CHEST

BackgroundAccurate prediction of exacerbation risk enables personalised chronic obstructive pulmonary disease (COPD) care. We developed and validated a generalisable model to predict the individualised rate and severity of COPD exacerbations. MethodsWe pooled data from three COPD trials on patients with a history of exacerbations. We developed a mixed-effect model to predict exacerbations over one-year. Severe exacerbations were those requiring inpatient care. Predictors were a history of exacerbations, age, sex, body mass index, smoking status, domiciliary oxygen therapy, lung function, symptom burden, and current medication use. ECLIPSE, a multicentre cohort study, was used for external validation. ResultsThe development dataset included 2,380 patients (mean 64{middle dot}7 years, 1373 [57{middle dot}7%] men, mean exacerbation rate 1{middle dot}42/year, 0{middle dot}29/year [20{middle dot}5%] severe). When validated against all COPD patients in ECLIPSE (n=1819, mean 63{middle dot}3 years, 1186 [65{middle dot}2%] men, mean exacerbation rate 1{middle dot}20/year, 0{middle dot}27/year [22{middle dot}2%] severe), the area-under-curve was 0{middle dot}81 (95%CI 0{middle dot}79-0{middle dot}83) for [≥]2 exacerbations and 0{middle dot}77 (95%CI 0{middle dot}74-0{middle dot}80) for [≥]1 severe exacerbation. Predicted rates were 0{middle dot}25/year for severe and 1{middle dot}31/year for all exacerbations, close to the observed rates (0{middle dot}27/year and 1{middle dot}20/year, respectively). In ECLIPSE patients with a prior exacerbation history (n=996, mean 63{middle dot}6 years, 611 (61{middle dot}3%) men, mean exacerbation rate 1{middle dot}82/year, 0{middle dot}40/year [22{middle dot}0%] severe), the area-under-curve was 0{middle dot}73 (95%CI 0{middle dot}70-0{middle dot}76) for [≥]2 exacerbations and 0{middle dot}74 (95%CI 0{middle dot}70-0{middle dot}78) for [≥]1 severe exacerbation. Calibration was accurate for severe exacerbations (predicted=0{middle dot}37/year, observed=0{middle dot}40/year) and all exacerbations (predicted=1{middle dot}80/year, observed=1{middle dot}82/year). The model is accessible at http://resp.core.ubc.ca/ipress/accept. InterpretationThis model can be used as a decision tool to personalise COPD treatment and prevent exacerbations. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSPreventing future exacerbations is a major goal in COPD care. Because of adverse effects, preventative treatments should be reserved for those at a higher risk of future exacerbations. Predicting exacerbation risk in individual patients can guide these clinical decisions. A 2017 systematic review reported that of the 27 identified COPD exacerbation prediction tools, only two had reported external validation and none was ready for clinical implementation. To find the studies that were published afterwards, we searched PubMed for articles on development and validation of COPD exacerbation prediction since 2015, using the search terms "COPD", "exacerbation", "model", and "validation". We included studies that reported prediction of either the risk or the rate of exacerbations and excluded studies that did not report external validation. Our literature search revealed two more prediction models neither of which was deemed generalisable due to lack of methodological rigour, or local and limited nature of the data available to investigators. Added value of this studyWe used data from three randomised trials to develop ACCEPT, a clinical prediction tool based on routinely available predictors for COPD exacerbations. We externally validated ACCEPT in a large, multinational prospective cohort. To our knowledge, ACCEPT is the first COPD exacerbation prediction tool that jointly estimates the individualised rate and severity of exacerbations. Successful external validation of ACCEPT showed that its generalisability can be expanded across geography and beyond the setting of therapeutic trials. ACCEPT is designed to be easily applicable in clinical practice and is readily accessible as a web application. Implications of all the available evidenceCurrent guidelines rely on a history of exacerbations as the sole predictor of future exacerbations. Simple clinical and demographic variables, in aggregate, can be used to predict COPD exacerbations with improved accuracy. ACCEPT enables a more personalised approach to treatment based on routinely collected clinical data by allowing clinicians to objectively differentiate risk profiles of patients with similar exacerbation history. Care providers and patients can use individualised exacerbation risk estimates to decide on preventive therapies based on objectively-established or patient-specific thresholds for treatment benefit and harm. COPD clinical researchers can use this tool to target enriched populations for enrolment in clinical trials.

ObjectivesTo examine whether high flow nasal oxygen (HFNO), continuous positive airway pressure (CPAP), or noninvasive ventilation (NIV) strategies impact mortality, the need for invasive mechanical ventilation (IMV), or hospital and intensive care unit (ICU) length of stay compared to standard oxygen therapy (SOT) or each other in patients with severe or critical COVID-19 with acute hypoxemic respiratory failure. MethodsA rapid review of randomized controlled trials (RCTs) identified through published systematic and rapid reviews supplemented with a search of bibliographic databases. RCTs were eligible if they compared HFNO, CPAP, or NIV to SOT or another ventilation strategy. Studies were screened, selected, and extracted by a single reviewer and checked by a second reviewer. We assessed risk of bias of included studies using the Cochrane Risk of bias tool and used the grading of recommendations, assessment, development, and evaluation (GRADE) approach to judge the certainty of the evidence for mortality, need for IMV, and hospital and ICU length of stay. We sought RCT evidence for non-COVID-19 patients with acute hypoxemic respiratory failure and acute respiratory distress to inform additional comparisons and to supplement the available data for COVID-19. ResultsA total of 5 RCTs comparing ventilation strategies in patients with severe or critical COVID-19 were included. Patient and study characteristics were extracted and evidence and certainty of evidence assessments were completed for comparisons of HFNO and CPAP to standard oxygen therapy and NIV and CPAP to HFNO. An additional 22 RCTs of non-COVID-19 patients were also included and considered. Results from meta-analysis suggest reductions in mortality and IMV with HFNO (RR mortality 0.87 (0.66-1.13), IMV 0.89 (0.77-1.03); low quality evidence) or CPAP (RR mortality 0.87 (0.64-1.18) low quality evidence, IMV 0.81 (0.67-0.98) moderate quality evidence) compared to SOT. Helmet NIV may reduce IMV (RR 0.69 (0.43-1.09)) and CPAP may reduce IMV (RR 0.69 (0.43-1.09)) and hospital (1.67 days fewer (5.43 fewer-2.09 more) or ICU length of stay (1.02 days fewer (3.97 fewer-1.93 more)) compared to HFNO (low quality evidence). ConclusionsThis rapid systematic review highlights the available evidence to support the use of noninvasive ventilation strategies including high flow nasal oxygen, noninvasive ventiltaion (e.g., BiPAP), or CPAP in hospitalized patients with severe or critical COVID-19 and acute hypoxemic respiratory failure who do not need emergent intubation. Findings based on moderate to very low certainty evidence suggest that noninvasive ventilation may be considered as an alternative to standard oxygen therapy to reduce hypoxemia and dyspnea. Additional high quality RCTs are warranted to reduce uncertainty and to fill in important knowledge gaps.

BackgroundMixed trial results suggest that some ventilated patients with acute respiratory distress syndrome (ARDS) benefit from high PEEP while others may be harmed, indicating heterogeneity of treatment effect (HTE). This study applies data-driven predictive approaches to uncover HTE and re-examines previously hypothesized HTE. This manuscript serves as a pre-registration of planned external validation of our trained models. MethodsWe identified eight randomized trials, and obtained individual patient data (IPD) from three of them (ALVEOLI, LOVS, EXPRESS), as our train cohort. We used effect modelling to predict individualized treatment effects (predicted 28-day mortality risk difference between PEEP strategies) across patient subgroups stratified by observed tertiles ([&le;]8 cmH2O, 9-11 cmH2O, [&ge;]12 cmH2O). Candidate effect modelling methods included meta-learners and technique-specific methods. Optimal methods were selected through leave-one-trial-out cross-validation, evaluating the methods performances in each PEEP tertile using AUC-benefit. We trained final models using the best performing methods implemented with or without forward selection (which yielded sufficient AUC-benefit), and additional final models by selecting the variables that yielded consistency in the forward selections performed in the cross validation, if any. We further evaluated earlier hypothesized HTE comparing (1) patients with baseline PaO2/FiO2 [&le;] 200 versus > 200 mmHg, and (2) patients with hypoinflammatory versus hyperinflammatory subphenotypes. Preliminary findingsIn the lower PEEP tertile ([&le;]8 cmH2O), an X-learner implemented without, and an S-learner implemented with forward selection (both with flexible base learners), yielded the highest AUC benefits and were used to train final models. In the high PEEP tertile ([&ge;]12 cmH2O), only the causal forest implemented with forward selection yielded an AUC benefit exceeding zero. Respiratory-system compliance (CRS) was consistently selected in the forward selections of cross validation, and was used to train an extra final causal forest model, with predicted effects shifting from harm to benefit for CRS 26.5 mL/cmH2O or higher. Higher PEEP benefited patients with baseline PaO2/FiO2 [&le;]200 mmHg (OR 0.80, 95% CI 0.66-0.98), incurred harm among those with PaO2/FiO2 >200 mmHg (OR 1.74, 95% CI 1.02-2.98; interaction P=0.01). This HTE was strongest when PaO2/FiO2 was measured at low PEEP ([&le;]8 cmH2O), reduced at mid-level PEEP (9-11 cmH2O), and negligible at high PEEP ([&ge;]12 cmH2O). A second-order interaction showed significant heterogeneity of HTE (ie, second-order heterogeneity) across PEEP tertiles (P=0.03). Preliminary ConclusionsOur preliminary findings indicated that baseline CRS [&ge;] 26.5 mL/cmH2O predicts benefit, while CRS < 26.5 mL/cmH2O predicts harm from high PEEP when CRS is measured at high baseline PEEP ([&ge;]12 cmH2O). Similarly, baseline PaO2/FiO2 [&le;] 200 mmHg predicts benefit, while PaO2/FiO2 > 200 mmHg predicts harm from high PEEP when PaO2/FiO2 is measured at a low baseline PEEP ([&le;]8 cmH2O). Using data from the LOVS trial, we investigated HTE for high PEEP between hypo- and hyperinflammatory subphenotypes but found none, despite significant HTE observed earlier in the ALVEOLI trial.

ObjectivesRisk prediction models are increasingly used at point of care to support personalized treatment decisions. This study created and evaluated two Information, Education, and Communication (IEC) resources to improve public understanding of a risk prediction tool for Chronic Obstructive Pulmonary Disease (COPD) management. MethodsWe created a 5-minute video and a pamphlet explaining the burden of COPD and how a prediction model generates quantitative estimates of, and benefit of certain treatments for, exacerbations of the disease. These tools were tested among students and researchers in public health. A patient partner was engaged throughout to ensure the materials were accessible and patient-centered. ResultsTwenty-five individuals participated (80% female; 60% aged 25-64). After reviewing the materials, 92% of participants agreed to the statement "I am familiar with the idea of precision medicine approach". Most (72%) felt they received sufficient information about the tool, and 92% believed such materials could support patient decision. Participants stated that the materials were clear, detailed, and written in plain language. Participants preferred the pamphlet (68%) over the video (44%). Suggestions for improvement included expanding content on how the tool works. ConclusionsThe findings of this study provided a better understanding of how to present complex medical information around precision medicine that is accessible and meaningful to diverse audiences. We will improve our materials based on these comments, and continue to make them available at https://resp.core.ubc.ca/show/patient_committee_2025

BackgroundManual bag-valve ventilation during cardiopulmonary resuscitation (CPR) is prone to substantial variability in tidal volume and respiratory rate, frequently resulting in hyperventilation. The clinical effectiveness of mechanical ventilation as an alternative strategy remains uncertain. ObjectivesThis systematic review and meta-analysis compared mechanical versus manual ventilation during adult CPR to assess return of spontaneous circulation (ROSC), survival to hospital discharge, and neurological outcomes. MethodsWe searched PubMed, Embase, and Scopus (inception through October 2025) for randomized controlled trials and observational studies comparing mechanical and manual ventilation during adult CPR. We conducted separate meta-analyses for randomized trials and observational studies using random-effects models and assessed evidence certainty using GRADE methodology. Primary outcomes were ROSC, survival to discharge, and favorable neurological outcome (Cerebral Performance Category 1-2). ResultsEight studies (5,130 patients) met inclusion criteria. Mechanical ventilation was associated with higher ROSC (odds ratio [OR] 1.22; 95% confidence interval [CI] 1.07-1.38; p=0.002; I{superscript 2}=8%), survival to discharge (OR 1.39; 95% CI 1.08-1.77; p=0.009; I{superscript 2}=0%), and favorable neurological outcome (OR 1.61; 95% CI 1.04-2.48; p=0.03; I{superscript 2}=0%) compared with manual ventilation. In randomized trials (n=120), mechanical ventilation showed a trend toward improved ROSC (OR 1.49; 95% CI 0.73-3.07; p=0.27) but lacked statistical significance. Observational studies (n=7,081) demonstrated an association between mechanical ventilation and higher ROSC (OR 1.21; 95% CI 1.03-1.42; p=0.02; I{superscript 2}=34%). Post-ROSC arterial blood gases showed improved oxygenation (mean difference 13.01 mmHg higher pO2 ; p<0.0001) and lower pCO2 levels (mean difference 15.12 mmHg lower; p<0.00001) with mechanical ventilation. GRADE assessment indicated low-certainty evidence for clinical outcomes and moderate-certainty evidence for physiological outcomes. ConclusionsMechanical ventilation during CPR was associated with higher rates of ROSC, survival, and favourable neurological outcomes, along with more controlled post-ROSC physiological parameters. However, the certainty of evidence is low, driven largely by confounded observational data and limited randomized trial evidence. These findings are hypothesis-generating and should not be interpreted as causal. Confirmation in adequately powered randomized controlled trials is required before changes to practice or guidelines can be recommended. WHAT IS NEW?O_LIThis systematic review and meta-analysis, stratified by study design, synthesizes the available evidence comparing mechanical and manual ventilation during adult cardiopulmonary resuscitation across eight studies involving 5,130 patients. C_LIO_LIMechanical ventilation was associated with higher rates of return of spontaneous circulation, survival to hospital discharge, and favorable neurological outcome compared with manual ventilation; however, these associations are derived largely from observational data with low certainty of evidence. C_LIO_LIMechanical ventilation demonstrated more consistent post-resuscitation arterial blood gas parameters--higher oxygenation and lower carbon dioxide levels--suggesting physiologic benefits, although these findings also require confirmation in randomized trials. C_LI CLINICAL IMPLICATIONS?O_LIMechanical ventilation may offer a more standardized approach to delivering tidal volumes and respiratory rates during CPR, potentially mitigating the variability and risk of hyperventilation inherent to manual bag-valve ventilation. C_LIO_LIBecause the evidence supporting improved clinical outcomes is low certainty and primarily observational, the observed associations should not be interpreted as causal. These results are hypothesis-generating and highlight an important area for further investigation rather than indicating definitive clinical benefit. C_LIO_LIIf mechanical ventilation is used during CPR, implementation should prioritize protocolized ventilator settings (e.g., tidal volume 6-7 mL/kg and respiratory rate 10 breaths/min) and strict adherence to high-quality chest compressions. C_LIO_LIAdequately powered randomized controlled trials are needed to determine whether mechanical ventilation confers true clinical benefit and to inform future guideline recommendations. C_LI

RationaleChronic obstructive pulmonary disease (COPD) is a leading cause of death but with variable progression. ObjectiveEstimate factors influencing transition rates between PRISm and GOLD stages. MethodsUsing a Markov multistate model, transition rates between GOLD-0, PRISm and GOLD-1, GOLD-2 and GOLD 3-4 were estimated for 5,728 US adult ever cigarette users from the COPDGene cohort over 10-years. We calculated one and five-year transition probabilities for progressive and regressive transitions and estimated the mean sojourn time for severity states. Main ResultsGOLD-1 and PRISm individuals spent the least time in any single stage (GOLD-1: 6 years; PRISm: 7 years). PRISm and GOLD-1 individuals were equally likely to transition to GOLD-2 vs. GOLD-0 (PRISm: HR 1.09, 95% confidence interval [CI] 0.90-1.33, GOLD-1 (HR 1.15, 95%CI 0.93-1.42) per five-year period, but rarely transition between PRISm and GOLD-1. Individuals at GOLD-0 were equally likely to progress to GOLD-1 or PRISm (HR 1.11, 95%CI 0.93-1.31) but the transient time for this stage was the longest of any GOLD stage (16 years, 95%CI 15.2-17.3). GOLD-2 was the most likely stage to progress (HR 2.4, 95%CI 1.9-3.02) to GOLD 3-4 vs. regress to GOLD-1. For GOLD-2 individuals, current smoking status (HR 0.84, 95%CI 0.67-1.06) or intensity (HR 0.84, 95%CI 0.54-1.29) was not associated with disease progression. ConclusionsGOLD-1 and PRISm are the most transient stages equally likely to regress to GOLD-0 or progress to GOLD-2 and may benefit from smoking cessation interventions. GOLD-2 individuals are the most likely to progress and may benefit most from targeted disease interventions.

Background-Research questionChronic Obstructive Pulmonary Disease (COPD) is a leading cause of mortality. Predicting mortality risk in COPD patients can be important for disease management strategies. Although scores for all-cause mortality have been developed previously, there is limited research on factors that may directly affect COPD-specific mortality. Study design-Methodsused probabilistic (causal) graphs to analyze clinical baseline COPDGene data, including demographics, spirometry, quantitative chest imaging, and symptom features, as well as gene expression data (from year-5). ResultsWe identified factors linked to all-cause and COPD-specific mortality. Although many were similar, there were differences in certain comorbidities (all-cause mortality model only) and forced vital capacity (COPD-specific mortality model only). Using our results, we developed VAPORED, a 7-variable COPD-specific mortality risk score, which we validated using the ECLIPSE 3-yr mortality data. We showed that the new model is more accurate than the existing ADO, BODE, and updated BODE indices. Additionally, we identified biological signatures linked to all-cause mortality, including a plasma cell mediated component. Finally, we developed a web page to help clinicians calculate mortality risk using VAPORED, ADO, and BODE indices. InterpretationGiven the importance of predicting COPD-specific and all-cause mortality risk in COPD patients, we showed that probabilistic graphs can identify the features most directly affecting them, and be used to build new, more accurate models of mortality risk. Novel biological features affecting mortality were also identified. This is an important step towards improving our identification of high-risk patients and potential biological mechanisms that drive COPD mortality.

BackgroundLarge population-based DNA biobanks linked to electronic health records (EHRs) may provide novel opportunities to identify genetic drivers of ARDS. Research QuestionCan we develop an EHR-based algorithm to identify ARDS in a biobank database, and can this validate a previously reported ARDS genetic risk factor? Study Design and MethodsWe analyzed two parallel genotyped cohorts: a prospective biomarker cohort of critically ill adults (VALID), and a retrospective cohort of hospitalized participants enrolled in a de-identified EHR biobank (BioVU). ARDS was identified by clinician-investigator review in VALID and an EHR algorithm in BioVU (EHR-ARDS). We tested the association between the MUC5B promoter polymorphism rs35705950 with development of ARDS, and assessed if age modified this genetic association in each cohort. ResultsIn VALID, 2,795 patients were included, age was 55 [43, 66] (median [IQR]) years, and 718 (25.7%) developed ARDS. In BioVU, 9,025 hospitalized participants were included, age was 60 [48, 70] years, and 1,056 (11.7%) developed EHR-ARDS. We observed a significant age-related interaction effect on ARDS in VALID: among older patients, rs35705950 was associated with increased ARDS risk (OR: 1.44; 95%CI 1.08-1.92; p=0.012) whereas among younger patients this effect was absent (OR: 0.84; 95%CI: 0.62-1.14; p=0.26). In BioVU, rs35705950 was associated with increased risk for EHR-ARDS among all participants (OR: 1.20; 95%CI: 1.00-1.43, p=0.043) and this did not vary by age. The polymorphism was also associated worse oxygenation in mechanically ventilated BioVU participants, but had no association with oxygenation in VALID. InterpretationThe MUC5B promoter polymorphism was associated with ARDS in two cohorts of at-risk adults. Although age-related effect modification was observed only in VALID, BioVU identified a consistent association between MUC5B and ARDS risk regardless of age, and a novel association with oxygenation impairment. Our study highlights the potential for EHR biobanks to enable precision-medicine ARDS studies.

BackgroundTrials have not directly compared biologics for the treatment of asthma. ObjectiveTo comparative the relative efficacy of biologics in asthma. MethodsWe searched MEDLINE, EMBASE, CENTRAL, and clinicaltrials.gov from inception to May 31, 2022, for randomized trials addressing biologic therapies for asthma. Reviewers worked independently and in duplicate to screen references, extract data, and assess risk of bias. We performed a frequentist network meta-analysis and assessed the certainty of evidence using the GRADE approach. We present dichotomous outcomes as absolute risk differences per 1000 patients and relative risk (RR) with 95% confidence intervals (95% CI) and continuous outcomes as mean difference (MD) and 95% CI. ResultsWe identified 64 trials, including 26,630 patients. For patients with eosinophilic asthma, tezepelumab (329 fewer exacerbations per 1000 [95% CI 272.6 to 366.6 fewer]) and dupilumab (319.6 fewer exacerbations per 1000 [95% CI 272.6 to 357.2 fewer]) reduce exacerbations compared to placebo (high certainty). Tezepelumab (MD 0.24 L [95% CI 0.16 to 0.32]) and dupilumab (0.25 L (95% CI 0.21 to 0.29) improve lung function (FEV1) compared to placebo (high certainty). Both tezepelumab (110.97 fewer hospital admissions per 1000 (95% CI 94.53 to 120.56 fewer) and dupilumab (97.27 fewer hospitalizations [4.11 to 124.67 fewer]) probably reduce hospital admissions compared to placebo (moderate certainty). For patients with low eosinophils, biologics probably do not improve asthma outcomes. For these patients, tezepelumab (MD 0.1 L [95% CI 0 to 0.19]) and dupilumab (MD 0.1 L [95% CI 0 to 0.20)] may improve lung function (low certainty). ConclusionTezepelumab and dupilumab are effective at reducing exacerbations. For patients with low eosinophils, however, clinicians should probably be more judicious in use of biologics, including tezepelumab since they probably do not confer substantial benefit.

BackgroundBoth continuous positive airway pressure (CPAP) and high-flow nasal oxygenation (HFNO) have been recommended for acute respiratory failure in COVID-19. However, uncertainty exists regarding effectiveness and safety. MethodsIn the Recovery-Respiratory Support multi-center, three-arm, open-label, adaptive, randomized controlled trial, adult hospitalized patients with acute respiratory failure due to COVID-19, deemed suitable for treatment escalation, were randomly assigned to receive CPAP, HFNO, or conventional oxygen therapy. Comparisons were made between each intervention and conventional oxygen therapy. The primary outcome was a composite of tracheal intubation or mortality within 30-days. ResultsOver 13-months, 1272 participants were randomized and included in the analysis (380 (29.9%) CPAP; 417 (32.8%) HFNO; 475 (37.3%) conventional oxygen therapy). The need for tracheal intubation or mortality within 30-days was lower in the CPAP group (CPAP 137 of 377 participants (36.3%) vs conventional oxygen therapy 158 of 356 participants (44.4%); unadjusted odds ratio 0.72; 95% CI 0.53 to 0.96, P=0.03). There was no difference between HFNO and conventional oxygen therapy (HFNO 184 of 414 participants (44.4%) vs conventional oxygen therapy 166 of 368 participants (45.1%); unadjusted odds ratio 0.97; 95% CI 0.73 to 1.29, P=0.85). ConclusionsCPAP, compared with conventional oxygen therapy, reduced the composite outcome of intubation or death within 30 days of randomisation in hospitalized adults with acute respiratory failure due to COVID-19. There was no effect observed, compared with conventional oxygen therapy, with the use of HFNO. (Funded by the UK National Institute for Health Research; ISRCTN 16912075).

RationaleAccelerated decline in lung function contributes to the development of chronic respiratory disease. Despite evidence for a genetic component, few genetic associations with lung function decline have been identified. ObjectivesTo evaluate genome-wide associations and putative downstream functionality of genetic variants with lung function decline in diverse general population cohorts. MethodsWe conducted genome-wide association study (GWAS) analyses of decline in the forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), and their ratio (FEV1/FVC) in participants across six cohorts in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the UK Biobank. Genotypes were imputed to TOPMed (CHARGE cohorts) or Haplotype Reference Consortium (HRC) (UK Biobank) reference panels, and GWAS analyses used generalized estimating equation models with robust standard error. Models were stratified by cohort, ancestry, and sex, and adjusted for important lung function confounders and genotype principal components. Results were combined in cross-ancestry and ancestry-specific meta-analyses. Selected top variants were tested for replication in two independent COPD-enriched cohorts. Measurements and Main ResultsOur discovery analyses included 52,056 self-reported White (N=44,988), Black (N=5,788), Hispanic (N=550), and Chinese American (N=730) participants with a mean of 2.3 spirometry measurements and 8.6 years of follow-up. Functional mapping of GWAS meta-analysis results identified 361 distinct genome-wide significant (p<5E-08) variants in one or more of the FEV1, FVC, and FEV1/FVC decline phenotypes, which overlapped with previously reported genetic signals for several related pulmonary traits. Of these, 8 variants, or 20.5% of the variant set available for replication testing, were nominally associated (p<0.05) with at least one decline phenotype in COPD-enriched cohorts (White [N=4,778] and Black [N=1,118]). Using the GWAS results, gene-level analysis implicated 38 genes, including eight (XIRP2, GRIN2D, SATB1, MARCHF4, SIPA1L2, ANO5, H2BC10, and FAF2) with consistent associations across ancestries or decline phenotypes. Annotation class analysis revealed significant enrichment of several regulatory processes for corticosteroid biosynthesis and metabolism. Drug repurposing analysis identified 43 approved compounds targeting eight of the implicated 38 genes. ConclusionsOur multi-ancestry GWAS meta-analyses identified numerous genetic loci associated with lung function decline. These findings contribute knowledge to the genetic architecture of lung function decline, provide evidence for a role of endogenous corticosteroids in the etiology of lung function decline, and identify drug targets that merit further study for potential repurposing to slow lung function decline and treat lung disease.

PurposeThe goal of this study was to compare noninvasive respiratory support to invasive mechanical ventilation as the initial respiratory support in COVID-19 patients with acute hypoxemic respiratory failure. MethodsAll patients admitted to a large healthcare network with acute hypoxemic respiratory failure associated with COVID-19 and requiring respiratory support were eligible for inclusion. We compared patients treated initially with noninvasive respiratory support (noninvasive positive pressure ventilation by facemask or high flow nasal oxygen) with patients treated initially with invasive mechanical ventilation. The primary outcome was time-to-in-hospital death analyzed using an inverse probability of treatment weighted Cox model adjusted for potential confounders. Secondary outcomes included unweighted and weighted assessments of mortality, lengths-of-stay (intensive care unit and hospital) and time-to-intubation. ResultsOver the study period, 2354 patients met inclusion criteria. Nearly half (47%) received invasive mechanical ventilation first and 53% received initial noninvasive respiratory support. There was an overall 38% in-hospital mortality (37% for invasive mechanical ventilation and 39% for noninvasive respiratory support). Initial noninvasive respiratory support was associated with an increased hazard of death compared to initial invasive mechanical ventilation (HR: 1.61, p < 0.0001, 95% CI: 1.33 - 1.94). However, patients on initial noninvasive respiratory support also experienced an increased hazard of leaving the hospital sooner, but the hazard ratio waned with time (HR: 0.97, p < 0.0001, 95% CI: 0.96 - 0.98). ConclusionThese data show that the COVID-19 patients with acute hypoxemic respiratory failure initially treated with noninvasive respiratory support had an increased hazard of in-hospital death.

BackgroundChronic obstructive pulmonary disease (COPD) is a complex disorder with a high degree of interindividual variability. Gastrointestinal dysfunction is common in COPD patients and has been proposed to influence the clinical progression of the disease. Using the presence of bile acid(s) (BA) in bronchoalveolar lavage fluid (BAL) as a marker of gastric aspiration, we evaluated the relationships between BAs, clinical outcomes, and bacterial lung colonisation. MethodsWe used BAL specimens from a cohort of COPD patients and healthy controls. Bile acids were profiled and quantified in BAL supernatants using mass spectrometry. Microbial DNA was extracted from BAL cell pellets and quantified using qPCR. We profiled the BAL microbiota using an amplicon sequencing approach targeting the V3-V4 region of the 16S rRNA gene. ResultsDetection of BAs in BAL was more likely at earliest clinical stages of COPD and was independent of the degree of airway obstruction. BAL specimens with BAs demonstrated higher bacterial biomass and lower diversity. Likewise, the odds of recovering bacterial cultures from BAL were higher if BAs were also detected. Detection of BAs in BAL was not associated with either inflammatory markers or clinical outcomes. We also observed different bacterial community types in BAL, which were associated with different clinical groups, levels of inflammatory markers, and the degree of airway obstruction. ConclusionDetection of BAs in BAL was associated with different parameters of airway ecology. Further studies are needed to evaluate whether BAs in BAL can be used to stratify patients and for predicting disease progression trajectories.

RationalePatients with idiopathic pulmonary fibrosis have poor health-related quality of life. ObjectiveDetermine whether a modified yoga program in patients with idiopathic pulmonary fibrosis improves quality of life compared to usual care. MethodsRandomized controlled, non-blinded, pilot clinical trial lasting 12 weeks with 2 arms involving 63 adults with idiopathic pulmonary fibrosis. The yoga program-arm consisted of interventions such as seated postures, breathing and meditation exercises designed by authors for idiopathic pulmonary fibrosis patients. The control arm continued with usual standard of care. Analysis of covariance was performed; no multiplicity adjustments were made on account of this being a pilot study. MeasurementsThe primary outcomes were week-12 scores in seven patient-reported instruments, each with sub-domains, totaling 21 outcomes. Results60 of the 63 participants (32 randomized to yoga; 31 receiving standard of care) completed the study (one death in each arm and one withdrawal in yoga group). Analysis of covariance for week-12 scores, adjusting for baseline scores and confounders, revealed significant treatment effects favoring yoga in the L-IPF cough domain (-9.29 points, 95% CI -18.37 to -0.20; p=0.045), in the L-IPF total score (-7.11, 95% CI -13.15 to -1.06; p=0.022), and in the R-scale-PF cough domain (-1.18, 95% CI - 2.27 to -0.10; p=0.034) in the study population. ConclusionPatients with idiopathic pulmonary fibrosis participating in a yoga program demonstrated significant improvement in quality of life assessed by the cough and total scores of L-IPF and the cough score of R-scale-PF than those receiving usual care. SummaryWe examine if the implementation of a modified yoga program improves patient-reported outcomes in patients with idiopathic pulmonary fibrosis. Our study reveals significant post-yoga improvements in the yoga group compared to the control group across different patient-reported outcomes. Such outcomes are meaningful endpoints in patients with idiopathic pulmonary fibrosis and serve as a critical endpoint in related clinical trials.

In pharmacoepidemiologic studies of COVID-19, there were concerns about bias from residual confounding. We applied high-dimensional propensity scores (HDPS) to a case study investigating the role of inhaled corticosteroids (ICS) in COVID-19 to adjust for unmeasured confounding. We selected patients with chronic obstructive pulmonary disease on 01 March 2020 from Clinical Practice Research Datalink (CPRD) Aurum, comparing ICS/LABA/(+-LAMA) and LABA/LAMA users. ICS effects on the outcomes COVID-19 hospitalisation and death were assessed through weighted and unweighted Cox proportional hazards models. HDPS were estimated from primary care clinical records, prescriptions and hospitalisations. SNOMED-CT codes and dictionary of medicines and devices codes from CPRD Aurum were mapped to International Classification of Disease 10th revision codes and British National Formulary paragraphs respectively. We estimated propensity scores (PS) combining prespecified and HDPS covariates, selecting the top 100, 250, 500, 750 and 1000 covariates ranked by confounding potential. When excluding triple therapy users, the conventional PS-weighted estimates showed weak evidence of increased risk of COVID-19 hospitalisation among ICS users (HR 1.19 (95% CI 0.92-1.54)). Results varied slightly based on the number of covariates included in HDPS (HR using 100 HDPS covariates 1.01 (95% CI 0.76-1.33), HR using 250 HDPS covariates 1.24 (95% CI 0.83-1.87)). For COVID-19 death, conventional PS-weighted models showed weak evidence of harm of ICS when excluding triple therapy users (HR 1.24 (95% CI 0.87-1.75)). HDPS-weighting moved estimates toward the null, suggesting no effect of ICS (HR using 250 HDPS covariates excluding triple therapy 1.08 (95% CI 0.73- 1.59)). HDPS may have provided better confounding control for COVID-19 deaths and may be able to partially compensate for suboptimal comparison groups. HDPS results can be sensitive to the number of covariates included, highlighting the importance of sensitivity analyses. Key pointsO_LIResidual confounding, including residual confounding by indication, is a major concern in pharmacoepidemiologic studies of COVID-19 outcomes. C_LIO_LIWe apply high-dimensional propensity scores (HDPS) to adjust for residual confounding in a case study of inhaled corticosteroids (ICS) on COVID-19 hospitalisation and death in CPRD Aurum. C_LIO_LIConventional PS-weighted analyses suggested harmful effects of ICS on COVID-19 hospitalisation and, to a lesser extent, deaths. C_LIO_LIHDPS weighted analyses of COVID-19 hospitalisations were sensitive to the number of covariates included, with results moving towards the null for smaller number of covariates and away from the null when including more covariates, while for deaths, estimates moved towards the null consistently. C_LIO_LIHDPS demonstrated promise in addressing confounding even when comparison groups are suboptimal, but its performance depends on the careful selection and ranking of covariates. C_LI Plain Language SummaryA key challenge when researching the effects of medications using electronic health records is accounting for the fact that people who receive different medications often differ in important ways. Such differences, called confounding, is typically accounted for using statistical methods which require researchers to pre-specify all important confounders. A newer method, called high-dimensional propensity scores (HDPS), uses a data-driven approach to select what confounders to account for instead. These methods have not yet been applied to studies of inhaled corticosteroids and COVID-19 outcomes, an area where studies have found conflicting findings. We used electronic health records from the UK to compare the risk of COVID-19 hospitalisation and death among patients with chronic obstructive pulmonary disease taking two different treatments (ICS/LABA and LABA/LAMA) using both conventional and HDPS methods. Our findings showed that HDPS can reduce important differences between patients (confounding), but that the results can be sensitive to the number of covariates included. This demonstrates the value of HDPS and the need for researchers to run their analysis using several different assumptions.

RationaleSmall airways disease (SAD) and hyperinflation are common in precapillary pulmonary hypertension (PH). Activin signaling plays an important role in airway and bronchial function. ObjectiveTo determine whether treatment with sotatercept, an activin signaling inhibitor, for severe precapillary PH is associated with improvements in physiologic markers of SAD and hyperinflation. MethodsWe conducted a single-center, retrospective cohort study of participants who received sotatercept for the treatment of severe precapillary PH despite background PH treatments who also had pulmonary function tests (PFT) before and after initiation of sotatercept treatment. Measurements and Main ResultsForty-eight participants were included (median age 68 years, 77% female). Median BMI was 26.7 kg/m2 (IQR 23.6-31.4). All participants were functional class III or IV. Follow-up PFTs obtained a median of 4.4 months after sotatercept initiation showed significant improvements: FEV1 +155 mL (11%, 95% confidence interval [CI], 100-215 mL; p<0.001), FVC +180 mL (10%, 95% CI, 125-245 mL; p<0.001), FEF25-75% +0.15 L/sec (16%, 95% CI, 0.03-0.28 L/sec; p=0.015), DLCO +0.79 mL/min/mmHg (10%, 95% CI, 0.30-1.25 mL/min/mmHg; p<0.01). In participants with paired lung volume measurements (n=22), RV decreased 210 mL (12%, 95% CI, -340 to -85 mL; p<0.01), RV/TLC decreased 5% (95% CI, -7% to -3%; p<0.001), and ERV increased 175 mL (29%, 95% CI, 50-385 mL; p=0.02). There was no overall change in TLC or FRC. ConclusionIn a real-world cohort of patients with severe precapillary PH from a variety of causes, sotatercept was associated with improvements in markers of SAD and hyperinflation.

ObjectiveInvasive mechanical ventilation can worsen lung injury. Ventilator dyssynchrony (VD) may propagate ventilator-induced lung injury (VILI) and is challenging to detect and systematically monitor because each patient takes approximately 25,000 breaths a day yet some types of VD are rare, accounting for less than 1% of all breaths. Therefore, we sought to develop and validate accurate machine learning (ML) algorithms to detect multiple types of VD by leveraging esophageal pressure waveform data to quantify patient effort with airway pressure, flow, and volume data generated during mechanical ventilation, building a computational pipeline to facilitate the study of VD. Materials and MethodsWe collected ventilator waveform and esophageal pressure data from 30 patients admitted to the ICU. Esophageal pressure allows the measurement of transpulmonary pressure and patient effort. Waveform data were cleaned, features considered essential to VD detection were calculated, and a set of 10,000 breaths were manually labeled. Four ML algorithms were trained to classify each type of VD: logistic regression, support vector classification, random forest, and XGBoost. ResultsWe trained ML models to detect different families and seven types of VD with high sensitivity (>90% and >80%, respectively). Three types of VD remained difficult for ML to classify because of their rarity and lack of sample size. XGBoost classified breaths with increased specificity compared to other ML algorithms. DiscussionWe developed ML models to detect multiple types of VD accurately. The ability to accurately detect multiple VD types addresses one of the significant limitations in understanding the role of VD in affecting patient outcomes. ConclusionML models identify multiple types of VD by utilizing esophageal pressure data and airway pressure, flow, and volume waveforms. The development of such computational pipelines will facilitate the identification of VD in a scalable fashion, allowing for the systematic study of VD and its impact on patient outcomes.

The novel coronavirus SARS-CoV-2 was identified as the causative agent of the ongoing pandemic COVID 19. COVID-19-associated deaths are mainly attributed to severe pneumonia and respiratory failure. Recent work demonstrated that SARS-CoV-2 binds to angiotensin converting enzyme 2 (ACE2) in the lung. To better understand ACE2 abundance and expression patterns in the lung we interrogated our in-house single-cell RNA-sequencing dataset containing 70,085 EPCAM+ lung epithelial cells from paired normal and lung adenocarcinoma tissues. Transcriptomic analysis revealed a diverse repertoire of airway lineages that included alveolar type I and II, bronchioalveolar, club/secretory, quiescent and proliferating basal, ciliated and malignant cells as well as rare populations such as ionocytes. While the fraction of lung epithelial cells expressing ACE2 was low (1.7% overall), alveolar type II (AT2, 2.2% ACE2+) cells exhibited highest levels of ACE2 expression among all cell subsets. Further analysis of the AT2 compartment (n = 27,235 cells) revealed a number of genes co-expressed with ACE2 that are important for lung pathobiology including those associated with chronic obstructive pulmonary disease (COPD; HHIP), pneumonia and infection (FGG and C4BPA) as well as malarial/bacterial (CD36) and viral (DMBT1) scavenging which, for the most part, were increased in smoker versus light or non-smoker cells. Notably, DMBT1 was highly expressed in AT2 cells relative to other lung epithelial subsets and its expression positively correlated with ACE2. We describe a population of ACE2-positive AT2 cells that co-express pathogen (including viral) receptors (e.g. DMBT1) with crucial roles in host defense thus comprising plausible phenotypic targets for treatment of COVID-19.

RationaleGenetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. ObjectivesDefine high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) and assess differences in clinical and molecular characteristics. MethodsWe defined high-risk groups based on PRS and TRS quantiles by maximizing differences in protein biomarkers in a COPDGene training set and identified these groups in COPDGene and ECLIPSE test sets. We tested multivariable associations of subgroups with clinical outcomes and compared protein-protein interaction networks and drug repurposing analyses between high-risk groups. Measurements and Main ResultsWe examined two high-risk omics-defined groups in non-overlapping test sets (n=1,133 NHW COPDGene, n=299 African American (AA) COPDGene, n=468 ECLIPSE). We defined "High activity" (low PRS/high TRS) and "severe risk" (high PRS/high TRS) subgroups. Participants in both subgroups had lower body-mass index (BMI), lower lung function, and alterations in metabolic, growth, and immune signaling processes compared to a low-risk (low PRS, low TRS) reference subgroup. "High activity" but not "severe risk" participants had greater prospective FEV1 decline (COPDGene: -51 mL/year; ECLIPSE: - 40 mL/year) and their proteomic profiles were enriched in gene sets perturbed by treatment with 5-lipoxygenase inhibitors and angiotensin-converting enzyme (ACE) inhibitors. ConclusionsConcomitant use of polygenic and transcriptional risk scores identified clinical and molecular heterogeneity amongst high-risk individuals. Proteomic and drug repurposing analysis identified subtype-specific enrichment for therapies and suggest prior drug repurposing failures may be explained by patient selection.

BackgroundHypoxemia is a common and life-threatening complication during emergency tracheal intubation of critically ill adults. The administration of supplemental oxygen prior to the procedure ("preoxygenation") decreases the risk of hypoxemia during intubation. Research QuestionWhether preoxygenation with noninvasive ventilation prevents hypoxemia during tracheal intubation of critically ill adults, compared to preoxygenation with oxygen mask, remains uncertain. Study Design and MethodsThe PRagmatic trial Examining OXygenation prior to Intubation (PREOXI) is a prospective, multicenter, non-blinded randomized comparative effectiveness trial being conducted in 7 emergency departments and 17 intensive care units across the United States. The trial compares preoxygenation with noninvasive ventilation versus oxygen mask among 1300 critically ill adults undergoing emergency tracheal intubation. Eligible patients are randomized in a 1:1 ratio to receive either noninvasive ventilation or an oxygen mask prior to induction. The primary outcome is the incidence of hypoxemia, defined as a peripheral oxygen saturation <85% between induction and 2 minutes after intubation. The secondary outcome is the lowest oxygen saturation between induction and 2 minutes after intubation. Enrollment began on 10 March 2022 and is expected to conclude in 2023. InterpretationThe PREOXI trial will provide important data on the effectiveness of noninvasive ventilation and oxygen mask preoxygenation for the prevention of hypoxemia during emergency tracheal intubation. Specifying the protocol and statistical analysis plan prior to the conclusion of enrollment increases the rigor, reproducibility, and interpretability of the trial. Clinical trial registration numberNCT05267652 HIGHLIGHTS* Hypoxemia is common during emergency tracheal intubation * Supplemental oxygen prior to intubation (preoxygenation) reduces risk of hypoxemia * The PREOXI trial compares noninvasive ventilation vs oxygen mask preoxygenation * This protocol describes the design, methods, and planned analyses * PREOXI is the largest trial of preoxygenation for emergency intubation to date