This technology is based on an algorithm able to provide the probability of being asthmatic with high accuracy. This probability is based on the evaluation of respiratory function and, specifically, of forced expiratory vital capacity in the first second (FEV1), in resting conditions, and 20 minutes after administration of a bronchodilator drug. The algorithm uses 2 FEV1 threshold values together with some information on the subject (gender, season at the visit, allergic sensitization to inhalant allergens - mold, pollen, pet epithelia, dust mite), to give an estimate of the probability of asthma. The result provides an immediate benefit for the clinical management of the patient constituting a step forward for good clinical practice.
The framework based on the new bronchoreversibility cut-offs creates 3 categories of risk: low (<7.9%), intermediate (7.9% -14.7%) and high (≥14.7%). By also accounting for the subject information, the proposed approach provides very good diagnostic accuracy in discriminating asthmatic and non-asthmatic subjects, outperforming the current rule-of thumb based on the 12% cut-off. For example, subjects with FEV1 <12% who would be, as of now, too rigidly classified as non-asthmatics could be classified differently, and perhaps more correctly, depending on other explanatory prognostic factors. Utimately, our algorithm would provide the competitive advantage of having better performance in terms of diagnostic accuracy for first level screening.