In:
PLOS ONE, Public Library of Science (PLoS), Vol. 15, No. 11 ( 2020-11-5), p. e0241920-
Abstract:
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.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0241920
DOI:
10.1371/journal.pone.0241920.g001
DOI:
10.1371/journal.pone.0241920.g002
DOI:
10.1371/journal.pone.0241920.g003
DOI:
10.1371/journal.pone.0241920.t001
DOI:
10.1371/journal.pone.0241920.t002
DOI:
10.1371/journal.pone.0241920.t003
DOI:
10.1371/journal.pone.0241920.t004
DOI:
10.1371/journal.pone.0241920.s001
DOI:
10.1371/journal.pone.0241920.s002
DOI:
10.1371/journal.pone.0241920.s003
DOI:
10.1371/journal.pone.0241920.s004
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2020
detail.hit.zdb_id:
2267670-3
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