title: Toward a More Robust Prediction of Pulmonary Embolism in Trauma Patients: A Risk Assessment Model Based on 38,000 Patients.
abstract: OBJECTIVE
  Pulmonary embolism (PE) is a rare but sometimes fatal complication of trauma. Risk stratification models identify patients at increased risk of PE; however, they are often complex and difficult to use. This research aims to develop a model, based on a large sample of trauma patients, which can be easily and quickly used at the time of admission to predict PE.
  
  
  METHODS
  This study used trauma registry data from 38,597 trauma patients. Of these, 239 (0.619%) developed a PE. We targeted demographic and injury data, prehospital information, and data on treatments and events during hospitalization. A multivariate binary logistic regression model was developed to predict the odds of developing a PE during hospitalization. The model was developed using a 50% randomly selected development subsample and then tested for accuracy using the remaining 50% validation sample.
  
  
  RESULTS
  We found 7 statistically significant predictors of PE, including (1) age [odds ratio (OR) = 1.01; 95% CI, 1.00-1.02; P = 0.05], (2) obesity (OR = 2.54; 95% CI, 1.29-4.99; P < 0.01), (3) injury from motorcycle accident (OR = 2.01; 95% CI, 1.25-3.22; P < 0.01), (4) arrival by helicopter (OR = 2.91; 95% CI, 1.16-7.27; P = 0.02), (5) emergency department admission pulse rate (OR = 1.01; 95% CI, 1.0-1.02; P = 0.06), (6) admission to intensive care unit (OR = 5.03; 95% CI, 3.12-8.12; P < 0.01), and (7) injury location, including thorax (OR = 1.57; 95% CI, 1.04-2.37; P = 0.03), abdomen (OR = 1.56; 95% CI, 1.04-2.33; P = 0.03), and lower extremity injuries (OR = 2.85; 95% CI, 3.12-8.12; P < 0.01). Our model was able to discriminate between predicted and actual PE events with a receiver operating characteristic area under the curve of 0.87. By identifying the top 25% high-risk patients, we were able to predict 80%-84% of pulmonary emboli.
  
  
  CONCLUSIONS
  This knowledge allows us to focus stronger thromboprophylactic efforts on patients at highest risk. This model can be used to rapidly identify trauma patients at high risk for PE.
  
  
  METHODS
  Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.
authors.affiliation: *Department of Orthopaedic Surgery, University of Texas Southwestern Medical Center, Dallas, TX; and †Epidemiology and Biostatistics, US Army Institute of Surgical Research, US Army Institute of Surgical Research, San Antonio, TX.
authors.forename: Sheena R
authors.initials: SR
authors.lastname: Black
authors.forename: Jeffrey T
authors.initials: JT
authors.lastname: Howard
authors.forename: Paul C
authors.initials: PC
authors.lastname: Chin
authors.forename: Adam J
authors.initials: AJ
authors.lastname: Starr
mesh_terms: D000328:Adult; D015984:Causality; D015897:Comorbidity; D003627:Data Interpretation, Statistical; D005260:Female; D006760:Hospitalization / Q000706:statistics & numerical data; D006801:Humans; D015994:Incidence; D008297:Male; D011379:Prognosis; D016016:Proportional Hazards Models; D011655:Pulmonary Embolism / Q000175:diagnosis* / Q000453:epidemiology*; D012042:Registries; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D012680:Sensitivity and Specificity; D014481:United States / Q000453:epidemiology; D014947:Wounds and Injuries / Q000175:diagnosis* / Q000453:epidemiology*