Cost-Effectiveness of Fractional Exhaled Nitric Oxide Suppression Testing as an Adherence Screening Tool Among Patients With Difficult-to-Control Asthma

Choosing the Right Biologic for the Right Patient With Severe Asthma

In this instalment of the How I Do It series on severe asthma, we tackle the clinical conundrum of choosing the right biologic for the right patient with severe asthma. With 6 biologics now approved for use in this area comprising 4 different targeting strategies (anti-Ig E: omalizumab; anti-IL-5 and anti-IL-5-receptor: mepolizumab, reslizumab, and benralizumab; anti-IL-4-receptor: dupilumab; anti-thymic stromal lymphopoietin: tezepelumab), this question is increasingly complex. Recognizing that no head-to-head trial has compared biologics, we based our review on the expected effects of inhibiting different aspects of type 2 airway inflammation, supported whenever possible by clinical trial and real-world data. We use 4 variations of a case of severe uncontrolled asthma to develop concepts and considerations introduced in the previous installment ("Workup of Severe Asthma") and discuss pregnancy-related, biomarker-related, comorbidity-related, and corticosteroid dependency-related considerations when choosing a biologic. The related questions of deciding when, why, and how to switch from one biologic to another also are discussed. Overall, we consider that the choice of biologics should be based on the available clinical trial data for the desired efficacy outcomes, the biomarker profile of the patient, safety profiles (eg, when pregnancy is considered), and opportunities to target 2 comorbidities with 1 biologic. Using systemic and airway biomarkers (blood eosinophils and exhaled nitric oxide) and other phenotypic characteristics, we suggest a framework to facilitate therapeutic decision-making. Post hoc studies and new comparative studies are needed urgently to test this framework and to determine whether it allows us to make other clinically useful predictions.

Change is in the air: key questions on the 'Treatable Traits' model for chronic airway diseases in primary care

Despite great advancements in the treatment of chronic airway diseases, improvements in morbidity and mortality have stalled in recent years. Asthma and chronic obstructive pulmonary disease are complex and heterogeneous diseases that require tailored management based on individual patient characteristics and needs. The Treatable Traits (TTs) approach aims to personalise and improve patient care through the identification and targeting of clinically relevant and modifiable pulmonary, extra-pulmonary and behavioural traits. In this article, we outline the rationale for TTs-based management and provide practical guidance for its application in primary care. To aid implementation, seven potential 'prime' traits are proposed: airflow obstruction, eosinophilic inflammation, adherence, inhaler technique, smoking, low body mass index/obesity and anxiety and depression-selected for their prevalence, recognisability and feasibility of use. Some of the key questions among healthcare professionals, that may be roadblocks to widespread application of a TTs model of care, are also addressed.

Ultrasensitive Online NO Sensor Based on a Distributed Parallel Self-Regulating Neural Network and Ultraviolet Differential Optical Absorption Spectroscopy for Exhaled Breath Diagnosis

The concentration of fractional exhaled nitric oxide (FeNO) is closely related to human respiratory inflammation, and the detection of its concentration plays a key role in aiding diagnosing inflammatory airway diseases. In this paper, we report a gas sensor system based on a distributed parallel self-regulating neural network (DPSRNN) model combined with ultraviolet differential optical absorption spectroscopy for detecting ppb-level FeNO concentrations. The noise signals in the spectrum are eliminated by discrete wavelet transform. The DPSRNN model is then built based on the separated multipeak characteristic absorption structure of the UV absorption spectrum of NO. Furthermore, a distributed parallel network structure is built based on each absorption feature region, which is given self-regulating weights and finally trained by a unified model structure. The final self-regulating weights obtained by the model indicate that each absorption feature region contributes a different weight to the concentration prediction. Compared with the regular convolutional neural network model structure, the proposed model has better performance by considering the effect of separated characteristic absorptions in the spectrum on the concentration and breaking the habit of bringing the spectrum as a whole into the model training in previous related studies. Lab-based results show that the sensor system can stably achieve high-precision detection of NO (2.59-750.66 ppb) with a mean absolute error of 0.17 ppb and a measurement accuracy of 0.84%, which is the best result to date. More interestingly, the proposed sensor system is capable of achieving high-precision online detection of FeNO, as confirmed by the exhaled breath analysis.