Barbosa Ronald R, Rowell Susan E, Diggs Brian S, Schreiber Martin A, Holcomb J B, Wade C E, Brasel K J, Vercruysse G, MacLeod J, Dutton R P, Hess J R, Duchesne J C, McSwain N E, Muskat P, Johannigamn J, Cryer H M, Tillou A, Cohen M J, Pittet J F, Knudson P, De Moya M A, Schreiber M A, Tieu B, Brundage S, Napolitano L M, Brunsvold M, Sihler K C, Beilman G, Peitzman A B, Zenait M S, Sperry J, Alarcon L, Croce M A, Minei J P, Kozar R, Gonzalez E A, Stewart R M, Cohn S M, Mickalek J E, Bulger E M, Cotton B A, Nunez T C, Ivatury R, Meredith J W, Miller P, Pomper G J, Marin B
Trauma Services, Legacy Emanuel Hospital and Health Center, Portland, Oregon 97227, USA.
J Trauma. 2011 Aug;71(2 Suppl 3):S364-9. doi: 10.1097/TA.0b013e318227f170.
Improvements in prehospital care and resuscitation have led to increases in the number of severely injured patients who are salvageable. Massive transfusion has been increasingly used. Patients often present with markedly abnormal physiologic and biochemical data. The purpose of this study was to identify objective data that can be used to identify clinical futility in massively transfused trauma patients to allow for early termination of resuscitative efforts.
A multicenter database was used. Initial physiologic and biochemical data were obtained, and mortality was determined for patients in the 5th and 10th percentiles for each variable. Raw data from the extreme outliers for each variable were also examined to determine whether a point of excessive mortality could be identified. Injury scoring data were also analyzed. A classification tree model was used to look for variable combinations that predict clinical futility.
The cohort included 704 patients. Overall mortality was 40.2%. The highest mortality rates were seen in patients in the 10th percentile for lactate (77%) and pH (72%). Survivors at the extreme ends of the distribution curves for each variable were not uncommon. The classification tree analysis failed to identify any biochemical and physiologic variable combination predictive of >90% mortality. Patients older than 65 years with severe head injuries had 100% mortality.
Consideration should be given to withholding massive transfusion for patients older than 65 years with severe head injuries. Otherwise we did not identify any objective variables that reliably predict clinical futility in individual cases. Significant survival rates can be expected even in patients with profoundly abnormal physiologic and biochemical data.
院前护理和复苏技术的改进使得可挽救的重伤患者数量增加。大量输血的使用越来越频繁。患者通常呈现出明显异常的生理和生化数据。本研究的目的是确定可用于识别大量输血创伤患者临床救治无效的客观数据,以便尽早终止复苏努力。
使用多中心数据库。获取初始生理和生化数据,并确定每个变量处于第5和第10百分位数的患者的死亡率。还检查了每个变量极端异常值的原始数据,以确定是否能识别出过高死亡率的点。对损伤评分数据也进行了分析。使用分类树模型寻找预测临床救治无效的变量组合。
该队列包括704例患者。总体死亡率为40.2%。乳酸处于第10百分位数的患者死亡率最高(77%),pH值处于第10百分位数的患者死亡率最高(72%)。每个变量分布曲线两端的幸存者并不罕见。分类树分析未能识别出任何预测死亡率>90%的生化和生理变量组合。65岁以上重度颅脑损伤患者的死亡率为100%。
对于65岁以上重度颅脑损伤患者,应考虑不进行大量输血。否则,我们未识别出任何能可靠预测个别病例临床救治无效的客观变量。即使是生理和生化数据严重异常的患者,也有望获得显著的生存率。