Abstract
BackgroundDue 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 resultsA 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.ConclusionWe developed and validated an AIHD prediction model for patients with atypical chest pain by using an XGB model.
Highlights
Acute ischemic heart disease (AIHD) is a major public health concern worldwide [1, 2]
After excluding patients who were transferred from other hospitals (N = 1,085), transferred to another hospital (N = 39), had symptom onset more than 7 days before their emergency department (ED) visit (N = 1,298), or had the highest triage level at presentation (N = 17), 5,415 patients remained
Prediction of acute ischemic heart disease in patients with atypical chest pain We found that the discrimination power was comparable between our model and the logistic regression model
Summary
Acute ischemic heart disease (AIHD) is a major public health concern worldwide [1, 2]. One case of AIHD occurs approximately every 40 seconds in the United States [3]. Chest pain is the most common presenting complaint of AIHD [4, 5]. 2% of patients with AIHD are missed on initial presentation to the emergency department (ED) [12]. 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
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