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IntroductionClinical Scientist-led breathlessness clinics within Community Diagnostic Centres (CDCs) enable timely diagnosis and management planning for patients with chronic breathlessness (Pritchard et al. 2025). However, Clinical Scientists in the UK do not currently hold prescribing rights. Where management requires initiation of pharmacological therapy, treatment implementation relies on subsequent action by primary care. The impact of this prescribing constraint on treatment delay has not been well characterised.MethodsA retrospective service evaluation was undertaken of patients seen and discharged from a Clinical Scientist-led breathlessness clinic between November 2023 and December 2025. Of 206 referrals, 139 patients were assessed and discharged at the point of analysis. Thirty-nine patients received a new diagnosis of asthma or chronic obstructive pulmonary disease with advice to initiate guideline-concordant inhaled therapy in line with national guidance (NICE 2024). Time-to-prescription was defined as days from clinic appointment to GP prescription issue date, determined by GP record review. Data are presented as median (interquartile range).ResultsOf the 39 patients advised to commence inhaled therapy, 32 had a documented GP prescription issue date and were included in delay analysis. The median time from clinic appointment to GP prescription was 46 days (IQR 29.5–68.5; range 6–414 days). Seven patients had no record of a new inhaled prescription within the study window, including two already prescribed inhaled therapy, three without the new diagnosis recorded in GP records, and one with an existing diagnosis.ConclusionIn a Clinical Scientist-led breathlessness clinic, absence of prescribing authority was associated with prolonged and highly variable delays to initiation of inhaled therapy following new respiratory diagnosis. Reliance on post-clinic GP prescribing risks undermining the benefits of rapid diagnostic pathways delivered within CDCs (NHS England 2020). These findings describe a system constraint within current service models rather than evaluating prescribing practice or professional scope, with implications for patient experience, safety, and service effectiveness.

BackgroundIt is estimated that one in four individuals report exercise-related respiratory symptoms, including shortness of breath, cough and/or wheeze.1 Untreated symptoms result in reduced participation in exercise activity or exercise avoidance, particularly in adolescents, with symptoms usually treated as exercise-induced bronchoconstriction (EIB) or asthma.2 It is now widely accepted that differential diagnoses such as exercise-induced laryngeal obstruction (EILO) and dysfunctional breathing (DB) should be considered, due to their high prevalence.1Considered the gold standard to confirm a diagnosis of EILO, continuous laryngoscopy during exercise (CLE) has limited availability in UK paediatric hospitals, likely exacerbating misdiagnosis of exercise-related respiratory symptoms in children and young people and leading to unnecessary treatments, in particular inhaled corticosteroids. Cardiopulmonary exercise testing (CPET) is an established objective test, but again is only available at tertiary level, such that differential diagnoses including DB may be missed in paediatric primary and secondary-level care.Study sourceThis case describes the successful diagnosis and treatment of EILO, DB and asthma overlap in a symptomatic 14-year old child.Primary resultsA 14-year old girl was referred to a tertiary children’s respiratory clinic with a 7-month history of exercise-related respiratory symptoms. Despite increasing, burdensome levels of inhaled treatments, symptoms continued to worsen and exercise avoidance ensued. Reported symptoms included inhalation difficulty with accompanying audible, noisy breathing. Diagnoses included exclusion of poorly-controlled asthma (fractional expired Nitric Oxide (FeNO) and bronchodilator response), exclusion of EIB from poorly-controlled asthma (exercise-induced bronchoprovocation fully-medicated), confirmation of DB and suspicion of EILO (CPET), and confirmation of underlying asthma (FeNO and exercise-induced bronchoprovocation off-treatment). Subsequent referral for CLE led to surgical intervention (supraglottoplasty), therapeutic intervention (physiotherapy) for DB and reduction of inhaled therapies.ConclusionThis case describes successful approaches to diagnosing and treating overlapping EILO, DB and asthma, with resolution of all exercise-related symptoms for this child. The improvement in patient well-being, reduction in treatment burden and return to exercise following successful management highlights the importance of synergy in clinical assessment, objective testing, treatment optimisation and effective interventions (surgical, medical and behavioural).ReferencesHull JH, et al. Thorax 2022;77:540–551.Johansson H, et al. Thorax 2015;70:57–63.

IntroductionCardiopulmonary exercise testing (CPET) is used to support pre-operative risk stratification, evaluate cardiopulmonary disease, and investigate unexplained breathlessness. A previous United Kingdom (UK) survey (Reeves et al., 2018) highlighted variation in practice and a lack of standardisation. This survey provides an update to current UK CPET services following the implementation of ARTP national guidelines (Pritchard et al., 2021).MethodsAn online survey was distributed via the ARTP and the International Prehabilitation and Perioperative Exercise Testing Society. Departmental and CPET service leads across England, Scotland, Wales, and Northern Ireland were contacted between August 2025 and February 2026.ResultsA total of 105 survey responses were received across the UK (figure 1). Over 25,000 CPETs are undertaken annually, with 73.3% of centres delivering adult-only services, 6.7% paediatric-only and 20% offering combined adult and paediatric services. Risk stratification was reported by 78 centres (74.3%), of which 71 (91.0%) utilised or adapted ARTP risk stratification to meet service needs. Services were led by senior physiologists or clinical scientists, particularly at Band 7 (40.0%) and Band 6 (30.5%) levels. Other services were led by doctors (7.6%) or anaesthetists (17.1%). Standard operating procedures (SOPs) were widely reported for undertaking CPET (87.6%); however, fewer centres had SOPs for reporting (60.0%) or interpretation (33.3%), and 11.4% of services reported no CPET-specific SOPs. Physiologist-led reporting was observed at 40.0% of sites, while anaesthetist-led and shared reporting responsibilities occurred at 25.7% and 18.1% of sites, respectively. Most services met ARTP guidance for report turnaround times of less than 10 days (90.4%). ECG training requirements varied. In-house training was most frequently reported (47.6%), followed by medical training pathways (23.8%), SCST accreditation, external course attendance (16.2%), while 16.2% of services reported no formal ECG training requirement. 54.2% of centres offer blood gas analysis, while 8 offer combined CPET-Continuous laryngeal endoscopy (CLE).ConclusionCPET services are widely established across the UK, despite the introduction of ARTP guidelines, significant variation remains in service structure, protocols, reporting practices, and training requirements. While most centres meet recommended reporting timelines, these findings highlight the need for greater national standardisation in CPET governance, training, and interpretation.Abstract P44 Figure 1United Kingdom CPET services (blue location markers = CPET centres; red location markers = CPET-CLE centres)

IntroductionAccurate CO2 monitoring during sleep studies is fundamental to optimising ventilation in children. While end-tidal CO2 (EtCO2) and Transcutaneous CO2 (TcCO2) are common alternatives to arterial blood gases (ABG), evidence regarding their agreement is mixed. TcCO2 has shown good agreement with Arterial CO2, whereas there’s mixed evidence regarding EtCO2 measurements during sleep (Orlikowski et al. Respiratory Medicine 2016; 117: 7–13; Won et al. American journal of physical medicine & rehabilitation 2016; 95.2: 91-95). To our knowledge, this is the first study to compare EtCO2 and TcCO2 in tracheostomy-ventilated children.MethodsWe conducted a prospective study of children on tracheostomy ventilation undergoing cardiorespiratory sleep studies at Great Ormond Street Hospital (Dec 2024-Dec 2025). EtCO2 (Masimo Rad-97® NemoLine® Capnography) and TcCO2 (Radiometer Ltd© TCM5 with Sensor-92) were recorded simultaneously. Stowood Scientific Instrument Ltd© VisiDownload software was used to analyse Rad-97®, and Natus® Embla® Remlogic for TCM5 data. Studies with <4 hours of good quality CO2 recording were excluded. We aimed to compare mean and max CO2, and percentage of sleep with CO2 > 50mmHg. Data was analysed using Bland-Altman plots with GraphPad Prism 10.Results20 children, Median (IQR) age 7 (4,13) years were studied; 8 (40%) were excluded for unreliable EtCO2 recording, leaving 12 for analysis. Mean CO2: EtCO2 showed a mean bias of -3.4 mmHg compared to TcCO2 (SD 6.1 mmHg), with 95% limits of agreement (LoA) of -8.5 to +15.4 mmHg. Max CO2: EtCO2 showed a mean bias of -0.34 mmHg compared to TcCO2 (SD 11.1 mmHg) with LoA of -22.1 to +21.4 mmHg. Insufficient data to compare percentage of sleep with CO2 >50mmHg.ConclusionA mean bias of -3.4mmHg suggests EtCO2 may systematically underestimate CO2 levels in tracheostomy ventilated children. Although numerically small, this bias combined with wide LoA and a high failure rate (40%) renders EtCO2 unreliable for accurate ventilation monitoring. Limitations of the study include a small sample size, and different software used for EtCO2 and TcCO2 analysis. Future research should focus on larger cohorts and methods to improve EtCO2 signal stability.

IntroductionDisruption to services due to the COVID-19 pandemic has led to an increased demand for pulmonary function testing (PFT). Healthcare systems, including the NHS, have responded by prioritising respiratory medicine; expanding diagnostic capacity through increasing activity and opening community diagnostic centres. As services scale and operate multiple PFT systems concurrently, healthcare professionals require confidence that patient results are consistent across devices. Assessment of inter- and intra-device variability is essential to support reliable clinical interpretation and longitudinal testing.AimsThe aim of this study was to assess inter- and intra-device variability for PFT measurements obtained using VitaloLAB PFT systems. We hypothesized that repeated measurements would show low variability both within and between devices, such that device-related effects would be low relative to expected biological variation.Methods19 healthy participants,18-65 years, completed 3 PFT sessions on separate days using 2 VitaloLAB PFT systems (VitaloLAB A and VitaloLAB B). Participants were randomised to perform two sessions on one device and one session on the other (e.g. AAB or BBA). Each session included spirometry, diffusing capacity (DLCO) and lung volumes (multiple breath nitrogen washout, MBN2) tests. Tests were conducted by trained respiratory physiologists in accordance with ERS/ATS technical standards. Inter- and intra-device variability was assessed using Bland-Altman analysis.ResultsDatasets were obtained for all 19 participants across all tests. Preliminary analyses indicate low within-subject variability across repeated test sessions on the same device and small absolute differences between devices across all test types. Observed variability was consistent with expected variation, accounting for biological and within test variation. The bias across both systems for FEV1 was 0.03 (SD 0.26), FVC was -0.04 (SD 0.19), TLCO was 0.39 (SD 0.82), KCO was 0.17 (SD 0.06), FRC was 0.15 (SD 0.41) and TLC was 0.23 (SD 0.48).ConclusionsIn healthy individuals, inter- and intra- device variability for key PFT parameters met ERS/ATS guidelines when testing was performed on identical VitaloLab PFT systems. These findings support the comparability of results when multiple VitaloLAB systems of the same type are used within a clinical service, offering confidence in the consistency of patient results across devices.

IntroductionContinuous positive airway pressure (CPAP) treatment has a consistently low adherence rate, with approximately 35% of patients not tolerating it long term (Rotenberg, Murariu, Pang 2016). What if non-adherence rates are influenced by anatomical airway differences? What if we can identify these before the struggle with CPAP even begins? This study investigates whether CPAP compliance is statistically related to anatomical structures identified in drug induced sedation endoscopy (DISE) procedures in patients with sleep apnoea. The primary hypothesis is that there will be a significant relation between CPAP compliance and anatomical structures identified during DISE.MethodData from patients using standard ResMed Airsense 10 & 11 machines (n=59) were compared within group. All machines automatically titrated pressure. Participants with mild, moderate, and severe apnoea were included. Treatment efficacy was measured by AHI average over 365 days. Compliancy was measured by the percentage of days CPAP was used >= 4 hours out of days CPAP was used over the last 365 of data as well as minutes used on average per 12-hour session. DISE procedures were performed at UCLH by in-house surgeons. Structure features included palette collapse, tonsil size, lateral wall collapse, tongue base collapse, epiglottis trap door collapse, turbinate size, and septum position.ResultsPRELIMARY DATA RESULTS. One-way ANOVAs were performed to compare the effect DISE identified structures on CPAP pressures.A one-way ANOVA revealed that there was a statistically significant difference in mean CPAP adherence usage per night when measuring the degree of lateral wall collapse p < 0.05 (F(2, 56) = [3.23], p = [0.047]). One-way ANOVAs performed for other DISE identified structures did not reveal statistically significant differences in CPAP adherence.Post Hoc tests and Multi-way ANOVAs are to be performed for the final data presentation.ConclusionsThere was a clinically relevant significant correlation between CPAP adherence and lateral wall collapse when identified in a DISE procedure. Therefore, the null hypothesis can be rejected. Further investigation on internal airway structures during sleep apnoea is required to investigate to what degree airway structures influence comfort and compliance of CPAP therapy.

IntroductionRespiratory disease is a national clinical priority, with delayed and inaccurate diagnoses contributing to inappropriate treatment and avoidable referrals to secondary care. Historically, access to quality-assured spirometry and fractional exhaled nitric oxide (FeNO) testing in primary care has been variable. A pharmacist-led Respiratory Diagnostic Hublet within North Central London improves access to timely, standardised respiratory diagnostics across East Haringey. This service evaluation describes early activity, diagnostic outcomes and service utilisation following implementation of this model in primary care.MethodsDiagnostic testing was primarily performed by trained Healthcare Assistants with interpretation provided by ARTP-trained clinical pharmacists. The RDH pathway is outlined schematically (figure 1), highlighting pharmacist-led referral triage, selection of appropriate diagnostic testing and integrated clinical interpretations beyond spirometry indices alone. A retrospective service evaluation was conducted using activity data collected from the RDH between July and December 2025, including all adult and paediatric referrals. Data extracted comprised number of referrals, waiting time from referral to appointment, diagnostic tests performed, diagnostic outcomes and escalation to the respiratory multidisciplinary team (MDT). Descriptive statistics were used to summarise outcomes.ResultsBetween July and December 2025, 394 patients (adults and children) were referred to the RDH for investigation of new respiratory presentations. Mean waiting time from referral to appointment was 11 days, enabling earlier diagnostic evaluation compared with traditional secondary care pathways. Diagnostic testing resulted in a confirmed respiratory diagnosis in 60% of patients, most commonly asthma and COPD. 31% of patients had normal respiratory test results and no diagnosis was made. Structured written interpretations of results were supplied to referring clinicians. Only 3% of patients required escalation for discussion at the respiratory MDT, with no paediatric cases requiring MDT input. The remaining 6% of patients were awaiting respiratory testing at the time of analysis.ConclusionsEarly evaluation demonstrates that a pharmacist-led RDH can deliver high-volume, quality-assured respiratory diagnostics within primary care, enabling earlier diagnostic clarification while reserving specialist MDT input for complex cases. This model supports accurate differentiation between asthma, COPD, overlap syndromes and restrictive disease, reduces inappropriate diagnostic labelling and provides an effective interface between primary and secondary care respiratory services.Abstract P7 Figure 1Pharmacist-led respiratory diagnostic hublet (RDH) pathway demonstrating referral triage, diagnostic testing and integrated interpretation to support appropriate use of specialist respiratory services

IntroductionTelemonitoring of home non-invasive ventilation (H-NIV) is gaining interest as a tool for detecting deteriorating patients in the community (Khirani, Patout and Arnal 2024). Although clinical alerts can be configured within telemonitoring software, evidence supporting its routine use remains limited.MethodsThis single-centre retrospective study analysed NIV telemonitoring data (NIV-TD) collected from AirView® for 70 H-NIV patients between 2020–2025. All patients were compliant with NIV (≥4 hours usage on ≥70% of days) and had experienced at least one acute hospital admission since NIV initiation (admission group). NIV-TD was collected for two 10-day periods: prior to a routine clinical review (stable period) and prior to hospital admission (pre-admission period). Stable-period data from a control group (n=70) were used for logistic regression analysis only.ResultsA Kruskal–Wallis test demonstrated significantly higher hourly NIV usage during the stable period in patients with neuromuscular disease (NMD) compared with obesity hypoventilation syndrome (OHS) (9.50 hours [IQR 8.13–14.44] vs 6.82 hours [3.88–7.72], H(4)=12.99, p=0.011). Wilcoxon signed-rank tests showed that during the pre-admission period, respiratory rate (p≤0.001, r=0.414) and spontaneous triggered breaths (p≤0.001, r=0.345) significantly increased with moderate effect sizes, while tidal volume significantly decreased (p=0.03, r=0.183) with a weak effect size (figure 1A). Multivariate logistic regression identified increases in respiratory rate (OR 1.211, 95% CI 1.010–1.450, p=0.038) and hourly usage (OR 1.211, 95% CI 1.026–1.431, p=0.024), alongside decreases in spontaneous cycled breaths (inverse OR 1.032, 95% CI 1.009–1.056, p=0.006) and compliance days (inverse OR 2.13, 95% CI 1.38–3.29, p<0.001), as significantly associated with hospital admission (RUSC model). ROC curve analysis of the RUSC model (figure 1B) showed fair predictive performance (AUC 0.784), improving to good performance with the addition of stable-period PaCO2 (AUC 0.850).ConclusionNIV usage differed significantly between NMD and OHS groups, helping address gaps in evidence on normal NIV-TD variation (Jeganathan et al., 2021). Changes in respiratory rate, spontaneous triggered breaths, and tidal volume were evident prior to hospital admission. The RUSC model could inform telemonitoring alerts for unwell H-NIV patients, though multi-centre studies are required to improve generalisability and predictive accuracy.Abstract P2 Figure 1ANIV telemonitoring variables: analysis between stable and pre-admission periods boxplots shown for the telemonitoring data calculated from AirView® across the 10-day stable period (blue) and pre-admission period (green) in the admission group, for respiratory rate (A), spontaneous triggered breaths (B) and tidal volume (C). Line: median, Box: interquartile range (IQR) (Q1-Q3), Whiskers: 1.5x IQR, Dots: outliers (1.5-3x IQR)Abstract P2 Figure 1BReceiver operating characteristic (ROC) curve for the multivariate rusc model & univariate single variable analysis area under the curve (AUC) displayed with associated significance level (p value <0.05). Higher AUC values indicate better predictive performance. RUSC, respiratory rate, hourly usage, spontaneous cycle & compliance; PaCO2, partial pressure of carbon dioxide in arterial blood

BackgroundObstructive sleep apnoea (OSA) is a condition whereby the upper airway partially or completely collapses during sleep, resulting in fragmentation of sleep and increased cardiovascular events. Mild OSA, defined as Apnoea-Hypopnoea Index (AHI) of 6-15 events per hour can cause significant daytime symptoms. The National Institute for Health and Care Excellence (NICE, 2021) recommends conservative treatment of mild OSA including weight loss, positional therapy and lifestyle measures. However, in patients with persistent daytime symptoms Continuous Positive Airway Pressure (CPAP) therapy should be offered (NICE, 2021). Garnadoux et al. (2016) found that CPAP therapy adherence in mild OSA was low. Specialist-led assessment, including input from Consultants and Advanced Clinical Scientists, may enhance patient understanding, engagement, and subsequent adherence to therapy.AimThis study aimed to explore CPAP adherence in patients with mild symptomatic OSA following assessment in a Clinical Scientist Led clinic and to evaluate whether scientist-led sleep services could support improved adherence and symptom control.MethodsA retrospective study of 122 patients diagnosed with mild OSA (AHI 6–15) who attended either Consultant or Advanced Clinical Scientist-led clinics. Of these, 45 patients were initiated on CPAP therapy. Data were collected at baseline (specialist clinic), at the first follow-up (14 days post-initiation), and at 180 days. Variables included patient demographics, comorbidities, AHI, T90 (percentage of sleep time with oxygen saturation <90%), Epworth Sleepiness Scale (ESS) scores, and CPAP adherence. Adherence was defined as CPAP use ≥4 hours per night for ≥70% of nights. Table 1ResultsAdherence at 14 days was 63% and 60% at 180 days, indicating relatively stable long-term usage. Mean ESS scores decreased over the study period, reflecting improvements in daytime sleepiness. These outcomes suggest that, in a cohort selected through specialist review, CPAP therapy is both clinically beneficial and well-tolerated in patients with mild OSA. Table 2ConclusionSpecialist-led assessment, including Advanced Clinical Scientist input, may enhance engagement and support sustained CPAP adherence in patients with mild symptomatic OSA. Scientist-led sleep services could represent a valuable model for optimising treatment uptake and improving long-term patient outcomes. Further prospective studies are warranted to confirm these findings and explore patient-centred strategies.Abstract P38 Table 1Baseline demographics of study groupTotal in Group (N=45)Age (y)Gender (m/f)Weight (kg)Height (cm)BMI (kg/m2)Collar size (cm)AHI (event/h)AHI supine (event/h)% of time in supineODI (event/h)PLMI (event/h)T-90 (hour/min)ESS48.44 ± 11.2416/2992.75 ± 20.76169.1 ± 8.8032.33 ± 7.0839.25 ± 4.329.35 ± 2.9813.61 ± 8.2948.20 ± 27.149.26 ± 2.9712.59 ± 20.360:07 ± 0:2510.19 ± 5.75Data presented as mean ± standard deviation (SD). Definitions of abbreviations: BMI = Body Mass Index, AHI= Apnoea/Hyponoea Index, ESS =Epworth Sleepiness Score (out of 24). AHI = Apnoea/Hypopnoea Index. ODI = Oxygen Desaturation Index. PLMI = Periodic Leg Movement Index. T-90 = Tim below 90%.Abstract P38 Table 2CPAP adherence at 14 days and 180 daysTotal in Group (N=45)Follow up 14 daysFollow up 180 daysAverage use in 24 hours (h/min)Average % >4 hours for 70% of the timeAHI (event/h)ESSAverage use in 24 hours (h/min)Average % >4 hours for 70% of the timeAHI (event/h)ESS04:31±2:4060.23±37.891.60±2.1010.72±5.265:32±2.0164.58±121.421.42±1.798.04±5.84Data presented as mean ± standard deviation (SD). Definitions of abbreviations: AHI = Apnoea/Hypopnoea Index. ESS =Epworth Sleepiness Score (out of 24).

BackgroundDot sign- punctate foci of diffusion restriction on MRI DWI (diffusion weighted imaging) has been established as a radiological finding in Transient Global Amnesia (TGA).1 This sign can also be seen in non TGA presentations, and we outline a status epilepticus presentation below.CaseA 66 year old scientist went to sleep at 8pm and woke up his wife at midnight with his generalised tonic clonic seizure like activity lasting minutes. His GCS (Glasgow coma scale) was 7 and he was intubated and ventilated in the regional Hospital Emergency department before being flown to a tertiary hospital Intensive care unit, for further review, and neurological investigations.Patient had no known history of seizures and his past medical history included ischaemic heart disease, undergoing coronary bypass graft in 2020, Hypertension and Type 2 Diabetes Mellitus.He spontaneously recovered and was extubated and back to baseline GCS 15 on the neurological inpatient ward.His MRI Brain demonstrated classic \"dot sign\" in the left hippocampal region.EEG demonstrated diffuse bilateral slowing.Conclusion\"Dot sign\" is an interesting finding, not always related to TGA, and has been shown to be associated with status epilepticus.2 References Park JH, Oh CG, Kim SH, Lee SH, Jang JW. Hippocampal lesions of diffusion weighted magnetic resonance image in patients with headache without symptoms of transient global amnesia. Dement Neurocogn Disord. 2017 Sep;16(3):87–90. Meletti S, Monti G, Mirandola L, Vaudano AE, Giovannini G. Neuroimaging of status epilepticus. Epilepsia. 2018;59(S2):113–119.