Gomez David, Sarrami Pooria, Singh Hardeep, Balogh Zsolt J, Dinh Michael, Hsu Jeremy
Department of Surgery, Division of General Surgery, University of Toronto, ON, Canada; Trauma Service, Westmead Hospital, Westmead, Sydney, NSW, Australia.
New South Wales Institute of Trauma and Injury Management, Sydney, NSW, Australia; South Western Sydney Clinical School, University of New, South Wales, NSW, Australia.
Injury. 2019 Jan;50(1):178-185. doi: 10.1016/j.injury.2018.09.037. Epub 2018 Sep 23.
Trauma centres and systems have been associated with improved morbidity and mortality after injury. However, variability in outcomes across centres within a given system have been demonstrated. Performance improvement initiatives, that utilize external benchmarking as the backbone, have demonstrated system-wide improvements in outcomes. This data driven approach has been lacking in Australia to date. Recent improvement in local data quality may provide the opportunity to engage in data driven performance improvement. Our objective was to generate risk-adjusted outcomes for the purpose of external benchmarking of trauma services in New South Wales (NSW) based on existing data standards.
Retrospective cohort study of the NSW Trauma Registry. We included adults (>16 years), with an Injury Severity Score >12, that received definitive care at either Major Trauma Services (MTS) or Regional Trauma Services (RTS) between 2012-2016. Hierarchical logistic regression models were then used to generate risk-adjusted outcomes. Our outcome measure was in-hospital death. Demographics, vital signs, transfer status, survival risk ratios, and injury characteristics were included as fixed-effects. Median odds ratios (MOR) and centre-specific odds ratios with 95% confidence intervals were generated. Centre-level variables were explored as sources of variability in outcomes.
14,452 patients received definitive care at one of seven MTS (n = 12,547) or ten RTS (n = 1905). Unadjusted mortality was lower at MTS (9.4%) compared to RTS (11.2%). After adjusting for case-mix, the MOR was 1.33, suggesting that the odds of death was 1.33-fold greater if a patient was admitted to a randomly selected centre with worse as opposed to better risk-adjusted mortality. Definitive care at an MTS was associated with a 41% lower likelihood of death compared to definitive care at an RTS (OR 0.59 95%CI 0.35-0.97). Similar findings were present in the elderly and isolated severe brain injury subgroups.
The NSW trauma system exhibited variability in risk-adjusted outcomes that did not appear to be explained by case-mix. A better understanding of the drivers of the described variation in outcomes is crucial to design targeted locally-relevant quality improvement interventions.
创伤中心和创伤系统与受伤后发病率和死亡率的改善相关。然而，已证明在给定系统内各中心的结果存在差异。以外部基准为支柱的绩效改进举措已证明可在全系统范围内改善结果。迄今为止，澳大利亚一直缺乏这种数据驱动的方法。当地数据质量最近的改善可能提供了参与数据驱动的绩效改进的机会。我们的目标是根据现有数据标准生成风险调整后的结果，以便对新南威尔士州（NSW）的创伤服务进行外部基准测试。
对新南威尔士州创伤登记处进行回顾性队列研究。我们纳入了年龄大于16岁、损伤严重度评分>12、在2012年至2016年期间在主要创伤服务（MTS）或区域创伤服务（RTS）接受确定性治疗的成年人。然后使用分层逻辑回归模型生成风险调整后的结果。我们的结局指标是院内死亡。人口统计学、生命体征、转运状态、生存风险比和损伤特征作为固定效应纳入。生成中位数优势比（MOR）和具有95%置信区间的中心特异性优势比。探索中心层面的变量作为结果差异的来源。
14452名患者在七个MTS之一（n = 12547）或十个RTS之一（n = 1905）接受了确定性治疗。MTS的未调整死亡率（9.4%）低于RTS（11.2%）。在对病例组合进行调整后，MOR为1.33，这表明如果患者被收治到风险调整后死亡率较差而非较好的随机选择的中心，死亡几率会高出1.33倍。与在RTS接受确定性治疗相比，在MTS接受确定性治疗的死亡可能性低41%（OR 0.59，95%CI 0.35 - 0.97）。在老年和孤立性严重脑损伤亚组中也有类似发现。
新南威尔士州创伤系统在风险调整后的结果方面存在差异，这种差异似乎无法用病例组合来解释。更好地理解所描述的结果差异的驱动因素对于设计针对性的、与当地相关的质量改进干预措施至关重要。
