Daily Respiratory Research Analysis

BACKGROUND: Respiratory allergies often begin in childhood and can progress over time, leading to increased disease burden. Allergen immunotherapy (AIT) is the only causal treatment for allergic respiratory diseases with disease-modifying potential. While randomised trials support its efficacy in controlling allergic rhinitis (AR) and asthma symptoms, long-term real-world data in children remain limited. METHODS: This paediatric study (n = 11,036) was conducted within the pre-defined framework of the REACT study, based on protocol-specified objectives. Children (< 18 years) with physician-diagnosed AR, with or without pre-existing asthma, were included. AIT-treated patients were matched 1:1 to non-AIT controls. Effectiveness was assessed over 9 years by comparing AR and asthma medication prescriptions, using a public database covering all reimbursable AIT products. Relative differences were calculated across the full observation period. RESULTS: AIT-treated children (mean age 11.4 years; 62.1% male) exhibited greater reductions in AR medication use than controls (additional 9% reduction beyond 61% in controls). In children with asthma, AIT was associated with additional reductions in asthma medication use (-21% beyond -48% in controls), severe exacerbations (-21% beyond -36%), and new oral corticosteroid prescriptions (-33% beyond -41%). Age stratification revealed more pronounced AR medication reductions in younger children (0-11 years) than in adolescents (12-17 years). CONCLUSION: This large-scale, real-world study supports the long-term effectiveness of AIT in children with AR, with or without asthma. The findings reflect improved disease control and suggest a disease-modifying effect of AIT. Early intervention, particularly in younger children, may help mitigate the progression of allergic disease.

OBJECTIVE AND DESIGN: The objective of the present study was to assess the discriminative accuracy of artificial intelligence (AI) software to identify COPD and other chronic respiratory diseases from primary care spirometry. This was a diagnostic study with blinded analysis. METHODS: Retrospective hand-held spirometry data from consecutive patients attending primary care clinics in Hillingdon (London, UK) between September 2015 and March 2019 were used. The index diagnosis was the "preferred" diagnosis determined by AI software (highest probability) using supervised random-forest machine learning to interpret raw spirometry data and basic demographics. The reference diagnosis was based on the consensus of expert pulmonologists with access to primary and secondary care medical notes and results of relevant investigations. Cross-tabulation of the index test results by the results of the reference standard for COPD and other respiratory disease categories provided the main outcome measures. RESULTS: In this primary care spirometry dataset from 1113 patients, 543 (48.8%) had a reference diagnosis of COPD. AI preferred diagnosis detected 456, achieving a sensitivity of 84.0% (95% CI 80.6-87.0%), specificity of 86.8% (83.8-89.5%), accuracy of 85.4% (83.2-87.5%) with area under curve (AUC) of 0.914 (0.896-0.930). AI preferred diagnosis identified 187 out of 249 patients with reference diagnosis of interstitial lung disease and 59 out of 107 patients with asthma, with AUCs of 0.900 (0.880-0.916) and 0.814 (0.790-0.836), respectively. CONCLUSION: AI software achieved high sensitivity and specificity in identifying COPD using spirometry and basic demographic data and may support accurate diagnosis of COPD in primary care. AI software performed less well for other chronic respiratory disease categories.

BACKGROUND: Post-COVID-19 Conditions (PCC) potentially affect millions of people, but it is unclear whether treating acute COVID-19 with nirmatrelvir-ritonavir may reduce the risk of PCC. METHODS: This is a retrospective cohort study using real-world, closed claims data to assess the relationship between nirmatrelvir-ritonavir and PCC by age group (12-17, 18-49, 50-64, ≥65 years). Eligible patients had a COVID-19 index date (positive laboratory test, ICD-10 diagnosis code, or nirmatrelvir-ritonavir prescription) from 1 April to 31 August 2022, in the outpatient, telehealth, or emergency department setting, and had a higher risk of severe COVID-19 based on age (≥50 years) or underlying risk factors. Treated patients (ie, received a nirmatrelvir-ritonavir prescription within ±5 days of index date) were matched 1:2 on age, sex, month of index date, and HHS region with untreated patients. PCC was defined by the presence of ≥1 of 45 new-onset symptoms or conditions recorded ≥60 days after index date. RESULTS: Of the treated patients, 291 433 were matched to 582 866 untreated patients. Treatment with nirmatrelvir-ritonavir reduced PCC risk in adults 50-64 years (adjusted hazard ratio [aHR] 0.93, 95% confidence interval [CI] 0.92-0.95) and ≥65 years (aHR 0.88, 95% CI 0.87-0.90). Treatment had minimal effect among high-risk adults 18-49 years (aHR 0.98, 95% CI 0.97-0.99) and no effect among high-risk adolescents 12-17 years (aHR 1.06, 95% CI 0.66-1.13). CONCLUSIONS: Results using real-world data suggest a protective relationship between nirmatrelvir-ritonavir during acute illness and PCC risk among older adults, but not among adolescents. Consideration may be given to outpatient treatment of mild to moderate COVID-19 with nirmatrelvir-ritonavir to reduce the risk of severe disease and PCC.