{smcl}
{* October 25, 2010}
{* Version 3.0}
help for {hi:ais_tmpm}
{hline}

{title:Title}

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{cmd:ais_tmpm} -- Trauma Mortality Prediction Model (AIS version)


{title:Version}

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Version 3.0.

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ICDPIC Version 3.0 requires STATA 8.0 or higher.  ICDPIC Version 3.0 has been tested in STATA 10 and STATA 11, but the authors believe it
should also work without incident in STATA 8 and STATA 9.  If you have any problems using ICDPIC Version 3.0 in STATA 8 or STATA 9, please
inform the authors.

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ICDPIC Version 3.0 may be installed from within STATA using the {bf:ssc} command.  If you installed a previous version of ICDPIC from the SSC
archives website using the {bf:ssc} command, we suggest that you first delete it by typing {bf:ssc uninstall icdpic} followed by
{bf:ssc install icdpic}.  Alternatively, you may use {bf:ssc install icdpic, replace}.  See help for {help ssc}.

{p 4 4 2}
If you installed any previous ICDPIC files obtained directly from the authors, please delete them {bf:ALL} (.ado, .hlp and .dta files) to avoid
any conflicts with ICDPIC 3.0 files.

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Please enter complete variable names in the ICDPIC Version 3.0 dialog boxes.  Do not use abbreviations.

{p 4 4 2}
New to Version 3.0 is the addition of a dialog box (.dlg) file associated with each individual ICDPIC Version 3.0 program (.ado) file.  To access
the ICDPIC dialog box, and all the ICDPIC programs, type: {cmd:db icdpic}.  Typing {cmd:icdpic}, as in earlier versions, will still work, but
{bf:ONLY} with {bf:icdpic}.  For example, to access the AIS TMPM program directly, type: {bf:db ais_tmpm}.  Typing {bf:ais_tmpm}, as in previous
versions, will produce an error.

{p 4 4 2}
Fixed in ICDPIC Version 3.0 is the ability to use path\file names containing spaces.

{p 4 4 2}
Fixed in ICDPIC Version 3.0 is the ability to run in STATA 11.0.

{p 4 4 2}
Fixed in ICDPIC Version 3.0 is a bug that caused the {bf:triss} program to crash if the {it:rts} variable was named anything other than "rts".

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New in ICDPIC Version 3.0 ({bf:trauma} program only) is the ability to choose whether an AIS value of 6 automatically forces an ISS of
75 or to automatically have all AIS values of 6 changed to an AIS value of 5 and then have the ISS calculated normally.

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All dialog boxes in ICDPIC Version 3.0 have memory.  Each time a dialog box is opened within the same STATA session, it will remember the values
last entered.

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All dialog boxes in ICDPIC Version 3.0 have the following buttons:

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{it:OK} executes the program and removes the dialog box from the screen.

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{it:SUBMIT} executes the program and leaves the dialog box on the screen.  Note that if an error message is generated the dialog box may
be minimized.

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{it:CANCEL} removes the dialog box from the screen and does nothing.  Clicking on the close icon of the dialog box does the same thing.

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{it:HELP} leaves the dialog box on the screen and presents the program help file.  The {it:HELP} button has a question mark on it.

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{it:COPY} leaves the dialog box on the screen and copies the program command to the clipboard.

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{it:RESET} resets the values of the controls in the dialog box to their initial state, just as if the dialog box were invoked
for the first time.  Each time a user invokes a dialog box, its controls will be filled in with the values the user last entered.
{it:RESET} restores the control values to their defaults.  The {it:RESET} button has an R on it.


{title:Syntax}

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{cmd:db ais_tmpm}

{p 4 4 2}
The {it:AIS TMPM (AIS Trauma Mortality Prediction Model)} dialog box will open.  Follow the instructions.  

{center:{bf:{it:OR}}}

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{cmd:db icdpic}

{p 4 4 2}
The {it:ICDPIC} dialog box will open.  Choose {it:AIS TMPM} and click {it:OK} or {it:Submit}.
The {it:AIS TMPM (AIS Trauma Mortality Prediction Model)} dialog box will open.  Follow the instructions.  


{title:Description}

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{cmd:ais_tmpm} estimates patient probability of death,


{center: Pd = normal(TMPM)}


{p 4}
where 


{center: TMPM = (C0 + (C1 X I1) + (C2 X I2) + (C3 X I3) + (C4 X I4) + (C5 X I5) + (C6 X S) + (C7 X I1 X I2)}


{p 4 4 2}
Pd is the mortality predicted by TMPM and normal is the standard normal distribution function.  I1 through I5 are the MARC
values ordered with the greatest MARC value (worst injury) first, second greatest MARC value second, up to the fifth worst
injury.  S is a Boolean value set to 1 if the two worst injuries occur in the same body region and 0 otherwise.  I1 X I2
represents the interaction of the worst and second worst injuries a patient has sustained.  C0 through C7 are coefficients.

{p 4 4 2}
See also the {bf:Options} and {bf:Remarks} sections for {bf:IMPORTANT} information on, and requirements for, {cmd:ais_tmpm}.


{title:Options}

None


{title:Remarks}

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The user's data must contain AIS codes.  AIS codes in the user's data must be of type string.  AIS codes should have a width
of 6 (predot portion of code only) or 8 (complete code).  The AIS code prefix must be the same for all AIS codes and
numbered sequentially starting with 1, for example, ais1...aisN.

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{cmd:ais_tmpm} adds the following variables to a new copy of the user's data stored on disk:


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{it:marc_1-marc_n}: MARC value for AIS codes 1..n {p_end}
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{it:inj1-inj5}: injuries (AIS codes) associated with the top 5 MARC values (5 worst injuries) {p_end}
{p 8 12 2}
{it:marc1-marc5}: top 5 MARC values (5 worst injuries) {p_end}
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{it:high1-high2}: AIS body region of the two worst injuries (top two MARC values) respectively {p_end}
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{it:same_reg}: Boolean variable to indicate if top two injuries occurred in the same body region {p_end}
{p 8 12 2}
{it:AIS_TMPM}: Trauma Mortality Prediction Model value {p_end}
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{it:AIS_POD}: probability of death {p_end}


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Variables {it:AIS_TMPM} and {it:AIS_POD} are rounded to the nearest 0.000001.

{p 4 4 2}
{cmd:ais_tmpm} requires the use of lookup table {bf:mais_s.dta}.  This data table is supplied.  See help for {help icdpic},
specifically the LOOKUP TABLES part of the {bf:Remarks} section.


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{bf:{ul:NOTES ON METHODOLOGY AND VALIDATION}}

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The Trauma Mortality Prediction Model (AIS TMPM) has the same goal as the ISS.  Both provide a single number summary of a
patient's injuries when those injuries are described in the AIS lexicon.  However ISS considers only at most three of a
patient's injuries, and provides only a relative measure of injury severity (0 to 75).  TMPM, by contrast, considers
as many as 5 of a patient's injuries and results in an actual prediction of that patient's mortality (0% to 100%) based
upon the injuries sustained.
 
{p 4 4 2} 
Although ISS and TMPM have similar goals, the models upon which they are based are quite different: ISS is simply the sum
of the worst AIS severities in each of the three most severely injured body regions.  TMPM, by contrast, is based upon a
probit model that includes the five worst injuries (regardless of where they occur in the body), as well as terms that
describe interactions and whether the two worst injuries are in the same body region.  A further difference is the source
of information that ISS and TMPM use to assign severity to individual injuries: ISS uses the AIS severity created by panels
of experts over the years while TMPM uses empirically derived values for each AIS code.  These "Model Adjusted Regression
Coefficients" (MARC values) were derived from 702,229 patients injured between 2001 and 2005 and cared for at trauma
centers that provided information to the National Trauma Data Bank.

{p 4 4 2}
The details of the mathematics behind the derivation of MARC values and construction and validation of the TMPM have been
published (Osler T, Glance L, et al, A trauma mortality prediction model based on the Anatomic Injury Scale, {it:Ann Surg}
2008;247:1041-1048).  In this study TMPM provided predictions that were substantially more accurate, both in terms of
discrimination and calibration, than did ISS.

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Perhaps adding to the appeal of the TMPM is the fact that a version of this model that uses injuries described in the
ICD-9 lexicon is also available. (Annals of Surgery, in press.)  Because administrative data often describe patient injuries
in either the AIS or ICD-9-CM lexicon, but not both, we anticipate the two TMPM programs (ICD-9-CM TMPM and AIS TMPM) will
be useful to researchers who perhaps are confronted with data sets in which patients are described in the AIS lexicon
in some and described in the ICD-9-CM lexicon in others.

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Note that, although TMPM uses only anatomic injuries in its predictions, there in nothing precluding the addition of other
information (age, GCS, etc.) to outcome predictions.  A logistic model predicting outcome for any patient data set can
easily be constructed with the anatomic injury component of the model encapsulated in the TMPM value.  Note, however, that
if TMPM is to be incorporated into a larger logistic model it should enter such a model as its logit (log(Ps /(1-Ps)))
transformation.


{title:Examples}

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None


{title:Authors}

{p 4}
David E. Clark, M.D.

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Maine Medical Center, Portland, Maine, USA {p_end}
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University of Vermont College of Medicine, Burlington, Vermont, USA {p_end}
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Harvard Injury Control Research Center, Harvard School of Public Health, Boston, Massachusetts, USA {p_end}

{p 4}
Turner M. Osler, M.D.

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University of Vermont College of Medicine, Burlington, Vermont, USA {p_end}

{p 8}
Correspondence to Dr. Osler, {p_end}
{p 8}
Department of Surgery {p_end}
{p 8}
University of Vermont {p_end}
{p 8}
789 Orchard Shore Road {p_end}
{p 8}
Colchester, VT 05446 {p_end}
{p 8} 
Email:  tosler@uvm.edu {p_end}

{p 4}
David R. Hahn

{p 8 12 2}
Maine Medical Center, Portland, Maine, USA {p_end}


{title:References}

{p 4 8 2}
Osler T, Glance L, Buzas JS, Mukamel D, Wagner J, Dick A.  A Trauma Mortality Prediction Model Based on the Anatomic Injury
Scale.  {it:Ann Surg} 2008;247:1041-1048.


{title:Also see}

{p 4}
help for {help icd9tmpm}

{p 4}
help for {help icdpic}

{p 4}
help for {help ais_tmpm}