Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at UTHealth, Houston, Texas; Center for Surgical Trials and Evidence-Based Practice, McGovern Medical School at UTHealth, Houston, Texas; Center for Translational Injury, McGovern Medical School at UTHealth, Houston, Texas.
Division of Acute Care Surgery, Department of Surgery, McGovern Medical School at UTHealth, Houston, Texas; Center for Surgical Trials and Evidence-Based Practice, McGovern Medical School at UTHealth, Houston, Texas; Center for Translational Injury, McGovern Medical School at UTHealth, Houston, Texas.
J Surg Res. 2024 Apr;296:465-471. doi: 10.1016/j.jss.2023.12.016. Epub 2024 Feb 5.
Risk stratification for poor outcomes is not currently age-specific. Risk stratification of older patients based on observational cohorts primarily composed of young patients may result in suboptimal clinical care and inaccurate quality benchmarking. We assessed two hypotheses. First, we hypothesized that risk factors for poor outcomes after trauma are age-dependent and, second, that the relative importance of various risk factors are also age-dependent.
A cohort study of severely injured adult trauma patients admitted to the intensive care unit 2014-2018 was performed using trauma registry data. Random forest algorithms predicting poor outcomes (death or complication) were built and validated using three cohorts: (1) patients of all ages, (2) younger patients, and (3) older patients. Older patients were defined as aged 55 y or more to maintain consistency with prior trauma literature. Complications assessed included acute renal failure, acute respiratory distress syndrome, cardiac arrest, unplanned intubation, unplanned intensive care unit admission, and unplanned return to the operating room, as defined by the trauma quality improvement program. Mean decrease in model accuracy (MDA), if each variable was removed and scaled to a Z-score, was calculated. MDA change ≥4 standard deviations between age cohorts was considered significant.
Of 5489 patients, 25% were older. Poor outcomes occurred in 12% of younger and 33% of older patients. Head injury was the most important predictor of poor outcome in all cohorts. In the full cohort, age was the most important predictor of poor outcomes after head injury. Within age cohorts, the most important predictors of poor outcomes, after head injury, were surgery requirement in younger patients and arrival Glasgow Coma Scale in older patients. Compared to younger patients, head injury and arrival Glasgow Coma Scale had the greatest increase in importance for older patients, while systolic blood pressure had the greatest decrease in importance.
Supervised machine learning identified differences in risk factors and their relative associations with poor outcomes based on age. Age-specific models may improve hospital benchmarking and identify quality improvement targets for older trauma patients.
目前，针对不良预后的风险分层并非针对特定年龄。基于主要由年轻患者组成的观察队列对老年患者进行风险分层，可能导致临床护理不佳和不准确的质量基准。我们评估了两个假设。首先，我们假设创伤后不良预后的危险因素与年龄有关；其次，各种危险因素的相对重要性也与年龄有关。
使用创伤登记数据进行了一项 2014 年至 2018 年期间入住重症监护病房的严重创伤成年患者的队列研究。使用随机森林算法构建并验证了预测不良预后（死亡或并发症）的模型，该模型使用了三个队列：（1）所有年龄段的患者；（2）年轻患者；（3）老年患者。老年患者的定义为年龄 55 岁或以上，以与先前的创伤文献保持一致。评估的并发症包括急性肾衰竭、急性呼吸窘迫综合征、心脏骤停、计划外插管、计划外入住重症监护病房和计划返回手术室，这些并发症由创伤质量改进计划定义。如果每个变量被移除并缩放到 Z 分数，则计算模型准确性的平均减少量（MDA）。如果在年龄队列之间 MDA 变化超过 4 个标准差，则认为差异显著。
在 5489 名患者中，25%为老年人。年轻患者中有 12%，老年患者中有 33%发生不良预后。头部损伤是所有队列中不良预后的最重要预测因素。在全队列中，年龄是头部损伤后不良预后的最重要预测因素。在年龄队列内，在头部损伤后，年轻患者的手术需求和老年患者的入院格拉斯哥昏迷量表是不良预后的最重要预测因素。与年轻患者相比，头部损伤和入院格拉斯哥昏迷量表对老年患者的重要性增加最大，而收缩压的重要性下降最大。
监督机器学习根据年龄确定了不良预后的危险因素及其相对关联的差异。基于年龄的特定模型可能会改善医院的基准，并确定老年创伤患者质量改进的目标。
