• Lynne Moore, Henry Thomas Stelfox, Alexis F Turgeon, Avery B Nathens, André Lavoie, Marcel Emond, Gilles Bourgeois, and Xavier Neveu.
• *Department of Social and Preventative Medicine, Université Laval, Quebec, Canada †Axe Santé des Populations-Pratiques Optimales en Santé (Population Health-Optimal Health Practices Research Unit), Traumatologie-Urgence-Soins Intensifs (Trauma-Emergency-Critical Care Medicine), Centre de Recherche du Centre Hospitalier Universitaire de Quebec (CHU de Québec-Hôpital de l'Enfant-Jésus), Université Laval, Quebec, Canada ‡Department of Critical Care Medicine, Medicine and Community Health Sciences, Institute for Public Health, University of Calgary, Calgary, Alberta, Canada §Department of Anesthesiology, Division of Critical Care Medicine, Quebec, Canada ‖Department of Surgery, St Michael's Hospital, University of Toronto, Toronto, Canada ¶Institut National d'excellence en santé et en Services Sociaux, Montréal, Quebec, Canada.
• Ann. Surg.. 2014 Dec 1;260(6):1121-7.

ObjectiveTo derive and internally validate a quality indicator (QI) for acute care length of stay (LOS) after admission for injury.BackgroundUnnecessary hospital days represent an estimated 20% of total LOS implying an important waste of resources as well as increased patient exposure to hospital-acquired infections and functional decline.MethodsThis study is based on a multicenter, retrospective cohort from a Canadian provincial trauma system (2005-2010; 57 trauma centers; n = 57,524). Data were abstracted from the provincial trauma registry and the hospital discharge database. Candidate risk factors were identified by expert consensus and selected for model derivation using bootstrap resampling. The validity of the QI was evaluated in terms of interhospital discrimination, construct validity, and forecasting.ResultsThe risk adjustment model explains 37% of the variation in LOS. The QI discriminates well across trauma centers (coefficient of variation = 0.02, 95% confidence interval: 0.011-0.028) and is correlated with the QI on processes of care (r = -0.32), complications (r = 0.66), unplanned readmissions (r = 0.38), and mortality (r = 0.35). Performance in 2005 to 2007 was predictive of performance in 2008 to 2010 (r = 0.80).ConclusionsWe have developed a QI on the basis of risk-adjusted LOS to evaluate trauma care that can be implemented with routinely collected data. The QI is based on a robust risk adjustment model with good internal and temporal validity, and demonstrates good properties in terms of discrimination, construct validity, and forecasting. This QI can be used to target interventions to reduce LOS, which will lead to more efficient resource use and may improve patient outcomes after injury.

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