Mueller Oliver S, Rentsch Katharina M, Nickel Christian H, Bingisser Roland
Emergency Department, University Hospital Basel, 4031 Basel, Switzerland.
Laboratory Medicine, University Hospital Basel, 4031 Basel, Switzerland.
J Clin Med. 2021 Mar 1;10(5):939. doi: 10.3390/jcm10050939.
Disposition is one of the main tasks in the emergency department. However, there is a lack of objective and reliable disposition criteria, and diagnosis-based risk prediction is not feasible at early time points. The aim was to derive a risk score (TRIAL) based on routinely collected baseline (TRIage level and Age) and Laboratory data-supporting disposition decisions by risk stratification based on mortality. We prospectively included consecutive patients presenting to the emergency department over 18 weeks. Data sets of routinely collected baseline (triage level and age) and laboratory data were used for multivariable logistic regression to develop the TRIAL risk score predicting mortality. Routine laboratory variables and disposition cut-offs were chosen beforehand by expert consensus. Risk stratification was based on low risk (<1%), intermediate risk (1-10%), and high risk (>10%) of in-hospital mortality. In total, 8687 data sets were analyzed. Variables identified to develop the TRIAL risk score were triage level (Emergency Severity Index), age, lactate dehydrogenase, creatinine, albumin, bilirubin, and leukocyte count. The area under the ROC curve for in-hospital mortality was 0.93. Stratification according to the TRIAL score showed that 67.5% of all patients were in the low-risk category. Mortality was 0.1% in low-risk, 3.5% in intermediate-risk, and 26.2% in high-risk patients. The TRIAL risk score based on routinely available baseline and laboratory data provides prognostic information for disposition decisions. TRIAL could be used to minimize admission in low-risk and to maximize observation in high-risk patients.
分流是急诊科的主要任务之一。然而，目前缺乏客观可靠的分流标准，且基于诊断的风险预测在早期时间点并不可行。本研究旨在基于常规收集的基线数据（分诊级别和年龄）以及实验室数据，通过基于死亡率的风险分层得出一个风险评分（TRIAL），以辅助分流决策。我们前瞻性纳入了连续18周以上在急诊科就诊的患者。将常规收集的基线数据（分诊级别和年龄）以及实验室数据用于多变量逻辑回归分析，以建立预测死亡率的TRIAL风险评分。常规实验室变量和分流临界值通过专家共识预先选定。风险分层基于院内死亡率的低风险（<1%）、中风险（1-10%）和高风险（>10%）。总共分析了8687个数据集。确定用于建立TRIAL风险评分的变量包括分诊级别（急诊严重程度指数）、年龄、乳酸脱氢酶、肌酐、白蛋白、胆红素和白细胞计数。预测院内死亡率的ROC曲线下面积为0.93。根据TRIAL评分进行分层显示，所有患者中有67.5%属于低风险类别。低风险患者的死亡率为0.1%，中风险患者为3.5%，高风险患者为26.2%。基于常规可得的基线和实验室数据的TRIAL风险评分为分流决策提供了预后信息。TRIAL可用于尽量减少低风险患者的住院人数，并最大限度地增加高风险患者的观察时间。
