| Why carry out this study? |
| Machine learning (ML) and artificial intelligence have the potential to augment clinical decision-making and advance personalized medicine for patients with asthma. |
| In this study, we applied ML algorithms to a relatively large dataset of patients with asthma whose data were extracted from their electronic health records (EHR), with the aim of identifying features of patients with asthma that were associated with exacerbations. |
| What was learned from the study? |
| Factors associated with exacerbations included a prior history of exacerbation, prednisone usage, high-dose albuterol usage, and elevated troponin I. Reduced probability of exacerbations was associated with receiving inhaled albuterol, vitamins, aspirin, statins, furosemide, and influenza vaccination. |
| This study on asthma in the real world confirmed previously known features associated with increased exacerbation risk, while uncovering not entirely understood features associated with reduced risk of exacerbations. |
| These findings suggest that ML algorithms can be used on electronic health records to assess features of asthma associated with future risk of exacerbations. Patients should be informed if such methods are used to augment clinical decision-making and establish prognosis. |
| These results are hypothesis-generating and are not meant to guide clinical decision-making or treatment selection for prevention of asthma exacerbations. Future research will focus on uncovering the causal relationships among the most predictive features identified in our study. |
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