Abstract Background Our study examined whether prevalent and incident comorbidities are increased in idiopathic pulmonary fibrosis (IPF) patients when compared to matched chronic obstructive pulmonary disease (COPD) patients and control subjects without IPF or COPD. Methods IPF and age, gender and smoking matched COPD patients, diagnosed between 01/01/1997 and 01/01/2019 were identified from the Clinical Practice Research Datalink GOLD database multiple registrations cohort at the first date an ICD-10 or read code mentioned IPF/COPD. A control cohort comprised age, gender and pack-year smoking matched subjects without IPF or COPD. Prevalent (prior to IPF/COPD diagnosis) and incident (after IPF/COPD diagnosis) comorbidities were examined. Group differences were estimated using a t-test. Mortality relationships were examined using multivariable Cox proportional hazards adjusted for patient age, gender and smoking status. Results Across 3055 IPF patients, 38% had 3 or more prevalent comorbidities versus 32% of COPD patients and 21% of matched control subjects. Survival time reduced as the number of comorbidities in an individual increased (p < 0.0001). In IPF, prevalent heart failure (Hazard ratio [HR] = 1.62, 95% Confidence Interval [CI]: 1.43–1.84, p < 0.001), chronic kidney disease (HR = 1.27, 95%CI: 1.10–1.47, p = 0.001), cerebrovascular disease (HR = 1.18, 95%CI: 1.02–1.35, p = 0.02), abdominal and peripheral vascular disease (HR = 1.29, 95%CI: 1.09–1.50, p = 0.003) independently associated with reduced survival. Key comorbidities showed increased incidence in IPF (versus COPD) 7–10 years prior to IPF diagnosis. Interpretation The mortality impact of excessive prevalent comorbidities in IPF versus COPD and smoking matched controls suggests that multiorgan mechanisms of injury need elucidation in patients that develop IPF.

The morphology and distribution of airway tree abnormalities enable diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. Furthermore, the segmentation of a complete airway tree is challenging as the intensity, scale/size and shape of airway segments and their walls change across generations. The existing classical techniques either provide an undersegmented or oversegmented airway tree, and manual intervention is required for optimal airway tree segmentation. The recent development of deep learning methods provides a fully automatic way of segmenting airway trees; however, these methods usually require high GPU memory usage and are difficult to implement in low computational resource environments. Therefore, in this study, we propose a data-centric deep learning technique with big interpolated data, Interpolation-Split, to boost the segmentation performance of the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway segments at different scales. In terms of average segmentation performance (dice similarity coefficient, DSC), our method (A) achieves 90.55%, 89.52%, and 85.80%; (B) outperforms the baseline models by 2.89%, 3.86%, and 3.87% on average; and (C) produces maximum segmentation performance gain by 14.11%, 9.28%, and 12.70% for individual cases when (1) nnU-Net with instant normalisation and leaky ReLU; (2) nnU-Net with batch normalisation and ReLU; and (3) modified dilated U-Net are used respectively. Our proposed method outperformed the state-of-the-art airway segmentation approaches. Furthermore, our proposed technique has low RAM and GPU memory usage, and it is GPU memory-efficient and highly flexible, enabling it to be deployed on any 2D deep learning model.

There remains a significant burden of long-term illness from COVID-19, but the possible causes are poorly understood, which limits mechanistic understanding and therapeutic intervention. Analysing the plasma proteome in acute COVID-19 has been able to identify distinct signatures and potential therapeutic targets. Utilising this method in long COVID could provide insights and identify individuals who are more likely to suffer from persistent symptoms. We used mass spectrometry (MS)-based proteomics to analyse 220 individual plasma samples. 5ul of plasma per sample was processed and analysed with the use of standard flow LC-MS in a data independent acquisition (DIA) mode. Raw data were processed by the DIANN software and the statistical analysis was performed in R (Messner, C.B. et al. Cell Systems 2020; 11(1):11-24). Of 220 plasma samples, 138 (63%) were from patients with confirmed SARS-CoV-2 infection. Median time from symptom onset to sampling date for all patients was 16 weeks (IQR 12-19). Median age was 48 years (IQR 39-61) and 55% were female. In those with confirmed infection, 80 (58%) were hospitalised and in the unconfirmed group 83 (99%) were community managed. We identified 190 unique proteins and in the hospitalised group, these cluster according to severity of illness with either increasing or decreasing levels; P<0.05. Unbiased hierarchical clustering was able to identify subsets of patients stratified according to severity of acute illness. Further analysis will explore defined clinical phenotypes correlating symptoms, physiology and imaging with protein abundance.

An estimated 2% of the UK population are affected by a post-COVID syndrome with symptoms persisting beyond 12 weeks but there is a paucity of data on exercise-related physiological parameters. We used a m-6MWT with pre- and post-test earlobe capillary blood gases (CBGs) to investigate oxygen desaturation and reduced exercise capacity. Our clinic reviewed patients following hospital discharge or community referral. Clinical assessment included a m-6MWT with continuous measurement of index finger-SpO2 and heart rate (HR) during resting, testing, and recovery phases, as well as pre- and post-test earlobe CBGs. Blood pressure (BP), HR, and dyspnoea and fatigue Borg scores were recorded. 87 patients were included; median distance walked (6MWD%) was 101% predicted [IQR 80-114]. Median SpO2 dropped from 96% [IQR 95-97] to a nadir of 91% [IQR 87-93] recovering post-test to 95% [IQR 94-96]. Median resting dyspnoea and fatigue modified Borg scores were 1 [IQR 0-1] and 0 [IQR 0-2] respectively increasing to 4 [IQR 2-6] and 3 [IQR 0-6] post-test. Despite no significant drop in capillary pO2 at rest and test completion blood lactate increased by 1.5 mmol/L (P<0.0001) (Figure 1). Blood lactate levels varied amongst patients and was not effort dependent. Further work is required to understand the physiology of this heterogenous response which could help inform individual rehabilitation programmes in this cohort.

The morphology and distribution of airway tree abnormalities enables diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. In this study, we propose a data-centric deep learning technique to segment the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway trees at different scales. In terms of segmentation performance (dice similarity coefficient), our method outperforms the baseline model by 2.5% on average when a combined loss is used. Further, our proposed technique has a low GPU usage and high flexibility enabling it to be deployed on any 2D deep learning model.

Background Computer quantification of baseline computed tomography (CT) radiological pleuroparenchymal fibroelastosis (PPFE) associates with mortality in idiopathic pulmonary fibrosis (IPF). We examined mortality associations of longitudinal change in computer-quantified PPFE-like lesions in IPF and fibrotic hypersensitivity pneumonitis (FHP). Methods Two CT scans 6–36 months apart were retrospectively examined in one IPF (n=414) and one FHP population (n=98). Annualised change in computerised upper-zone pleural surface area comprising radiological PPFE-like lesions (Δ-PPFE) was calculated. Δ-PPFE >1.25% defined progressive PPFE above scan noise. Mixed-effects models evaluated Δ-PPFE against change in visual CT interstitial lung disease (ILD) extent and annualised forced vital capacity (FVC) decline. Multivariable models were adjusted for age, sex, smoking history, baseline emphysema presence, antifibrotic use and diffusion capacity of the lung for carbon monoxide. Mortality analyses further adjusted for baseline presence of clinically important PPFE-like lesions and ILD change. Results Δ-PPFE associated weakly with ILD and FVC change. 22–26% of IPF and FHP cohorts demonstrated progressive PPFE-like lesions which independently associated with mortality in the IPF cohort (hazard ratio 1.25, 95% CI 1.16–1.34, p<0.0001) and the FHP cohort (hazard ratio 1.16, 95% CI 1.00–1.35, p=0.045). Interpretation Progression of PPFE-like lesions independently associates with mortality in IPF and FHP but does not associate strongly with measures of fibrosis progression.

Objectives The study examined whether quantified airway metrics associate with mortality in idiopathic pulmonary fibrosis (IPF). Methods In an observational cohort study ( n = 90) of IPF patients from Ege University Hospital, an airway analysis tool AirQuant calculated median airway intersegmental tapering and segmental tortuosity across the 2nd to 6th airway generations. Intersegmental tapering measures the difference in median diameter between adjacent airway segments. Tortuosity evaluates the ratio of measured segmental length against direct end-to-end segmental length. Univariable linear regression analyses examined relationships between AirQuant variables, clinical variables, and lung function tests. Univariable and multivariable Cox proportional hazards models estimated mortality risk with the latter adjusted for patient age, gender, smoking status, antifibrotic use, CT usual interstitial pneumonia (UIP) pattern, and either forced vital capacity (FVC) or diffusion capacity of carbon monoxide (DLco) if obtained within 3 months of the CT. Results No significant collinearity existed between AirQuant variables and clinical or functional variables. On univariable Cox analyses, male gender, smoking history, no antifibrotic use, reduced DLco, reduced intersegmental tapering, and increased segmental tortuosity associated with increased risk of death. On multivariable Cox analyses (adjusted using FVC), intersegmental tapering (hazard ratio (HR) = 0.75, 95% CI = 0.66–0.85, p < 0.001) and segmental tortuosity (HR = 1.74, 95% CI = 1.22–2.47, p = 0.002) independently associated with mortality. Results were maintained with adjustment using DLco. Conclusions AirQuant generated measures of intersegmental tapering and segmental tortuosity independently associate with mortality in IPF patients. Abnormalities in proximal airway generations, which are not typically considered to be abnormal in IPF, have prognostic value. Clinical relevance statement Quantitative measurements of intersegmental tapering and segmental tortuosity, in proximal (second to sixth) generation airway segments, independently associate with mortality in IPF. Automated airway analysis can estimate disease severity, which in IPF is not restricted to the distal airway tree. Key Points • AirQuant generates measures of intersegmental tapering and segmental tortuosity. • Automated airway quantification associates with mortality in IPF independent of established measures of disease severity. • Automated airway analysis could be used to refine patient selection for therapeutic trials in IPF. Graphical Abstract