The School of Nursing, Fujian Medical University, Fuzhou, Fujian, China.
Department of Emergency, Fujian Provincial Hospital, Fuzhou, Fujian, China.
BMC Emerg Med. 2021 Oct 7;21(1):112. doi: 10.1186/s12873-021-00501-8.
Currently, the risk stratification of critically ill patient with chest pain is a challenge. We aimed to use machine learning approach to predict the critical care outcomes in patients with chest pain, and simultaneously compare its performance with HEART, GRACE, and TIMI scores.
This was a retrospective, case-control study in patients with acute non-traumatic chest pain who presented to the emergency department (ED) between January 2017 and December 2019. The outcomes included cardiac arrest, transfer to ICU, and death during treatment in ED. In the randomly sampled training set (70%), a LASSO regression model was developed, and presented with nomogram. The performance was measured in both training set (70% participants) and testing set (30% participants), and findings were compared with the three widely used scores.
We proposed a LASSO regression model incorporating mode of arrival, reperfusion therapy, Killip class, systolic BP, serum creatinine, creatine kinase-MB, and brain natriuretic peptide as independent predictors of critical care outcomes in patients with chest pain. Our model significantly outperformed the HEART, GRACE, TIMI score with AUC of 0.953 (95%CI: 0.922-0.984), 0.754 (95%CI: 0.675-0.832), 0.747 (95%CI: 0.664-0.829), 0.735 (95%CI: 0.655-0.815), respectively. Consistently, our model demonstrated better outcomes regarding the metrics of accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score. Similarly, the decision curve analysis elucidated a greater net benefit of our model over the full ranges of clinical thresholds.
We present an accurate model for predicting the critical care outcomes in patients with chest pain, and provide substantial support to its application as a decision-making tool in ED.
目前，对胸痛危重症患者进行风险分层是一个挑战。我们旨在采用机器学习方法预测胸痛患者的重症监护结局，并同时比较其与 HEART、GRACE 和 TIMI 评分的性能。
这是一项回顾性病例对照研究，纳入 2017 年 1 月至 2019 年 12 月期间因急性非创伤性胸痛就诊于急诊科的患者。结局包括心脏骤停、转入 ICU 和 ED 治疗期间死亡。在随机抽样的训练集中（70%的参与者），建立了 LASSO 回归模型，并呈现为列线图。在训练集（70%的参与者）和测试集（30%的参与者）中测量性能，并与三种广泛使用的评分进行比较。
我们提出了一个 LASSO 回归模型，将到达方式、再灌注治疗、Killip 分级、收缩压、血清肌酐、肌酸激酶同工酶和脑利钠肽作为胸痛患者重症监护结局的独立预测因子。我们的模型在 AUC 方面显著优于 HEART、GRACE 和 TIMI 评分，分别为 0.953（95%CI：0.922-0.984）、0.754（95%CI：0.675-0.832）和 0.747（95%CI：0.664-0.829）、0.735（95%CI：0.655-0.815）。同样，我们的模型在准确性、敏感性、特异性、阳性预测值、阴性预测值和 F1 评分等方面的表现都更好。同样，决策曲线分析表明，与全范围临床阈值相比，我们的模型具有更大的净获益。
我们提出了一种用于预测胸痛患者重症监护结局的准确模型，并为其作为 ED 决策工具的应用提供了有力支持。
