Secondary Prevention of Work-Exacerbated Asthma

Work-exacerbated asthma (WEA), defined as a “pre-existing or concurrent asthma that is worsened by workplace conditions,”1 is a growing medical and public health problem, occurring in almost a quarter of adults with asthma.1 Occupational asthma (OA) is defined as asthma “due to causes and conditions attributable to a particular occupational environment and not to stimuli encountered outside the workplace.”2 Compared with workers with OA, those with WEA are more frequently exposed to ammonia, engine exhaust fumes, silica, mineral fibers, aerosol propellants, and solvents; and less exposed to animal-derived dust and enzymes.3 As recently reviewed,1 patients with WEA are more symptomatic, use more health care resources, and have a poorer quality of life compared with those with non–work-related asthma (NWRA). Patients with OA and WEA account for 10-fold higher asthma-related direct medical costs compared with patients with NWRA,4 and are more likely to suffer from psychiatric disorders compared with the general population.5,6 Nevertheless, WEA is difficult to differentiate from OA. No difference has been reported between workers with OA and WEA in terms of asthma severity, medication requirements, and socioeconomic outcomes.1,5 Taken together, these considerations make WEA a major public health concern, and outline a critical role for secondary prevention, including the early identification of symptomatic workers. We have recently demonstrated that a simple self-report respiratory screening questionnaire, consisting of 8 items combined with information on age and work duration, could be an efficient tool to identify subjects with OA in workers referred to a work-related asthma-specialized clinic for possible OA.7 The objective of this study was to evaluate the performance of the same screening questionnaire to identify workers with WEA. The study population, questionnaire, and design have been previously described.7 Briefly, between January 2009 and December 2011, 169 consecutive subjects who were referred to the Department of Chest Medicine, Hôpital du Sacré-Coeur de Montréal (Quebec, Canada), for suspected OA, answered the questionnaire before clinical evaluation. Seventy-three had WEA; 20 had OA; and 76 had other conditions (reactive airway dysfunction syndrome, occupational rhinitis, eosinophilic bronchitis, hyperventilation syndrome, vocal cord dysfunction, and chronic obstructive pulmonary disease). The diagnosis of WEA was made by one of four senior physicians with expertise in the field of WRA. Their diagnosis was based on the presence of asthma with work-exacerbated symptoms and a negative evaluation for OA (ie, negative-specific inhalation challenge and/or negative peak expiratory flow monitoring). The senior physicians were blinded to the questionnaire data. We used logistic regression analysis to refit the prediction model for WEA that consisted of eight respiratory questionnaire items.8 The control group was composed of workers with OA and other conditions. The discriminative ability of the model was determined with the area under the receiver operating characteristic curve (AUC).9 All statistical analyses were performed with SPSS 19.0 (IBM Inc, Chicago, IL). We present the characteristics of the sample in Table 1. Compared with controls, workers with WEA have higher percentages of low forced expiratory volume in 1 second (FEV1), low FEV1/forced vital capacity (FVC) ratio, bronchial hyperresponsiveness, and use of inhaled corticosteroids. As per guidelines, bronchial hyperresponsiveness was defined as having a methacholine concentration of 16 mg/mL or less that causes a 20% fall in forced expiratory volume in 1 second.10TABLE 1: Characteristics of the Study Participants by the Presence of WEAThe fact that WEA and OA are two different entities was well reflected by how each questionnaire item performed differently in the models (Table 2). For example, in line with the working definitions, wheezing at work (item 7) appeared to be the strongest positive predictor of OA, although it was a negative predictor of WEA. On the contrary, the actual use of asthma medication (item 5) was a negative predictor of OA, whereas it was a strong positive predictor of WEA. This is concordant with clinical practice, where patients with WEA need more medication than patients with NWRA or OA.5 These findings support the need to refit the model for WEA instead of using the same regression coefficients from the original model for OA.TABLE 2: Multivariable Models for OA and WEA on the Basis of Items of the Screening QuestionnaireThe discriminative ability of the refitted model for WEA is fair (AUC = 0.75; 95% confidence interval [CI] = 0.68 to 0.82). An AUC of 0.75 means we correctly differentiated workers with and without WEA in 75% of our sample. This AUC is better than the initial model for OA (AUC = 0.69; 95% CI = 0.58 to 0.80), although the difference is not statistically significant (delta = +0.06; 95% CI = −0.07 to 0.19). Figure 1 clearly shows that subjects with other diagnosis had the lowest mean predicted probabilities of having OA and WEA. Subjects with actual OA had the highest mean predicted probability of having OA and a fair mean predicted probability of having WEA. On the contrary, subjects with actual WEA had the highest mean predicted probability of having WEA and a fair mean predicted probability of having OA.FIGURE 1: (A) Subjects with actual OA had the highest mean predicted probability of having OA calculated from the model (mean = 18.4%; 95% CI = 12.7% to 24.0%), followed by subjects with WEA (mean = 11.6%; 95% CI = 9.8% to 13.4%), and subjects with other conditions (mean = 10.3%; 95% CI = 8.4% to 12.2%). (B) Subjects with actual WEA had the highest mean predicted probability of having WEA calculated from the model (mean = 52.8%; 95% CI = 48.9% to 56.7%), followed by subjects with OA (mean = 41.2%; 95% CI = 34.4% to 47.9%), and subjects with other conditions (mean = 34.5%; 95% CI = 29.8% to 39.2%).In prediction modeling, it is important to maintain the ideal ratio of number of events per predictor variable (EPV) of 10:1 to avoid overoptimistic model (ie, too high or too low estimates).11 We had 73 WEA cases with 8 predictors (EPV = 9:1), which is close to the ideal ratio. External validation of the models in an independent population is required before the models could be used with confidence.8 In conclusion, we have two distinct prediction models on the basis of the same questionnaire. Our results suggest promising ability of the models to discriminate OA, WEA, and other diagnoses in a clinical setting. Given that the questionnaire was completed in a clinical setting with highly selected patients referred for a suspected diagnosis of WRA, the model needs a further validation to be used as a screening tool in the workplace setting. Both models need an additional validation in clinical settings that use other operational definitions for OA and WEA. Once validated, the next steps would be transforming the models to easy-to-use clinical score and selecting the threshold of probabilities for referral purposes. Cost-effectiveness analysis of the implementation of such models for secondary prevention is also an important issue for future research. Jacques A. Pralong, MD, MSc Institute for Work and Health, Epalinges-Lausanne, Switzerland G. Moullec, PhD Research Center, Hôpital du Sacré-Coeur de Montréal, Canada V. Dorribo, MD Institute for Work and Health, Epalinges-Lausanne, Switzerland C. Lemiere, MD, MSc Research Center, Hôpital du Sacré-Coeur de Montréal, Canada E. Suarthana, MD, PhD Research Center, Hôpital du Sacré-Coeur de Montréal, Canada

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.