Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
Health Services Research Centre, Singapore Health Services, Singapore, Singapore.
BMC Med Res Methodol. 2021 Apr 17;21(1):74. doi: 10.1186/s12874-021-01265-2.
Chest pain is among the most common presenting complaints in the emergency department (ED). Swift and accurate risk stratification of chest pain patients in the ED may improve patient outcomes and reduce unnecessary costs. Traditional logistic regression with stepwise variable selection has been used to build risk prediction models for ED chest pain patients. In this study, we aimed to investigate if machine learning dimensionality reduction methods can improve performance in deriving risk stratification models.
A retrospective analysis was conducted on the data of patients > 20 years old who presented to the ED of Singapore General Hospital with chest pain between September 2010 and July 2015. Variables used included demographics, medical history, laboratory findings, heart rate variability (HRV), and heart rate n-variability (HRnV) parameters calculated from five to six-minute electrocardiograms (ECGs). The primary outcome was 30-day major adverse cardiac events (MACE), which included death, acute myocardial infarction, and revascularization within 30 days of ED presentation. We used eight machine learning dimensionality reduction methods and logistic regression to create different prediction models. We further excluded cardiac troponin from candidate variables and derived a separate set of models to evaluate the performance of models without using laboratory tests. Receiver operating characteristic (ROC) and calibration analysis was used to compare model performance.
Seven hundred ninety-five patients were included in the analysis, of which 247 (31%) met the primary outcome of 30-day MACE. Patients with MACE were older and more likely to be male. All eight dimensionality reduction methods achieved comparable performance with the traditional stepwise variable selection; The multidimensional scaling algorithm performed the best with an area under the curve of 0.901. All prediction models generated in this study outperformed several existing clinical scores in ROC analysis.
Dimensionality reduction models showed marginal value in improving the prediction of 30-day MACE for ED chest pain patients. Moreover, they are black box models, making them difficult to explain and interpret in clinical practice.
胸痛是急诊科（ED）最常见的就诊主诉之一。ED 胸痛患者的快速、准确风险分层可能改善患者结局并降低不必要的成本。传统的逐步变量选择逻辑回归已用于构建 ED 胸痛患者的风险预测模型。在这项研究中，我们旨在研究机器学习降维方法是否可以改善风险分层模型的推导性能。
对 2010 年 9 月至 2015 年 7 月期间因胸痛到新加坡综合医院急诊科就诊的年龄＞20 岁的患者进行回顾性分析。使用的变量包括人口统计学、病史、实验室检查结果、心率变异性（HRV）和从 5 到 6 分钟心电图（ECG）计算的心率 n 变异性（HRnV）参数。主要结局为 30 天内主要不良心脏事件（MACE），包括 ED 就诊后 30 天内死亡、急性心肌梗死和血运重建。我们使用了八种机器学习降维方法和逻辑回归来创建不同的预测模型。我们进一步排除了肌钙蛋白，并推导了一组不使用实验室检查的模型，以评估没有使用实验室检查的模型的性能。使用接收者操作特征（ROC）和校准分析来比较模型性能。
共纳入 795 例患者，其中 247 例（31%）发生 30 天内 MACE。MACE 患者年龄较大，且更可能为男性。所有八种降维方法与传统的逐步变量选择法相比均具有相当的性能；多维尺度算法的曲线下面积最佳，为 0.901。本研究中生成的所有预测模型在 ROC 分析中均优于几种现有的临床评分。
降维模型在改善 ED 胸痛患者 30 天内 MACE 预测方面显示出了一定的价值。此外，它们是黑箱模型，在临床实践中难以解释和解释。
