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BMC Emergency Medicine

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Open Access 01-12-2021 | Care | Research

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

Authors: Ting Ting Wu, Ruo Fei Zheng, Zhi Zhong Lin, Hai Rong Gong, Hong Li

Published in: BMC Emergency Medicine | Issue 1/2021

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.

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Title: A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department
Authors: Ting Ting Wu
Ruo Fei Zheng
Zhi Zhong Lin
Hai Rong Gong
Hong Li
Publication date: 01-12-2021
Publisher: BioMed Central
Keyword: Care
Published in: BMC Emergency Medicine / Issue 1/2021
Electronic ISSN: 1471-227X
DOI: https://doi.org/10.1186/s12873-021-00501-8

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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