriatric patients (5.0% [IQR 1.0%, 19.0%] vs 1.0% [IQR 0%, 7.0%], p < 0.001). In-hospital mortality was 3 times higher in geriatric patients (18.1% vs 6.0%, p < 0.001). The UHC-EM had superior AUC to TRISS in both geriatric (0.89 [95% CI 0.87, 0.91] vs 0.81 [95% CI 0.78, 0.84], p < 0.05) and nongeriatric (0.93 [95% CI 0.92, 0.94] vs 0.90 [95% CI 0.89, 0.91], p < 0.05) patients.
  
  
  CONCLUSIONS
  An ICD-9-based algorithm, such as the UHC-EM, which incorporates injuries and comorbidities, may be superior to algorithms based on vital signs and injury patterns without comorbidities in predicting mortality after trauma in the geriatric population.
authors.affiliation: Division of Trauma and Surgical Critical Care, Vanderbilt University Medical Center, Nashville, TN.
authors.forename: Steven E
authors.initials: SE
authors.lastname: Brooks
authors.affiliation: Division of Trauma and Surgical Critical Care, Vanderbilt University Medical Center, Nashville, TN.
authors.forename: Kaushik
authors.initials: K
authors.lastname: Mukherjee
authors.affiliation: Division of Trauma and Surgical Critical Care, Vanderbilt University Medical Center, Nashville, TN.
authors.forename: Oliver L
authors.initials: OL
authors.lastname: Gunter
authors.affiliation: Division of Trauma and Surgical Critical Care, Vanderbilt University Medical Center, Nashville, TN.
authors.forename: Oscar D
authors.initials: OD
authors.lastname: Guillamondegui
authors.affiliation: Division of Trauma and Surgical Critical Care, Vanderbilt University Medical Center, Nashville, TN.
authors.forename: Judith M
authors.initials: JM
authors.lastname: Jenkins
authors.affiliation: Division of Trauma and Surgical Critical Care, Vanderbilt University Medical Center, Nashville, TN.
authors.forename: Richard S
authors.initials: RS
authors.lastname: Miller
authors.affiliation: Division of Trauma and Surgical Critical Care, Vanderbilt University Medical Center, Nashville, TN. Electronic address: addison.may@vanderbilt.edu.
authors.forename: Addison K
authors.initials: AK
authors.lastname: May
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D015897:Comorbidity; D003661:Decision Support Techniques; D017052:Hospital Mortality; D006801:Humans; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D012189:Retrospective Studies; D015599:Trauma Severity Indices; D055986:Vital Signs; D014947:Wounds and Injuries / Q000453:epidemiology* / Q000401:mortality