Abstract
BackgroundCurrently, 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.MethodsThis 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.ResultsWe 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.ConclusionWe 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.
Highlights
The risk stratification of critically ill patient with chest pain is a challenge
Several established clinical outcome scores have been used for risk stratification of patients with chest pain presenting to the emergency department (ED), including the History, Electrocardiography (ECG), Age, Risk factors, and Troponin (HEART) [10]; the Thrombolysis in Myocardial Infarction (TIMI) [11]; and the Global Registry of Acute Coronary Events (GRACE) score [12]
The HEART score is most accurate and widely used for risk stratification of patients with chest pain [16]. It is used for safe discharge of low-risk patients [17], or identifying high-risk patient for occurrence of major adverse cardiovascular events (MACE) [18]. Considering all these facts, it can be concluded that a little or no attention is being paid to the prediction of outcomes in critically ill patients presenting with chest pain
Summary
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. Several established clinical outcome scores have been used for risk stratification of patients with chest pain presenting to the ED, including the History, Electrocardiography (ECG), Age, Risk factors, and Troponin (HEART) [10]; the Thrombolysis in Myocardial Infarction (TIMI) [11]; and the Global Registry of Acute Coronary Events (GRACE) score [12]. It is used for safe discharge of low-risk patients [17], or identifying high-risk patient for occurrence of MACE [18] Considering all these facts, it can be concluded that a little or no attention is being paid to the prediction of outcomes in critically ill patients presenting with chest pain. A great challenge lies ahead in constructing a promising prediction model to identify this group of patients
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