West TA, Rivara FP, Cummings P, Jurkovich GJ, Maier RV.

J Trauma 2000;49:530-541.

 To develop a scoring system to better estimate probability of mortality on the basis of information that is readily available from the hospital discharge sheet and does not rely on physiologic data

 There have been several attempts to develop a scoring system that can accurately reflect the severity of a trauma patient’s injuries, particularly with respect to the effect of injury on survival  Current methodologies require unreliable physiologic data for the assignment of a survival probability and fail to account for the potential synergism of different injury combinations

 Four problems with current models: 1. Information often lost – physiologic/anatomic data combined into intermediate scores, then combined to achieve final probability of survival score 2. Injuries modelled as if effects are independent – but some combinations more lethal than models predict 3. None account for pre-existing disease – widely acknowledged contributor to outcome 4. Pre-hospital/emergency department physiologic data often missing – making probability calculation of survival impossible

 This injury severity classification method attempts to explicitly address the possibility that certain injury combinations might contribute to mortality beyond their independent effects  In addition, it takes comorbid disease into account when predicting mortality  This method uses data that are readily available for all patients without relying on missing or inaccurate physiologic data

 Records from the trauma registry from Harborview Medical Center (an urban Level I trauma centre) were analysed using logistic regression  Information obtained for all trauma admissions and emergency room deaths between 1 st July 1985 and 31 st December 1997  No treatment-related variables were included in the analysis; only those variables determined upon or before the individual’s arrival at the hospital

 Resulting data split into two roughly equal groups:  A ‘design set’ to determine the best prediction model  A ‘validation set’ to test the accuracy of the model on an independent set of data  Statistical analysis performed using Stata (College Station, TX)  ICD-9 codes representing injuries (codes 800-959.9; n = 2,034) reclassified into 109 anatomically-similar injury categories  ICD-9 codes that corresponded to Abbreviated Injury Scale (AIS) severity scores <3 (e.g. minor injuries) were excluded  Burns & burn-related injuries also excluded

Methods (2)

 Included in the regression were International Classification of Diseases-9 th Rev (ICD-9-CM) codes for anatomic injury, mechanism, intent, and pre-existing medical conditions, as well as age.

 Two-way interaction terms for several combinations of injuries were also included in the regression model.  The resulting Harborview Assessment for Risk of Mortality (HARM) score takes the form of a probability between 0 and 1 of in hospital mortality.

Methods (3)

 HARM model compared to ICISS (ICD-9-CM Injury Severity Score) and TRISS (Trauma and Injury Severity Score) to discriminate between survivors and nonsurvivors from this dataset, using an ROC curve.

 Area under the curve (AUC) calculated for each model and compared using the Hosmer-Lemeshow (HL) statistic.

 33,990 admissions recorded in Harborview Medical Center Trauma Registry between 1/7/85-31/12/97. Excluded readmissions for same injury  final data: 32,207 admissions ▪ ▪ 16,185  ‘design set’ 16,122  ‘validation set’  Study population predominantly young and male. Most injuries resulted from blunt trauma. No significant differences between design and validation sets with respect to age, gender, mortality, etc.

 Final logistic regression model contained 80 variables.

 51 injury categories included  Six comorbid conditions included - cirrhosis, IHD, hypertension, psychoses, alcohol/drug dependence, and congenital coagulopathy

Results (1)

   HARM model calculated probability of mortality in 16,097 of the 16,122 admissions (99.9%) ICISS only managed 15,820 (98.1%) TRISS only 9,923 (61.4%) because of missing physiologic data   HARM had a better fit to the validation data (HL statistics = 21.37; p = 0.0315) than ICISS (HL = 712.4; p = 0.0005) and TRISS (HL = 59.54; p = <0.005).

NB smaller HL = better fit to actual data.

  Specificity of HARM was 83.4% ICISS = 78.2%, TRISS = 72.1%

Results (2)

 TRISS was the “standard” for mortality prediction among trauma patients for many years, but has limitations:  Most importantly, its applicability to patients with missing physiologic data.

 HARM score has excellent power in discriminating between survivors and nonsurvivors, with better calibration than either TRISS or ICISS  Comorbidities found to be important include:  Cirrhosis  IHD  Congenital coagulopathy

 Ten most lethal injuries according to HARM model Independent Variable Loss of consciousness >24hrs (irreversible) Full-thickness cardiac laceration Unspecified cardiac injury Complete spinal cord injury C4 or above Superior vena cava or innominate vein Pulmonary laceration Cardiac contusion Traumatic amputation above the knee Major laceration of liver Thoracic aorta or great vessels Adjusted Odds Radio 95.2

67.2

32.0

30.9

28.4

27.3

22.4

21.4

14.6

13.5

 Injury severity scores based on ICD-9 codes predict mortality with as much or more accuracy than those based on Abbreviated Injury Scale (AIS) scores, with considerably less effort and expense  Further, predictive power of HARM does not require the use of physiologic data  HARM is an effective tool for predicting in-hospital mortality for trauma patients, outperforming both TRISS and ICDISS with respect to discrimination and calibration, using information readily available from hospital discharge coding, without requiring physiologic data

 Does not use physiologic data  Two patients with the same injuries, mechanism of injury, comorbidities and age have same score regardless of vital signs on admission  BUT….point of HARM is to predict survival on bases of factors established at time of injury itself.

 Avoids inherent problems of using physiologic data: ▪ E.g. often time elapsed since injury to admission to hospital is unknown

 Applicability of findings to other centres?

 Harborview Medical Center patient population may be homogeneous when compared to other hospital populations  Accuracy of ICD-9 coding?

▪ Data usually coded by non-clinicians ▪ ICD-10 thought of as more accurate, with a more comprehensive list of possible diagnoses and diagnostic codes.

 ICISS and TRISS models applied to vastly different databases to that of HARM  Ideally, should have derived TRISS coefficients and ICISS risk ratios from Harborview dataset and then compared all three models using either Harborview or an independent dataset  Calibration comparisons between the three models inappropriate when underlying population mortality rates are different (whole of N. america for TRISS, N. Carolina for ICISS, Seattle, WA for HARM)