Alan Cook

A comparison of the Injury Severity Score and the Trauma Mortality Prediction Model

BACKGROUND Performance benchmarking requires accurate measurement of injury severity. Despite its shortcomings, the Injury Severity Score (ISS) remains the industry standard 40 years after its creation. A new severity measure, the Trauma Mortality Prediction Model (TMPM), uses either the Abbreviated Injury Scale (AIS) or DRG International Classification of Diseases—9th Rev. (ICD-9) lexicons and may better quantify injury severity compared with ISS. We compared the performance of TMPM with ISS and other measures of injury severity in a single cohort of patients. METHODS We included 337,359 patient records with injuries reliably described in both the AIS and the ICD-9 lexicons from the National Trauma Data Bank. Five injury severity measures (ISS, maximum AIS score, New Injury Severity Score [NISS], ICD-9–Based Injury Severity Score [ICISS], TMPM) were computed using either the AIS or ICD-9 codes. These measures were compared for discrimination (area under the receiver operating characteristic curve), an estimate of proximity to a model that perfectly predicts the outcome (Akaike information criterion), and model calibration curves. RESULTS TMPM demonstrated superior receiver operating characteristic curve, Akaike information criterion, and calibration using either the AIS or ICD-9 lexicons. Calibration plots demonstrate the monotonic characteristics of the TMPM models contrasted by the nonmonotonic features of the other prediction models. CONCLUSION Severity measures were more accurate with the AIS lexicon rather than ICD-9. NISS proved superior to ISS in either lexicon. Since NISS is simpler to compute, it should replace ISS when a quick estimate of injury severity is required for AIS-coded injuries. Calibration curves suggest that the nonmonotonic nature of ISS may undermine its performance. TMPM demonstrated superior overall mortality prediction compared with all other models including ISS whether the AIS or ICD-9 lexicons were used. Because TMPM provides an absolute probability of death, it may allow clinicians to communicate more precisely with one another and with patients and families. LEVEL OF EVIDENCE Disagnostic study, level I; prognostic study, level II.

Chest radiographs of limited utility in the diagnosis of blunt traumatic aortic laceration.

BACKGROUND The radiographic diagnosis of blunt traumatic aortic laceration (BTAL) remains problematic. We reviewed our experience with chest radiographic signs of BTAL at a single trauma center. METHODS The chest radiographs of 188 consecutive blunt trauma patients with suspected BTAL who underwent portable chest radiography and aortography were retrospectively reviewed by a thoracic radiologist. The presence or absence of 15 radiographic findings were recorded, and the sensitivity and specificity of individual radiographic signs and combinations of signs were determined. RESULTS There were 10 patients with BTAL. Although three signs showed greater than 90% sensitivity for BTAL, these signs showed low specificity, and no significant improvement in overall accuracy was achieved by combining radiographic findings. CONCLUSION The experience at our institution suggests that chest radiographs have limited utility in the accurate diagnosis of blunt traumatic aortic laceration. Cross-sectional imaging techniques will likely become the preferred imaging procedures for evaluating patients with suspected BTAL.

The differential mortality of Glasgow Coma Score in patients with and without head injury

IMPORTANCE The GCS was created forty years ago as a measure of impaired consciousness following head injury and thus the association of GCS with mortality in patients with traumatic brain injury (TBI) is expected. The association of GCS with mortality in patients without TBI (non-TBI) has been assumed to be similar. However, if this assumption is incorrect mortality prediction models incorporating GCS as a predictor will need to be revised. OBJECTIVE To determine if the association of GCS with mortality is influenced by the presence of TBI. DESIGN/SETTING/PARTICIPANTS Using the National Trauma Data Bank (2012; N=639,549) we categorized patients as isolated TBI (12.8%), isolated non-TBI (33%), both (4.8%), or neither (49.4%) based on the presence of AIS codes of severity 3 or greater. We compared the ability GCS to discriminate survivors from non-survivors in TBI and in non-TBI patients using logistic models. We also estimated the odds ratios of death for TBI and non-TBI patients at each value of GCS using linear combinations of coefficients. MAIN OUTCOME MEASURE Death during hospital admission. RESULTS As the sole predictor in a logistic model GCS discriminated survivors from non-survivors at an acceptable level (c-statistic=0.76), but discriminated better in the case of TBI patients (c-statistic=0.81) than non-TBI patients (c-statistic=0.70). In both unadjusted and covariate adjusted models TBI patients were about twice as likely to die as non-TBI patients with the same GCS for GCS values<8; for GCS values>8 TBI and non-TBI patients were at similar risk of dying. CONCLUSIONS A depressed GCS predicts death better in TBI patients than non-TBI patients, likely because in non-TBI patients a depressed GCS may simply be the result of entirely reversible intoxication by alcohol or drugs; in TBI patients, by contrast, a depressed GCS is more ominous because it is likely due to a head injury with its attendant threat to survival. Accounting for this observation into trauma mortality datasets and models may improve the accuracy of outcome prediction.

Trauma patient readmissions: Why do they come back for more?

BACKGROUND Hospital readmissions are a frequent challenge. Speculation exists that rates of readmission following traumatic injury will be publicly disclosed. The primary aim of this study was to characterize and model 1-year readmission patterns to multiple institutions among patients originally admitted to a single, urban Level I trauma center. Additional analyses within the superutilizers subgroup identified predictors of 30-day readmissions as well as patient loyalty for readmission to their index hospital. We hypothesized that hospital readmission among trauma patients would be associated with socioeconomic, demographic, and clinical features and superutilizers would be identifiable during initial hospitalization. METHODS Data were retrospectively gathered for 2,411 unique trauma patients admitted to a Level I American College of Surgeons–certified trauma center over 1 year, with readmissions identified 1 year after index admission. A regional hospital database was queried for readmissions. Outcomes of all readmission encounters were analyzed using a binary logistic regression model including demographic, diagnoses, Injury Severity Score (ISS), procedures, Elixhauser comorbidities, insurance, and disposition data. Subset analysis of superutilizers was also performed to examine patterns among superutilizers. RESULTS A total of 434 patients (21%) were readmitted during the study period, accounting for 720 readmission encounters. Sixty-three patients accounting for 269 encounters were identified as superutilizers (3+ readmissions). A total of 136 patients (6%) were readmitted within 30 days of initial discharge. Fifty-seven percent of readmissions returned to the originating hospital. CONCLUSION Complications including comorbid disease (diabetes and congestive heart failure), septicemia, weight loss, and trauma recidivism distinguish the superutilizer trauma patient. Having Medicaid funding increased the odds of readmission by 274%. It is imperative that interventions be developed and targeted toward those at high risk of superutilization of health care resources to curb spending. These results strongly support continuation of longitudinal readmission research in trauma patients conducted in multicenter settings. LEVEL OF EVIDENCE Epidemiologic study, level III.

Vena Cava Filter Use in Trauma and Rates of Pulmonary Embolism, 2003-2015

Importance Vena cava filter (VCF) placement for pulmonary embolism (PE) prophylaxis in trauma is controversial. Limited research exists detailing trends in VCF use and occurrence of PE over time. Objective To analyze state and nationwide temporal trends in VCF placement and PE occurrence from 2003 to 2015 using available data sets. Design, Setting, and Participants A retrospective trauma cohort study was conducted using data from the Pennsylvania Trauma Outcome Study (PTOS) (461 974 patients from 2003 to 2015), the National Trauma Data Bank (NTDB) (5 755 095 patients from 2003 to 2014), and the National (Nationwide) Inpatient Sample (NIS) (24 449 476 patients from 2003 to 2013) databases. Main Outcomes and Measures Temporal trends in VCF placement and PE rates, filter type (prophylactic or therapeutic), and established predictors of PE (obesity, pregnancy, cancer, deep vein thrombosis, major procedure, spinal cord paralysis, venous injury, lower extremity fracture, pelvic fracture, central line, intracranial hemorrhage, and blood transfusion). Prophylactic filters were defined as VCFs placed before or without an existing PE, while therapeutic filters were defined as VCFs placed after a PE. Results Of the 461 974 patients in PTOS, the mean (SD) age was 47.2 (26.4) and 61.6% (284 621) were men; of the 5 755 095 patients in NTDB, the mean age (SD) was 42.0 (24.3) and 63.7% (3 666 504) were men; and of the 24 449 476 patients in NIS, the mean (SD) age was 58.0 (25.2) and 49.7% (12 160 231) were men. Of patients receiving a filter (11 405 in the PTOS, 71 029 in the NTDB, and 189 957 in the NIS), most were prophylactic VCFs (93.6% in the PTOS, 93.5% in the NTDB, and 93.3% in the NIS). Unadjusted and adjusted temporal trends for the PTOS and NTDB showed initial increases in filter placement followed by significant declines (unadjusted reductions in VCF placement rates, 76.8% in the PTOS and 53.3% in the NTDB). The NIS demonstrated a similar unadjusted trend, with a slight increase and modest decline (22.2%) in VCF placement rates over time; however, adjusted trends showed a slight but significant increase in filter rates. Adjusted PE rates for the PTOS and NTDB showed significant initial increases followed by slight decreases, with limited variation during the declining filter use periods. The NIS showed an initial increase in PE rates followed by a period of stagnation. Conclusions and Relevance Despite a precipitous decline of VCF use in trauma, PE rates remained unchanged during this period. Taking this association into consideration, VCFs may have limited utility in influencing rates of PE. More judicious identification of at-risk patients is warranted to determine individuals who would most benefit from a VCF.

Gunshot wounds resulting in hospitalization in the United States: 2004–2013

INTRODUCTION The United States (US) leads all high income countries in gunshot wound (GSW) deaths. However, as a result of two decades of reduced federal support, study of GSW has been largely neglected. In this paper we describe the current state of GSW hospitalizations in the US using population-based data. PATIENTS AND METHODS We conducted an observational study of patients hospitalized for GSW in the National (Nationwide) Inpatient Sample (NIS) 2004 -2013. Our primary outcome is mortality after admission and we model its associations with gender, race, age, intent, severity of injury and weapon type, as well as providing temporal trends in hospital charges. RESULTS Each year approximately 30,000 patients are hospitalized for GSW, and 2500 die in hospital. Men are 9 times as likely to be hospitalized for GSW as women, but are less likely to die. Twice as many blacks are hospitalized for GSW as non-Hispanic whites. In-hospital mortality for blacks and non-Hispanic whites was similar when controlled for other factors. Most GSW (63%) are the result of assaults which overwhelmingly involve blacks; accidents are also common (23%) and more commonly involve non-Hispanic whites. Although suicide is much less common (8.3%), it accounts for 32% of all deaths; most of which are older non-Hispanic white males. Handguns are the most common weapon reported, and have the highest mortality rate (8.4%). During the study period, the annual rate of hospitalizations for GSW remained stable at 80 per 100,000 hospital admissions; median inflation-adjusted hospital charges have steadily increased by approximately 20% annually from

Is the Trauma Mortality Prediction Model (TMPM-ICD-9) a valid predictor of mortality in pediatric trauma patients?

30,000 to

Variation in readmission rates among hospitals following admission for traumatic injury

56,000 per hospitalization. The adjusted odds for mortality decreased over the study period. Although extensively reported, GSW inflicted by police and terrorists represent few hospitalizations and very few deaths. CONCLUSIONS The preponderance GSW hospitalizations resulting from assaults on young black males and suicides among older non-Hispanic white males have continued unabated over the last decade with escalating costs. As with other widespread threats to the public wellbeing, federally funded research is required if effective interventions are to be developed.

Comparison of two prognostic models in trauma outcome

BACKGROUND/PURPOSE Researchers are constantly challenged to identify optimal mortality risk adjustment methodologies that perform accurately in pediatric trauma patients. This study evaluated the new Trauma Mortality Prediction Model (TMPM-ICD-9) in pediatric trauma patients. METHODS Data were analyzed on 107,104 pediatric trauma patients included in the NTDB® in 2010 who had both a valid ISS and probability of death using TMPM-ICD-9. Discrimination was compared using the area under the receiver operator characteristic curve (AUC) and by age, blunt vs penetrating, intent, Glasgow Coma Scale (GCS), and number of injuries. RESULTS The AUC for TMPM-ICD-9 demonstrated excellent discrimination in predicting mortality versus ISS overall, 11 to 17years of age (0.96 vs 0.93), by injury type, intent, and in the lowest GCS scores. The TMPM-ICD-9 showed superior discrimination over ISS in patients with more than two injuries. CONCLUSIONS The TMPM demonstrated superior discrimination compared to ISS. The TMPM shows promise of a much needed and simple to use risk adjustment tool with application to both adult and pediatric patients. Researchers should continue to validate this tool in robust pediatric data sets.

A novel approach to optimal placement of new trauma centers within an existing trauma system using geospatial mapping

INTRODUCTION Readmission following hospital discharge is both common and costly. The Hospital Readmission Reduction Program (HRRP) financially penalizes hospitals for readmission following admission for some conditions, but this approach may not be appropriate for all conditions. We wished to determine if hospitals differed in their adjusted readmission rates following an index hospital admission for traumatic injury. PATIENTS AND METHODS We extracted from the AHRQ National Readmission Dataset (NRD) all non-elderly adult patients hospitalized following traumatic injury in 2014. We estimated hierarchal logistic regression models to predicted readmission within 30 days. Models included either patient level predictors, hospital level predictors, or both. We quantified the extent of hospital variability in readmissions using the median odds ratio. Additionally, we computed hospital specific risk-adjusted rates of readmission and number of excess readmissions. RESULTS Of the 177,322 patients admitted for traumatic injury 11,940 (6.7%) were readmitted within 30 days. Unadjusted hospital readmission rates for the 637 hospitals in our study varied from 0% to 20%. After controlling for sources of variability the range for hospital readmission rates was between 5.5% and 8.5%. Only 2% of hospitals had a random intercept coefficient significantly different from zero, suggesting that their readmission rates differed from the mean level of all hospitals. We also estimated that in 2014 only 11% of hospitals had more than 2 excess readmissions. Our multilevel model discriminated patients who were readmitted from those not readmitted at an acceptable level (C = 0.74). CONCLUSIONS We found little evidence that hospitals differ in their readmission rates following an index admission for traumatic injury. There is little justification for penalizing hospitals based on readmissions after traumatic injury.