Wheeze at rest, traits can predict small airways dysfunction in patients with asthma
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Key takeaways:
- A model with knowledge of wheeze when resting and simple patient traits predicted odds for small airways dysfunction.
- Adding lung function test outcomes improved the accuracy of the model.
Wheeze when resting, age, age when diagnosed with asthma and BMI can “reasonably well” predict patients with asthma who are likely to have small airways dysfunction, according to study results published in European Respiratory Journal.
“This study showed that with limited resources one could reasonably well discriminate patients who were more likely to have small airways dysfunction (SAD) from patients who were less likely to have SAD,” Janwillem Kocks, MD, PhD, managing director and co-founder of the General Practitioners Research Institute and professor of inhalation medicine at the Observational & Pragmatic Research Institute, and colleagues wrote. “If access to respiratory tests is limited, which is the case in primary care in many parts of the world, asking about wheezing at rest and a few patient characteristics will support health care providers in identifying patients with SAD and providing proper care for these patients.”
In this study, Kocks and colleagues analyzed 764 adults with asthma who had varying likelihoods for SAD from the ATLANTIS (Assessment of Small Airways Involvement in Asthma) study to figure out what information could be used in a tool to predict the disease in patients who do not have access to lung function tests.
When designing this tool, researchers used the most relevant items from the 63-question Small Airways Dysfunction Tool (SADT), which they found through several tests.
When separated according to items suggestive for more SAD or less SAD, the item stating, “I sometimes wheeze when I am sitting or lying quietly,” had the greatest odds for SAD (adjusted OR = 2.17; P < .001) and a selection frequency of 95 in the 100 bootstep samples of the logistic regression models.
According to researchers, two items that suggested less SAD were found but contributed little when added into models with the item on wheeze, so neither were included in the final models.
Through a combination of the one SADT item, routine patient characteristics and two lung function tests, researchers created three logistic regression models based on varying availability of data to detect the likelihood for SAD.
Researchers used both area under the receiving operating characteristic curve (AUC) and positive likelihood ratio (LR+) to assess each model’s diagnostic ability.
Of the total cohort, 452 patients (median age, 43 years; 57% women; median FEV1 percent predicted, 90.2%) had a low likelihood for SAD, whereas 312 patients (median age, 50 years; 60% women; median FEV1 percent predicted, 70.1%) had a high likelihood for SAD.
In the model that captured a patients’ age, age when diagnosed with asthma, BMI and whether they experience wheezing when sitting or lying down, a “reasonably well” ability to detect SAD was found (AUC = 0.74; LR+ = 2.3), according to researchers.
Additionally, researchers observed improved accuracy of SAD prediction when spirometry test values (AUC = 0.87; LR+ = 5) and spirometry plus oscillometry test values (AUC = 0.96; LR+ = 12.8) were factored into the tool.
“The SADT developed in this study needs to be further validated in external datasets,” Kocks and colleagues wrote. “For implementation of the SADT in clinical practice, the models need to be converted into a simple calculator to be feasible as a point-of-care test.”
Perspective
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I read with great interest the paper written by Kocks et al. SAD in asthma is important to diagnose because of its association with more severe bronchial hyperreactivity, worse asthma control and higher number of exacerbations. However, it is difficult to diagnose and often requires specialized testing at larger tertiary care centers. The study’s use of the SADT to aid in the diagnosis of SAD is one step further to helping everyday clinicians screen for this entity and perhaps refer for more specialized testing and treatment.
Importantly it’s not the whole questionnaire that helps point to the diagnosis of SAD. A “yes” response to the question “I sometimes wheeze when I am sitting or lying quietly,” along with BMI, age and age at diagnosis of asthma reasonably predicts SAD. Adding the results of FEV1% from simple spirometry can aid the diagnostic accuracy of SAD.
These findings are significant in the fact they give clinicians who see asthma patients an easy prediction tool to triage those who need more advanced testing. While these results certainly help clinicians diagnose SAD and refer for further testing, it would be interesting to see how this would play out in a more resource-limited setting. Would it spur more empiric treatment with extra fine particle inhaled steroids since these agents are shown to improve SAD and overall asthma control in these individuals? It would indeed be interesting if future research utilized these simple tools to diagnose SAD and, in this cohort of patients, gauge response to extra fine particle inhalers or even biologics.
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