Key result
An extreme gradient boosting model using routinely collected emergency department data predicted acute ischemic heart disease with an AUROC of 0.75, demonstrating similar discrimination power to troponin and logistic regression models.
Why the study?
Patients with acute ischemic heart disease increasingly present to the emergency department with atypical chest pain, motivating the development and validation of a prediction model for this population.
Does an extreme gradient boosting (XGB) model using routine ED data predict acute ischemic heart disease as accurately as troponin or logistic regression in patients with atypical chest pain?
Observational (n=4,978)
No
Does an extreme gradient boosting (XGB) model using routine ED data predict acute ischemic heart disease as accurately as troponin or logistic regression in patients with atypical chest pain?
A machine learning model using routinely collected ED data performs comparably to troponin testing for predicting acute ischemic heart disease in patients with atypical chest pain.
BACKGROUND: Due to an aging population and the increasing proportion of patients with various comorbidities, the number of patients with acute ischemic heart disease (AIHD) who present to the emergency department (ED) with atypical chest pain is increasing. The aim of this study was to develop and validate a prediction model for AIHD in patients with atypical chest pain. METHODS AND RESULTS: A chest pain workup registry, ED administrative database, and clinical data warehouse database were analyzed and integrated by using nonidentifiable key factors to create a comprehensive clinical dataset in a single academic ED from 2014 to 2018. Demographic findings, vital signs, and routine laboratory test results were assessed for their ability to predict AIHD. An extreme gradient boosting (XGB) model was developed and evaluated, and its performance was compared to that of a single-variable model and logistic regression model. The area under the receiver operating characteristic curve (AUROC) was calculated to assess discrimination. A calibration plot and partial dependence plots were also used in the analyses. Overall, 4,978 patients were analyzed. Of the 3,833 patients in the training cohort, 453 (11.8%) had AIHD; of the 1,145 patients in the validation cohort, 166 (14.5%) had AIHD. XGB, troponin (single-variable), and logistic regression models showed similar discrimination power (AUROC [95% confidence interval]: XGB model, 0.75 [0.71-0.79]; troponin model, 0.73 [0.69-0.77]; logistic regression model, 0.73 [0.70-0.79]). Most patients were classified as non-AIHD; calibration was good in patients with a low predicted probability of AIHD in all prediction models. Unlike in the logistic regression model, a nonlinear relationship-like threshold and U-shaped relationship between variables and the probability of AIHD were revealed in the XGB model. CONCLUSION: We developed and validated an AIHD prediction model for patients with atypical chest pain by using an XGB model.
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Kim et al. (2020) conducted an observational in Atypical chest pain / suspected acute ischemic heart disease (n=4,978). Extreme gradient boosting (XGB) prediction model vs. Troponin (single-variable) and logistic regression models was evaluated on Discrimination power for acute ischemic heart disease (AUROC) (AUROC 0.75, 95% CI 0.71-0.79). An extreme gradient boosting model using routinely collected emergency department data predicted acute ischemic heart disease with an AUROC of 0.75, demonstrating similar discrimination power to troponin and logistic regression models.
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