Novel approaches in occupational asthma diagnosis and management

Multivariate analysis and data mining help predict asthma exacerbations

BACKGROUND

Work-related asthma has become a highly prevalent occupational lung disorder.

OBJECTIVE

Our study aims to evaluate occupational exposure as a predictor for asthma exacerbation.

METHOD

We performed a retrospective evaluation of 584 consecutive patients diagnosed and treated for asthma between October 2017 and December 2019 in four clinics from Western Romania. We evaluated the enrolled patients for their asthma control level by employing the Asthma Control Test (ACT < 20 represents uncontrolled asthma), the medical record of asthma exacerbations, occupational exposure, and lung function (i.e. spirometry). Then, we used statistical and data mining methods to explore the most important predictors for asthma exacerbations.

RESULTS

We identified essential predictors by calculating the odds ratios (OR) for the exacerbation in a logistic regression model. The average age was 45.42 ± 11.74 years (19-85 years), and 422 (72.26%) participants were females. 42.97% of participants had exacerbations in the past year, and 31.16% had a history of occupational exposure. In a multivariate model analysis adjusted for age and gender, the most important predictors for exacerbation were uncontrolled asthma (OR 4.79, p < .001), occupational exposure (OR 4.65, p < .001), and lung function impairment (FEV1 < 80%) (OR 1.15, p = .011). The ensemble machine learning experiments on combined patient features harnessed by our data mining approach reveal that the best predictor is professional exposure, followed by ACT.

CONCLUSIONS

Machine learning ensemble methods and statistical analysis concordantly indicate that occupational exposure and ACT < 20 are strong predictors for asthma exacerbation.