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Canadian benchmarks for acute injury care
Lynne Moore1, David Evans1, Natalie L Yanchar1
1From the Department of Social and Preventative Medicine, Université Laval, Québec, Que. (Moore); the Axe Santé des Populations et Pratiques Optimales en Santé, Traumatologie-Urgence-Soins intensifs, Centre de Recherche du CHU de Québec, Hôpital de l'Enfant-Jésus, Université Laval, Québec, Que. (Moore, Lauzier, Turgeon); the Department of Surgery, University of Calgary, Calgary, Alta. (Yanchar); the Department of Surgery, University of British Columbia, Vancouver, BC (Evans, Thakore, Hameed); the Department of Critical Care Medicine, Medicine and Community Health Sciences (Stelfox), O'Brien Institute for Public Health, University of Calgary, Calgary, Alta. (Stelfox); the Department of Surgery, Division of General Surgery and Division of Critical Care, University of Calgary, Calgary, Alta. (Kortbeek); the Institut national d'excellence en santé et en services sociaux, Québec, Que. (Clément); the Department of Surgery, Université Laval, Québec, Que. (Clément); and the Department of Anesthesiology and Critical Care Medicine, Division of Critical Care Medicine, Université Laval, Québec, Que. (Lauzier, Turgeon).
Background:
Acute care injury outcomes vary substantially across Canadian provinces and trauma centres. Our aim was to develop Canadian benchmarks to monitor mortality and hospital length of stay (LOS) for injury admissions.
Methods:
Benchmarks were derived using data from the Canadian National Trauma Registry on patients with major trauma admitted to any level I or II trauma centre in Canada and from the following patient subgroups: isolated traumatic brain injury (TBI), isolated thoracoabdominal injury, multisystem blunt injury, age 65 years or older. We assessed predictive validity using measures of discrimination and calibration, and performed sensitivity analyses to assess the impact of replacing analytically complex methods (multiple imputation, shrinkage estimates and flexible modelling) with simple models that can be implemented locally.
Results:
The mortality risk adjustment model had excellent discrimination and calibration (area under the receiver operating characteristic curve 0.886, Hosmer-Lemeshow 36). The LOS risk-adjustment model predicted 29% of the variation in LOS. Overall, observed:expected ratios of mortality and mean LOS generated by an analytically simple model correlated strongly with those generated by analytically complex models (r > 0.95, κ on outliers > 0.90).
Conclusion:
We propose Canadian benchmarks that can be used to monitor quality of care in Canadian trauma centres using Excel (see the appendices, available at canjsurg.ca). The program can be implemented using local trauma registries, providing that at least 100 patients are available for analysis.
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