title: Simplified alternative to the TRISS method for resource-constrained settings.
abstract: BACKGROUND
  We developed simple methods of risk adjustment for evaluating the quality of injury care (predicting survival probabilities of the injured) by fully utilizing routinely collected data in injury surveillance and clinical practices. Widely used methods of risk adjustment require additional data that are difficult to collect in resource-constrained settings.
  
  
  METHODS
  We developed logistic regression models that predict survival using data obtained from 9,840 victims aged 15 years or older with blunt traumatic injuries who were registered in the Japan Trauma Data Bank, Japan's national trauma registry, between January 2004 and December 2007. The models included three predictors: age, an anatomical injury severity parameter such as a simplified severity categorization (minor, moderate, and severe) described in the Injury Surveillance Guidelines, and a physiological status parameter. The models' abilities to predict survival probabilities were evaluated using the area under the receiver-operating characteristic curve (AUROCC).
  
  
  RESULTS
  The simplified three-predictor models showed good performance with the AUROCC ranging from 0.86 to 0.94. In particular, the models with a consciousness level indicator as a physiological parameter showed a high AUROCC, ranging from 0.93 to 0.94, which was not much different from the performance of the widely used method that shows an AUROCC of 0.96.
  
  
  CONCLUSIONS
  Simplified methods of risk adjustment that require only routinely collected data will facilitate evaluation and improvement in the quality of injury care in resource-constrained low- and middle-income countries, where injuries are a growing public health concern.
authors.affiliation: Department of Preventive Medicine, St Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa 216-8511, Japan. snakahara@marianna-u.ac.jp
authors.forename: Shinji
authors.initials: S
authors.lastname: Nakahara
authors.forename: Masao
authors.initials: M
authors.lastname: Ichikawa
authors.forename: Akio
authors.initials: A
authors.lastname: Kimura
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D002423:Cause of Death; D005260:Female; D017052:Hospital Mortality / Q000639:trends; D006801:Humans; D007564:Japan; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D058996:Quality Improvement; D012042:Registries; D020379:Risk Adjustment / Q000592:standards*; D018570:Risk Assessment; D016019:Survival Analysis; D015599:Trauma Severity Indices; D014949:Wounds, Nonpenetrating / Q000175:diagnosis* / Q000401:mortality* / Q000628:therapy; D055815:Young Adult