October 7, 2021BMC Emergency MedicineOpen Access

A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department

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Why the study?

Risk stratification of critically ill patients presenting with chest pain is currently challenging.

Does a LASSO regression machine learning model improve prediction of critical care outcomes compared to HEART, GRACE, and TIMI scores in patients with acute non-traumatic chest pain in the ED?

Structured PICO

Does a LASSO regression machine learning model improve prediction of critical care outcomes compared to HEART, GRACE, and TIMI scores in patients with acute non-traumatic chest pain in the ED?

Population

Patients with acute non-traumatic chest pain who presented to the emergency department (ED) between January 2017 and December 2019.

Intervention

LASSO regression machine learning model incorporating mode of arrival, reperfusion therapy, Killip class, systolic BP, serum creatinine, creatine kinase-MB, and brain natriuretic peptide

Comparator

HEART, GRACE, and TIMI scores

Outcome

Critical care outcomes (composite of cardiac arrest, transfer to ICU, and death during treatment in ED)composite

A novel LASSO regression machine learning model demonstrated superior accuracy over traditional risk scores (HEART, GRACE, TIMI) for predicting critical care outcomes in ED patients with acute chest pain.

Implication

ML models may aid ED chest pain risk stratification; leaves open superiority over HEART/GRACE/TIMI without prospective validation.

Abstract

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.