A Propensity Score Analysis of Prehospital Factors and Directness of Transport of Major Trauma Patients to a Level I Trauma Center

BackgroundAlthough helmets are associated with reduction in mortality from motorcycle collisions, many states have failed to adopt universal helmet laws for motorcyclists, in part on the grounds that prior research is limited by study design (historical controls) and confounding variables. The goal of this study was to evaluate the association of helmet use in motorcycle collisions with hospital charges and mortality in trauma patients with propensity score analysis in a state without a universal helmet law.MethodsMotorcycle collision data from the Arizona State Trauma Registry from 2014 to 2017 were propensity score matched by regressing helmet use on patient age, sex, race/ethnicity, alcohol intoxication, illicit drug use, and comorbidities. Linear and logistic regression models were used to evaluate the impact of helmet use.ResultsOur sample consisted of 6849 cases, of which 3699 (54.0%) were helmeted and 3150 (46.0%) without helmets. The cohort was 88.1% male with an average age of 40.9±16.0 years. Helmeted patients were less likely to be admitted to the intensive care unit (20.3% vs. 23.7%, OR 0.82 (0.72–0.93)) and ventilated (7.8% vs. 12.0%, OR 0.62 (0.52–0.75)). Propensity-matched analyses consisted of 2541 pairs and demonstrated helmet use to be associated with an 8% decrease in hospital charges (B −0.075 (0.034)) and a 56% decrease in mortality (OR 0.44 (0.31–0.58)).DiscussionIn a state without mandated helmet use for all motorcyclists, the burden of the unhelmeted rider is significant with respect to lives lost and healthcare charges incurred. Although the helmet law debate with respect to civil liberties is complex and unsettled, it appears clear that helmet use is strongly associated with both survival and less economic encumbrance on the state.Level of evidenceLevel III, prognostic and epidemiological.

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A Propensity Score Analysis of Clostridium difficile Infection among Adult Trauma Patients

Surgical Infections ◽

10.1089/sur.2018.110 ◽

2018 ◽

Vol 19 (7) ◽

pp. 661-666 ◽

Cited By ~ 1

Author(s):

Viktor Gabriel ◽

Areg Grigorian ◽

Jacquelyn L. Phillips ◽

Sebastian D. Schubl ◽

Cristobal Barrios ◽

...

Keyword(s):

Clostridium Difficile ◽

Propensity Score ◽

Clostridium Difficile Infection ◽

Propensity Score Analysis ◽

Trauma Patients ◽

Score Analysis

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Association of Direct Helicopter Versus Ground Transport and In-hospital Mortality in Trauma Patients: A Propensity Score Analysis

Academic Emergency Medicine ◽

10.1111/j.1553-2712.2011.01207.x ◽

2011 ◽

Vol 18 (11) ◽

pp. 1208-1216 ◽

Cited By ~ 64

Author(s):

Kenneth E. Stewart ◽

Linda D. Cowan ◽

David M. Thompson ◽

John C. Sacra ◽

Roxie Albrecht

Keyword(s):

Propensity Score ◽

Hospital Mortality ◽

Propensity Score Analysis ◽

Trauma Patients ◽

Score Analysis

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Directness of Transport of Major Trauma Patients to a Level I Trauma Center: A Propensity-Adjusted Survival Analysis of the Impact on Short-Term Mortality

Journal of Trauma and Acute Care Surgery ◽

10.1097/ta.0b013e3181e243b8 ◽

2011 ◽

Vol 70 (5) ◽

pp. 1118-1127 ◽

Cited By ~ 68

Author(s):

Tabitha Garwe ◽

Linda D. Cowan ◽

Barbara R. Neas ◽

John C. Sacra ◽

Roxie M. Albrecht

Keyword(s):

Survival Analysis ◽

Trauma Center ◽

Major Trauma ◽

Trauma Patients ◽

Short Term ◽

Short Term Mortality ◽

Level I ◽

The Impact

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Longer Prehospital Time Decreases Reliability of Vital Signs in the Field: A Dual Center Study

The American Surgeon ◽

10.1177/0003134820956941 ◽

2020 ◽

pp. 000313482095694

Author(s):

Morgan Schellenberg ◽

Subarna Biswas ◽

James M. Bardes ◽

Marc D. Trust ◽

Daniel Grabo ◽

...

Keyword(s):

Linear Regression ◽

Propensity Score ◽

Injury Severity ◽

Propensity Score Analysis ◽

Vital Signs ◽

Trauma Team ◽

Trauma Patients ◽

Study Objective ◽

Score Analysis ◽

Prehospital Time

Background Field vital signs are integral in the American College of Surgeons (ASA) Committee on Trauma (COT) triage criteria for trauma team activation (TTA). Reliability of field vital signs in predicting first emergency department (ED) vital signs, however, may depend upon prehospital time. The study objective was to define the effect of prehospital time on correlation between field and first ED vital signs. Methods All highest level TTAs at two Level I trauma centers (2008-2018) were screened. Exclusions were unrecorded prehospital vital signs and those dead on arrival. Demographics, prehospital time (scene time + transport time), injury data, and vital signs were collected. Differences between field and first ED vitals were determined using the paired Student’s t test. Propensity score analysis, adjusting for age, sex, injury severity score (ISS), and mechanism of injury compared outcomes among patients with ISS ≥16. Multivariate linear regression determined impact of prehospital time on vital sign differences between field and ED among propensity-matched patients. Results After exclusions, 21 499 patients remained. Mean prehospital time was 32 vs. 41 minutes ( P < .001). On propensity score analysis, longer prehospital time was associated with significantly greater differences in systolic blood pressure (SBP) ( P < .001), pulse pressure (PP) ( P = .003), and Glasgow Coma Scale (GCS) ( P < .001). On multivariate analysis, linear regression that demonstrated longer prehospital time was associated with greater differences in SBP, heart rate (HR), and PP ( P < .001). Conclusions Field vital signs are less likely to reflect initial ED vital signs when prehospital times are longer. Given the reliance of trauma triage criteria on prehospital vital signs, medical providers must be cognizant of this pitfall during the prehospital assessment of trauma patients.

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