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TMPM.ado: The Trauma Mortality Prediction Model is Robust to ICD-9 and AIS Coding Lexicons

Listed author(s):
  • Alan Cook


    (Baylor University Medical Center - Dallas, Texas)

  • Turner M. Osler

    (University of Vermont College of Medicine, Department of Surgery)

Registered author(s):

    Many methods have been developed to predict mortality following trauma. Two classification systems are used to provide a taxonomy for diseases, including injuries. The ICD-9 is the classification system for administrative data in the U.S.A. AIS was developed for characterization of injuries alone. The Trauma Mortality Prediction Model (TMPM) is based on empiric estimates of severity for each injury in the ICD-9 and AIS lexicons. Each probability of mortality (POD) is estimated from the five worst injuries per patient. TMPM has been rigorously tested against other mortality prediction models using ICD-9 and AIS data and found superior. The TMPM.ado command allows Stata users to efficiently apply TMPM to data sets using ICD-9 or AIS. The command makes use of model-averaged regression coefficients (MARC) that assign empirically derived severity measures for each of the 1,322 AIS codes and 1,579 ICD-9 injury codes. The injury codes are sorted into body regions then merged with the MARC table to assemble a set of regression coefficients. A logit model is generated to calculate the probability of death. TMPM.ado accommodates either AIS or ICD-9 lexicons from a single command and adds the POD for each patient to the original dataset as a new variable.

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    Paper provided by Stata Users Group in its series SAN12 Stata Conference with number 20.

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    Date of creation: 01 Aug 2012
    Handle: RePEc:boc:scon12:20
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