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Objective: Evaluate interhospital variation in resource use for in-hospital injury deaths., Background: Significant variation in resource use for end-of-life care has been observed in the US for chronic diseases. However, there is an important knowledge gap on end-of-life resource use for trauma patients., Methods: We conducted a multicenter, retrospective cohort study of injury deaths following hospitalization in any of the 57 trauma centers in a Canadian trauma system (2013-2016). Resource use intensity was measured using activity-based costing (2016 $CAN) according to time of death (72 h, 3-14 d, ≥14 d). We used multilevel log-linear regression to model resource use and estimated interhospital variation using intraclass correlation coefficients (ICC)., Results: Our study population comprised 2044 injury deaths. Variation in resource use between hospitals was observed for all 3 time frames (ICC = 6.5%, 6.6%, and 5.9% for < 72 h, 3-14 d, and ≥14 d, respectively). Interhospital variation was stronger for allied health services (ICC = 18 to 26%), medical imaging (ICC = 4 to 10%), and the ICU (ICC = 5 to 6%) than other activity centers. We observed stronger interhospital variation for patients < 65 years of age (ICC = 11 to 34%) than those ≥65 (ICC = 5 to 6%) and for traumatic brain injury (ICC = 5 to 13%) than other injuries (ICC = 1 to 8%)., Conclusions: We observed variation in resource use intensity for injury deaths across trauma centers. Strongest variation was observed for younger patients and those with traumatic brain injury. Results may reflect variation in level of care decisions and the incidence of withdrawal of life-sustaining therapies., Competing Interests: The authors report no conflicts of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Injuries represent one of the leading causes of preventable morbidity and mortality. For countries with ageing populations, admissions of injured older patients are increasing exponentially. Yet, we know little about hospital resource use for injured older patients. Our primary objective was to evaluate inter-hospital variation in the risk-adjusted resource use for injured older patients. Secondary objectives were to identify the determinants of resource use and evaluate its association with clinical outcomes., Methods: We conducted a multicenter retrospective cohort study of injured older patients (≥65 years) admitted to any trauma centres in the province of Quebec (2013-2016, N = 33,184). Resource use was estimated using activity-based costing and modelled with multilevel linear models. We conducted separate subgroup analyses for patients with trauma and fragility fractures., Results: Risk-adjusted resource use varied significantly across trauma centres, more for older patients with fragility fractures (intra-class correlation coefficients [ICC] = 0.093, 95% CI [0.079, 0.102]) than with trauma (ICC = 0.047, 95% CI = 0.035-0.051). Risk-adjusted resource use increased with age, and the number of comorbidities, and varied with discharge destination (P < 0.001). Higher hospital resource use was associated with higher incidence of complications for trauma (Pearson correlation coefficient [r] = 0.5, 95% CI = 0.3-0.7) and fragility fractures (r = 0.5, 95% CI = 0.3-0.7) and with higher mortality for fragility fractures (r = 0.4, 95% CI = 0.2-0.6)., Conclusions: We observed significant inter-hospital variations in resource use for injured older patients. Hospitals with higher resource use did not have better clinical outcomes. Hospital resource use may not always positively impact patient care and outcomes. Future studies should evaluate mechanisms, by which hospital resource use impacts care., (© The Author(s) 2019. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Evaluate the predictive validity of complications derived using expert consensus methodology to monitor the quality of trauma care. Secondary objectives were to assess the predictive validity of complications not selected by consensus and identify determinants of complications., Background: A list of complications to monitor the quality of trauma care has recently been derived using Delphi consensus methodology. However, the predictive validity of consensus complications has not yet been demonstrated., Methods: We conducted a multicenter cohort study of adults admitted to the 57 adult trauma centers of a Canadian integrated trauma system (2007-2012; n = 84,216). Multiple generalized linear models were used to assess the influence of complications on mortality and acute care length of stay (LOS) and to identify determinants of consensus complications., Results: The presence of at least 1 consensus complication was associated with a 2.7-fold [95% confidence interval (CI): 2.45-2.90] and 2.2-fold (95% CI: 2.11-2.19) increase in the odds of mortality and mean LOS, respectively. Nonselected complications were associated with no increase in mortality (odds ratio = 0.90, 95% CI: 0.80-1.01) and a 60% increase in LOS (geometric mean ratio = 1.60, 95% CI: 1.57-1.62). Patient-related factors and factors related to treatment explained 66% and 34% of the variation in complication rates, respectively., Conclusions: In addition to the face and content validity ensured by consensus methodology, this study suggests that consensus complications have good predictive validity. Monitoring these complications as part of quality improvement activities would provide an opportunity to improve outcome and resource use for injury admissions.

Objective: To derive and internally validate a quality indicator (QI) for acute care length of stay (LOS) after admission for injury., Background: Unnecessary 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., Methods: This 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., Results: The 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)., Conclusions: We 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.

Objective: To describe acute care length of stay (LOS) over all consecutive hospitalizations for the injury and according to level of care [intensive care unit (ICU), intermediate care, general ward], compare observed and expected LOS, and identify predictors of LOS., Background: Prolonged LOS has important consequences in terms of costs and outcome, yet detailed information on LOS after trauma is lacking., Methods: This multicenter retrospective cohort study was based on adults discharged alive from a Canadian trauma system (1999-2010; n = 126,513). Registry data were used to calculate index LOS (LOS in trauma center with highest designation level) and were linked to hospital discharge data to calculate total LOS (all consecutive hospitalizations for the injury). Expected LOS was obtained by matching general provincial discharge statistics to study data by year, age, and sex. Potential predictors of LOS were evaluated using linear regression., Results: Mean index and total LOS were 8.6 and 9.4 days, respectively. ICU, intermediate care unit, and general ward care constituted 8.9%, 2.5%, and 88.6% of total hospital days. Observed mean index and ICU LOS in our trauma patients were 2.9 and 1.3 days longer than expected LOS (P < 0.0001). The strongest determinants of index LOS were discharge destination, age, transfer status, and injury severity., Conclusions: Results suggest that acute care LOS after injury is underestimated when only information on the index hospitalization is used and that ICU or intermediate care constitute an important part of LOS. This information should be used to inform the development of an informative and actionable quality indicator.

Background: Unplanned readmissions represent 20% of all admissions and cost $12 billion annually in the United States. Despite the burden of injuries for the health care system, no quality indicator (QI) based on readmissions is available to evaluate trauma care. The objective of this study was to derive and internally validate a QI for a 30-day unplanned hospital readmission to evaluate trauma care., Methods: We performed a multicenter retrospective cohort study in a Canadian integrated provincial trauma system. We included adults admitted to any of the 57 provincial trauma centers between 2005 and 2010 (n = 57,524). Data were abstracted from the provincial trauma registry and linked to the hospital discharge database. The primary outcome was unplanned readmission to an acute care hospital within 30 days of discharge. Candidate risk factors were identified by expert consensus and selected for derivation of the risk adjustment model using bootstrap resampling. The validity of the QI was evaluated in terms of interhospital discrimination, construct validity, and forecasting., Results: The risk adjustment model includes patient age, sex, the Injury Severity Score (ISS), region of the most severe injury, and 11 comorbid conditions. The QI discriminates well across trauma centers (coefficient of variation, 0.02) and is correlated with QIs that measure hospital performance in terms of clinical processes (r = -0.38), risk-adjusted mortality (r = 0.32), and complication rates (r = 0.38). In addition, performance in 2005 to 2007 was predictive of performance in 2008 to 2010 (r = 0.59)., Conclusion: We have developed a QI based on risk-adjusted 30-day rates of unplanned readmission, which can be used to evaluate trauma care with routinely collected data. The QI is based on a comprehensive risk adjustment model with good internal and temporal validity and demonstrates good properties in terms of discrimination, construct validity, and forecasting. This research represents an essential step toward reducing unplanned readmission rates to improve resource use and patient outcomes following injury., Level of Evidence: Prognostic study, level III.

Objective: 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., Background: Hospital readmissions represent an important burden in terms of mortality, morbidity, and resource use but information on unplanned rehospitalization after injury admissions is scarce., Methods: 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., Results: 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., Conclusions: 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.