Spirometry services in England post-pandemic and the potential role of AI support software: a qualitative study of challenges and opportunities.

BACKGROUND: Spirometry services to diagnose and monitor lung disease in primary care were identified as a priority in the NHS Long Term Plan, and are restarting post-COVID-19 pandemic in England; however, evidence regarding best practice is limited. AIM: To explore perspectives on spirometry provision in primary care, and the potential for artificial intelligence (AI) decision support software to aid quality and interpretation. DESIGN AND SETTING: Semi-structured interviews with stakeholders in spirometry services across England. METHOD: Participants were recruited by snowball sampling. Interviews explored the pre- pandemic delivery of spirometry, restarting of services, and perceptions of the role of AI. Transcripts were analysed thematically. RESULTS: In total, 28 participants (mean years' clinical experience = 21.6 [standard deviation 9.4, range 3-40]) were interviewed between April and June 2022. Participants included clinicians (n = 25) and commissioners (n = 3); eight held regional and/or national respiratory network advisory roles. Four themes were identified: 1) historical challenges in provision of spirometry services; 2) inequity in post- pandemic spirometry provision and challenges to restarting spirometry in primary care; 3) future delivery closer to patients' homes by appropriately trained staff; and 4) the potential for AI to have supportive roles in spirometry. CONCLUSION: Stakeholders highlighted historic challenges and the damaging effects of the pandemic contributing to inequity in provision of spirometry, which must be addressed. Overall, stakeholders were positive about the potential of AI to support clinicians in quality assessment and interpretation of spirometry. However, it was evident that validation of the software must be sufficiently robust for clinicians and healthcare commissioners to have trust in the process.

Implementation of a primary care asthma management quality improvement programme across 68 general practice sites.

Despite national and international guidelines, asthma is frequently misdiagnosed, control is poor and unnecessary deaths are far too common. Large scale asthma management programme such as that undertaken in Finland, can improve asthma outcomes. A primary care asthma management quality improvement programme was developed with the support of the British Lung Foundation (now Asthma + Lung UK) and Optimum Patient Care (OPC) Limited. It was delivered and cascaded to all relevant staff at participating practices in three Clinical Commissioning Groups. The programme focussed on improving diagnostic accuracy, management of risk and control, patient self-management and overall asthma control. Patient data were extracted by OPC for the 12 months before (baseline) and after (outcome) the intervention. In the three CCGs, 68 GP practices participated in the programme. Uptake from practices was higher in the CCG that included asthma in its incentivised quality improvement programme. Asthma outcome data were successfully extracted from 64 practices caring for 673,593 patients. Primary outcome (Royal College of Physicians Three Questions [RCP3Q]) data were available in both the baseline and outcome periods for 10,328 patients in whom good asthma control (RCP3Q = 0) increased from 36.0% to 39.2% (p < 0.001) after the intervention. The odds ratio of reporting good asthma control following the intervention was 1.15 (95% CI 1.09-1.22), p < 0.0001. This asthma management programme produced modest but highly statistically significant improvements in asthma outcomes. Key lessons learnt from this small-scale implementation will enable the methodology to be improved to maximise benefit in a larger scale role out.

Residual Lung Abnormalities after COVID-19 Hospitalization: Interim Analysis of the UKILD Post-COVID-19 Study.

Rationale: Shared symptoms and genetic architecture between coronavirus disease (COVID-19) and lung fibrosis suggest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection may lead to progressive lung damage. Objectives: The UK Interstitial Lung Disease Consortium (UKILD) post-COVID-19 study interim analysis was planned to estimate the prevalence of residual lung abnormalities in people hospitalized with COVID-19 on the basis of risk strata. Methods: The PHOSP-COVID-19 (Post-Hospitalization COVID-19) study was used to capture routine and research follow-up within 240 days from discharge. Thoracic computed tomography linked by PHOSP-COVID-19 identifiers was scored for the percentage of residual lung abnormalities (ground-glass opacities and reticulations). Risk factors in linked computed tomography were estimated with Bayesian binomial regression, and risk strata were generated. Numbers within strata were used to estimate posthospitalization prevalence using Bayesian binomial distributions. Sensitivity analysis was restricted to participants with protocol-driven research follow-up. Measurements and Main Results: The interim cohort comprised 3,700 people. Of 209 subjects with linked computed tomography (median, 119 d; interquartile range, 83-155), 166 people (79.4%) had more than 10% involvement of residual lung abnormalities. Risk factors included abnormal chest X-ray (risk ratio [RR], 1.21; 95% credible interval [CrI], 1.05-1.40), percent predicted DlCO less than 80% (RR, 1.25; 95% CrI, 1.00-1.56), and severe admission requiring ventilation support (RR, 1.27; 95% CrI, 1.07-1.55). In the remaining 3,491 people, moderate to very high risk of residual lung abnormalities was classified at 7.8%, and posthospitalization prevalence was estimated at 8.5% (95% CrI, 7.6-9.5), rising to 11.7% (95% CrI, 10.3-13.1) in the sensitivity analysis. Conclusions: Residual lung abnormalities were estimated in up to 11% of people discharged after COVID-19-related hospitalization. Health services should monitor at-risk individuals to elucidate long-term functional implications.

Epidemiology of bronchiectasis in the UK: Findings from the British lung foundation's 'Respiratory health of the nation' project.

Key findings of this national survey of non-cystic fibrosis bronchiectasis epidemiology were that its prevalence, incidence and mortality have all increased over recent years; we estimate that around 212,000 people are currently living with bronchiectasis in the UK, very much higher than commonly quoted figures. Bronchiectasis is more common in females than males; 60% of diagnoses are made in the over-70 age group. Regional differences in prevalence, incidence, mortality, and hospital admission were identified. An intriguing finding was that bronchiectasis is more commonly diagnosed in the least deprived sections of the population, in contrast to other respiratory disorders.

The use of an online Epworth Sleepiness Scale to assess excessive daytime sleepiness.

PURPOSE: Excessive daytime sleepiness is the most common complaint reported in sleep clinics. We hypothesised that utilising modern media to deliver an online Epworth Sleepiness Scale, age- and gender-related differences in subjective daytime sleepiness could be assessed. METHODS: Age, gender and online Epworth Sleepiness Scale (range 0-24 points) of 39,448 subjects were recorded between January 2013 and November 2015. RESULTS: A significant trend, for males but not females, was found between age and Epworth score (p < 0.001). Average scores were higher for female subjects in their 1st and 2nd (p = 0.014), 3rd (p < 0.011) and 4th lifetime decade (p = 0.011), whereas male subjects conveyed significantly higher levels of sleepiness in their 7th lifetime decade (p < 0.001). Individual item analysis found differences between gender; females scored significantly higher than males in items 1, 4 and 5, while male subjects had higher scores for items 3, 6, 7 and 8. Lowest levels of sleepiness were reported for item 8 and highest scores for item 5. CONCLUSIONS: The use of an online Epworth Sleepiness Scale identifies gender- and age-specific differences and facilitates new pathways in the delivery of chronic care.

The use of an online pictorial Epworth Sleepiness Scale in the assessment of age and gender specific differences in excessive daytime sleepiness.

BACKGROUND: Excessive daytime sleepiness (EDS) is a non-specific but highly prevalent cardinal symptom of sleep disorders. We hypothesized that with modern media and an online pictorial Epworth Sleepiness Scale (ESS) age and gender specific differences of EDS could be identified on a large scale. This could be helpful in the screening of patients with sleep disorders. PATIENTS AND METHODS: In 8,098 subjects, age and gender were recorded in addition to an online pictorial ESS (range 0-24 points). The cut-off for EDS (ESS >10 points) was chosen in line with the traditional ESS. RESULTS: The prevalence of EDS was slightly higher in male subjects (45% vs. 43%, P=0.033). When age was considered, female subjects tended to be sleepier in their 3(rd) and 4(th) lifetime decade (P=0.01 and P=0.003, respectively), whilst male subjects scored significantly higher in their 7(th) decade (P<0.0001); there was a trend to more daytime symptoms with higher age (P for trend <0.001). CONCLUSIONS: The online pictorial ESS identifies gender differences in EDS and reveals increased levels of sleepiness associated with higher age. The use of modern media facilitates reaching out to the general population to raise awareness of conditions associated with EDS such as sleep apnoea.

Predicted relative prevalence estimates for obstructive sleep apnoea and the associated healthcare provision across the UK.

We surveyed the UK distribution of five factors commonly associated with obstructive sleep apnoea (OSA) to produce an overall risk map that could be used to predict relative prevalence estimates. The weighting and mapping of selected risk factors associated with OSA highlighted significant regional variation in predicted prevalence estimates. These data provide the first attempt to systematically map the UK for OSA and identify areas where the condition is likely to be more prevalent. The data show a significant mismatch between areas identified as having a high predicted prevalence estimate and the distribution of existing sleep centres.