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Update on the Management of Occupational Asthma and Work-Exacerbated Asthma
Ambrose Lau,Susan M. Tarlo 대한천식알레르기학회 2019 Allergy, Asthma & Immunology Research Vol.11 No.2
Work-related asthma is the most common occupational lung disease encountered in clinical practice. In adult asthmatics, work-relatedness can account for 15%–33% of cases, but delays in diagnosis remain common and lead to worse outcomes. Accurate diagnosis of asthma is the first step to managing occupational asthma, which can be sensitizer-induced or irritant-induced asthma. While latency has traditionally been recognized as a hallmark of sensitizer-induced asthma and rapid-onset a defining feature of irritant-induced asthma (as in Reactive Airway Dysfunction Syndrome), there is epidemiological evidence for irritant-induced asthma with latency from chronic moderate exposure. Diagnostic testing while the patient is still in the workplace significantly improves sensitivity. While specific inhalational challenges remain the gold-standard for the diagnosis of occupational asthma, they are not available outside of specialized centers. Commonly available tests including bronchoprovocation challenges and peak flow monitoring are important tools for practicing clinicians. Management of sensitizer-induced occupational asthma is notable for the central importance of removal from the causative agent: ideally, removal of the culprit agent; but if not feasible, this may require changes in the work process or ultimately, removal of the worker from the workplace. While workers' compensation programs may reduce income loss, these are not universal and there can be significant socio-economic impact from work-related asthma. Primary prevention remains the preferred method of reducing the burden of occupational asthma, which may include modification to work processes, better worker education and substitution of sensitizing agents from the workplace with safer compounds.
Ory Jesse,Tradewell Michael B.,Blankstein Udi,Lima Thiago F.,Nackeeran Sirpi,Gonzalez Daniel C.,Nwefo Elie,Moryousef Joseph,Madhusoodanan Vinayak,Lau Susan,Jarvi Keith,Ramasamy Ranjith 대한남성과학회 2022 The World Journal of Men's Health Vol.40 No.4
Purpose: Varicocele repair is recommended in the presence of a clinical varicocele together with at least one abnormal semen parameter, and male infertility. Unfortunately, up to 50% of men who meet criteria for repair will not see meaningful benefit in outcomes despite successful treatment. We developed an artificial intelligence (AI) model to predict which men with varicocele will benefit from treatment. Materials and Methods: We identified men with infertility, clinical varicocele, and at least one abnormal semen parameter from two large urology centers in North America (Miami and Toronto) between 2006 and 2020. We collected pre and postoperative clinical and hormonal data following treatment. Clinical upgrading was defined as an increase in sperm concentration that would allow a couple to access previously unavailable reproductive options. The tiers used for upgrading were: 1–5 million/mL (ICSI/IVF), 5–15 million/mL (IUI) and >15 million/mL (natural conception). Thus moving from ICSI/IVF to IUI, or from IUI to natural conception, would be considered an upgrade. AI models were trained and tested using R to predict which patients were likely to upgrade after surgery. The model sorted men into categories that defined how likely they were to upgrade after surgery (likely, equivocal, and unlikely). Results: Data from 240 men were included from both centers. A total of 45.6% of men experienced an upgrade in sperm concentration following surgery, 48.1% did not change, and 6.3% downgraded. The data from Miami were used to create a random forest model for predicting upgrade in sperm concentration. On external validation using Toronto data, the model accurately predicted upgrade in 87% of men deemed likely to improve, and in 49% and 36% of men who were equivocal and unlikely to improve, respectively. Overall, the personalized prediction for patients in the validation cohort was accurate (AUC 0.72). Conclusions: A machine learning model performed well in predicting clinically meaningful post-varicocelectomy sperm parameters using pre-operative hormonal, clinical, and semen analysis data. To our knowledge, this is the first prediction model to show the utility of hormonal data, as well as the first to use machine learning models to predict clinically meaningful upgrading. This model will be published online as a clinical calculator that can be used in the preoperative counseling of patients.