Slide descriptions, Toyota --Wake Forest University School of Medicine
CIREN Center,

"What Constitutes an Elderly Motor Vehicle Crash Victim -- The Age
Threshold At Which Specific Injuries Increase The Risk Of Mortality"

1.  Our title is called "What constitutes an elderly motor vehicle crash
    victim -- the age at which specific injuries increase the risk of
    mortality, by the Toyota -- WFU School of Medicine CIREN Team

2.  The elderly population in America is increasing. This data from the
    US Census shows projected changes of different age groups as a
    percentage of the total from 2000 -- 2050. The most marked increases
    are in the higher age groups particularly 85+.

3.  This is a graphic showing the cumulative distribution of US Elderly
    Drivers from the CIREN database, for both drivers and right front
    passengers.

4.  This is a slide showing the % killed or injured by age from NHTSA
    traffic safety facts, 2005.

5.  Replotting the previous graph, the ratio of % killed to % injured
    increases dramatically from about 1 from 16-20 to 45-54, to up to
    over 2.5 for age 74 or greater.

6.  These are some of the challenges for policy makers: record #'s of
    elderly drivers, very little legislation, and additional challenges.

7.  The elderly constitute 15% of MVC trauma victims, and mortality rate
    for this group is 2.54%, versus 1.39% for everyone else. What are
    the reasons for this?

8.  Age as a risk factor: Patient age has utility in risk prediction and
    adjustment models. Usually, age is used as a surrogate for
    preexisting comorbidities. There are at least two problems with this
    approach: PEC effect can be better estimated, and age and PEC
    technically measure different though related quantities.

9.  Most risk adjustment models approach model age as a dichotomous
    variable. Age 55 is the commonly accepted threshold for the aged
    based solely on clinical acumen. The adjusted odds ratios for age
    threshold from the original TRISS score are 5.71, versus our own
    NTDB score, they are 6.36.

10. The global estimate of 55 at best over generalizes the age
    contribution. Certainly, immunity from death will depend both on age
    and the specific set of injuries of the patient.

11. Our study objectives were for specific injuries common to elderly
    car crash victims, to ascertain whether age is an important
    determinant of survival, and to identify the age at which this
    injury becomes a major threat to survival.

12. The CIREN database was interrogated for the 10 most frequently
    occurring injuries in persons over age 45. The regions are split
    into 9 regions as usual.

13. The NTDB was used, the national trauma databank: this is sponsored
    by American college of surgeons, and includes 1.5 million trauma
    cases from 904 hospitals in every state. It includes more than 200
    acs-verified trauma centers.

14. The NTDB consists of 35% of patients treated who were in motor
    vehicle crashes. Data collected are pre hospital demographics,
    injury variables, outcome data. But crash characteristics are not
    collected.

15. NTDB case selection: For a specific injury I, all NTDB cases meeting
    the following criteria were chosen: ECODE range 810.0 to 825.9, age
    \> 25 years, and among top 10 injuries per body region subset from
    the CIREN database.

16. For each injury I, a study dataset containing only patients with
    this injury is constructed. Also collected are known covariates, or
    other factors associated with death. The primary variable of
    interest though is age.

17. Covariates are: ISS, ED GCS Motor, ED GCS Verbal, ED GCS Eye, Payor
    status, Race, Gender, Year of Injury, and Max AIS in each body
    region.

18. For each injury I a multiple logistic A Multiple Logistic Regression
    (MLR) model was constructed to relate death as a function of age
    controlling for the covariates. The impact of age in terms of
    statistical significance was determined, and adjusted odds ratios
    (OR) for death are calculated for continuous age.

19. Missing data: Missing data are present in some of the covariates.
    Multiple Imputation (MI) methods were used to maximize the amount of
    data considered via MCMC iterations. The goals of MI are: Impute
    values that will maintain the covariance structure of the regression
    and maximize the information used. The goal is NOT to "replace the
    data with a plausible" value

20. Area under ROC curve AUROC analysis was undertaken. This measures
    discrimination -- the model's ability to distinguish between
    survivors and non-survivors at each specific age threshold. It uses
    a sliding scale of sensitivity and specificity, is bounded by 0.5
    and 1.0, where values closer to 0.5 indicate poor discrimination,
    and values closer to 1.0 indicate excellent survival. So the goal is
    to find threshold with highest area under the curve.

21. The adjusted AUROC takes a randomly paired survivor and non-survivor
    -- the probability that the survivor will have a higher predicted
    survival than the non-survivor is calculated. The MLR (regression)
    model produces adjusted estimates of mortality risk.

22. Here are some example BCI ROC curves for Troponin, CKMB, and
    CKMB/CK. They demonstrate the usefulness of the sensitivity
    specificity curve and the ability of a test to distinguish. These
    are analogous to our consideration of ages and the ability to
    predict fatality.

23. The ROC curve analysis is implemented as an iterative algorithm --
    the way it works is to define a binary threshold for age (starting
    with 25), and use this term in the adjusted model instead of age,
    and record the AUROC.

24. Then, Shift the threshold for age up one unit (for example from 30
    to 31), use this new threshold in the adjusted model, record the
    AUROC for the new threshold, keep repeating until all binary ages
    have been cycled through, and record the age whose binary threshold
    produced the largest AUROC.

25. These are the results for the top head injuries in NTDB using our
    selection criteria.

26. Top face injuries in NTDB using our selection criteria.

27. Top neck injuries in NTDB using our selection criteria.

28. Top thorax injuries in NTDB using our selection criteria.

29. Top abdomen injuries in NTDB using our selection criteria.

30. Top spine injuries in NTDB using our selection criteria.

31. Top upper extremity injuries in NTDB using our selection criteria.

32. Top lower extremity injuries in NTDB using our selection criteria.

33. This is an example of our analysis results for one injury. For
    example for subarachnoid hemorrhage, there are 13,282 patients in
    NTDB with 140684.3 code. Mortality -- 22.7%. Males 64.3%, and Mean
    ISS = 24.2.

34. Significant terms in the model include ISS, EDverbal, EDmotor, SES,
    Age (importantly), and YOinj.

35. Now, the age that maximizes area under this curve is 40 years old,
    and the curve is shown. This is a pretty good
    specificity/sensitivity curve. Area is .868.

36. As a comparison of adjusted and unadjusted curves, the adjusted area
    is .868, while unadjusted is .556, which demonstrates how important
    it is to control for other covariates and isolate age.

37. These are Head injury age thresholds, areas under the ROC curve, and
    the odds ratios along with a lower and upper confidence intervals
    for the odds ratios.

38. These are Face injury age thresholds, areas under the ROC curve, and
    the odds ratios along with a lower and upper confidence intervals
    for the odds ratios.

39. These are Thorax injury age thresholds, areas under the ROC curve,
    and the odds ratios along with a lower and upper confidence
    intervals for the odds ratios.

40. These are Abdomen injury age thresholds, areas under the ROC curve,
    and the odds ratios along with a lower and upper confidence
    intervals for the odds ratios.

41. These are Spinal injury age thresholds, areas under the ROC curve,
    and the odds ratios along with a lower and upper confidence
    intervals for the odds ratios.

42. These are Spinal injury age thresholds continued, areas under the
    ROC curve, and the odds ratios along with a lower and upper
    confidence intervals for the odds ratios.

43. These are Upper extremity injury age thresholds, areas under the ROC
    curve, and the odds ratios along with a lower and upper confidence
    intervals for the odds ratios.

44. These are Lower extremity injury age thresholds, areas under the ROC
    curve, and the odds ratios along with a lower and upper confidence
    intervals for the odds ratios.

45. These are Lower extremity injury age thresholds continued, areas
    under the ROC curve, and the odds ratios along with a lower and
    upper confidence intervals for the odds ratios.

46. We can conclude that this is a comprehensive method to identify age
    thresholds where injury becomes especially lethal. It allows for
    refinements to be made to the overall prediction of fatality based
    on its age dependence.

47. We would like to thank Toyota Motor Corporation for sponsorship of
    our CIREN center. Work was performed for the Crash Injury Research
    and Engineering Network (CIREN) Project at Wake Forest University
    School of Medicine in cooperation with the United States Department
    of Transportation/National Highway Traffic Safety Administration
    (USDOT/NHTSA). Funding has been provided by Toyota Motor North
    America Inc. under Cooperative Agreement Number DTNH22-05-H-61001.
    Views expressed are those of the authors and do not represent the
    views of any of the sponsors or NHTSA.