IDMEC, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
Hospital da Luz Learning Health, Lisbon, Portugal.
PLoS One. 2020 Apr 2;15(4):e0230876. doi: 10.1371/journal.pone.0230876. eCollection 2020.
Emergency department triage is the first point in time when a patient's acuity level is determined. The time to assign a priority at triage is short and it is vital to accurately stratify patients at this stage, since under-triage can lead to increased morbidity, mortality and costs. Our aim was to present a model that can assist healthcare professionals in triage decision making, namely in the stratification of patients through the risk prediction of a composite critical outcome-mortality and cardiopulmonary arrest. Our study cohort consisted of 235826 adult patients triaged at a Portuguese Emergency Department from 2012 to 2016. Patients were assigned to emergent, very urgent or urgent priorities of the Manchester Triage System (MTS). Demographics, clinical variables routinely collected at triage and the patients' chief complaint were used. Logistic regression, random forests and extreme gradient boosting were developed using all available variables. The term frequency-inverse document frequency (TF-IDF) natural language processing weighting factor was applied to vectorize the chief complaint. Stratified random sampling was used to split the data into train (70%) and test (30%) data sets. Ten-fold cross validation was performed in train to optimize model hyper-parameters. The performance obtained with the best model was compared against the reference model-a regularized logistic regression trained using only triage priorities. Extreme gradient boosting exhibited good calibration properties and yielded areas under the receiver operating characteristic and precision-recall curves of 0.96 (95% CI 0.95-0.97) and 0.31 (95% CI 0.26-0.36), respectively. The predictors ranked with higher importance by this model were the Glasgow coma score, the patients' age, pulse oximetry and arrival mode. Compared to the reference, the extreme gradient boosting model using clinical variables and the chief complaint presented higher recall for patients assigned MTS-3 and can identify those who are at risk of the composite outcome.
急诊科分诊是确定患者严重程度的第一时间。在分诊时分配优先级的时间很短，因此在这个阶段准确分层患者至关重要，因为分诊不足可能导致发病率、死亡率和成本增加。我们的目的是提出一个模型，帮助医疗保健专业人员做出分诊决策，即通过对复合关键结局——死亡率和心肺骤停的风险预测对患者进行分层。我们的研究队列包括 2012 年至 2016 年在葡萄牙急诊科分诊的 235826 名成年患者。患者被分配到曼彻斯特分诊系统（MTS）的紧急、非常紧急或紧急优先级。使用常规收集的人口统计学、临床变量和患者的主要诉求。使用所有可用变量开发逻辑回归、随机森林和极端梯度增强。词频-逆文档频率（TF-IDF）自然语言处理加权因子用于向量化主要诉求。采用分层随机抽样将数据分为训练（70%）和测试（30%）数据集。在训练中进行了 10 折交叉验证以优化模型超参数。使用最佳模型获得的性能与参考模型（仅使用分诊优先级训练的正则化逻辑回归）进行比较。极端梯度增强具有良好的校准特性，产生的接收器操作特性和精度-召回曲线下面积分别为 0.96（95%CI 0.95-0.97）和 0.31（95%CI 0.26-0.36）。该模型排名较高的预测因子是格拉斯哥昏迷评分、患者年龄、脉搏血氧饱和度和到达方式。与参考相比，使用临床变量和主要诉求的极端梯度增强模型对 MTS-3 分配的患者具有更高的召回率，可以识别那些存在复合结局风险的患者。
