An Evaluation of Pediatric Secondary Overtriage in the Pennsylvania Trauma System.

BACKGROUND: We sought to determine the secondary overtriage rate of pediatric trauma patients admitted to pediatric trauma centers. We hypothesized that pediatric secondary overtriage (POT) would constitute a large percentage of admissions to PTC. MATERIALS AND METHODS: The Pennsylvania Trauma Outcome Study database was retrospectively queried from 2003 to 2017 for pediatric (age ≤ 18 y) trauma patients transferred to accredited pediatric trauma centers in Pennsylvania (n = 6). Patients were stratified based on discharge within (early) and beyond (late) 24 h following admission. POT was defined as patients transferred to a PTC with an early discharge. Multilevel mixed-effects logistic regression model controlling for demographic and injury severity covariates were utilized to determine the adjusted impact of injury patterns on early discharge. RESULTS: A total of 37,653 patients met inclusion criteria. For transfers, POT compromised 18,752 (49.8%) patients. Compared to POT, non-POT were more severely injured (ISS: 10 versus 6;P < 0.001) and spent less time in the ED (Min: 181 versus 207;P < 0.001). In adjusted analysis, concussion, closed skull vault fractures, supracondylar humerus fractures, and consults to neurosurgery were associated with increased odds of POT. Overall, femur fracture, child abuse evaluation, and consults to plastic surgery, orthopedics, and ophthalmology were all associated with a decreased risk of being POT. CONCLUSIONS: POT comprises 49.8% of PTC transfer admissions in Pennsylvania's trauma system. Improving community resources for management of pediatric concussion and mild TBI could result in decreased rates of POT to PTCs. Developing better inter-facility transfer guidelines and increased education of adult TC and nontrauma center hospitals is needed to decrease POT. LEVEL OF EVIDENCE: Epidemiologic study, level III.

Practice Variation in Vena Cava Filter Use Among Trauma Centers in the National Trauma Database.

BACKGROUND: Agreement regarding indications for vena cava filter (VCF) utilization in trauma patients has been in flux since the filter's introduction. As VCF technology and practice guidelines have evolved, the use of VCF in trauma patients has changed. This study examines variation in VCF placement among trauma centers. MATERIALS AND METHODS: A retrospective study was performed using data from the National Trauma Data Bank (2005-2014). Trauma centers were grouped according to whether they placed VCFs during the study period (VCF+/VCF-). A multivariable probit regression model was fit to predict the number of VCFs used among the VCF+ centers (the expected [E] number of VCF per center). The ratio of observed VCF placement (O) to expected VCFs (O:E) was computed and rank ordered to compare interfacility practice variation. RESULTS: In total, 65,482 VCFs were placed by 448 centers. Twenty centers (4.3%) placed no VCFs. The greatest predictors of VCF placement were deep vein thrombosis, spinal cord paralysis, and major procedure. The strongest negative predictor of VCF placement was admission during the year 2014. Among the VCF+ centers, O:E varied by nearly 500%. One hundred fifty centers had an O:E greater than one. One hundred sixty-nine centers had an O:E less than one. CONCLUSIONS: Substantial variation in practice is present in VCF placement. This variation cannot be explained only by the characteristics of the patients treated at these centers but could be also due to conflicting guidelines, changing evidence, decreasing reimbursement rates, or the culture of trauma centers.

Then we all fall down: fall mortality by trauma center level.

BACKGROUND: Ground-level falls (GLFs) are the predominant mechanism of injury in US trauma centers and accompany a spectrum of comorbidities, injury severity, and physiologic derangement. Trauma center levels define tiers of capability to treat injured patients. We hypothesized that risk-adjusted observed-to-expected mortality (O:E) by trauma center level would evaluate the degree to which need for care was met by provision of care. MATERIALS AND METHODS: This retrospective cohort study used National Trauma Data Bank files for 2007-2014. Trauma center level was defined as American College of Surgeons (ACS) level I/II, ACS III/IV, State I/II, and State III/IV for within-group homogeneity. Risk-adjusted expected mortality was estimated using hierarchical, multivariable regression techniques. RESULTS: Analysis of 812,053 patients' data revealed the proportion of GLF in the National Trauma Data Bank increased 8.7% (14.1%-22.8%) over the 8 y studied. Mortality was 4.21% overall with a three-fold increase for those aged 60 y and older versus younger than 60 y (4.93% versus 1.46%, P < 0.001). O:E was lowest for ACS III/IV, (0.973, 95% CI: 0.971-0.975) and highest for State III/IV (1.043, 95% CI: 1.041-1.044). CONCLUSIONS: Risk-adjusted outcomes can be measured and meaningfully compared among groups of trauma centers. Differential O:E for ACS III/IV and State III/IV centers suggests that factors beyond case mix alone influence outcomes for GLF patients. More work is needed to optimize trauma care for GLF patients across the spectrum of trauma center capability.

An epidemiologic overview of 13 years of firearm hospitalizations in Pennsylvania.

BACKGROUND: Gun violence is a controversial public health issue plagued by a lack of recent research. We sought to provide a 13-y overview of firearm hospitalizations in Pennsylvania, analyzing trends in mode, intent, and outcome. We hypothesized that no adjusted change in mortality or functional status at discharge (FSD) would be observed for gunshot wound (GSW) victims over the study period. METHODS: All admissions to the Pennsylvania Trauma Outcome Study database from 2003 to 2015 were queried. GSWs were identified by external cause-of-injury codes. Collected variables included patient demographics, firearm type, intent (assault and attempted suicide), FSD, and mortality. Multilevel mixed-effects logistic regression models and ordinal regression analyses using generalized linear mixed models assessed the impact of admission year (continuous) on adjusted mortality and FSD score, respectively. Significance was set at P < 0.05. RESULTS: Of the 462,081 patients presenting to Pennsylvania trauma centers from 2003 to 2015, 19,342 were GSWs (4.2%). Handguns were the most common weapon of injury (n = 7007; 86.7%) among cases with specified firearm type. Most GSWs were coded as assaults (n = 15,415; 79.7%), with suicide attempts accounting 1866 hospitalizations (9.2%). Suicide attempts were most prevalent among young and middle-aged white males, whereas assaults were more common in young black males. Rates of firearm hospitalizations decreased over time (test of trend P = 0.001); however, admission year was not associated with improved adjusted survival (adjusted odds ratio: 0.99, 95% confidence interval: 0.97-1.01; P = 0.353) or FSD (adjusted odds ratio: 0.99, 95% confidence interval: 0.98-1.00; P = 0.089) while controlling for demographic and injury severity covariates. CONCLUSIONS: Temporal trends in outcomes suggest rates of firearm hospitalizations are declining in Pennsylvania; however, outcomes remain unchanged. To combat this epidemic, a multidisciplinary, demographic-specific approach to prevention should be the focus of future scientific pursuits.

A Criteria to Reduce Interhospital Transfer of Traumatic Brain Injuries in Greater East Texas.

BACKGROUND: Traumatic brain injury (TBI) due to single-level falls (SLF) are frequent and often require interhospital transfer. This retrospective cohort study aimed to assess the safety of a criteria for non-transfer among a subset of TBI patients who could be observed at their local hospital, vs mandatory transfer to a level 1 trauma center (L1TC). METHODS: We conducted a 7-year review of patients with TBI due to SLF at a rural L1TC. Patients were classified as transfer/non-transfer according to the Brain Injuries in Greater East Texas (BIGTEX) criteria. The primary outcome measure was the occurrence of a critical event defined as deteriorating repeat head computed tomography (CT) scan or neurological status, neurosurgical intervention, or death. RESULTS: Of the 689 included patients, 63 (9.1%) were classified as non-transfer. Although there were 4 cases with a neurological change and one with a head CT change among the non-transfer group, there were no neurosurgical procedures or deaths. The Cox Proportional Hazard model showed a near 3-fold increased risk of experiencing a critical event if classified as a non-transfer. The multivariable regression model showed patients with an Abbreviated Injury Scale (AIS) of 3 was twice as likely to experience a critical event, with an AIS of 4, three times, and 3 times more likely to be classified to transfer. DISCUSSION: The BIGTEX criteria identify a subset of patients who can safely be observed at their local hospital. To confirm the safety and efficacy of this transfer criteria recommendation, a prospective study is warranted.

Trends in Traumatic Brain Injury Related to Consumer Products Among U.S. School-aged Children Between 2000 and 2019.

INTRODUCTION: Consumer product‒related traumatic brain injury in children is common, but long-term trends have not been well characterized. Understanding the long-term trends in consumer product‒related traumatic brain injury may inform prevention efforts. The study objective is to examine the trends in consumer product‒related traumatic brain injury in school-aged children. METHODS: Data were extracted from the National Electronic Injury Surveillance System-All Injury Program for initial emergency department visits for consumer product‒related traumatic brain injury (2000-2019) in school-aged children and analyzed in 2021. RESULTS: Approximately 6.2 million children presented to emergency department with consumer product‒related traumatic brain injury during 2000-2019. Consumer product‒related traumatic brain injury increased from 4.5% of overall consumer product‒emergency department visits in 2000 to 12.3% in 2019, and its incidence rate (cases per 100,000 population) was higher in males (681.2; 95% CI=611.2, 751.2) than in females (375.8; 95% CI=324.1, 427.6). The annual percentage change in consumer product‒related traumatic brain injury was 3.6% from 2000 to 2008, 13.3% from 2008 to 2012, and ‒2.0% through 2019. Average annual percentage change was higher in females (5.1%; 95% CI=3.4, 6.8) than in males (2.8%; 95% CI=1.6, 3.9). Consumer product‒related traumatic brain injury increased from 2000 to 2012 in females and then remained stable. In males, annual percentage change increased from 2008 to 2012 and then declined through 2019. CONCLUSIONS: Traumatic brain injury incidence rate in school-aged children increased from 2000 to 2019, peaked in 2012, and then declined in males but not in females. Percentage increases were highest in females. Prevention strategies should continue, with a specific focus on reducing consumer product‒related traumatic brain injury in female children.

Geographic Coverage and Verification of Trauma Centers in a Rural State: Highlighting the Utility of Location Allocationfor Trauma System Planning.

BACKGROUND: Care at verified trauma centers has improved survival and functional outcomes, yet determining the appropriate location of potential trauma centers is often driven by factors other than optimizing system-level patient care. Given the importance of transport time in trauma, we analyzed trauma transport patterns in a rural state lacking an organized trauma system and implemented a geographic information system to inform potential future trauma center locations. STUDY DESIGN: Data were collected on trauma ground transport during a 3-year period (2014 through 2016) from the Statewide Incident Reporting Network database. Geographic information system mapping and location-allocation modeling of the best-fit facility for trauma center verification was computed using trauma transport patterns, population density, road network layout, and 60-minute emergency medical services transport time based on current transport protocols. RESULTS: Location-allocation modeling identified 2 regional facilities positioned to become the next verified trauma centers. The proportion of the Vermont population without access to trauma center care within 60 minutes would be reduced from the current 29.68% to 5.81% if the identified facilities become verified centers. CONCLUSIONS: Through geospatial mapping and location-allocation modeling, we were able to identify gaps and suggest optimal trauma center locations to maximize population coverage in a rural state lacking a formal, organized trauma system. These findings could inform future decision-making for targeted capacity improvement and system design that emphasizes more equitable access to trauma center care in Vermont.

Geriatric Trauma Mortality: Does Trauma Center Level Matter?

BACKGROUND: Given their mostly rural/suburban locations, level II trauma centers (TCs) may offer greater exposure to and experience in managing geriatric trauma patients. We hypothesized that geriatric patients would have improved outcomes at level II TCs compared to level I TCs. METHODS: The Pennsylvania Trauma Outcome Study (PTOS) database was retrospectively queried from 2003 to 2017 for geriatric (age ≥65 years) trauma patients admitted to level I and II TCs in Pennsylvania. Patient demographics, injury severity, and clinical outcomes were compared to assess differences in care between level I and II TCs. A multivariate logistic regression model assessed the adjusted impact of care at level I vs II TCs on mortality, complications, and functional status at discharge (FSD). The National Trauma Data Bank (NTDB) was retrospectively queried for geriatric (age ≥65 years) trauma admissions to state-accredited level I or level II TCs in 2013. RESULTS: 112 648 patients met inclusion criteria. The proportion of geriatric trauma patients across level I and level II TCs were determined to be 29.1% and 36.2% (P <.001), respectively. In adjusted analysis, there was no difference in mortality (adjusted odds ratio [AOR]: 1.13; P = .375), complications (AOR: 1.25; P = .080) or FSD (AOR: 1.09; P = .493) when comparing level I to level II TCs. Adjusted analysis from the NTDB (n = 144 622) also found that mortality was not associated with TC level (AOR: 1.04; P = .182). DISCUSSION: Level I and level II TCs had similar rates of mortality, complications, and functional outcomes despite a higher proportion (but lower absolute number) of geriatric patients being admitted to level II TCs. Future consideration for location of centers of excellence in geriatric trauma should include both level I and II TCs.

Predictors of Trauma High Resource Consumers in a Mature Trauma System.

BACKGROUND: Extended hospital length of stay (LOS) is widely associated with significant healthcare costs. Since LOS is a known surrogate for cost, we sought to evaluate outliers. We hypothesized that particular characteristics are likely predictive of trauma high resource consumers (THRC) and can be used to more effectively manage care of this population. METHODS: The Pennsylvania Trauma Outcome Study database was retrospectively queried from 2003-2017 for all adult (age ≥15) trauma patients admitted to accredited trauma centers in Pennsylvania. THRC were defined as patients with hospital LOS two standard deviations above the population mean or ≥22 days (p<0.05). Patient demographics, comorbid conditions and clinical variables were compared between THRC and non-THRC to identify potential predictor variables. A multilevel mixed-effects logistic regression model controlling for age, gender, injury severity, admission Glasgow coma score, systolic blood pressure, and injury year assessed the adjusted impact of clinical factors in predicting THRC status. The National Trauma Data Bank (NTDB) was retrospectively queried from 2014-2016 for all adult (age ≥15) trauma patients admitted to state-accredited trauma centers and likewise were assessed for factors associated with THRC. RESULTS: A total of 465,601 patients met inclusion criteria [THRC: 16,818 (3.6%); non-THRC 448,783 (96.4%)]. Compared to non-THRC counterparts, THRC patients were significantly more severely injured (median ISS: 9 vs. 22, p<0.001). In adjusted analysis, gunshot wound (GSW) to the abdomen, undergoing major surgery and reintubation along with injury to the spine, upper or lower extremities were significantly associated with THRC. From the NTDB, 2 323 945 patients met inclusion criteria. In adjusted analysis, GSW to the abdomen was significantly associated with THRC. Penetrating injury overall was associated with decreased risk of being a THRC in the NTDB dataset. Those who had either GSW to abdomen, surgery, or reintubation required significantly longer LOS (p<0.001). CONCLUSIONS: Reintubation, major surgery, gunshot wound to abdomen, along with injury to the spine, upper or lower extremities are all strongly predictive of THRC. Understanding the profile of the THRC will allow clinicians and case management to proactively put processes in place to streamline care and potentially reduce costs and LOS.

An analysis of overtriage and undertriage by advanced life support transport in a mature trauma system.

BACKGROUND: While issues regarding triage of severely injured trauma patients are well publicized, little information exists concerning the difference between triage rates for patients transported by advanced life support (ALS) and basic life support (BLS). We sought to analyze statewide trends in undertriage (UT) and overtriage (OT) to address this question, hypothesizing that there would be a difference between the UT and OT rates for ALS compared with BLS over a 13-year period. METHODS: All patients submitted to Pennsylvania Trauma Outcomes Study database from 2003 to 2015 were analyzed. Undertriage was defined as not calling a trauma alert for patients with an Injury Severity Score (ISS) of 16 or greater. Overtriage was defined as calling a trauma alert for patients with an ISS of 9 or less. A logistic regression was used to assess mortality between triage groups in ALS and BLS. A multinomial logistic regression assessed the adjusted impact of ALS versus BLS transport on UT and OT versus normal triage while controlling for age, sex, Glasgow Coma Scale, systolic blood pressure (SBP), pulse, Shock Index and injury year. RESULTS: A total of 462,830 patients met inclusion criteria, of which 115,825 had an ISS of 16 or greater and 257,855 had an ISS of 9 or less. Both ALS and BLS had significantly increased mortality when patients were undertriaged compared with the reference group. Multivariate analysis in the form of a multinomial logistic regression revealed that patients transported by ALS had a decreased adjusted rate of undertriage (relative risk ratio, 0.92; 95% confidence interval, 0.87-0.97; p = 0.003) and an increased adjusted rate of OT (relative risk ratio, 1.59; 95% confidence interval, 1.54-1.64; p < 0.001) compared with patients transported by BLS. CONCLUSION: Compared with their BLS counterparts, while UT is significantly lower, OT is substantially higher in ALS-further increasing the high levels of resource (over)utilization in trauma patients. Undertriage in both ALS and BLS are associated with increased mortality rates. Additional education, especially in the BLS provider, on identifying the major trauma victim may be warranted based on the results of this study. LEVEL OF EVIDENCE: Epidemiological, Level III.

An analysis of outcomes and predictors of intensive care unit bouncebacks in a mature trauma system.

BACKGROUND: With the recent birth of the Pennsylvania TQIP Collaborative, statewide data identified unplanned admissions to the intensive care unit (ICU) as an overarching issue plaguing the state trauma community. To better understand the impact of this unique population, we sought to determine the effect of unplanned ICU admission/readmission on mortality to identify potential predictors of this population. We hypothesized that ICU bounceback (ICUBB) patients would experience increased mortality compared with non-ICUBB controls and would likely be associated with specific patterns of complications. METHODS: The Pennsylvania Trauma Outcome Study database was retrospectively queried from 2012 to 2015 for all ICU admissions. Unadjusted mortality rates were compared between ICUBB and non-ICUBB counterparts. Multilevel mixed-effects logistic regression models assessed the adjusted impact of ICUBB on mortality and the adjusted predictive impact of 8 complications on ICUBB. RESULTS: A total of 58,013 ICU admissions were identified from 2012 to 2015. From these, 53,715 survived their ICU index admission. The ICUBB rate was determined to be 3.82% (2,054/53,715). Compared with the non-ICUBB population, ICUBB patients had a significantly higher mortality rate (12% vs. 8%; p < 0.001). In adjusted analysis, ICUBB was associated with a 70% increased odds ratio for mortality (adjusted odds ratio, 1.70; 95% confidence interval, 1.44-2.00; p < 0.001). Adjusted analysis of predictive variables revealed unplanned intubation, sepsis, and pulmonary embolism as the strongest predictors of ICUBB. CONCLUSION: Intensive care unit bouncebacks are associated with worse outcomes and are disproportionately burdened by respiratory complications. These findings emphasize the importance of the TQIP Collaborative in identifying statewide issues in need of performance improvement within mature trauma systems. LEVEL OF EVIDENCE: Epidemiological study, level III.

The geriatric trauma patient: A neglected individual in a mature trauma system.

BACKGROUND: Those older than 65 years represent the fastest growing demographic in the United States. As such, their care has been emphasized by trauma entities such as the American College of Surgeons Committee on Trauma. Unfortunately, much of that focus has been of their care once they reach the hospital with little attention on the access of geriatric trauma patients to trauma centers (TCs). We sought to determine the rate of geriatric undertriage (UT) to TCs within a mature trauma system and hypothesized that there would be variation and clustering of the geriatric undertriage rate (UTR) within a mature trauma system because of the admission of geriatric trauma patient to nontrauma centers (NTCs). METHODS: From 2003 to 2015, all geriatric (age >65 years) admissions with an Injury Severity Score of greater than 9 from the Pennsylvania Trauma Systems Foundation (PTSF) registry and those meeting trauma criteria (International Classification of Diseases, Ninth Revision: 800-959) from the Pennsylvania Health Care Cost Containment Council (PHC4) database were included. Undertriage rate was defined as patients not admitted to TCs (n = 27) divided by the total number of patients as from the PHC4 database. The PHC4 contains all inpatient admissions within Pennsylvania (PA), while PTSF reports admissions to PA TCs. The zip code of residence was used to aggregate calculations of UTR as well as other aggregate patient and census demographics, and UTR was categorized into lower, middle box, and upper quartiles. ArcGIS Desktop: Version 10.7, ESRI, Redlands, CA and GeoDa: Version 1.14.0, Open source license were used for geospatial mapping of UT with a spatial empirical Bayesian smoothed UTR, and Stata: Version 16.1, Stata Corp., College Station TX was used for statistical analyses. RESULTS: Pennsylvania Trauma Systems Foundation had 58,336 cases, while PHC4 had 111,626 that met the inclusion criteria, resulting in a median (Q1-Q3) smoothed UTR of 50.5% (38.2-60.1%) across PA zip code tabulation areas. Geospatial mapping reveals significant clusters of UT regions with high UTR in some of the rural regions with limited access to a TC. The lowest quartile UTR regions tended to have higher population density relative to the middle or upper quartile UTR regions. At the patient level, the lowest UTR regions had more racial and ethnic diversity, a higher injury severity, and higher rates of treatment at a TC. Undertriage rate regions that were closer to NTCs had a higher odds of being in the upper UTR quartile; 4.48 (2.52-7.99) for NTC with less than 200 beds and 8.53 (4.70-15.47) for NTC with 200 beds or greater compared with zip code tabulation areas with a TC as the closest hospital. CONCLUSION: There are significant clusters of geriatric UT within a mature trauma system. Increased emphasis needs to focus prehospital on identifying the severely injured geriatric patient including specific geriatric triage protocols. LEVEL OF EVIDENCE: Epidemiological, Level III.

A comparison of adolescent penetrating trauma patients managed at pediatric versus adult trauma centers in a mature trauma system.

BACKGROUND: While there is little debate that pediatric trauma centers (PTC) are uniquely equipped to manage pediatric trauma patients, the extent to which adolescents benefit from treatment there remains controversial. We sought to elucidate differences in management approach and outcome between PTC and adult trauma centers (ATC) for the adolescent penetrating trauma population. We hypothesized that improved mortality would be observed at ATC for this subset of patients. METHODS: Adolescent patients (age, 15-18 years), presenting to Pennsylvania-accredited trauma centers between 2003 and 2017 with penetrating injury, were queried from the Pennsylvania Trauma Outcome Study database. Dead on arrival, transfer patients, and those admitted to a Level III or Level IV trauma center were excluded from analysis. Patient length of stay, number of complications, surgical intervention, and mortality were compared between ATC and PTC. Multilevel mixed effects logistic regression models with trauma center as the clustering variable were used to assess the impact of center type (ATC/PTC) on management approach and mortality adjusted for appropriate covariates. RESULTS: A total of 2,630 adolescent patients met inclusion criteria (PTC: n = 428 [16.3%]; ATC: n = 2,202 [83.7%]). Pediatric trauma centers had a lower adjusted odds of mortality (adjusted odds ratio [AOR], 0.35; 95% confidence interval [CI], 0.17-0.74; p = 0.006) and a lower adjusted odds of surgery (AOR, 0.67; 95% CI, 0.0.48-0.93; p = 0.016) than their ATC counterparts. There were no differences in complication rates (AOR, 0.94; 95% CI, 0.57-1.55; p = 0.793) or length of stay longer than 4 days (AOR, 0.95; 95% CI, 0.61-1.48; p = 0.812) between the PTCs and ATCs. There were also differences in penetrating injury type between PTC and ATC. CONCLUSION: The adolescent penetrating trauma patient population treated at PTC had less surgery performed with improved mortality compared with ATC. LEVEL OF EVIDENCE: Therapeutic, Level IV.

An analysis of pediatric trauma center undertriage in a mature trauma system.

BACKGROUND: Improved mortality as a result of appropriate triage has been well established in adult trauma and may be generalizable to the pediatric trauma population as well. We sought to determine the overall undertriage rate (UTR) in the pediatric trauma population within Pennsylvania (PA). We hypothesized that a significant portion of pediatric trauma population would be undertriaged. METHODS: All pediatric (age younger than 15) admissions meeting trauma criteria (International Classification of Diseases, Ninth Revision: 800-959) from 2003 to 2015 were extracted from the Pennsylvania Health Care Cost Containment Council (PHC4) database and the Pennsylvania Trauma Systems Foundation (PTSF) registry. Undertriage was defined as patients not admitted to PTSF-verified pediatric trauma centers (n = 6). The PHC4 contains inpatient admissions within PA, while PTSF only reports admissions to PA trauma centers. ArcGIS Desktop was used for geospatial mapping of undertriage. RESULTS: A total of 37,607 cases in PTSF and 63,954 cases in PHC4 met criteria, suggesting UTR of 45.8% across PA. Geospatial mapping reveals significant clusters of undertriage regions with high UTR in the eastern half of the state compared to low UTR in the western half. High UTR seems to be centered around nonpediatric facilities. The UTR for patients with a probability of death 1% or less was 39.2%. CONCLUSION: Undertriage is clustered in eastern PA, with most areas of high undertriage located around existing trauma centers in high-density population areas. This pattern may suggest pediatric undertriage is related to a system issue as opposed to inadequate access. LEVEL OF EVIDENCE: Retrospective study, without negative criteria, Level III.

A preliminary analysis of Level IV trauma centers within an organized trauma system.

BACKGROUND: The effect of Level IV trauma center (TC) accreditation within an existing trauma network remains understudied. This study compared preaccreditation to postaccreditation data from Level IV TCs within a mature trauma system in Pennsylvania to determine whether TC designation affected time to and/or rate of transfer to definitive care. Level IV TCs were hypothesized to have a decreased time to transfer following accreditation and improved mortality. METHODS: The Pennsylvania Trauma Systems Foundation collects predesignation and postdesignation data from hospitals pursuing accreditation. Data from Pennsylvania Trauma Systems Foundation between 2012 and 2017 were analyzed. Variables of interest included patient demographics, injury severity, mortality, and incidence of surgical interventions precredentialingto postcredentialing. A multilevel mixed-effects logistic regression model assessed the adjusted impact of Level IV TC accreditation on transfer rate. ArcGIS Desktop was used for geospatial mapping of lives and geographic area covered by the addition of Level IV TCs in Pennsylvania. RESULTS: Five hospitals underwent Level IV credentialing from 2012 to 2017, providing data on 5,076 cases (pre, 2,395 [47.2%]; post, 2,681 [52.8%]). No significant difference in age, admission Glasgow Coma Scale score, or shock index was observed preaccreditation to postaccreditation. A difference in transfer rate was observed after credentialing in unadjusted (62.7% vs. 63.3%; p < 0.014) and adjusted analyses (adjusted odds ratios, 1.13, p = 0.389). There was a trend toward reduced odds of mortality postcredentialing (adjusted odds ratios, 0.59, p = 0.261). Major surgical intervention decreased (Pre, 0.42%; Post, 0.04%; p = 0.004). CONCLUSION: Level IV TC accreditation has beneficial effects on increased transfer rates and may improve mortality. It is important to continue to observe the impact of Level IV TCs on patient outcomes within a mature trauma system. LEVEL OF EVIDENCE: Prognostic and epidemiological, level III.

The magic number: Are improved outcomes observed at trauma centers with undertriage rates below 5%?

BACKGROUND: The American College of Surgeons Committee on Trauma (ACSCOT) advises trauma centers maintain <5% undertriage rate (UTR), but provides limited rationale for this figure. We sought to determine whether patients managed at Level I/II trauma centers with a UTR less than 5% had improved outcomes compared with centers with greater than 5% UTR. We hypothesized that similar overall adjusted outcomes would be observed at trauma centers in Pennsylvania regardless of their compliance with ACSCOT undertriage recommendation. METHODS: The Pennsylvania Trauma Outcome Study database was retrospectively queried for all trauma patients managed at accredited adult Level I/II trauma centers (n = 27) from 2003 to 2015. Patients with missing data on Injury Severity Score and/or Trauma Activation Status were excluded from the analysis. Institutional UTR were calculated for all trauma centers based on ACSCOT criteria (Injury Severity Score >15; no trauma activation) and were categorized into less than 5% or greater than 5% subgroups. A multilevel mixed-effects logistic regression model assessed the adjusted impact of management at centers with less than 5% undertriage. Statistical significance was set at p less than 0.05. RESULTS: A total of 404,315 patients from 27 trauma centers met inclusion criteria. Institutional UTRs ranged from 0% to 20.5%, with 15 centers exhibiting UTR less than 5% and 12 centers with UTR greater than 5%. No clinically meaningful difference in unadjusted mortality rate was observed between subgroups (<5% UTR: 5.19%; >5% UTR: 5.20%; p < 0.001). In adjusted analysis, no difference in mortality was found for patients managed at centers with less than 5% UTR compared to those with greater than 5% UTR (adjusted odds ratio, 1.06; 95% confidence interval, 0.85-1.33; p = 0.608). CONCLUSION: Achieving ACSCOT less than 5% undertriage standards appears to have limited impact on institutional mortality. Further research should seek to identify new triage criteria that can be uniformly applied to all trauma centers. LEVEL OF EVIDENCE: Epidemiological study, level III.

Increasing Onshore Oil Production: An Unexpected Explosion in Trauma Patients.

INTRODUCTION: Few data currently exist which are focused on type and severity of onshore oil extraction-related injuries. The purpose of this study was to evaluate injury patterns among onshore oil field operations. METHODS: A retrospective review was conducted of all trauma patients aged 18 and older with an onshore oil field-related injury admitted to an American College of Surgeons-verified level 1 trauma center between January 1, 2003 and June 30, 2012. Data collected included demographics, injury severity and details, hospital outcomes, and disposition. RESULTS: A total of 66 patients met inclusion criteria. All patients were male, of which the majority were Caucasian (81.8%, n = 54) with an average age of 36.5 ± 11.8 years, injury severity score of 9.4 ± 8.9, and Glasgow Coma Scale score of 13.8 ± 3.4. Extremity injuries were the most common (43.9%, n = 29), and most were the result of being struck by an object (40.9%, n = 27). Approximately one-third of patients (34.8%, n = 23) were admitted to the intensive care unit. Nine patients (13.6%) required mechanical ventilation while 27 (40.9%) underwent operative treatment. The average hospital length of stay was 5.8 ± 16.6 days, and most patients (78.8%, n = 52) were discharged home. Four patients suffered permanent disabilities, and there were two deaths. CONCLUSION: Increased domestic onshore oil production inevitably will result in higher numbers of oil field-related traumas. By focusing on employees who are at the greatest risk for injuries and by targeting the main causes of injuries, training programs can lead to a decrease in injury incidence.

Undertriage in trauma: Does an organized trauma network capture the major trauma victim? A statewide analysis.

BACKGROUND: Proper triage of critically injured trauma patients to accredited trauma centers (TCs) is essential for survival and patient outcomes. We sought to determine the percentage of patients meeting trauma criteria who received care at non-TCs (NTCs) within the statewide trauma system that exists in the state of Pennsylvania. We hypothesized that a substantial proportion of the trauma population would be undertriaged to NTCs with undertriage rates (UTR) decreasing with increasing severity of injury. METHODS: All adult (age ≥15) hospital admissions meeting trauma criteria (ICD-9, 800-959; Injury Severity Score [ISS], > 9 or > 15) from 2003 to 2015 were extracted from the Pennsylvania Health Care Cost Containment Council (PHC4) database, and compared with the corresponding trauma population within the Pennsylvania Trauma Systems Foundation (PTSF) registry. PHC4 contains all hospital admissions within PA while PTSF collects data on all trauma cases managed at designated TCs (Level I-IV). The percentage of patients meeting trauma criteria who are undertriaged to NTCs was determined and Network Analyst Location-Allocation function in ArcGIS Desktop was used to generate geospatial representations of undertriage based on ISSs throughout the state. RESULTS: For ISS > 9, 173,022 cases were identified from 2003 to 2015 in PTSF, while 255,263 cases meeting trauma criteria were found in the PHC4 database over the same timeframe suggesting UTR of 32.2%. For ISS > 15, UTR was determined to be 33.6%. Visual geospatial analysis suggests regions with limited access to TCs comprise the highest proportion of undertriaged trauma patients. CONCLUSION: Despite the existence of a statewide trauma framework for over 30 years, approximately, a third of severely injured trauma patients are managed at hospitals outside of the trauma system in PA. Intelligent trauma system design should include an objective process like geospatial mapping rather than the current system which is driven by competitive models of financial and health care system imperatives. LEVEL OF EVIDENCE: Epidemiological study, level III; Therapeutic, level IV.

Development of a trauma system and optimal placement of trauma centers using geospatial mapping.

BACKGROUND: The care of patients at individual trauma centers (TCs) has been carefully optimized, but not the placement of TCs within the trauma systems. We sought to objectively determine the optimal placement of trauma centers in Pennsylvania using geospatial mapping. METHODS: We used the Pennsylvania Trauma Systems Foundation (PTSF) and Pennsylvania Health Care Cost Containment Council (PHC4) registries for adult (age ≥15) trauma between 2003 and 2015 (n = 377,540 and n = 255,263). TCs and zip codes outside of PA were included to account for edge effects with trauma cases aggregated to the Zip Code Tabulation Area centroid of residence. Model assumptions included no previous TCs (clean slate); travel time intervals of 45, 60, 90, and 120 minutes; TC capacity based on trauma cases per bed size; and candidate hospitals ≥200 beds. We used Network Analyst Location-Allocation function in ArcGIS Desktop to generate models optimally placing 1 to 27 TCs (27 current PA TCs) and assessed model outcomes. RESULTS: At a travel time of 60 minutes and 27 sites, optimally placed models for PTSF and PHC4 covered 95.6% and 96.8% of trauma cases in comparison with the existing network reaching 92.3% or 90.6% of trauma cases based on PTSF or PHC4 inclusion. When controlled for existing coverage, the optimal numbers of TCs for PTSF and PHC4 were determined to be 22 and 16, respectively. CONCLUSIONS: The clean slate model clearly demonstrates that the optimal trauma system for the state of Pennsylvania differs significantly from the existing system. Geospatial mapping should be considered as a tool for informed decision-making when organizing a statewide trauma system. LEVEL OF EVIDENCE: Epidemiological study/Care management, level III.

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

BACKGROUND: Trauma system expansion is a complex process often governed by financial and health care system imperatives. We sought to propose a new, informed approach to trauma system expansion through the use of geospatial mapping. We hypothesized that geospatial mapping set to specific parameters could effectively identify optimal placement of new trauma centers (TC) within an existing trauma system. METHODS: We used Pennsylvania Trauma Systems Foundation registry data of adult (age, ≥ 15 years) trauma for calendar years 2003 to 2015 (n = 408,432), hospital demographics, road networks, and US Census data files. We included TCs and zip codes outside of Pennsylvania to account for edge effects with trauma cases aggregated to the zip code centroid of residence. Our model assumptions included existing Pennsylvania Trauma Systems Foundation Level I and II TCs, a maximum travel time of 60 minutes to the TC, capacity based on mean statewide ratios of trauma cases per hospital bed size, Injury Severity Score, candidate hospitals with 200 or more licensed beds and 30 minutes or longer or 15 minutes or longer from an existing TC in nonurban/urban areas, respectively. We used the Network Analyst Location-Allocation function in ArcGIS Desktop to generate spatial models. RESULTS: Of the 130 candidate sites, only 14 met the bed size and travel time criteria from an existing TC. Approximately 70% of zip codes and 91% of cases were within 60 minutes of an existing TC. Adding one to six new optimally paced TCs increased to a maximum of 82% of zip codes and 96% of cases within 60 minutes of an existing TC. Changes to model assumptions had an impact on which candidate sites were selected. CONCLUSION: Intelligent trauma system design should include an objective process like geospatial to determine the optimum locations for new TCs within existing trauma networks. LEVEL OF EVIDENCE: Epidemiological study, level III.