Lynch, David A.

Abstract Rationale Impaired lung health may serve as a risk factor for, and a post-acute sequela of, SARS-CoV-2 infection. Yet most prior studies of lung health sequelae of COVID-19 lacked information on pre-infection lung health, a major antecedent confounder. To identify associations of COVID-19 with changes in lung health, we leveraged paired pre- and post-pandemic data on respiratory symptoms, lung function, and lung structure in a general population-based cohort. Methods We studied participants in the Multi-Ethnic Study of Atherosclerosis Lung Study with paired pre-pandemic (2016-2018) and post-pandemic (2022-2024) assessments of respiratory symptoms, spirometry, and lung CT. SARS-CoV-2 infection (2020-2023) was ascertained using a composite of questionnaires, serology, and medical record adjudication, and classified as severe (requiring hospitalization) or non-severe (not requiring hospitalization). Lung health was assessed by questionnaire (mMRC grade ≥2 dyspnea, chronic cough, chronic phlegm, chronic bronchitis), pre-bronchodilator spirometry (FEV1, FVC, FEV1:FVC), and lung density on non-contrast lung CT (% low attenuation areas, % high attenuation areas). Each post-pandemic lung health measure was regressed on infection history and the corresponding pre-pandemic lung health measure, adjusting for age at post-pandemic exam, sex, race/ethnicity, smoking status, and vaccination status at infection. Sensitivity analyses were included- (a) restriction to participants without pre-pandemic respiratory symptoms and (b) with additional adjustment for time since infection and variant wave. Results Among 2,015 participants (mean age 77.2±7.5 years; 55% female; 40% non-Hispanic White; 25% Black; 11% Asian; 23% Hispanic/Latino), 59 (3%) experienced severe and 506 (25%) non-severe SARS-CoV-2 infection. The prevalence of respiratory symptoms was similar or slightly higher at the post- vs. pre-pandemic exam (grade ≥2 dyspnea: 14% vs. 10%; chronic cough: 10% vs. 8%; chronic phlegm: 9% vs. 8%; chronic bronchitis: 4% vs. 4%; p-values <0.0001). Compared with participants without infection, those with severe infection had higher odds of chronic cough (OR:2.9, 95%CI:1.3-6.3) and chronic bronchitis (OR:3.3, 95%CI:1.2-9.0) at the post-pandemic exam (Table). Associations were stronger among individuals infected >90 days prior to the post-pandemic assessment and during pre-Omicron waves. Results were similar when restricted to participants without pre-pandemic symptoms. No significant associations were observed for non-severe infection (vs. no infection) or for any other lung health outcomes. Conclusions Controlling for pre-pandemic lung health, severe COVID-19 was associated with increased odds of chronic cough and chronic bronchitis but not with changes in lung function or structure in a general population-based sample. Non-severe COVID-19 was not associated with lung health outcomes. This abstract is funded by: OT2HL156812, R21HL165405, R01HL077612, 75N92025D00022, 75N92025D00026, 75N92020D00002, 75N92025D00024, 75N92025D00027, 75N92025D00025, 75N92025D00028

Abstract Rationale Interstitial lung abnormalities (ILA) are abnormal findings on chest CT scans that may represent early-stages of interstitial lung disease. The presence of fibrosis (traction bronchiectasis and/or honeycombing) identifies those with ILA at an increased risk for mortality, however, determining who will progress remains a challenge, and the role of blood-based biomarkers in ILA risk-stratification is unknown. Methods Data from four prospective cohort studies were used to identify associations between plasma proteins measured with DNA aptamers (SomaScan) and ILA, fibrosis, and mortality. The Genetic Epidemiology of COPD Study (COPDGene) was used to identify protein associations, and findings were replicated by meta-analysis of the Age, Gene/Environment Susceptibility-Reykjavik study (AGES-Reykjavik), SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS), and Framingham Heart Study (FHS). Protein associations with ILA and with fibrotic ILA compared to non-fibrotic ILA were performed using multivariable logistic regression; among those with ILA, Cox proportional hazards models were used to identify protein associations with mortality. Models were adjusted for age, gender, race, BMI, smoking status, pack-year smoking history, and for mortality, COPD. Benjamini-Hochberg false discovery rate p-values <0.05 were considered significant. Results 524 (10.7%) of 4,899 COPDGene participants had ILA, of whom 57 (10.9%) had fibrosis; of 8,146 AGES-Reykjavik, SPIROMICS and FHS participants included in the meta-analysis, 666 (8.2%) had ILA, of whom 198 (29.7%) had fibrosis. In COPDGene, 230 proteins were associated with ILA; the most significant of which were SFTPD (adjusted odds ratio per standard deviation [SD] of protein [OR]=2.21, p = 3.7E-41) and SFTPB (OR = 1.82, p = 7.9E-37). In the meta-analysis, 159 of the ILA-associated proteins were replicated. Of the 230 ILA-associated proteins in COPDGene, 9 were associated with fibrosis, and 5 of these 9 proteins were associated with mortality among those with ILA. The 5 proteins associated with all three outcomes were WFDC2 (ORILA=1.90, p = 2.6E-34; ORFibrosis=2.00, p = 0.0001; adjusted hazards of mortality per SD of protein [HR]=1.59, p = 3.4E-05), GDF15 (ORILA=1.67, p = 3.6E-23; ORFibrosis=1.78, p = 0.0006; HR = 1.63, p = 1.4E-06), SCGB3A1 (ORILA=1.52, p = 4.5e-14; ORFibrosis=1.80, p = 0.0002; HR = 1.25, p = 0.02), TREM1 (ORILA=1.44, p = 1.4e-14; ORFibrosis=1.62, p = 0.001; HR = 1.25, p = 0.02) ICAM5 (ORILA=1.42, p = 1.2e-11; ORFibrosis=1.79, p = 0.0001; HR = 1.31, p = 0.002). In the meta-analysis, 6 of the 9 fibrosis-associated proteins and all 5 ILA mortality-associated proteins replicated. Conclusion This represents the first plasma proteomic screen demonstrating reproducible biomarker associations not only with ILA presence, but also with measures of severity and adverse outcomes. These results provide compelling targets for further study, including their prospective evaluation as clinical biomarkers for early disease identification and prognostication. This abstract is funded by: Multiple Sources

Abstract Rationale Quantitative CT using multiple instance learning (MIL)-usual interstitial pneumonia (UIP) classifier and data-driven textural analysis (DTA) provides objective and reproducible measurements of disease pattern and extent, respectively. However, the understanding of their individual or combined relationships with outcomes in fibrotic hypersensitivity pneumonitis (fHP) remains limited. Objectives To determine whether visual CT patterns of fHP or UIP, a MIL-UIP classifier, baseline and changes in DTA over 12-months and DTA at 12 months are associated with progression-free survival (PFS) in fHP. Methods The primary cohort included 134 participants with fHP. Associations between CT variables and subsequent lung function change, and PFS were assessed using linear mixed models and Cox hazards models. Results were validated in a combined dataset from three independent populations (N = 216). Results Visual patterns of fHP or UIP were not associated with PFS. A baseline MIL-UIP classification and DTA were significantly associated with 24-month PFS. However, the MIL-UIP did not predict PFS after adjusting for DTA fibrosis extent. Over 12 months, a 1-point increase in DTA across and within patients resulted in a significant decline in FVC% and DLCO%. A 12-month change in DTA (HR = 1.07, 95%CI 1.01-1.13) and DTA at 12-months (HR = 1.03, 95%CI 1.01-1.05) were associated with subsequent 12-month PFS. Similar estimates were observed in the validation cohort. Conclusions In fHP, the objectively measured extent of lung fibrosis is more prognostic than either a UIP classifier or visual CT patterns of fHP or UIP. Baseline DTA, DTA at 12 months, and its change over 12-months are associated with PFS.

Abstract Background Despite advances in diagnosis and management, the interstitial lung disease (ILD) lexicon is plagued by ambiguous and inconsistent terminology that complicates communication and impedes knowledge generation. The objective of this Fleischner Society Consensus Statement was to produce standardized terminology for ILD multidisciplinary diagnoses and major phenotypes. Methods Interviews with 10 experts were used to identify ILD clinical diagnoses and major phenotypes. The preferred terms for each entity and potential alternatives were identified, alongside the rationale for the preferred term. Entities with more than one potential term were the subject of an online modified Delphi survey posed to the 29 committee members, aiming to achieve consensus. Committee members rated their agreement with the initially preferred term—5 (strongly agree), 4 (agree), 3 (neutral/unsure), 2 (disagree), and 1 (strongly disagree)—with the option to provide additional comments. Median score ≥4 and interquartile range ≤1 were considered consensus agreement. Terms not reaching agreement were discussed by video conference, followed by an additional survey that incorporated feedback. Results From the 60 initial terms, there were two root terms that required upfront consensus before survey initiation (ILD and interstitial pneumonia) and another eight terms that had no alternative suggested by the committee or in the literature. Agreement was met by 47/50 terms (94%) in Round 1 of the survey. The three terms (6%) that did not reach agreement met agreement in Round 2. Conclusions This document provides standardized recommended terms for ILD multidisciplinary diagnoses and major phenotypes that will facilitate communication among clinicians, researchers, patients, and other stakeholders.

Abstract RATIONALE: Susceptibility to severe COVID-19 and long COVID may be influenced by pre-existing lung structure. ECG-gated, non-contrast cardiac CT scans, originally acquired for assessing coronary artery calcium, are available in multiple longitudinal cohort studies and have been previously validated against lung CT scans for densitometry; however, protocol variation across cohorts may hinder direct comparison. To address this, we developed a quality control (QC) rubric to grade cardiac CT scans for standardized quantitative lung measures across cohorts. METHODS: C4R assessed SARS-CoV-2 infection and outcomes in 14 NHLBI cohorts, of which 10 (six community-based and four longitudinal case-control studies) included CT scans (12,459 participants with lung and 13,752 with cardiac CTs). Logic rules were applied to categorize scans (Table): Grade “A-B” scans had 2-3.5mm slice thickness, at least 20 axial slices, no lung cropping in axial view and lung and airway volumes greater than the cohort-specific 1st percentile. “C-D” scans had one lung obscured by cropping around the heart, so quantitative measures were restricted to the lung in complete view. “F-H” scans were deemed unusable, suffering from low slice count (<20), severe image cropping, low lung or airway volumes (< 1st%ile). Visual inspection was performed on a sample from each category to ensure accuracy. Percent of lung voxels in low attenuation areas < -950 HU (LAA950), high attenuation areas (-600 to -250 HU, HAA) and 15th percentile HU value (perc15) were measured. Within-participant variability was quantified using the intraclass correlation coefficient (ICC). RESULTS: We performed QC on cardiac CT scans from an initial sample of 6,275 participants from three C4R cohorts (ARIC, CARDIA, MASALA), imaged with GE or Siemens scanners. Of these, 5,125 (81.7%) were graded “A-B”, 496 (7.9%) were “C-D” and 654 (10.4%) were “F-H”. The percentage of “F-H” scans varied widely across cohorts [<1% to 26%] due to image cropping. Among the 5,621 cardiac CT scans graded A-D, the mean±SD for LAA950, HAA and perc15 respectively were 1.1±2.1%, 7.2±4.7% and -887±33 HU. Repeated imaging was available for 107 participants (time interval 5.1±0.7y). For paired scans in grades A-B (n=101/107), the within-participant correlation for LAA950, HAA and perc15 were 0.91, 0.38 and 0.74 respectively. CONCLUSION: Initial application of QC to cardiac CT scans identified scans with high reproducibility, with the aim to provide valid quantitative lung metrics on over 20,000 highly characterized participants in C4R.

Abstract Rationale Increased risk of coronavirus disease (COVID-19) hospitalization and death has been reported among patients with clinical lung disease. Objectives To test the association of objective measures of prepandemic lung function and structure with COVID-19 outcomes in U.S. adults. Methods Prepandemic obstruction (FEV1/FVC < 0.70) and restriction (FEV1/FVC ⩾ 0.7, FVC < 80%) were defined based on the most recent spirometry exam conducted in 11 prospective U.S. general population–based cohorts. Severe obstruction was classified by FEV1 < 50%. Percentage emphysema, percentage high-attenuation areas, and interstitial lung abnormalities were defined on computed tomography in a subset. Incident COVID-19 was ascertained via questionnaires, serosurvey, and medical records from 2020 to 2023 and classified as severe (hospitalized or fatal) or nonsevere. Cause-specific hazard models were adjusted for sociodemographics, anthropometry, smoking, comorbidities, and COVID-19 vaccination status. Measurements and Main Results Among 29,323 participants (mean age, 67 yr), there were 748 severe incident COVID-19 cases over median follow-up of 17.3 months from March 1, 2020. Greater hazards of severe COVID-19 were associated with severe obstruction (vs. normal; adjusted hazard ratio [aHR], 2.11; 95% confidence interval [CI], 1.02–1.27), restriction (vs. normal; aHR, 1.40; 95% CI, 1.12–1.76), and percentage emphysema (highest vs. lowest quartile; aHR, 1.64; 95% CI, 1.03–2.61), but not greater high-attenuation areas or interstitial lung abnormalities. COVID-19 vaccination provided greater absolute risk reduction in these groups. Results were similar in participants without smoking, obesity, or clinical cardiopulmonary disease. Conclusions Prepandemic severe spirometric obstruction, spirometric restriction, and greater percentage emphysema lung on computed tomography were associated with risk of severe COVID-19. These findings support enhanced COVID-19 risk mitigation for individuals with impaired lung health and warrant further mechanistic studies on interactions of lung function, structure, and vulnerability to acute respiratory illnesses.

Abstract Emphysema’s significant morbidity and mortality underscore the need for reliable outcome metrics in clinical trials. However, commonly accepted chronic obstructive pulmonary disease outcome measures do not adequately capture emphysema severity or progression. Computed tomography (CT) metrics have been validated as accurate indicators of pathological emphysema and predictors of chronic obstructive pulmonary disease progression, exacerbations, and mortality. This position paper reviews the evidence supporting CT densitometry as a biomarker for emphysema, establishes implementation standards, and highlights areas for future research. A systematic literature review addressed three key questions: whether CT densitometry can be used as a diagnostic biomarker of emphysema, whether CT densitometry can be used as a prognostic biomarker, and whether longitudinal change in densitometry can be used as a disease progression monitoring biomarker. Emphysema metrics, such as the percentage of low attenuation areas below −950 Hounsfield units, are validated, highly reproducible diagnostic and prognostic biomarkers. Volume-adjusted lung density is recommended for disease monitoring. Both metrics demonstrate a scan–rescan intraclass correlation coefficient of 0.99 with proper technique. The paper also discusses relevant CT physics, techniques, and sources of variation, including technical factors, physiological changes, and software analysis. Key recommendations for clinical trials include using standardized CT techniques, proper subject selection, and longitudinal evaluation with volume-adjusted lung density.

Abstract Rationale Idiopathic pulmonary fibrosis (IPF) is a complex and heterogeneous disease. Given this, we reasoned that differences in genetic profiles may be associated with unique clinical and radiologic features. Computational image analysis, sometimes referred to as radiomics, provides objective, quantitative assessments of radiologic features in subjects with pulmonary fibrosis. Objectives To determine if the genetic risk profile of patients with IPF identifies unique computational imaging phenotypes. Methods Participants with IPF were included in this study if they had genotype data and computed tomography (CT) scans of the chest available for computational image analysis. The extent of lung fibrosis and the likelihood of a usual interstitial pneumonia (UIP) pattern were scored automatically using two separate, previously validated deep learning techniques for CT analysis. UIP pattern was also classified visually by radiologists according to established criteria. Results Among 329 participants with IPF, MUC5B and ZKSCAN1 were independently associated with the deep learning–based UIP score. None of the common variants were associated with fibrosis extent by computational imaging. We did not find an association between MUC5B or ZKSCAN1 and visually assessed UIP pattern. Conclusions Select genetic variants are associated with computer-based classification of UIP on CT in this IPF cohort. Analysis of radiologic features using deep learning may enhance our ability to identify important genotype–phenotype associations in fibrotic lung diseases.