Outcome of hospitalization for COVID-19 in patients with interstitial lung disease. An international multicenter study

Rationale: The impact of coronavirus disease (COVID-19) on patients with interstitial lung disease (ILD) has not been established.Objectives: To assess outcomes in patients with ILD hospitalized for COVID-19 versus those without ILD in a contemporaneous age-, sex-, and comorbidity-matched population.Methods: An international multicenter audit of patients with a prior diagnosis of ILD admitted to the hospital with COVID-19 between March 1 and May 1, 2020, was undertaken and compared with patients without ILD, obtained from the ISARIC4C (International Severe Acute Respiratory and Emerging Infection Consortium Coronavirus Clinical Characterisation Consortium) cohort, admitted with COVID-19 over the same period. The primary outcome was survival. Secondary analysis distinguished idiopathic pulmonary fibrosis from non-idiopathic pulmonary fibrosis ILD and used lung function to determine the greatest risks of death.Measurements and Main Results: Data from 349 patients with ILD across Europe were included, of whom 161 were admitted to the hospital with laboratory or clinical evidence of COVID-19 and eligible for propensity score matching. Overall mortality was 49% (79/161) in patients with ILD with COVID-19. After matching, patients with ILD with COVID-19 had significantly poorer survival (hazard ratio [HR], 1.60; confidence interval, 1.17-2.18; P = 0.003) than age-, sex-, and comorbidity-matched controls without ILD. Patients with an FVC of Conclusions: Patients with ILD are at increased risk of death from COVID-19, particularly those with poor lung function and obesity. Stringent precautions should be taken to avoid COVID-19 in patients with ILD.</div

Genetic overlap between idiopathic pulmonary fibrosis and COVID-19.

Coronavirus disease 2019 (COVID-19) is an infectious disease potentially leading to long lasting respiratory symptoms and has resulted in over 4 million deaths worldwide. Idiopathic pulmonary fibrosis (IPF) is a chronic interstitial lung disease (ILD) characterised by an aberrant response to alveolar injury leading to progressive scarring of the lungs. Individuals with ILD are at a higher risk of death from COVID-19 [1] </p

The Use of Genetic Information to Define Idiopathic Pulmonary Fibrosis in UK Biobank

To the Editor: Idiopathic pulmonary fibrosis (IPF) is a rare disease with prevalence of 50 in 100,000 cases in the UK.1 Genome-wide association studies have identified 20 independent single nucleotide polymorphisms (SNPs) that are associated with IPF risk to date.2, 3, 4 A single common SNP in the MUC5B gene promoter region (rs35705950) has a large effect on IPF risk with each copy of the T allele that is associated with a 4- to 5-fold increased risk of IPF.4,5 Most datasets for genetic studies of IPF were derived from dedicated IPF cohort studies, registries, and clinical trials, which are usually modest in size. Large general population cohorts, such as UK Biobank, represent a valuable resource for increasing IPF case sample sizes for molecular epidemiologic studies. However, observed effect size estimates for rs35705950 on IPF risk in general population cohorts, for which cases are defined with the use of the International Classification of Diseases, revision 10 (ICD-10)6 J84.1 code, are smaller than those that are estimated in clinically-derived datasets.7 Although this attenuation could be explained by misclassification of IPF cases, the misclassification may be mitigated by the substantial gain in statistical power that can be leveraged from very large biobanks. However, more accurate classification of cases and control subjects in biobanks could provide more accurate effect estimates for use in further analyses. Given this, we proposed that the IPF risk effect size of rs35705950 could be used to evaluate and refine the choice of codes to define IPF cases. We applied this approach in UK Biobank.</p

Proportion of Idiopathic Pulmonary Fibrosis Risk Explained by Known Common Genetic Loci in European Populations

Understanding how genetic factors contribute to disease risk improves our understanding of pathogenesis, supports drug development, and aids risk prediction (1). Appropriate quantification and interpretation of this contribution is essential for measuring the impact of genetic variation and in motivating and informing future studies. </p

Genome-wide association study across five cohorts identifies five novel loci associated with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic lung condition with poor survival times. We previously published a genome-wide meta-analysis of IPF risk across three studies with independent replication of associated variants in two additional studies. To maximise power and to generate more accurate effect size estimates, we performed a genome-wide meta-analysis across all five studies included in the previous IPF risk genome-wide association studies. We used the distribution of effect sizes across the five studies to assess the replicability of the results and identified five robust novel genetic association signals implicating mTOR (mammalian target of rapamycin) signalling, telomere maintenance and spindle assembly genes in IPF risk

Analysis of Forced Vital Capacity (FVC) trajectories in Idiopathic Pulmonary Fibrosis (IPF) identifies four distinct clusters of disease behaviour

Background: Idiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease with a variable clinical trajectory. Decline in Forced Vital Capacity (FVC) is the main indicator of progression, however missingness prevents long-term analysis of lung function patterns. We used Machine Learning (ML) techniques to identify patterns of lung function trajectory. Methods: Longitudinal FVC data were collected from 415 participants with IPF. The imputation performance of conventional and ML techniques to impute missing data was evaluated, then the fully imputed dataset was analysed by unsupervised clustering using Self-Organizing Maps (SOM). Anthropometrics, genomic associations, blood biomarkers and clinical outcomes were compared between clusters. Replication was performed using an independent dataset. Results: An unsupervised ML algorithm had the lowest imputation error amongst tested methods, and SOM identified four distinct clusters (CL1 to CL4), confirmed by sensitivity analysis. CL1 (n=140): linear decline over three years; CL2 (n=100): initial improvement in FVC before declining; CL3 (n=113): initial FVC decline before stabilisation; CL4(n=62): stable lung function. Median survival was shortest in CL1 (2.87 - 95%CI: 2.29–3.40) and longest in CL4 (5.65 - 95%CI: 5.18–6.62). Baseline FEV1/FVC ratio and biomarker SPD levels were significantly higher among clusters CL1 and CL3. Similar lung function clusters with some shared anthropometric characteristics were identified in the replication dataset. Conclusions: Using a data-driven unsupervised approach, we identified four clusters of lung function trajectory with distinct clinical and biochemical features. Enriching or stratifying longitudinal spirometric data into clusters may optimise evaluation of intervention efficacy during clinical trials and patient managemen

Effects of sleep disturbance on dyspnoea and impaired lung function following hospital admission due to COVID-19 in the UK: a prospective multicentre cohort study.

BackgroundSleep disturbance is common following hospital admission both for COVID-19 and other causes. The clinical associations of this for recovery after hospital admission are poorly understood despite sleep disturbance contributing to morbidity in other scenarios. We aimed to investigate the prevalence and nature of sleep disturbance after discharge following hospital admission for COVID-19 and to assess whether this was associated with dyspnoea.MethodsCircCOVID was a prospective multicentre cohort substudy designed to investigate the effects of circadian disruption and sleep disturbance on recovery after COVID-19 in a cohort of participants aged 18 years or older, admitted to hospital for COVID-19 in the UK, and discharged between March, 2020, and October, 2021. Participants were recruited from the Post-hospitalisation COVID-19 study (PHOSP-COVID). Follow-up data were collected at two timepoints: an early time point 2-7 months after hospital discharge and a later time point 10-14 months after hospital discharge. Sleep quality was assessed subjectively using the Pittsburgh Sleep Quality Index questionnaire and a numerical rating scale. Sleep quality was also assessed with an accelerometer worn on the wrist (actigraphy) for 14 days. Participants were also clinically phenotyped, including assessment of symptoms (ie, anxiety [Generalised Anxiety Disorder 7-item scale questionnaire], muscle function [SARC-F questionnaire], dyspnoea [Dyspnoea-12 questionnaire] and measurement of lung function), at the early timepoint after discharge. Actigraphy results were also compared to a matched UK Biobank cohort (non-hospitalised individuals and recently hospitalised individuals). Multivariable linear regression was used to define associations of sleep disturbance with the primary outcome of breathlessness and the other clinical symptoms. PHOSP-COVID is registered on the ISRCTN Registry (ISRCTN10980107).Findings2320 of 2468 participants in the PHOSP-COVID study attended an early timepoint research visit a median of 5 months (IQR 4-6) following discharge from 83 hospitals in the UK. Data for sleep quality were assessed by subjective measures (the Pittsburgh Sleep Quality Index questionnaire and the numerical rating scale) for 638 participants at the early time point. Sleep quality was also assessed using device-based measures (actigraphy) a median of 7 months (IQR 5-8 months) after discharge from hospital for 729 participants. After discharge from hospital, the majority (396 [62%] of 638) of participants who had been admitted to hospital for COVID-19 reported poor sleep quality in response to the Pittsburgh Sleep Quality Index questionnaire. A comparable proportion (338 [53%] of 638) of participants felt their sleep quality had deteriorated following discharge after COVID-19 admission, as assessed by the numerical rating scale. Device-based measurements were compared to an age-matched, sex-matched, BMI-matched, and time from discharge-matched UK Biobank cohort who had recently been admitted to hospital. Compared to the recently hospitalised matched UK Biobank cohort, participants in our study slept on average 65 min (95% CI 59 to 71) longer, had a lower sleep regularity index (-19%; 95% CI -20 to -16), and a lower sleep efficiency (3·83 percentage points; 95% CI 3·40 to 4·26). Similar results were obtained when comparisons were made with the non-hospitalised UK Biobank cohort. Overall sleep quality (unadjusted effect estimate 3·94; 95% CI 2·78 to 5·10), deterioration in sleep quality following hospital admission (3·00; 1·82 to 4·28), and sleep regularity (4·38; 2·10 to 6·65) were associated with higher dyspnoea scores. Poor sleep quality, deterioration in sleep quality, and sleep regularity were also associated with impaired lung function, as assessed by forced vital capacity. Depending on the sleep metric, anxiety mediated 18-39% of the effect of sleep disturbance on dyspnoea, while muscle weakness mediated 27-41% of this effect.InterpretationSleep disturbance following hospital admission for COVID-19 is associated with dyspnoea, anxiety, and muscle weakness. Due to the association with multiple symptoms, targeting sleep disturbance might be beneficial in treating the post-COVID-19 condition.FundingUK Research and Innovation, National Institute for Health Research, and Engineering and Physical Sciences Research Council

Cohort Profile: Post-hospitalisation COVID-19 study (PHOSP-COVID)

Key Features The Post-Hospitalisation COVID-19 (PHOSP-COVID) study is a national UK multicentre cohort study of patients who were hospitalized for COVID-19 and subsequently discharged. PHOSP-COVID was established to investigate the medium- and long-term sequelae of severe COVID-19 requiring hospitalization, understand the underlying mechanisms of these sequelae, evaluate the medium- and long-term effects of COVID-19 treatments and to serve as a platform to enable future studies, including clinical trials. Data collected covered a wide range of physical measures, biological samples and patient-reported outcome measures (PROMs). Participants could join the cohort either in Tier 1 only with remote data collection using hospital records, a PROMs app and postal saliva sample for DNA; or in Tier 2 in which they were invited to attend two specific research visits for further data collection and biological research sampling. These research visits occurred at 5 (range 2–7) months and 12 (range 10–14) months post-discharge. Participants could also participate in specific nested studies (Tier 3) at selected sites. All participants were asked to consent to further follow-up for 25 years via linkage to their electronic healthcare records and to be re-contacted for further research. In total, 7935 participants were recruited from 83 UK sites: 5238 to Tier 1 and 2697 to Tier 2, between August 2020 and March 2022. Cohort data are held in a Trusted Research Environment and samples stored in a central biobank. Data and samples can be accessed upon request and subject to approvals from https://www.phosp.org/data-sample-request/.</p