Canadian Benchmarks in Trauma

Bergeron, Éric; Simons, Richard K.; Linton, Cassandra; Fang, Yang; Tallon, John M.; Stewart, Tanya Charyk; Guia, Nicole de; Stephens, Mary

doi:10.1097/01.ta.0000202483.67135.f3

Cited by 11 publications

(17 citation statements)

References 22 publications

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“…It would be interesting to continuing the idea of Bergeron et al 78 idea and benchmark the different hospitals in a nationwide study within the same level of care (trauma centre, non-trauma centre), and by comparing different levels of trauma care with each other, particularly as Celso et al in 2006 99 estimated the survival benefit in trauma centres at a staggering 15%.…”

Section: Further Directions For Researchmentioning

confidence: 99%

Risk-Adjustment for Swedish In-Hospital Trauma Mortality using International Classification of disease Injury Severity Score (ICISS) : issues with description and methods

Larsen

2019

Linköping University Medical Dissertations

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1ABSTRACT Introduction Different methods have been used to describe the epidemiology of trauma with varying results. Crude mortality outcome data differ significantly from risk-adjusted information. A previous standard method for risk-adjustment in trauma was the Injury Severity Score (ISS), although it has several shortcomings. In this thesis I examine Swedish injury statistics from an epidemiological perspective using crude and risk-adjusted mortality, and to adjust for injury I used the International Classification of disease Injury Severity Score (ICISS). The groups of most lethal injuries (fall, traffic, and assault) were examined separately using an ICISS mortality prediction model that focused particularly on the effects on the prediction of mortality by adding coexisting conditions (comorbidity) to it. Differences in mortality between the sexes and changes over time were tested separately. Material and MethodsData from all patients with ICD-10 based diagnoses of injury (ICD-10: V01 to Y36) in the Swedish National Patient Registry and Cause of Death Registry were collected from 1999 to 2012 and used for assessment of mortality and comorbidity. A subgroup (patients in hospital) from 2001-2011 were selected as the study group. Their injuries were in the subgroups of falls, traffic, and assaults, and are the focus of this thesis. Mortality within 30 days of injury was used as the endpoint. The severity of injury was adjusted for using the ICISS, which was first described by Osler et al. The model was also adjusted for age, sex, and comorbidities. ResultsThe study group comprised 815 846 patients (of whom 17 721 died). There was a decrease over time in injuries caused by falls and traffic (coefficient -4.71, p=0.047 and coefficient -5.37, p<0.001), whereas there was no change in assault-related injuries/100 000 inhabitants. The risk-adjusted 30-day mortality showed a decrease in injuries related to traffic and assault (OR 0.95, p<0.001 and OR 0.93, p=0.022) whereas for falls it remained unchanged. There was also a risk-adjusted survival benefit for women, which increased with increasing age. Adjusting for comorbidities made the prediction of 30-day mortality by the ICISS model better (accuracy, calibration, and discrimination). However, most of this effect was found to be the result of the other characteristics of the fall related injury group (they were older, and had more coexisting conditions). ConclusionDuring a 10-year period, there has been a significant overall decrease in crude as well as risk-adjusted mortality for these three injury groups combined. Within these groups there is a clear, risk-adjusted, female survival advantage. The ICISS model for the prediction of mortality improves when comorbidities are added, but this effect is minor and seen mainly among the injuries caused by falls, where comorbidity is significant. The ICISS method was a valuable adjunct in the investigation of data on Swedish mortality after injury that has been gathered from health care registry data.Abstract .

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Section: Further Directions For Researchmentioning

confidence: 99%

Risk-Adjustment for Swedish In-Hospital Trauma Mortality using International Classification of disease Injury Severity Score (ICISS) : issues with description and methods

Larsen

2019

Linköping University Medical Dissertations

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“…There were an insufficient number of in-hospital deaths to calculate diagnosis-specific injury survival probabilities and make any meaningful comparisons with provincial or national survival probability data. 21 Outcomes for patients injured in NW BC and who were subsequently transferred to tertiary trauma facilities in Van- couver were assessed using the BCTR. Patients transferred to Vancouver General Hospital from the NHA with a diagnosis of severe head injury (Glasgow Coma Scale score 3-8) had a mortality of 23.5% compared with a mortality of 43.9% for similar patients with severe head injury admitted directly from scene to Vancouver General Hospital.…”

Section: In-community Trauma Carementioning

confidence: 99%

A Population-Based Analysis of Injury-Related Deaths and Access to Trauma Care in Rural-Remote Northwest British Columbia

Simons

Brasher

Taulu

et al. 2010

Journal of Trauma: Injury, Infection &Amp; Critical Care

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“…For example, in a study of emergency department visits for supraventricular tachycardia (SVT), the selection of cases of SVT was based on the descendants of 2 highlevel ICD codes. Analogously, [132] calculated survival risk ratios for trauma patients for various groups of hierarchically-defined diagnostic categories in ICD and [133] used highlevel ICD codes in a study of stroke hospitalization over time. Many other ontologies are used for data selection purposes, including SNOMED CT, used for querying clinical data warehouses [134].…”

Section: Data Selectionmentioning

confidence: 99%

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

2008

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SummaryObjectives: To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. Methods: Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. Results: The ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery). Conclusions: Ontologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed.

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Canadian Benchmarks in Trauma

Cited by 11 publications

References 22 publications

Risk-Adjustment for Swedish In-Hospital Trauma Mortality using International Classification of disease Injury Severity Score (ICISS) : issues with description and methods

Risk-Adjustment for Swedish In-Hospital Trauma Mortality using International Classification of disease Injury Severity Score (ICISS) : issues with description and methods

A Population-Based Analysis of Injury-Related Deaths and Access to Trauma Care in Rural-Remote Northwest British Columbia

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

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