title: Predicting Mortality and Independence at Discharge in the Aging Traumatic Brain Injury Population Using Data Available at Admission.
abstract: BACKGROUND
  Aging worsens outcome in traumatic brain injury (TBI), but available studies may not provide accurate outcomes predictions due to confounding associated injuries. Our goal was to develop a predictive tool using variables available at admission to predict outcomes related to severity of brain injury in aging patients.
  
  
  METHODS
  Characteristics and outcomes of blunt trauma patients, aged 50 or older, with isolated TBI, in the National Trauma Data Bank (NTDB), were evaluated. Equations predicting survival and independence at discharge (IDC) were developed and validated using patients from our trauma registry, comparing predicted with actual outcomes.
  
  
  RESULTS
  Logistic regression for survival and IDC was performed in 57,588 patients using age, sex, Glasgow Coma Scale score (GCS), and Revised Trauma Score (RTS). All variables were independent predictors of outcome. Two models were developed using these data. The first included age, sex, and GCS. The second substituted RTS for GCS. C statistics from the models for survival and IDC were 0.90 and 0.82 in the GCS model. In the RTS model, C statistics were 0.80 and 0.67. The use of GCS provided better discrimination and was chosen for further examination. Using a predictive equation derived from the logistic regression model, outcome probabilities were calculated for 894 similar patients from our trauma registry (January 2012 to March 2016). The survival and IDC models both showed excellent discrimination (p < 0.0001). Survival and IDC generally decreased by decade: age 50 to 59 (80% IDC, 6.5% mortality), 60 to 69 (82% IDC, 7.0% mortality), 70 to 79 (76% IDC, 8.9% mortality), and 80 to 89 (67% IDC, 13.4% mortality).
  
  
  CONCLUSIONS
  These models can assist in predicting the probability of survival and IDC for aging patients with TBI. This provides important data for loved ones of these patients when addressing goals of care.
authors.affiliation: Department of Surgery, Wake Forest University, Winston-Salem, NC. Electronic address: pmiller@wakehealth.edu.
authors.forename: Preston R
authors.initials: PR
authors.lastname: Miller
authors.affiliation: Department of Surgery, Wake Forest University, Winston-Salem, NC.
authors.forename: Michael C
authors.initials: MC
authors.lastname: Chang
authors.affiliation: Department of Surgery, Wake Forest University, Winston-Salem, NC.
authors.forename: J Jason
authors.initials: JJ
authors.lastname: Hoth
authors.affiliation: Department of Surgery, Wake Forest University, Winston-Salem, NC.
authors.forename: Amy N
authors.initials: AN
authors.lastname: Hildreth
authors.affiliation: Department of Neurosurgery, Wake Forest University, Winston-Salem, NC.
authors.forename: Stacey Q
authors.initials: SQ
authors.lastname: Wolfe
authors.affiliation: Department of Surgery, Wake Forest University, Winston-Salem, NC.
authors.forename: Jessica L
authors.initials: JL
authors.lastname: Gross
authors.affiliation: Department of Surgery, Wake Forest University, Winston-Salem, NC.
authors.forename: R Shayn
authors.initials: RS
authors.lastname: Martin
authors.affiliation: Department of Surgery, Wake Forest University, Winston-Salem, NC.
authors.forename: Jeffrey E
authors.initials: JE
authors.lastname: Carter
authors.affiliation: Department of Surgery, Wake Forest University, Winston-Salem, NC.
authors.forename: J Wayne
authors.initials: JW
authors.lastname: Meredith
authors.affiliation: Wake Forest Health Science Department of Biostatistical Sciences, Wake Forest University, Winston-Salem, NC.
authors.forename: Ralph
authors.initials: R
authors.lastname: D'Agostino
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000070642:Brain Injuries, Traumatic / Q000175:diagnosis* / Q000401:mortality*; D016208:Databases, Factual; D003661:Decision Support Techniques; D005260:Female; D006801:Humans; D057187:Independent Living / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D010343:Patient Admission / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data; D011379:Prognosis; D012189:Retrospective Studies; D014481:United States / Q000453:epidemiology; D014949:Wounds, Nonpenetrating / Q000175:diagnosis* / Q000401:mortality*