Breathlessness, or dyspnoea, is a complex symptom influenced by respiratory, cardiovascular and neural mechanisms, necessitating a systematic and tiered approach for accurate diagnosis and effective management. This review presents a structured, three-tier diagnostic framework, comprising history-taking, static testing (such as pulmonary function tests and thoracic imaging), and dynamic testing (e.g., 6-minute walk test and cardiopulmonary exercise testing) for comprehensive assessment. Each tier is designed to progressively investigate and characterise underlying conditions. This framework is specifically tailored for use in an outpatient general respiratory clinic setting, where clinicians evaluate chronic or unexplained dyspnoea in non-acute patients. Literature and guidelines support this approach, highlighting the importance of combining clinical examination, imaging, laboratory testing and dynamic assessments to capture both static and exertional components of dyspnoea. Emphasising a patient-centred approach, this framework aims to improve diagnostic accuracy and guide targeted therapeutic interventions.

Relating Type 2 status to steroid exposure, cytokine expression and symptoms T2 low asthma remains poorly understood even though it may account for up to 50% of patients. We aimed to characterise the inflammatory signature and symptom burden in T2 low patients and relate T2 status to corticosteroid exposure. We evaluated 200 patients' T2 status from the INCA-SUN RCT over 6 visits. Four groups were identified based on frequency of T2 inflammation: Consistently T2 low (n=40), Usually T2 low (n=48), Usually T2 high (n=64) and Consistently T2 high (n=48). Inhaled (ICS) and oral corticosteroid burden (OCS) were measured by digital inhaler monitors and pharmacy records. Multiplex assays of inflammatory pathways (Th-1, T2, Th-17 and TRegs) were performed in these patients and healthy controls. Symptom burden was assessed using the Asthma Control Test (ACT). Low T2 biomarkers were independently associated with higher ICS and OCS exposure, OR 1.03 per mg fluticasone propionate (p=.011), OR 1.44 per course of OCS, p=.018. The Consistently T2 high group had higher periostin (p<.0001), IgE (p<.0001), IL-4 (p=.0061) and IL-5 (p<.0001) compared to the other cohorts. Increasing prevalence of T2 high status was associated with worse and more unstable lung function. The Consistently T2 low patients showed no evidence of alternate inflammatory pathways. Their cytokine results most closely resembled healthy controls but their symptom burden was similar to other groups. ACT scores related poorly to objective markers of asthma. Persistently low ACT scores were associated with co-morbidities such as GORD (p=.006) and anxiety/depression (p=.028) T2 low asthma may be explained by steroid exposure and co-morbid conditions.

In little over a generation, the ingenuity of scientists and clinician researchers has developed inhaled medications and pathway-specific biological agents that control the inflammation and physiology of asthma. Unfortunately, whether it is because of cost or difficulty understanding why or how to use inhaled medications, patients often do not take these medications. The consequences of poor treatment adherence, loss of control and exacerbations, are the same as if the condition remained untreated. Furthermore, poor adherence is difficult to detect without direct measurement. Together this means that poor treatment adherence is easily overlooked and, instead of addressing the cause of poor adherence, additional medicines may be prescribed. In other words, poor treatment adherence is a risk for the patient and adds cost to healthcare systems. In this article, we discuss the rationale for and the delivery of successful interventions to improve medication adherence in asthma. We contextualize these interventions by describing the causes of poor treatment adherence and how adherence is assessed. Finally, future perspectives on the design of new interventions are described.

BACKGROUND: Exposure to any form of glucocorticoid preparation is associated with a risk of adrenal insufficiency (AI). OBJECTIVE: To establish the contribution of oral corticosteroid (OCS) and inhaled corticosteroid (ICS) exposure to the risk of AI in a cohort of patients (n = 80) with severe, uncontrolled asthma. METHODS: We compiled individualized cumulative OCS and ICS exposure data using a combination of health care records and electronic inhaler monitoring using an Inhaler Compliance Assessment device and estimated the risk of AI for each participant using a morning serum cortisol concentration. RESULTS: The predicted prevalence of AI based on morning cortisol concentrations was 25% (20 of 80). Participants on maintenance OCS therapy had the highest risk of AI at 60% (6 of 10) compared with 17% (11 of 65) in those with no recent OCS exposure. Morning serum cortisol correlated negatively with both OCS exposure (mg/kg prednisolone) (r = -0.4; P < .0002) and ICS exposure (mg/kg fluticasone propionate) (r = -0.26; P = .019). Logistic regression of risk of AI against the number of standard treatment courses of OCS demonstrated a positive relationship although this did not reach statistical significance (odds ratio, 1.41; 95% CI, 0.97-2.05; P = .073). Logistic regression analysis, categorizing patients as high-risk AI (cortisol <130 nmol/L) or not (cortisol >130 nmol/L), showed that cumulative ICS exposure remained a significant predictor of AI, even when exposure to OCS was controlled for (odds ratio, 2.17 per 1 mg/kg increase in cumulative fluticasone propionate exposure; 95% CI, 1.06-4.42; P = .033). CONCLUSIONS: Our data suggest that AI is common among patients with asthma and highlights that the risk of AI is associated with both high-dose ICS therapy and intermittent treatment courses of OCS. (C) 2022 American Academy of Allergy, Asthma & Immunology

Abstract The extent to which inhaled glucocorticoid exposure (ICS) contributes to risk of adrenal insufficiency (AI) is not fully understood. The aim of this study was to establish the relative contribution of both oral (OCS) and inhaled (ICS) glucocorticoid exposure to risk of AI.82 patients with severe asthma treated with fluticasone propionate (FP) who participated in a 32-week prospective randomised trial INCA SUN, (NCT02307669) were studied. Cumulative ICS exposure was calculated using a unique digital device, which creates an acoustic recording of inhaler adherence and technique. Analysis of this data provides an exact measure of the ICS dose received by each patient. Morning serum samples collected during the final study visit (week 32) were analysed for serum cortisol concentration (cortisol) using Roche Elecsys Cortisol II immunoassay. Participants were then stratified into three groups based on cortisol concentration to predict risk of AI; cortisol < 100nmol/l (high risk), 100–315 nmol/l (indeterminate risk) and > 315 nmol/l (low risk) based on locally derived reference ranges. 21% participants were classified as low risk, 18% as high risk and the remaining 61% at indeterminate risk of AI. Median morning cortisol in the low risk group was ten-fold higher than those in the high risk group (380 vs 38.5 nmol/l, p=0.001). OCS exposure was a significant predictor of risk of AI (OR 1.1 [1.03–1.17] per mg/kg increase in prednisolone exposure, p=0.004)). Participants at high risk were more likely to be on maintenance OCS (33% vs 0%, p=0.015) and had a greater median cumulative OCS exposure over the study period (7.55 vs 0.66 mg/kg prednisolone, p=0.002). ICS exposure was also associated with risk of AI. Participants at high risk AI had a greater adherence to ICS therapy (78% vs 62%, p=0.049) and greater cumulative received ICS dose over the study duration than those at low risk AI (178.2 vs 127.9 mg, p=0.036). ICS exposure remained a significant predictor of AI even when OCS exposure is controlled for (OR 2.49 [1.06–5.82] per 1mg/kg increase in FP exposure). Both the asthma control test (ACT) & asthma quality of life questionnaire (AQLQ) scores correlate with morning cortisol concentration (ACT r=0.2, p=0.068, AQLQ r=0.26, p=0.019). Interestingly, participants with cortisol < 100nmol/l reported worse asthma control (ACT score 16 vs 20, p=0.07) and a lower AQLQ score (4.1 vs 5.8, p=0.02) than the low risk group despite objectively better lung function (FEV1 90.6 vs 77.6% predicted). Our data suggests that both cumulative oral and inhaled glucocorticoid exposure contribute independently to cortisol suppression and risk of AI. The discrepancy between objective (FEV1) and more subjective measures of asthma control (ACT score) in the high risk group suggests that undiagnosed AI, as well as other non-airway co-morbidities, may contribute to the symptom burden experienced by these patients.

Respiratory rate (RR) is routinely used to monitor patients with infectious, cardiac and respiratory diseases and is a component of early warning scores used to predict patient deterioration. However, it is often measured visually with considerable bias and inaccuracy. Objectives. Firstly, to compare distribution and accuracy of electronically measured RR (EMRR) and visually measured RR (VMRR). Secondly, to determine whether, and how far in advance, continuous electronic RR monitoring can predict oncoming hypoxic and pyrexic episodes in infectious respiratory disease. Approach. A retrospective cohort study analysing the difference between EMRR and VMRR was conducted using patient data from a large tertiary hospital. Cox proportional hazards models were used to determine whether continuous, EMRR measurements could predict oncoming hypoxic (SpO2 < 92%) and pyrexic (temperature >38 °C) episodes. Main results. Data were gathered from 34 COVID-19 patients, from which a total of 3445 observations of VMRR (independent of Hawthorne effect), peripheral oxygen saturation and temperature and 729 117 observations of EMRR were collected. VMRR had peaks in distribution at 18 and 20 breaths per minute. 70.9% of patients would have had a change of treatment during their admission based on the UK’s National Early Warning System if EMRR was used in place of VMRR. An elevated EMRR was predictive of hypoxic (hazard ratio: 1.8 (1.05–3.07)) and pyrexic (hazard ratio: 9.7 (3.8–25)) episodes over the following 12 h. Significance. Continuous EMRR values are systematically different to VMRR values, and results suggest it is a better indicator of true RR as it has lower kurtosis, higher variance, a lack of peaks at expected values (18 and 20) and it measures a physiological component of breathing directly (abdominal movement). Results suggest EMRR is a strong marker of oncoming hypoxia and is highly predictive of oncoming pyrexic events in the following 12 h. In many diseases, this could provide an early window to escalate care prior to deterioration, potentially preventing morbidity and mortality.

BACKGROUND: Goal-orientated health care accounts for patient preferences and values, not just physician treatment aims. The Global Initiative for Asthma (GINA) management strategy states that clinicians should elicit patients' own treatment goals as a central part of care. Despite this recommendation, data on patients' treatment goals are sparse among patients with severe asthma. OBJECTIVE: The objective of this study is to investigate the relationship between rates of treatment adherence and goal achievement, and patient-selected goals. METHODS: Thematic analysis was used to characterize patient selected goals. Previously undescribed goal categories in asthma were identified, quantified, and related to clinical characteristics. Goal achievement was aligned with objectively measured trea ment adherence. RESULTS: Three categories of patients-selected goals were identified from 2 randomized control trials: disease-specific (n = 98 [51%] and n = 92 [54%], respectively), function-related (n = 90 [48%] and n = 61 [36%]), and knowledge (n = 1 [1%]and n = 17 [10%]). Only 53% of goals aligned with clinician treatment goals. Patients who chose disease-specific goals were more likely to achieve both control and their specified goal (n = 98 [45%], odds ratio: 1.789, confidence interval: 1.066-3.001). Male participants are more likely to focus on disease-specific goals. Patients who achieved their goals were more likely to be T2-high, have an elevated fractional exhaled nitric oxide (FeNO) at their first visit, and have a lower FeNO value at their final visit. Interestingly, adherence rates decline significantly for those who achieve their goals. CONCLUSION: Almost half of patient-selected goals do not align with GINA clinical asthma management goals. Participants who chose goals that do align with clinicians were more likely to achieve them. (c) 2021 American Academy of Allergy, Asthma & Immunology (J Allergy Clin Immunol Pract 2021;9:2732-41)

For a physician, the final step of a consultation consists of developing a treatment plan and prescription. For the patient, this is the start of a process. First, their role in the treatment plan must be clarified, then they may have to obtain an alternative prescription from their general practitioner. Next, they must have the prescription filled and dispensed from the pharmacy and, finally, they must take the treatment on time and for the required duration. For people with chronic conditions, this requires repeatedly returning to the pharmacy for the prescription to be renewed and dispensed. Given that many patients are on multiple treatment regimens and may have poor health literacy, this becomes a complex process and it is not surprising that this can, and frequently does, go wrong. Research shows that when a patient does not adhere to standard asthma or COPD treatment, they report poor control and overuse of rescue beta-agonists, experience frequent exacerbations and are often prescribed add-on treatments such as biological agents. In short, poor treatment adherence can manifest in the same way as a refractory condition. These clinical features should prompt a clinician to investigate poor adherence as they might investigate a new blood or radiological finding. Examining a patient's prescription refill records or a digitally enabled inhaler can demonstrate a number of patterns of inhaler use. A small minority regularly use their treatment as prescribed but many appear to be "cluster users": a group of patients who use their treatment correctly when they are unwell, but once some level of personal control is attained, they cease or reduce their use. Others may cease using their treatment because they are not perceiving a benefit or because an alternative condition accounts for their symptoms. In other words, clinicians can consider that treatment adherence is like a clinical sign: something to be investigated so that they may understand the patient's condition better.

Poor adherence to treatment is a common reason why patients with chronic disease have worse outcomes than might be expected. Poor treatment adherence is of particular concern among people with airways disease because, apart from not taking treatment as prescribed, inhaled medication can also be administered incorrectly. Recently, a number of technological advances that accurately document when an inhaled treatment has been used and, in certain instances, how it was used have been developed. There is good evidence from a number of research groups that these devices, either by patient reminders or physician feedback, promote adherence to inhaled treatments. What is less certain is how, in a real-world setting, these devices change outcomes. In this perspective article, the role of electronic devices in quantifying treatment use and addressing poor treatment adherence and their potential role in clinical practice outside of clinical validation trials are described.