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Access to specialised trauma care is an important measure of trauma system efficiency. However, few data are available on access to integrated trauma systems. We aimed to describe access to trauma centres (TCs) in an integrated Canadian trauma system and identify its determinants.
We conducted a population-based cohort study including all injured adults admitted to acute care hospitals in the province of Québec between 2006 and 2011. Proportions of injured patients transported directly or transferred to TCs were assessed. Determinants of access were identified through a modified Poisson regression model and a relative importance analysis was used to determine the contribution of each independent variable to predicting access.
Of the 135,653 injury admissions selected, 75% were treated within the trauma system. Among 25,522 patients with major injuries [International Classification of diseases Injury Severity Score (ICISS<0.85)], 90% had access to TCs. Access was higher for patients aged under 65, men and among patients living in more remote areas (p-value <0.001). The region of residence followed by injury mechanism, number of trauma diagnoses, injury severity and age were the most important determinants of access to trauma care.
In an integrated, mature trauma system, we observed high access to TCs. However, problems in access were observed for the elderly, women and in urban areas where there are many non-designated hospitals. Access to trauma care should be monitored as part of quality of care improvement activities and pre-hospital guidelines for trauma patients should be applied uniformly throughout the province.
To read the full-text of this research, you can request a copy directly from the authors.
... Contribution to the registry is mandatory for all patients that meet any of the uniform inclusion criteria described above. Population coverage of the registry is high; over 92 % of patients admitted for major trauma in the province are treated in a designated trauma hospital and therefore included in the registry . This proportion remained stable over the study period . ...
... We performed a retrospective cohort study with excellent population coverage-over 90 % of major trauma is treated within the system . In addition, the study was based on a registry with excellent data quality. ...
The introduction of trauma systems in many countries worldwide has been shown to improve injury survival. However, few data are available on the long-term evolution of outcomes other than mortality. The objective of this study was to describe trends in mortality, unplanned readmission, complications, and length of stay in a mature inclusive trauma system from 1999 to 2012.
This retrospective cohort study was based on the inclusive trauma system of Quebec, Canada. Data were drawn from the trauma registry linked to the hospital discharge database. Time trends were evaluated using generalized linear mixed models with a correction for hospital clusters and cohort effects.
Between 1999 and 2012, risk-adjusted mortality decreased from 5.8 to 4.2 % for all patients and from 14.9 to 13.1 % for major trauma (p < 0.0001). Mean LOS decreased from 9.5 days to 8.0 days for all patients and from 15.5 days to 11.5 days for major trauma (p < 0.0001). Unplanned readmission and complication rates remained stable over the observation period at around 6.6 and 11.6 % for all patients and 7.6 and 25.6 % for major trauma, respectively.
The results of this study suggest that there have been significant decreases in patient mortality and hospital length of stay in the inclusive trauma system of Québec over the last decade. Results also suggest that efforts should be made to reduce in-hospital complications and unplanned readmissions. Future research should attempt to identify determinants of observed decreases in mortality and LOS and assess whether similar improvements have occurred in functional outcomes.
... This study population is representative of moderate to major trauma admissions in the province (population based) because it includes admissions to all trauma centers (level I to level IV) in a fully integrated system. Previous research has shown that over 90 % of patients hospitalized for major trauma in the province are treated within the trauma system . In addition, the trauma registry used in this study is audited periodically to ensure data quality and audit results suggest high data accuracy (only 76 errors in 65 data fields × 80 patient files; data not published). ...
... Considering these patients are likely to have higher levels of material and social deprivation and worse outcomes than those included, SES-LOS associations may again have been underestimated. Third, approximately 10 % of major trauma cases are treated in non-designated centers in the province and so were not included in this study . However, we have no reason to believe that the association between SES and LOS would not be observed in trauma patients treated outside the system. ...
Injury is second only to cardiovascular disease in terms of acute care costs in North America. One key to improving injury care efficiency is to generate knowledge on the determinants of resource use. Socio-economic status (SES) is a documented risk factor for injury severity and mortality but its impact on length of stay (LOS) for injury admissions is unknown. This study aimed to examine the relationship between SES and LOS following injury. This multicenter retrospective cohort study was based on adults discharged alive from any trauma center (2007-2012; 57 hospitals; 65,486 patients) in a Canadian integrated provincial trauma system. SES was determined using ecological indices of material and social deprivation. Mean differences in LOS adjusted for age, gender, comorbidities, and injury severity were generated using multivariate linear regression.
Mean LOS was 13.5 days. Patients in the highest quintile of material/social deprivation had a mean LOS 0.5 days (95 % CI 0.1-0.9)/1.4 days (1.1-1.8) longer than those in the lowest quintile. Patients in the highest quintiles of both social and material deprivation had a mean LOS 2.6 days (1.8-3.5) longer than those in the lowest quintiles.
Results suggest that patients admitted for traumatic injury who suffer from high social and/or material deprivation have longer acute care LOS in a universal-access health care system. The reasons behind observed differences need to be further explored but may indicate that discharge planning should take patient SES into consideration.
... The use of the Quebec trauma registry provides high population coverage of moderate to severe cases of TBI in the province, as 90% of major trauma cases are treated in trauma centres. 42 Furthermore, rigorous data quality assurance processes including standardized procedures for data collection, unified inclusion criteria, coding forums and coding quality algorithms ensure high data quality (98% accuracy in stratified random sample chart reabstraction [L.M. and Amina Belcaïd, Centre de recherche du Centre hospitalier universitaire de Québec, Hôpital de l'Enfant-Jésus: unpublished data, 2017]). Another strength of this study is the reproducibility of the method used to estimate hospital resource use. ...
The knowledge gap regarding acute care resource use for patients with traumatic brain injury (TBI) impedes efforts to improve the efficiency and quality of the care of these patients. Our objective was to evaluate interhospital variation in resource use for patients with TBI, identify determinants of high resource use and assess the association between hospital resource use and clinical outcomes.
We conducted a multicentre retrospective cohort study including patients aged 16 years and older admitted to the inclusive trauma system of Quebec following TBI, between 2013 and 2016. We estimated resource use using activity-based costs. Clinical outcomes included mortality, complications and unplanned hospital readmission. Interhospital variation was evaluated using intraclass correlation coefficients (ICCs) with 95% confidence intervals (CIs). Correlations between hospital resource use and clinical outcomes were evaluated using correlation coefficients on weighted, risk-adjusted estimates with 95% CIs.
We included 6319 patients. We observed significant interhospital variation in resource use for patients discharged alive, which was not explained by patient case mix (ICC 0.052, 95% CI 0.043 to 0.061). Adjusted mean resource use for patients discharged to long-term care was more than twice that of patients discharged home. Hospitals with higher resource use tended to have a lower incidence of mortality (r -0.347, 95% CI -0.559 to -0.087) and unplanned readmission (r -0.249, 95% CI -0.481 to 0.020) but a higher incidence of complications (r 0.491, 95% CI 0.255 to 0.666).
Resource use for TBI varies significantly among hospitals and may be associated with differences in mortality and morbidity. Negative associations with mortality and positive associations with complications should be interpreted with caution but suggest there may be a trade-off between adverse events and survival that should be evaluated further.
... Little is known on the population-based effects of access in integrated trauma systems which consist of a network of TCs that cover the whole health service territory and include service corridors with pre-hospital transport and inter-hospital transfer agreements [12,13]. Research has suggested that even in integrated trauma systems, up to 15% of patients with major injuries in some areas are still treated in a non-designated hospital . Background: Few data are available on population-based access to specialised trauma care and its influence on patient outcomes in an integrated trauma system. ...
... Indeed, over 90% of major trauma admissions in the province are admitted to a hospital within the trauma system.  In addition, the trauma registry is based on uniform inclusion criteria and standardized data collection with rigorous data quality control mechanisms. We also used a validated risk adjustment model which includes age as a continuous variable while respecting its nonlinear association with the logit of mortality. ...
To assess whether the definition of an IHF used as an exclusion criterion influences the results of trauma center benchmarking.
We conducted a multicenter retrospective cohort study with data from an integrated Canadian trauma system. The study population included all patients admitted between 1999 and 2010 to any of the 57 adult trauma centers. Seven definitions of IHF based on diagnostic codes, age, mechanism of injury, and secondary injuries, identified in a systematic review, were used. Trauma centers were benchmarked using risk-adjusted mortality estimates generated using the Trauma Risk Adjustment Model. The agreement between benchmarking results generated under different IHF definitions was evaluated with correlation coefficients on adjusted mortality estimates. Correlation coefficients >0.95 were considered to convey acceptable agreement.
The study population consisted of 172,872 patients before exclusion of IHF and between 128,094 and 139,588 patients after exclusion. Correlation coefficients between risk-adjusted mortality estimates generated in populations including and excluding IHF varied between 0.86 and 0.90. Correlation coefficients of estimates generated under different definitions of IHF varied between 0.97 and 0.99, even when analyses were restricted to patients aged ≥65 years.
Although the exclusion of patients with IHF has an influence on the results of trauma center benchmarking based on mortality, the definition of IHF in terms of diagnostic codes, age, mechanism of injury and secondary injury has no significant impact on benchmarking results. Results suggest that there is no need to obtain formal consensus on the definition of IHF for benchmarking activities.
... The focus has then shifted towards more inclusive and integrated systems where both trauma centers and non-trauma hospitals are important for delivering trauma care to the region regardless of habitation status and have the ability to match the patients' injury with the right level of care [9,24]. Some recent studies from Canada have focused on integrated systems [24,25], but more research is needed to evaluate these systems in terms of performance and cost effectiveness. The majority of the studies on trauma systems and trauma outcomes have been conducted outside of Europe  and Scandinavia  therefore the results may not be applicable to our slightly different trauma systems. ...
Trauma systems and regionalized trauma care have been shown to improve outcome in severely injured trauma patients. The aim of this study was to evaluate the implementation of a prehospital trauma care protocol and transport directive, and to determine its effects on the number of primary admissions and secondary trauma transfers in a large Scandinavian city.
We performed a retrospective observational study based on local trauma registries and hospital and ambulance records in Stockholm County; patients > 15 years of age with an Injury Severity Score (ISS) > 15 transported to any emergency care hospitals in the Stockholm area were included for the years 2006 and 2008. We also included secondary transferred patients to the regional trauma center during 2006, 2008, and 2013.
A total of 693 primarily admitted trauma patients were included for the years 2006 and 2008. For the years 2006, 2008 and 2013, we included 114 secondarily transported trauma patients. The number of primary patient transports to the trauma center increased during the years by 20.2 %, (p < 0.001); patients primarily transported to the trauma center had a significantly higher Injury Severity Score in 2008 than in 2006, and the number of patients transported secondarily to the trauma center in 2006 was higher compared to 2008 and to 2013 (p < 0.001, all 3 years).
Our data indicate that implementation of a prehospital trauma care protocol may have an effect on transportation of severely injured trauma patients. A decrease in secondarily transported trauma patients to the regional trauma center was noted after 1 year and persisted at 7 years after the organizational change. Patients primarily admitted to the trauma center after the change had more severe injuries than patients transported to other emergency hospitals in the area even if 20 % of patients were not admitted primarily to a trauma center. This does not imply that the transport directives or the criteria were not followed but rather reveals the difficulties and uncertainties of field triage.
With the introduction of a prehospital trauma transport directive in a large urban city, an increase in patients transported to the regional trauma center and a decrease in secondary transfers were detected, but a considerable number of severely injured patients were still transported to local hospitals.
... This study has important strengths which support the validity of study results and the potential impact of conclusions. We conducted a large study with near population coverage of TBI (over 90% of severe TBI are treated in a designated trauma center) 58 , data subject to rigorous quality control procedures (chart re-abstractions suggest 92% data accuracy), consideration of all consecutive acute care stays for the injury, and evaluation of both patient and treatment-related determinants of LOS. ...
Traumatic brain injury (TBI) is the leading cause of disability in children and young adults and costs CAD$3 billion annually in Canada. Stakeholders have expressed the urgent need to obtain information on resource use for TBI to improve the quality and efficiency of acute care in this patient population. We aimed to assess the components and determinants of hospital and ICU LOS for TBI admissions.
We performed a retrospective multicenter cohort study on 11,199 adults admitted for TBI between 2007 and 2012 in an inclusive Canadian trauma system. Our primary outcome measure was index hospital LOS (admission to the hospital with the highest designation level). Index LOS was compared to total LOS (all consecutive admissions related to the injury). Expected LOS was calculated by matching TBI admissions to all-diagnosis hospital admissions by age, gender, and year of admission. LOS determinants were identified using multilevel linear regression.
Geometric mean total LOS was 1day longer than geometric mean index LOS (12.6 versus 11.7 days). Observed index and ICU LOS were respectively 4.2days and 2.5days longer than that expected according to all-diagnosis admissions. The six most important determinants of LOS were discharge destination, severity of concomitant injuries, extracranial complications, GCS, TBI severity, and mechanical ventilation, accounting for 80% of explained variation.
Results of this multicenter retrospective cohort study suggest that hospital and ICU LOS for TBI admissions are 56% and 119% longer than expected according to all-diagnosis admissions, respectively. In addition, hospital LOS is underestimated when only the index visit is considered and is largely influenced by discharge destination and extracranial complications, suggesting that improvements could be achieved with better discharge planning and interventions targeting prevention of in-hospital complications. This study highlights the importance of considering TBI patients as a distinct population when allocating resources or planning quality improvement interventions.
... 12,17,34 Similar studies have found that the elderly are more likely to be transported to non-trauma center and have the greatest risk of not reaching a trauma center at all. 10,12,17, Our finding of older age being associated with indirect admission to a RHSCIR acute facility is consistent with these studies, and suggests that an etiology other tha SCI was considered for this population in which cognitive deficits and mild neurological symptoms resulting from low-energy falls are common. This growing evidence highlights the need to explore the impact of delayed access to specialized care in the elderly population in future studies. ...
Current research indicates that more than half of patients with traumatic spinal cord injury (tSCI) experience delays in transfer and receive surgery more than 24 hours post-injury. The objectives of this study were to determine the geographic distribution of tSCI in Canada relative to specialized treatment facilities, to assess clinical and logistical factors at play for indirect admissions to those facilities, and to explore differences in current time to admission and simulated scenarios in an attempt to assess the potential impact of changes to triage protocols. This study included data from 876 patients with tSCI enrolled in the prospectively collected acute Rick Hansen Spinal Cord Injury Registry (RHSCIR) between January 1, 2010 and December 31, 2013 who had data on the location of their injury. Patients transported directly to a RHSCIR acute facility were more likely to reach the facility within 1 h of injury while those transported indirectly were more likely to arrive 7 h later. Considering the injuries occurring within 40 km of a RHSCIR acute facility (n=323), 249 patients (77%) were directly and 74 (23%) were indirectly admitted. In the multivariate regression analysis, only older age and longer road distance remained significantly associated with being indirectly admitted to a RHSCIR facility. Compared to the current status, the median time to admission decreased by 20% (3.5 h) in the 100% direct admission scenario; and increased by 102% (8.9 h) in the 100% indirect admission scenario.
... Level I and II trauma centres across the country are well represented in the registry, and patients with moderate or severe traumatic brain injury are rarely treated outside highly specialised centres.  Therefore, undercoverage from patients being treated in trauma centres that are not included in the registry or in non-trauma centres should not have had a major effect on our frequency estimate. However, we could not link 21.9% of the National Trauma Registry to the Discharge Abstract Database. ...
Optimisation of healthcare practices in patients sustaining a traumatic brain injury is of major concern given the high incidence of death and long-term disabilities. Considering the brain's susceptibility to ischaemia, strategies to optimise oxygenation to brain are needed. While red blood cell (RBC) transfusion is one such strategy, specific RBC strategies are debated. We aimed to evaluate RBC transfusion frequency, determinants of transfusions and associated clinical outcomes.
We conducted a retrospective multicentre cohort study using data from the National Trauma Registry of Canada. Patients admitted with moderate or severe traumatic brain injury to participating hospitals between April 2005 and March 2013 were eligible. Patient information on blood products, comorbidities, interventions and complications from the Discharge Abstract Database were linked to the National Trauma Registry data. Relative weights analyses evaluated the contribution of each determinant. We conducted multivariate robust Poisson regression to evaluate the association between potential determinants, mortality, complications, hospital-to-home discharge and RBC transfusion. We also used proportional hazard models to evaluate length of stay for time to discharge from ICU and hospital.
Among the 7062 patients with traumatic brain injury, 1991 patients received at least one RBC transfusion during their hospital stay. Female sex, anaemia, coagulopathy, sepsis, bleeding, hypovolemic shock, other comorbid illnesses, serious extracerebral trauma injuries were all significantly associated with RBC transfusion. Serious extracerebral injuries altogether explained 61% of the observed variation in RBC transfusion. Mortality (risk ratio (RR) 1.23 (95% CI 1.13 to 1.33)), trauma complications (RR 1.38 (95% CI 1.32 to 1.44)) and discharge elsewhere than home (RR 1.88 (95% CI 1.75 to 2.04)) were increased in patients who received RBC transfusion. Discharge from ICU and hospital were also delayed in transfused patients.
RBC transfusion is common in patients with traumatic brain injury and associated with unfavourable outcomes. Trauma severity is an important determinant of RBC transfusion. Prospective studies are needed to further evaluate optimal transfusion strategies in traumatic brain injury.
... Since participation in the provincial trauma registry is mandatory for all trauma centres and more than 90% of major trauma is treated within the trauma system, this study provides excellent representation of patients with hemorrhagic shock who survive transport to a trauma centre. 28 Other strengths include the availability of extensive clinical information for risk adjustment and the simulation of missing data, which enabled us to include all eligible patients. ...
Hemorrhagic shock is responsible for 45% of injury fatalities in North America, and 50% of these occur within 2 h of injury. There is currently a lack of evidence regarding the trajectories of patients in hemorrhagic shock and the potential benefit of level I/II care for these patients. We aimed to compare mortality across trauma centre designation levels for patients in hemorrhagic shock. Secondary objectives were to compare surgical delays, complications and hospital length of stay (LOS).
We performed a retrospective cohort study based on a Canadian inclusive trauma system (1999-2012), including adults with systolic blood pressure (SBP) < 90 mm Hg on arrival who required urgent surgical care (< 6 h). Logistic regression was used to examine the influence of trauma centre designation level on risk-adjusted surgical delays, mortality and complications. Linear regression was used to examine LOS.
Compared with level I centres, adjusted odds ratios (and 95% confidence intervals [CI]) of mortality for level III and IV centres were 1.71 (1.03-2.85) and 2.25 (1.08-4.73), respectively. Surgical delays did not vary across designation levels, but mean LOS and complications were lower in level II-IV centres than level I centres.
Level I/II centres may offer a survival advantage over level III/IV centres for patients requiring emergency intervention for hemorrhagic shock. Further research with larger sample sizes is required to confirm these results and to identify optimal transport time thresholds for bypassing level III/IV centres in favour of level I/II centres.
... T HE APPORTIONMENT OF TRAUMA CENTERS (TC) in Florida by the Florida Department of Health is based in part on the number of severely injured patients in a geographic region. Injury severity in this context is assessed using the International Classification Injury Severity Score (ICISS), with severely injured patients being classified as those having an ICISS <0.85. 1 This set point translates into patients with greater than 15 per cent mortality risk, 2 has been reported in previous studies,  and is observed in multiple TCs. 6,7 In this way, the state establishes the threshold by which they consider the need for a TC. ...
Florida considers the International Classification Injury Severity Score (ICISS) from hospital discharges within a geographic region in the apportionment of trauma centers (TCs). Patients with an ICISS <0.85 are considered to require triage to a TC, yet many are triaged to an emergency department (ED). We assess outcomes of those with an ICISS <0.85 by the actual triage decision of emergency medical services (EMS). From October 2011 to October 2013, 39,021 consecutive admissions with injury ICD-9 codes were analyzed. ICISS was calculated from the product of the survival risk ratios for a patient's three worst injuries. Outcomes were compared between patients with ICISS <0.85 either triaged to the ED or its separate, neighboring, free-standing TC at a large urban hospital. A total of 32,191 (83%) patients were triaged to the ED by EMS and 6,827 (17%) were triaged to the TC. Of these, 2544 had an ICISS <0.85, with 2145 (84%) being triaged to the TC and 399 (16%) to the ED. In these patients, those taken to the TC more often required admission, and those taken to the ED had better outcomes. When the confounders influencing triage to an ED or a TC are eliminated, those triaged by EMS to the ED rather than the TC had better overall outcomes. EMS providers better identified patients at risk for mortality than did the retrospective application of ICISS. ICISS <0.85 does not identify the absolute need for TC as EMS providers were able to appropriately triage a large portion of this population to the ED.
... The authors also found that injury severity [23,24], the body region of the worst injury , age , and the Glasgow Coma Score  were determinants of resource use. Our study has gone further by providing resource use estimates using a registry that includes data on 92% of major injury admissions , exploring additional determinants, and investigating inter-hospital variation. ...
Variations in adjusted costs have been observed among trauma centres in the United States but patient outcomes were not better in centres with higher costs. Attempts to improve injury care efficiency are hampered by insufficient patient-level information on resource use and on the drivers of resource use intensity.
To estimate patient-level resource use for injury admissions, identify determinants of resource use intensity, and evaluate inter-hospital variations in resource use.
We conducted a retrospective cohort study including ≥16-year-olds admitted to adult trauma centres in a mature, inclusive Canadian trauma system between 2014 and 2016. We extracted data from the trauma registry and hospital financial reports. We estimated resource use with activity-based costs, identified determinants of resource use intensity using a multilevel linear model and assessed the relative importance of each determinant with Cohen's f2. We evaluated inter-provider variations with intraclass correlation coefficients (ICC) and 95% confidence intervals.
We included 32,411 patients. Median costs per admission were $4857 (Quartiles 1 and 3 2961-8448). The most important contributors to total resource use were the medical ward (57%), followed by the operating room (OR; 23%) and the intensive care unit (13%). The strongest determinant of resource use intensity was discharge destination (Cohen's f2 = 7%). The most resource intense patient group was spinal cord injuries with $11,193 (7115-17,606) per admission. While resource use increased with increasing age for the medical ward, it decreased with increasing age for the OR. Resource use was 18% higher in level I centres compared to level IV centres and we observed significant variations in resource use across centres (ICC = 5% [4-6]), particularly for the OR (28% [20-40]).
Resource use for acute injury care in Quebec is not solely due to the clinical status of patients. We identified determinants of resource use that can be used to establish evidence-based resource allocations and improve injury care efficiency. The method we developed for estimating patient-level, in-hospital resource use for injury admissions and identifying related determinants could be reproduced using local trauma registry data and our unit costs or unit costs specific to each setting.
... Population coverage is high because 90% of major trauma patients are admitted to a trauma centre. 21 ...
Guidelines for injury care are increasingly moving away from surgical management towards less invasive procedures but there is a knowledge gap on how these recommendations are influencing practice. We aimed to assess inter‐hospital variation in surgical intensity for injury admissions and evaluate the correlation between hospital surgical intensity and mortality / complications.
We included adults admitted for major trauma between 2006 and 2016 in a Canadian provincial trauma system. Analyses were stratified for orthopedic (n=16,887), neurological (n=12,888) and torso injuries (n=9816). Surgical intensity was quantified with the number of surgical procedures < 72h. Inter‐hospital variation was assessed with the intra‐class correlation coefficient (ICC). We assessed the correlation between risk‐adjusted mean number of surgical procedures and risk‐adjusted incidence of mortality and complications using Pearson correlation coefficients (r).
Moderate inter‐hospital variation was observed for orthopedic surgery (ICC = 14.0%) whereas variation was low for torso surgery (ICC = 2.7%) and neurosurgery (ICC = 0.8%). Surgical intensity was negatively correlated with hospital mortality for torso injury (r=‐0.32, p= 0.02) and neurotrauma (r=‐0.65, p = 0.08). A strong positive correlation was observed with hospital complications for orthopedic injuries (r= 0.36, p= 0.006) whereas the opposite was observed for neurotrauma (r= ‐0.71, p= 0.05).
Results should be interpreted with caution as they may be due to residual confounding. However, they may suggest that there are opportunities for quality improvement in surgical care for injury admissions, particularly for orthopedic injuries. Moving forward, we should aim to prospectively evaluate adherence to guidelines on non‐operative management and their impact on mortality and morbidity.
To evaluate the incidence, determinants and impact on outcome of in-hospital complications in adults with traumatic brain injury (TBI).
Materials and methods
We conducted a multicenter cohort study of TBI patients admitted between 2007 and 2012 in an inclusive Canadian trauma system. Risk ratios of complications, odds ratios of mortality and geometric mean ratios of length of stay (LOS) were calculated using generalized linear models with adjustment for prognostic indicators and hospital cluster effects.
Of 12,887 patients, 3.2% had at least one neurological complication and 22.6% a non-neurological complication. Mechanical ventilation, head injury severity, blood transfusion and neurosurgical intervention had the strongest correlation with neurological complications. Mechanical ventilation, the Glasgow Coma Scale, blood transfusion and concomitant injuries had the strongest correlation with non-neurological complications. Neurological and non-neurological complications were associated with a 85% and 53% increase in the odds of mortality, and a 60% and two-fold increases in LOS, respectively.
More than 20% of patients with TBI developed a complication. Many of these complications were associated with increased mortality and LOS. Results highlight the importance of prevention strategies adapted to treatment decisions and underline the need to improve knowledge on the underuse and overuse of clinical interventions.
According to Donabedian's health care quality model, improvements in the structure of care should lead to improvements in clinical processes that should in turn improve patient outcome. This model has been widely adopted by the trauma community but has not yet been validated in a trauma system. The objective of this study was to assess the performance of an integrated trauma system in terms of structure, process, and outcome and evaluate the correlation between quality domains.
Quality of care was evaluated for patients treated in a Canadian provincial trauma system (2005-2010; 57 centers, n = 63,971) using quality indicators (QIs) developed and validated previously. Structural performance was measured by transposing on-site accreditation visit reports onto an evaluation grid according to American College of Surgeons criteria. The composite process QI was calculated as the average sum of proportions of conformity to 15 process QIs derived from literature review and expert opinion. Outcome performance was measured using risk-adjusted rates of mortality, complications, and readmission as well as hospital length of stay (LOS). Correlation was assessed with Pearson's correlation coefficients.
Statistically significant correlations were observed between structure and process QIs (r = 0.33), and process and outcome QIs (r = -0.33 for readmission, r = -0.27 for LOS). Significant positive correlations were also observed between outcome QIs (r = 0.37 for mortality-readmission; r = 0.39 for mortality-LOS and readmission-LOS; r = 0.45 for mortality-complications; r = 0.34 for readmission-complications; 0.63 for complications-LOS).
Significant correlations between quality domains observed in this study suggest that Donabedian's structure-process-outcome model is a valid model for evaluating trauma care. Trauma centers that perform well in terms of structure also tend to perform well in terms of clinical processes, which in turn has a favorable influence on patient outcomes.
Prognostic study, level III.
Trauma centers (TCs) are inconsistently distributed throughout the US. It is unclear if new TCs improve care and decrease mortality. We tested the hypothesis that increases in TCs are associated with decreases in injury-related mortality (IRM) at the state level.
We used data from the American Trauma Society to geolocate every state-designated or ACS-verified TC in all 50 states and DC from 2014-2018. These data were merged with publicly available IRM data from the Centers for Disease Control and Prevention. We used geographic information systems methods to map and study the relationships between TC locations and state-level IRM over time. Regression analysis, accounting for state-level fixed effects, was used to calculate the effect of total statewide number of TC on IRM and year-to-year changes in statewide TC with the IRM (shown as deaths per additional TC per 100,000 population, p-value).
Nationwide between 2014 and 2018, the number of TC increased from 2039 to 2153. IRM also increased over time. There was notable interstate variation, from 1 to 284 TCs. Four patterns in statewide TC changes emerged: static (12), increased (29), decreased (5), or variable (4). Of states with TC increases, 26 (90%) had increased IRM between 2014 and 2017, while the remaining 3 saw a decline. Regression analysis demonstrated that having more trauma centers in a state was associated with a significantly higher IRM rate (0.38, p=0.03); adding new trauma centers was not associated with changes in IRM (0.02, p=0.8).
Having more TC and increasing the number of TC within a state is not associated with decreases in state-level IRM. In this case, more is not better. However, more work is needed identify the optimal number and location of trauma centers to improve IRM.
Level of evidence:
Background: Quality improvement activities in trauma systems are widely based on comparisons between trauma centers within the same system. Comparisons across different trauma systems may reveal further opportunities for quality improvement.
Objectives: This study aimed to compare the integrated trauma systems in Québec, Canada and in Victoria, Australia, regarding their structures, care processes and patient outcomes.
Methodology: The elements recommended by the American College of Surgeons were used to compare trauma systems structures. Comparisons of care processes and patient outcomes were based on data from major trauma admissions extracted from trauma registries (2013 and 2017). Care processes included time to reach a definitive care facility, time spent in the emergency department, and time lapsed before the first head computed tomography (CT) scan. These care processes were compared using a z-test of log-transformed times. Hospital mortality and hospital length of stay (LOS) were compared using indirect standardization based on multiple logistic and linear regression.
Results: Major differences in trauma system structure were Advanced Trauma Life Support at the scene of injury (Victoria), the use of validated prehospital triage tools (Québec), and mandatory accreditation of all trauma centers (Québec). Patients in Québec arrived at their definitive care hospital earlier than their counterparts in Victoria (median: 1.93 vs. 2.13h, p=0.002), but spent longer in the emergency department (median: 8.23 vs. 5.15 h, p<0.0001) and waited longer before having their first head CT (median: 1.90 vs. 1.52 h, p<0.0001). In-hospital mortality and hospital LOS were higher in Québec than in Victoria (standardized mortality ratio: 1.15, 95% CI: 1.09 - 1.20; standardized LOS ratio: 1.10, 95% CI: 1.09 - 1.11).
Conclusion: We observed important differences in the structural components and care processes in Québec and Victoria's trauma systems, which might explain some of the observed differences in patient outcomes. This study shows the potential value of international comparisons in trauma care and identifies possible opportunities for quality improvement.
Closures of hospital trauma centers have accelerated since 2001. These closures may disproportionately affect disadvantaged communities. We evaluate how driving time between ZIP code areas and the nearest trauma centers-a proxy for access, given the time-sensitive nature of trauma care-changed nationwide during 2001-07. By 2007, sixty-nine million Americans (24 percent of the population) had to travel farther to the nearest trauma center than they did in 2001, and almost sixteen million people had to travel an additional thirty minutes or more. Communities with disproportionately high numbers of African American residents, uninsured people, and people living in poverty, as well as people living in rural areas, were more likely than others to be thus affected. Because mortality from traumatic injuries has also worsened for these vulnerable populations, policy makers should learn more about the possible connections-and consider such measures as paying trauma centers serving these communities higher amounts for treatment of injuries.
To estimate the likelihood of trauma center admission for injured elderly patients with trauma, determine trends in trauma center admissions, and identify factors associated with trauma center use for elderly patients with trauma.
Acute care hospitals in California.
All patients hospitalized for acute traumatic injuries during the period from January 1, 1999, to December 31, 2008 (n = 430,081). Patients who had scheduled admissions for nonacute or minor trauma were excluded.
Likelihood of admission to level I or II trauma center was calculated according to age categories after adjusting for patient and system factors.
Of 430,081 patients admitted to California acute care hospitals for trauma-related diagnoses, 27% were older than 65 years. After adjusting for demographic, clinical, and system factors, compared with trauma patients aged 18-25 years, the odds of admission to a trauma center decreased with increasing age; patients aged 26-45 years had lower odds (odds ratio [OR], 0.75; 95% confidence interval [CI], 0.71-0.80) of being admitted to a trauma center for their injuries than did patients 46-65 years of age (OR, 0.57; 95% CI, 0.54-0.60), patients 66-85 years of age (OR, 0.35; 95% CI, 0.30-0.41), and patients older than 85 years (OR, 0.30; 95% CI, 0.25-0.36). Similar patterns were found when stratifying the analysis by trauma type and severity. Living more than 50 miles away from a trauma center (OR, 0.03; 95% CI, 0.01-0.06) and lack of county trauma center (OR, 0.17; 95% CI, 0.09-0.35) were also predictors of not receiving trauma care.
Age and likelihood of admission to a trauma center for injured patients were observed to be inversely proportional after controlling for other factors. System-level factors play a major role in determining which injured patients receive trauma care.
Trauma is a leading cause of morbidity, potential years of life lost and health care expenditure in Canada and around the world. Trauma systems have been established across North America to provide comprehensive injury care and to lead injury control efforts. We sought to describe the current status of trauma systems in Canada and Canadians' access to acute, multidisciplinary trauma care.
A national survey was used to identify the locations and capabilities of adult trauma centers across Canada and to identify the catchment populations they serve. Geographic information science methods were used to map the locations of Level I and Level II trauma centers and to define 1-hour road travel times around each trauma center. Data from the 2006 Canadian Census were used to estimate populations within and outside 1-hour access to definitive trauma care.
In Canada, 32 Level I and Level II trauma centers provide definitive trauma care and coordinate the efforts of their surrounding trauma systems. Most Canadians (77.5%) reside within 1-hour road travel catchments of Level I or Level II centers. However, marked geographic disparities in access persist. Of the 22.5% of Canadians who live more than an hour away from a Level I or Level II trauma centers, all are in rural and remote regions.
Access to high quality acute trauma care is well established across parts of Canada but a clear urban/rural divide persists. Regional efforts to improve short- and long-term outcomes after severe trauma should focus on the optimization of access to pre-hospital care and acute trauma care in rural communities using locally relevant strategies or novel care delivery options.
To assess the performance of the International Classification of Diseases (ICD) based injury severity score, ICISS, when applied to two versions of the 10th edition of ICD, ICD-10 and ICD-10-AM.
ICISS was assessed on its ability to predict threat to life using logistic regression modelling. Models used ICISS and age as predictors and survival as the outcome.
Australia and New Zealand. Patients or
Hospitalisations with an ICD-10-AM principal diagnosis in the range S00-T89 from 1 July 1999 to 30 June 2001 (Australia) or 1 July 1999 to 31 December 2001 (New Zealand).
The models were assessed in terms of their discrimination, measured by the concordance score, and calibration, measured using calibration curves and the Hosmer-Lemeshow statistic.
523 633 Australian and 124 767 New Zealand hospitalisations were selected, including 7230 and 1565 deaths respectively. Discrimination was high in all the fitted models with concordance scores of 0.885 to 0.910. Calibration results were also promising with all calibration curves being close to linear, though ICISS appeared to underestimate mortality somewhat for cases with an ICISS score less than 0.6. Overall ICISS performed better when applied to the Australian than the New Zealand hospitalisations. Australian and New Zealand hospitalisations were very similar. ICISS was also only a little more successful when ICD-10-AM rather than mapped ICD-10 was used.
ICISS appears to be a reasonable way to estimate severity for databases using ICD-10 or ICD-10-AM. It is also likely to work well for other clinical variants of ICD-10.
Hospitals have difficulty justifying the expense of maintaining trauma centers without strong evidence of their effectiveness. To address this gap, we examined differences in mortality between level 1 trauma centers and hospitals without a trauma center (non-trauma centers).
Mortality outcomes were compared among patients treated in 18 hospitals with a level 1 trauma center and 51 hospitals non-trauma centers located in 14 states. Patients 18 to 84 years old with a moderate-to-severe injury were eligible. Complete data were obtained for 1104 patients who died in the hospital and 4087 patients who were discharged alive. We used propensity-score weighting to adjust for observable differences between patients treated at trauma centers and those treated at non-trauma centers.
After adjustment for differences in the case mix, the in-hospital mortality rate was significantly lower at trauma centers than at non-trauma centers (7.6 percent vs. 9.5 percent; relative risk, 0.80; 95 percent confidence interval, 0.66 to 0.98), as was the one-year mortality rate (10.4 percent vs. 13.8 percent; relative risk, 0.75; 95 percent confidence interval, 0.60 to 0.95). The effects of treatment at a trauma center varied according to the severity of injury, with evidence to suggest that differences in mortality rates were primarily confined to patients with more severe injuries.
Our findings show that the risk of death is significantly lower when care is provided in a trauma center than in a non-trauma center and argue for continued efforts at regionalization.
It is usually preferable to model and estimate prevalence ratios instead of odds ratios in cross-sectional studies when diseases or injuries are not rare. Problems with existing methods of modeling prevalence ratios include lack of convergence, overestimated standard errors, and extrapolation of simple univariate formulas to multivariable models. We compare two of the newer methods using simulated data and real data from SAS online examples.
The Robust Poisson method, which uses the Poisson distribution and a sandwich variance estimator, is compared to the log-binomial method, which uses the binomial distribution to obtain maximum likelihood estimates, using computer simulations and real data.
For very high prevalences and moderate sample size, the Robust Poisson method yields less biased estimates of the prevalence ratios than the log-binomial method. However, for moderate prevalences and moderate sample size, the log-binomial method yields slightly less biased estimates than the Robust Poisson method. In nearly all cases, the log-binomial method yielded slightly higher power and smaller standard errors than the Robust Poisson method.
Although the Robust Poisson often gives reasonable estimates of the prevalence ratio and is very easy to use, the log-binomial method results in less bias in most common situations, and because it fits the correct model and obtains maximum likelihood estimates, it generally results in slightly higher power, smaller standard errors, and, unlike the Robust Poisson, it always yields estimated prevalences between zero and one.
Objectives: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. Methods: ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD- 10, codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Results: Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. Conclusions: These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
Background: The Injury Severity Score (ISS) has served as the standard summary measure of human trauma for 20 years. Despite its stalwart service, the ISS has two weaknesses: it relies upon the consensus derived severity estimates for each Abbreviated Injury Scale (AIS) injury and considers, at most, only three of an individual patient's injuries, three injuries that often are not even the patient's most severe injuries. Additionally, the ISS requires that all patients have their injuries described in the AIS lexicon, an expensive step that is currently taken only at hospitals with a zealous commitment to trauma care. We hypothesized that a data driven alternative to ISS that used empirically derived injury severities and considered all of an individual patient's injuries would more accurately predict survival. Methods: Survival risk ratios were derived for every International Classification of Disease 9th Edition (ICD-9) injury category (800-959.9) using the North Carolina State Discharge Database experience with 300,000 trauma patients over 5 years. An ICD-9 Injury Severity Score (ICISS) was then defined as the product of all survival risk ratios for an individual patient's traumatic ICD-9 codes. We compared the performance of ISS and ICISS in a group of 3,142 patients accrued at the University of New Mexico Trauma Center over 4 years. These patients had both AIS and ICD-9 descriptors meticulously assigned prospectively by designated trauma data base personnel. Results: ICISS outperformed ISS at a level that was highly statistically significant (p < 0.0001) and may be clinically important: ISS misclassification rate 7.67%, ISS Receiver Operator Characteristic Curve area = 0.872; ICISS misclassification rate 5.95%, ICISS Receiver Operator Characteristic Curve area = 0.921. Moreover, these improvements are largely preserved when ICISS is used in a probability of survival model that includes age, mechanism, and revised trauma score. About half of ICISS's improvement in predictive power is because of its use of an individual patient's worst three injuries regardless of body region. The remainder is because of better modeling of individual injuries and allowing all injuries to contribute to the final score. Conclusions: We conclude that ICISS is a much better predictor of survival than ISS in injured patients. The use of the ICD-9 lexicon may avoid the need for AIS coding, and thus may add an economic incentive to the statistical appeal of ICISS. It is possible that a similar data driven revision of ISS using the AIS vocabulary might perform as well or better than ICISS. Indeed, the actual lexicon used to divide up the injury "landscape" into individual injuries may be of little consequence so long as all injuries are considered and empirically derived SRRs are used to calculate the final injury measure.
This study aimed to (i) describe unplanned readmission rates after injury according to time, reason, and place; (ii) compare observed rates with general population rates, and (iii) identify determinants of 30-day readmission.
Hospital readmissions represent an important burden in terms of mortality, morbidity, and resource use but information on unplanned rehospitalization after injury admissions is scarce.
This multicenter retrospective cohort study was based on adults discharged alive from a Canadian provincial trauma system (1998-2010; n = 115,329). Trauma registry data were linked to hospital discharge data to obtain information on readmission up to 12 months postdischarge. Provincial admission rates were matched to study data by age and gender to obtain expected rates. Determinants of readmission were identified using multiple logistic regression.
Cumulative readmission rates at 30 days, 3 months, 6 months, and 12 months were 5.9%, 10.9%, 15.5%, and 21.1%, respectively. Observed rates persisted above expected rates up to 11 months postdischarge. Thirty percent of 30-day readmissions were due to potential complications of injury compared with 3% for general provincial admissions. Only 23% of readmissions were to the same hospital. The strongest independent predictors of readmission were the number of prior admissions, discharge destination, the number of comorbidities, and age.
Unplanned readmissions after discharge from acute care for traumatic injury are frequent, persist beyond 30 days, and are often related to potential complications of injury. Several patient-, injury-, and hospital-related factors are associated with the risk of readmission. Injury readmission rates should be monitored as part of trauma quality assurance efforts.
Trauma systems are designed to deliver timely and appropriate care. Prehospital triage regulations and interfacility transfer guidelines are the primary determinants of system efficacy. We analyzed the effectiveness of the Florida trauma system in delivering trauma patients to trauma centers over time.
Injured patients were identified by ICD-9 codes from a statewide discharge dataset, and they were categorized as children (less than 16 years old), adult (16 to 65 years old), or elderly (over 65 years old). Severe injury was defined by International Classification Injury Severity Scores (ICISS) < 0.85. Residence ZIP codes were used as a surrogate for injury location.
Severe injury discharges increased at designated trauma centers (DTCs) and decreased at nontrauma centers (NTCs). The proportion of patients with severe injuries discharged from DTCs increased for all age groups, capturing nearly all severely injured children and adults. Access to DTCs was dependent on proximity for severely injured elderly but not for severely injured children and adults.
Triage improved over time, enabling near complete capture of at-risk children and adults independent of DTC proximity. Because distance from a DTC does not limit access for children and adults, existing trauma system resources are sufficient to meet the current demands. Efforts are needed to determine the trauma resource and triage needs of the severely injured elderly.
More than a third of patients with severe injury who receive initial care at nontrauma centers (NTCs) are not transferred to trauma center care. In those who are transferred, significant delays have been described. The availability of specialists, imaging modalities, or critical care resources might significantly affect transfer practices.
We undertook a population-based retrospective cohort study of adult patients with severe injury who were transported from the scene to an NTC. NTCs were characterized based on the availability of general and orthopedic surgeons, computed tomographic scanners, intensive care units, and emergency department staffing. NTCs that had all of the resources were characterized as resource rich, while those with none were characterized as resource limited. We evaluated the relationships between NTC resources and the likelihood and timeliness of interfacility transfer through the use of hierarchical regression modeling.
We identified 15,906 patients with severe injury across 192 NTCs (22% were resource limited, 57% were resource intermediate, and 21% were resource rich). Patients at resource rich centers, as compared with those at resource limited centers, were less likely to be transferred (27% vs. 50%, p < 0.001). This association persisted after adjustment for confounders (odds ratio, 0.66; 95% confidence interval, 0.47-0.92). Among patients who were transferred, median emergency department length of stay (ED-LOS) was 3.5 hours (interquartile range, 1.7-4.6 hours). However, ED-LOS varied significantly because resource rich centers had a greater proportion of patients experiencing prolonged ED-LOS when compared with resource limited centers (31% vs. 15%, p < 0.001). This association also persisted on multivariable analysis (odds ratio, 2.02; 95% confidence interval, 1.19-3.43).
Severely injured patients who received initial care in resource rich NTCs were less likely to be transferred to a trauma center compared with resource limited NTCs. Significant delays in the transfer process were identified. However, patients transferred from resource rich centers were more likely to experience prolonged ED-LOS compared with resource limited NTCs.
Level of evidence:
Epidemiologic study, level II.
Disparities in access to services across genders have been reported in many healthcare settings. The extent to which this occurs in the case of emergency surgical care is unknown. We set out to evaluate whether gender is a determinant of access to trauma center care, particularly in the setting where trauma triage guidelines are strong facilitators to ensure that access is determined by physiologic status and injury characteristics.
Population-based retrospective cohort analysis of severely injured (Injury Severity Score >15) adults surviving to reach hospital. Differential in access to trauma center care was evaluated for females compared with males. Secondary analyses evaluated gender-based differences in direct transport from the scene and transfer from nontrauma centers. The adjusted odd of trauma center care was determined using logistic regression models. Separate models were used to stratify patients based on age, mechanism, and injury severity.
We identified 26,861 severely injured patients; 35% were women. A smaller proportion of females received trauma center care compared with males (49% vs 62%; P < .0001), an association that persisted after adjustment for confounders (odds ratio [OR], 0.87; 95% confidence interval [CI], 0.79-0.96). Emergency medical service personnel were less likely to transport females from the field to a trauma center compared with males (OR, 0.88; 95% CI, 0.81-0.97). Similarly, physicians were less likely to transfer females to trauma centers compared with males (OR, 0.85; 95% CI, 0.73-0.99).
Severely injured women were less likely to be directed to a trauma center across 2 types of providers. The reasons for this differential in access might be related to perceived difference in injury severity, likelihood of benefiting from trauma center care, or subconscious gender bias.
Despite decades of trauma system development, many severely injured patients fail to reach a trauma center for definitive care. The purpose of this study was to define the regions served by Florida's designated trauma centers and define the geographic distribution of severely injured patients who do not access the state's trauma system.
Severely injured patients discharged from Florida hospitals were identified using the 2009 Florida Agency for Health Care Administration database. The home zip codes of patients discharged from trauma and nontrauma center hospitals were used as a surrogate for injury location and plotted on a map. A radial distance containing 75% of trauma center discharges defined trauma center catchment area.
Only 52% of severely injured patients were discharged from trauma centers. The catchment areas varied from 204 square miles to 12,682 square miles and together encompassed 92% state's area. Although 93% of patients lived within a trauma center catchment area, the proportion treated at a trauma center in each catchment area varied from 13% to 58%. Mapping of patient residences identified regions of limited access to the trauma system despite proximity to trauma centers.
The distribution of severely injured patients who do not reach trauma centers presents an opportunity for trauma system improvement. Those in proximity to trauma centers may benefit from improved and secondary triage guidelines and interfacility transfer agreements, whereas those distant from trauma centers may suggest a need for additional trauma system resources.
Epidemiologic study, level III.
This article advocates for the wider use of relative importance indices as a supplement to multiple regression analyses. The
goal of such analyses is to partition explained variance among multiple predictors to better understand the role played by
each predictor in a regression equation. Unfortunately, when predictors are correlated, typically relied upon metrics are
flawed indicators of variable importance. To that end, we highlight the key benefits of two relative importance analyses,
dominance analysis and relative weight analysis, over estimates produced by multiple regression analysis. We also describe
numerous situations where relative importance weights should be used, while simultaneously cautioning readers about the limitations
and misconceptions regarding the use of these weights. Finally, we present step-by-step recommendations for researchers interested
in incorporating these analyses in their own work and point them to available web resources to assist them in producing these
KeywordsRelative importance–Predictor importance–Relative weight analysis–Dominance analysis–Multiple regression
Hospital administrative databases are a useful source of population-level data on injured patients; however, these databases use the International Classification of Diseases (ICD) system, which does not provide a direct means of estimating injury severity. We created and validated a crosswalk to derive Abbreviated Injury Scale (AIS) scores from injury-related diagnostic codes in the tenth revision of the ICD (ICD-10).
We assessed the validity of the crosswalk using data from the Ontario Trauma Registry Comprehensive Data Set (OTRCDS). The AIS and Injury Severity Scores (ISS) derived using the algorithm were compared with those assigned by expert abstractors. We evaluated the ability of the algorithm to identify patients with AIS scores of 3 or greater. We used κ and intraclass correlation coefficients (ICC) as measures of concordance.
In total, 10 431 patients were identified in the OTRCDS. The algorithm accurately identified patients with at least 1 AIS score of 3 or greater (κ 0.65), as well as patients with a head AIS score of 3 or greater (κ 0.78). Mapped and abstracted ISS were similar; ICC across the entire cohort was 0.83 (95% confidence interval 0.81-0.84), indicating good agreement. When comparing mapped and abstracted ISS, the difference between scores was 10 or less in 87% of patients. Concordance between mapped and abstracted ISS was similar across strata of age, mechanism of injury and mortality.
Our ICD-10-to-AIS algorithm produces reliable estimates of injury severity from data available in administrative databases. This algorithm can facilitate the use of administrative data for population-based injury research in jurisdictions using ICD-10.
The evaluation of care and the surveillance of disease are important in respect to cardiovascular disease because it is prevalent and costly. In Canada, medico-administrative hospital data are readily available, continuously updated, and offer comprehensive coverage of the patient population. However, there is concern about the quality of the information.
The reliability and predictive capability of comorbidity data contained within Québec's hospital discharge database were assessed in comparison with data collected by clinical medical record reabstraction in a sample of 1989 patients hospitalized from 2002 to 2006 in a mix of 13 hospitals. Patients either had a principal diagnosis of myocardial infarction or underwent angioplasty or bypass surgery. Twenty-one comorbidities included in the Charlson comorbidity index or known to be associated with mortality were validated via medical record reabstraction.
Of 14 comorbidities with > 2% prevalence, 8 had excellent agreement with medical record review (κ > 0.8) while 6 had substantial agreement (κ > 0.6). In general, positive predictive values were high, while measures of sensitivity were more variable. Univariate associations between comorbidities and 30-day and 1-year mortality were generally similar in the 2 data sources. Comorbidities retained in the final multivariate stepwise regression models from each data source were almost identical, as were the 2 models' abilities to predict mortality.
Hospital discharge data in Québec are, in general, reliably coded and compare favourably with clinical medical record review in their ability to predict mortality. It appears sufficiently reliable to provide useful information about clinical outcomes of cardiac care and to identify problems that warrant investigation.
Mortality is widely used as a performance indicator to evaluate the quality of trauma care, but there is no consensus on the most appropriate definition. Our objective was to evaluate the influence of the definition of mortality in terms of the place (in-hospital or postdischarge) and time (30 days and 3, 6, and 12 months) of death on the results of trauma center performance evaluations according to the patients' ages.
Multicenter retrospective cohort study.
Inclusive Canadian provincial trauma system.
Adults admitted between 1999 and 2006 with a maximum abbreviated injury severity score≥3 (n=47,261).
Trauma registry data were linked to vital statistics data to obtain mortality up to 12 months postadmission. Observed mortality was compared to that expected according to provincial population mortality rates. Trauma center performance was evaluated with risk-adjusted mortality estimates. Agreement between performance results based on different definitions of mortality was evaluated with correlation coefficients; >.9 was considered acceptable. Analyses were stratified by predefined age categories (16-64, 65-84, and ≥85 yrs). A total of 3,338 patients (7%) died in-hospital, and 1,794 patients (4%) died postdischarge. Among patients 16-64 yrs old, 30-day hospital mortality represented 83% of all deaths and correlation coefficients across all definitions of mortality were >.9. In patients 65-84 yrs old, 30-day hospital mortality represented 52% of all deaths, observed mortality reached expected rates at around 6 months, and agreement across mortality definitions was low.
We observed an important variation in performance evaluation results across definitions of mortality, specifically in patients aged≥65 yrs. Half of the deaths among elders occurred later than 30 days following admission, including a significant number postdischarge. Results suggest that if performance evaluations include elderly patients, data on postdischarge mortality up to 6 months following admission are required.
Although efforts have been made to address disparities in access to trauma care in the past decade, there is little evidence to show if utilization has changed. We use patient-level data to describe the changes in utilization of trauma centers (TCs) in an 8-year period in California.
We analyzed all statewide trauma admissions (n = 752,706) using the California Office of Statewide Health Planning and Discharge Patient Discharge Database from the period of 1999 to 2006, and determined the trends in admissions and place of care.
The proportion of severe injuries admitted increased by 3.6% (p < 0.05), with a concomitant rise in the proportion of patients with trauma to TCs, from 39.3% (95% CI: 39.0%-39.7%) to 49.7% (49.4%-50.0%). Within the severely injured with injury severity scores (ISS) >15, 82.4% were treated in a TC if they resided in a county with a TC, compared with 30.8% of patients who did not live in a county with a TC. After adjustment, patients living greater than 50 miles away from a TC still had a likelihood ratio of 0.11 (p < 0.0001) of receiving care in a TC compared with those less than 10 miles away. Similarly, even severely injured patients not living in a county with a TC had a likelihood ratio of 0.35 (p < 0.0001) of being admitted to a TC compared with those residing in counties with TCs.
Admissions to TCs for all categories of injury severity are increasing. There remains, however, a large disparity in TC care depending on geographical distance and availability of a TC within county.
To determine whether age bias is a factor in triage errors.
Retrospective analysis of 10 years (1995-2004) of prospectively collected data in the statewide Maryland Ambulance Information System followed by surveys of emergency medical services (EMS) and trauma center personnel at regional EMS conferences and level I trauma centers, respectively.
Trauma patients were defined as those who met American College of Surgeons physiology, injury, and/or mechanism criteria and were subjectively declared priority I status by EMS personnel.
Undertriage, defined as when trauma patients were not transported to a state-designated trauma center.
The registry analysis identified 26 565 trauma patients. The undertriage rate was significantly higher in patients aged 65 years or older than in younger patients (49.9% vs 17.8%, P < .001). On multivariate analysis, this decrease in trauma center transports was found to start at age 50 years (odds ratio, 0.67; 95% confidence interval, 0.57-0.77), with another decrease at age 70 years (odds ratio, 0.45; 95% confidence interval, 0.39-0.53) compared with patients younger than 50 years. A total of 166 respondents participated in the follow-up surveys and ranked the top 3 causal factors for this undertriage as inadequate training, unfamiliarity with protocol, and possible age bias.
Even when trauma is recognized and acknowledged by EMS, providers are consistently less likely to consider transporting elderly patients to a trauma center. Unconscious age bias, in both EMS in the field and receiving trauma center personnel, was identified as a possible cause.
The Injury Severity Score (ISS) has served as the standard summary measure of human trauma for 20 years. Despite its stalwart service, the ISS has two weaknesses: it relies upon the consensus derived severity estimates for each Abbreviated Injury Scale (AIS) injury and considers, at most, only three of an individual patient's injuries, three injuries that often are not even the patient's most severe injuries. Additionally, the ISS requires that all patients have their injuries described in the AIS lexicon, an expensive step that is currently taken only at hospitals with a zealous commitment to trauma care. We hypothesized that a data driven alternative to ISS that used empirically derived injury severities and considered all of an individual patient's injuries would more accurately predict survival.
Survival risk ratios were derived for every International Classification of Disease 9th Edition (ICD-9) injury category (800-959.9) using the North Carolina State Discharge Database experience with 300,000 trauma patients over 5 years. An ICD-9 Injury Severity Score (ICISS) was then defined as the product of all survival risk ratios for an individual patient's traumatic ICD-9 codes. We compared the performance of ISS and ICISS in a group of 3,142 patients accrued at the University of New Mexico Trauma Center over 4 years. These patients had both AIS and ICD-9 descriptors meticulously assigned prospectively by designated trauma data base personnel.
ICISS outperformed ISS at a level that was highly statistically significant (p < 0.0001) and may be clinically important: ISS misclassification rate 7.67%, ISS Receiver Operator Characteristic Curve area = 0.872; ICISS misclassification rate 5.95%, ICISS Receiver Operator Characteristic Curve area = 0.921. Moreover, these improvements are largely preserved when ICISS is used in a probability of survival model that includes age, mechanism, and revised trauma score. About half of ICISS's improvement in predictive power is because of its use of an individual patient's worst three injuries regardless of body region. The remainder is because of better modeling of individual injuries and allowing all injuries to contribute to the final score.
We conclude that ICISS is a much better predictor of survival than ISS in injured patients. The use of the ICD-9 lexicon may avoid the need for AIS coding, and thus may add an economic incentive to the statistical appeal of ICISS. It is possible that a similar data driven revision of ISS using the AIS vocabulary might perform as well or better than ICISS. Indeed, the actual lexicon used to divide up the injury "landscape" into individual injuries may be of little consequence so long as all injuries are considered and empirically derived SRRs are used to calculate the final injury measure.
The primary purpose of this study was to evaluate whether severely injured geriatric patients were as likely to be treated at designated trauma centers (TCs) within the statewide trauma system. The secondary objective was to compare the demographic and injury characteristics of severely injured older and young patients who received care in TCs with the characteristics of those patients cared for in non-TCs.
The authors reviewed files for all acute injury discharges in Pennsylvania for 1997. Injury diagnoses were mapped to Abbreviated Injury Scale (AIS) scores using ICDMAP software; the Injury Severity Score (ISS) was computed. The frequency of hospital discharges for injury from TC and non-TC hospitals in both the older (E) (>/=65 years) and younger (Y) (<65 years) groups were computed, and compared using chi-square testing for significance. Logistic regression was performed to assess the influence of various factors. Severity of injury was controlled for with both ISS and Maximum AIS (MAIS).
107,358 patients were admitted to hospitals in the state because of injury; 8,980 had an ISS > 15; 5,855 were Y and 3,125 were E. Forty-seven percent of the Y patients received TC care compared with only 36.6% of the E patients (p < 0.001). Logistic regression analysis showed that age was a strongly negative predictor for TC care when injury severity was controlled.
Seriously injured older patients were less likely to receive care in a trauma center than younger patients.
The resources needed and those available to support trauma care for a given region are currently unknown. Resource use and availability were evaluated for injured subjects across a large sample of the United States.
This population-based study of trauma-related discharges in 18 states represented all four geographic regions of the United States. Hospital discharge and bed-utilization rates as a function of injury severity were assessed. Resource availability was evaluated by determining state trauma center density.
This study evaluated 523,780 trauma patients discharged from 2,317 hospitals. The discharge rate for all trauma was 412 per 100,000 person-years, whereas the rate for major trauma was only 44 per 100,000 person-years. More than one third of the patients with major trauma received care at centers not designated for trauma care. The hospital bed utilization rate was 2,095 days per 100,000 person-years. The availability of trauma centers varied greatly across states, ranging from 0.9 to 6.6 centers per million population.
A substantial minority of major trauma patients in the United States are treated in nondesignated trauma centers. The variability in the availability of trauma resources indicates a lack of consensus with respect to the resources required for trauma system implementation.
Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms.
ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms.
Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm.
These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.
Trauma systems decrease injury-related mortality, but not all systems have the same configuration. In some systems, nearly all acute care hospitals participate to the extent that their resources allow (inclusive systems), whereas in others, relatively few high-level centers participate (exclusive systems). We postulate that inclusive systems assure that severely injured patients are more likely to be triaged to a level I or II regional trauma center, and this greater degree of participation would lead to lower mortality.
We used administrative discharge data for 2001 in 24 states with formal systems, and we included all urgently hospitalized adults with an Injury Severity Score>or=16. We categorized states by trauma system configuration ("exclusive", "more inclusive", "most inclusive") based on the proportion of all hospitals designated as a Level I through V trauma center (0-13%, 14-37%, 38-100%, respectively). We compared the rates of triage to a regional trauma center and inpatient death in inclusive states relative to exclusive states, while adjusting for patient- and state-level factors.
Out of 61,496 patients, 40,706 (66.2%) were hospitalized at regional trauma centers. Inpatient mortality was 14.7%. After adjusting for patient age, primary payer status, and system maturity, the odds of triage to a regional trauma center were similar in inclusive and exclusive systems. After adjusting for primary payer status, mechanism of injury, and system maturity, the odds of death were similar in more inclusive and exclusive systems (odds ratio, 0.93; 95% confidence interval, 0.80-1.08) but were significantly lower in the most inclusive systems (odds ratio, 0.77; 95% confidence interval, 0.60-0.99).
Severely injured trauma patients have greater inpatient survival in inclusive trauma systems even though they are no more likely to be hospitalized at a regional trauma center. Consideration should be given to continuing implementation of systems with an inclusive configuration, especially in light of other theoretical benefits of these systems, such as better dispersing of trauma care resources in the event of natural disasters or terrorist events.
The majority of inpatient trauma care resources are consumed by a small proportion of severely injured patients.
Hospital lengths of stay (LOS), resource consumption, and postdischarge placement were abstracted from the institutional trauma registry.
Patients (n = 4,070) were evaluated by the trauma service during the study period. The overall mean LOS was 4.4 days, and beds were occupied on 18,005 days. Two hundred forty-four (6%) patients remained in the hospital >14 days after injury and occupied beds on 8,560 (47%) days. These patients were older, more severely injured, and required proportionately more intensive care unit and operative care. Injuries to the head, abdomen, and extremities were independently associated with longer LOS. Most patients with longer LOS were placed in long-term acute care or received home nursing care after discharge.
Almost half of inpatient trauma bed-days are occupied by a small proportion of patients with long-term care needs.
Although studies have shown that treatment at a trauma center reduces a patient's risk of dying following major trauma, important questions remain as to the effect of trauma centers on functional outcomes, especially among patients who have sustained major lower-limb trauma.
Domain-specific scores on the Medical Outcomes Study Short Form Health Survey (SF-36) supplemented by scores on the mobility subscale of the Musculoskeletal Function Assessment (MFA) and the Revised Center for Epidemiologic Studies Depression Scale (CESD-R) were compared among patients treated in eighteen hospitals with a level-I trauma center and fifty-one hospitals without a trauma center. Included in the study were 1389 adults, eighteen to eighty-four years of age, with at least one lower-limb injury with a score of >/=3 points according to the Abbreviated Injury Scale (AIS). To account for the competing risk of death, we estimated the survivors' average causal effect. Estimates were derived for all patients with a lower-limb injury and separately for a subset of patients without associated injuries of the head or spinal cord.
For patients with a lower-limb injury resulting from a high-energy force, care at a trauma center yielded modest but clinically meaningful improvements in physical functioning and overall vitality at one year after the injury. After adjustment for differences in case mix and the competing risk of death, the average differences in the SF-36 physical functioning and vitality scores and the MFA mobility score were 7.82 points (95% confidence interval: 2.65, 12.98), 6.80 points (95% confidence interval: 2.53, 11.07), and 6.31 points (95% confidence interval: 0.25, 12.36), respectively. These results were similar when the analysis was restricted to patients without associated injuries to the head or spine. Treatment at a trauma center resulted in negligible differences in outcome for the subset of patients with injuries resulting from low-energy forces.
This study provides evidence that patients who sustain high-energy lower-limb trauma benefit from treatment at a level-I trauma center.
Anatomic injury severity scores can be grouped into two classes; consensus-derived and data-derived. The former, including the Injury Severity Score (ISS), the New Injury Severity Score (NISS), and the Anatomic Profile Score (APS), are based on the severity score of the Abbreviated Injury Scale (AIS), assigned by clinical experts. The latter, including the International Classification of Disease Injury Severity Score (ICISS) and the Trauma Registry Abbreviated Injury Scale Score (TRAIS) are based on survival probabilities calculated in large trauma databases. We aimed to compare the predictive accuracy of consensus-derived and data-derived severity scores when considered alone and in combination with age and physiologic status.
Analyses were based on 25,111 patients from the trauma registries of the four Level I trauma centers in the province of Quebec, Canada, abstracted between April 1998 and March 2005. The predictive validity of each severity score was evaluated in logistic regression models predicting hospital mortality using measures of discrimination (Area Under the Receiver Operating Characteristics curve [AUC]) and calibration (Hosmer-Lemeshow statistic [HL]).
Data-derived scores had consistently better predictive accuracy than consensus-derived scores in univariate models (p < 0.0001) but very little difference between scores was observed in models including information on age and physiologic status. The difference in AUC between the least accurate severity score (ISS) and the most accurate severity score (TRAIS) was 15% in anatomic-only models but fell to 2% in models including age and physiologic status.
Data-derived scores provide more accurate mortality prediction than consensus-derived scores do when only anatomic injury severity is considered but offer little advantage if age and physiologic status are taken into account. This may be because of the fact that data-derived scores are not an independent measure of anatomic injury severity.
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