A prognostic model of excessive lung function decline among Québec apprentices: a cohort exposed to occupational sensitizing agents

Author:

Parfi Alfi AfadiyantiORCID,Taghiakbari MahsaORCID,Achore MeshackORCID,Gautrin Denyse,Bezgin Gleb,Suarthana Eva

Abstract

BACKGROUND Forced expiratory volume in 1 second (FEV1) decline as a predictor of lung-related health problems is widely observed, but not fully investigated. This study aimed to develop models to predict FEV1 decline among apprentices exposed to sensitizing agents. METHODS Of 692 apprentices recruited and followed in 3.6–17.3 years, 292 were exposed to low-molecular weight agents. The analysis was restricted to 357 apprentices with complete lung function assessment at the end of their training with a minimum of 5-year follow-up. According to the American Thoracic Society guideline, a mean FEV₁ decline >60 ml/year was defined as “accelerated.” Descriptive statistics and Cox regression analysis were utilized to determine its predictors. To develop the prognostic models, we used a logistic regression analysis adjusted for the follow-up duration. The accuracy of the models was quantified using calibration and discrimination measures. RESULTS Of 357 subjects, 62 (17.4%) had an excessive FEV1 decline post-apprenticeship. The questionnaire model (model 1), which included male sex, wheezing, and exposure to isocyanate or animal allergens during the apprenticeship, showed a reasonable discriminative ability (area under the receiver operating characteristics curve [AUC] of 0.67, 95% CI = 0.59–0.75). Adding the percent-predicted FEV₁ value at the end of apprenticeship significantly increased the discriminative ability of the model (model 4) (AUC = 0.762, 95% CI = 0.694–0.829) with a good calibration and reasonable internal validity. CONCLUSIONS We developed a model for accelerated lung function decline with a good accuracy and internal validity. However, external validation of the model is necessary.

Publisher

Faculty of Medicine, Universitas Indonesia

Subject

General Medicine

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