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Novel Machine Learning Can Predict Acute Asthma Exacerbation

Published:January 10, 2021DOI:https://doi.org/10.1016/j.chest.2020.12.051

      Background

      Asthma exacerbations result in significant health and economic burden, but are difficult to predict.

      Research Question

      Can machine learning (ML) models with large-scale outpatient data predict asthma exacerbations?

      Study Design and Methods

      We analyzed data extracted from electronic health records (EHRs) of asthma patients treated at the Cleveland Clinic from 2010 through 2018. Demographic information, comorbidities, laboratory values, and asthma medications were included as covariates. Three different models were built with logistic regression, random forests, and a gradient boosting decision tree to predict: (1) nonsevere asthma exacerbation requiring oral glucocorticoid burst, (2) ED visits, and (3) hospitalizations.

      Results

      Of 60,302 patients, 19,772 (32.8%) had at least one nonsevere exacerbation requiring oral glucocorticoid burst, 1,748 (2.9%) requiring and ED visit and 902 (1.5%) requiring hospitalization. Nonsevere exacerbation, ED visit, and hospitalization were predicted best by light gradient boosting machine, an algorithm used in ML to fit predictive analytic models, and had an area under the receiver operating characteristic curve of 0.71 (95% CI, 0.70-0.72), 0.88 (95% CI, 0.86-0.89), and 0.85 (95% CI, 0.82-0.88), respectively. Risk factors for all three outcomes included age, long-acting β agonist, high-dose inhaled glucocorticoid, or chronic oral glucocorticoid therapy. In subgroup analysis of 9,448 patients with spirometry data, low FEV1 and FEV1 to FVC ratio were identified as top risk factors for asthma exacerbation, ED visits, and hospitalization. However, adding pulmonary function tests did not improve models’ prediction performance.

      Interpretation

      Models built with an ML algorithm from real-world outpatient EHR data accurately predicted asthma exacerbation and can be incorporated into clinical decision tools to enhance outpatient care and to prevent adverse outcomes.

      Key Words

      Abbreviations:

      AI (artificial intelligent), AUC (area under the receiver operating characteristic curve), EHR (electronic health record), HDiCS (high-dose inhaled corticosteroid), iCS (inhaled corticosteroid), LABA (long-acting β-agonist), LightGBM (light gradient boosting machine), ML (machine learning), PFT (pulmonary function testing), SHAP (shapley additive explanation)
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