To evaluate three commercial AI software tools for pulmonary nodule detection and segmentation and to assess their impact on guideline-based management recommendations. A total of 740 CT and PET-CT studies from clinical routine were analyzed using three software tools (S1, S2, S3). We compared the total number of detected nodules and “actionable” nodules (per British Thoracic Society (BTS) definition). We further evaluated how measurement variations between tools affected hypothetical management according to Fleischner Society and BTS guidelines for incidental nodules. The tools differed significantly in the total number of detections (S1: 1336; S2: 1060; S3: 1536; p < 0.001) and wrong findings (S1: 965; S2: 720; S3: 1169; p < 0.001). However, the detection of actionable nodules was comparable across all tools (S1: 375; S2: 341; S3: 373; p = 0.73). While no statistically significant differences were found in mean diameter or volume measurements, small absolute variations led to significant differences in management. Specifically, S2 triggered significantly more 1-year follow-up recommendations than S3 under BTS guidelines (p < 0.001). No significant management differences were observed when applying Fleischner Society guidelines. While the three included AI tools show comparable performance in detecting actionable nodules, minor measurement variations significantly impact downstream management when using guidelines with narrow thresholds, such as the BTS criteria. Fleischner Society guidelines appear more robust to these inter-software variations. Question How do commercial software tools for pulmonary nodule detection perform in real-world settings and impact hypothetical management under BTS and Fleischner guidelines? Findings Detection of actionable nodules was comparable across all tools, but small absolute measurement variations triggered significantly more 1-year follow-up recommendations under BTS guidelines. Clinical relevance AI software can cause inconsistent BTS-based management due to narrow thresholds, while Fleischner criteria appear more stable. Frequent detection of benign lesions potentially poses a risk of overdiagnosis and overtreatment in standalone AI-based reporting.

Interleukin-33 (IL-33) is a promising therapeutic target in chronic obstructive pulmonary disease (COPD). However, recent clinical trial setbacks have highlighted the complexity of its biology. This review examines the functions of IL-33 in inflammation, host defense, and tissue remodeling and discusses the potential benefits and harms of targeting this pathway in patients with COPD. We synthesize data from clinical trials of anti-IL-33/ST2 agents and integrate these findings with recent molecular and cellular immunology research to elucidate IL-33’s dual functionality, induced by the reduced IL-33 and oxidized IL-33 isoforms. Additionally, we highlight the impact of modifying factors, including multimorbidity, exogenous exposures, and microbial pathogens, on the IL-33 cascade. Divergent trial outcomes in patients with COPD treated with anti-IL-33 and anti-ST2 antibodies revealed critical mechanistic insights. Inconsistent efficacy of single-pathway inhibitors likely reflect incomplete targeting of IL-33’s dual functionality. While the IL-33red/ST2 pathway drives type 2 inflammation, it is also essential for neutrophil-mediated antibacterial host defense. In contrast, IL-33ox signals through the RAGE/EGFR axis to promote steroid-resistant epithelial remodelling and mucus hypersecretion, a pathway not addressed by ST2-targeted therapies. Experimental data suggesting a potential increase in infection risk associated with anti-IL-33 therapies further underscore the clinical relevance of these mechanistic considerations. Effective IL-33-targeted therapy in COPD will require a mechanistically comprehensive strategy with precision medicine approaches that address patient heterogeneity, including infection risk and comorbidity burden, and incorporate assessment of IL-33 redox states, soluble ST2, and soluble RAGE.

Background:Identifying and addressing long-term health and societal challenges after COVID-19 is a research priority. Objectives:To create an international, multidisciplinary COVID-19 database, and synthesise long-term outcomes, predictors and costs. Design:Systematic identification of COVID-19 data sets and meta-analysis of individual participant data on long-term outcomes after COVID-19. Setting:Contributed data were collected in clinical, community and research settings. Interventions:Interventions from original studies were included as covariates in models. Data sources:MEDLINE, Cochrane Central Register of Controlled Trials, EMBASE, Web of Science, PsycInfo® (American Psychological Association, Washington, DC, USA), Cumulative Index to Nursing and Allied Health Literature, World Health Organization Global Index Medicus, Epistemonikos, LitCOVID; World Health Organization International Clinical Trials Registry Platform; ClinicalTrials.gov and supplementary searches for studies (November 2019-November 2021) were searched for studies on > 10 people from cohort, case-control, survey or randomised controlled trial studies, across any setting, describing validated assessment instruments, symptoms, hospitalisation, discharge destination or mortality beyond 28-days after COVID-19 onset. Data were extracted by two independent reviewers. Methods:Principal investigators contributed fully anonymised individual participant data. Demography, equity and symptoms were described. Assessment instruments were mapped to the International Classification of Functioning, Disability and Health. Factors associated with outcomes at 3-6 months, 9-12 months and beyond 12 months of index infection, for n > 500 individual participant data and > 1 data set were described using ratio of difference, point estimates, odds ratio and 95% confidence interval, as appropriate. The Mixed Methods Appraisal Tool described study quality; models were appraised using a Grading of Recommendations Assessment, Development and Evaluation-informed approach; heterogeneity was described using I2. Outcome measures:Included overall perception of health, multidomain cognitive function, anxiety, depression, stress, post-traumatic stress disorder, fatigue, strength, walking ability, mobility, coping with daily life, breathlessness, mortality, later hospitalisation and health-related quality of life. Results:PRECIOUS collated 116 data sets from 40 countries (individual participant data = 62,849), comprising 20 randomised controlled trials, 13 case-control, 60 cohort 2 longitudinal, 1 survey and 20 other study types. Participants' median age was 58 years interquartile range (45-68); 34,185 (54.4%) were female; 158 unique symptoms and 137 unique assessment instruments were captured, predominantly describing International Classification of Function, Disability and Health-body functions. Women had poorer outcomes across 30/37 models, compared with men. In 15/37 models, pre-existing lung disease and increasing age were associated with poorer outcomes; hospitalisation, diabetes and chronic kidney disease were each associated with poorer outcomes in 8/37 models. Initial hospitalisation resulted in lower health-related quality of life that did not recover for up to 2 years after initial infection. Heterogeneity was low in 34/37 models; 22/37 models were of moderate and 11/37 were of low quality. Limitations:Use of secondary data limits available covariates, outcomes and time points to those included in primary data sets; evidence was primarily based on high-income countries. There was a lack of data on longer-term healthcare resource use to estimate the costs to the healthcare system. Conclusions:PRECIOUS contributes to the overall picture of long-term COVID-19 outcomes beyond the long-COVID condition and highlights poorer long-term outcomes in women and people with pre-existing comorbidities. Future work:Evidence gaps included healthcare resource use, isolation, loneliness, societal participation and return to work outcomes. Data are needed on the role of health inequity on long-term outcomes. Study registration:This study is registered as PROSPERO (CRD42020224323, IRAS ID: 293578). Funding:This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR132895) and is published in full in Health and Social Care Delivery Research; Vol. 14, No. 29. See the NIHR Funding and Awards website for further award information.

Pulmonary arterial hypertension (PAH) is a progressive and life-limiting condition caused by pulmonary vascular remodeling due to heterogeneous aetiologies. Despite significant advances in treatment, PAH remains incurable and is associated with a poor prognosis, highlighting the need for novel therapeutic options. Recent advances in molecular and translational research have uncovered key pathways involved in PAH, including endothelial dysfunction, smooth muscle cell proliferation, inflammation, and thrombosis. These insights have guided the development of novel therapeutic strategies aimed at modulating these mechanisms. Promising compounds currently under investigation include agents targeting the bone morphogenic protein/transforming growth factor-beta axis, epigenetic modulators, receptor tyrosine kinase inhibitors, and immunomodulatory biologics. In addition, vasodilators acting on the renin–angiotensin–aldosterone system and the soluble guanylate cyclase pathway, as well as metabolic and hormonal modulators are under investigation, with clinical trials showing encouraging results for several agents. This review outlines the current understanding of PAH pathobiology and the most promising emerging treatments for pulmonary hypertension.

Respiratory cilia play a crucial role in clearing pathogens from the airway, and understanding how Pseudomonas aeruginosa (PA) impairs their function is essential for developing targeted therapies to enhance airway epithelial defense and hamper bacterial invasion. Thus, we investigated the mechanisms by which PA impairs respiratory cilia function and identified pharmacological interventions to restore cilia motility. We used mucociliary differentiated human airway organoids expressing motile cilia on the apical side, to model the initial stage of PA infection. We show that co-culturing of organoids with PA down-regulates the expression of genes associated with cilia formation, structure and function. Electron microscopy confirmed ciliary structural damage and membrane disruption in infected organoids and slowed ciliary beating frequency as quantified by slow-motion video recording. Reduction of cilia function resulted in increased bacterial cell invasion, and we identified the bacterial toxin Pyocyanin as one causative cilia damaging factor. We could further demonstrate, that the selective phosphodiesterase-4 inhibitor Roflumilast can maintain ciliary function and beating frequency upon infection of organoids with PA, thereby reducing bacterial invasion of epithelia in our model system. Our findings emphasize the critical role of motile cilia in innate host defense against PA infection, implying that treatment strategies aimed at restoring ciliary function can enhance epithelial robustness during bacterial invasion. Our human organoid infection model enables the evaluation of cellular and functional responses of the airway epithelium to respiratory pathogens and to test potential therapeutic strategies aimed at restoring ciliary function and improving infection control.

Pulmonary vein stenosis is a rare but serious complication after atrial fibrillation ablation. We present a 41-year-old man with a history of paroxysmal atrial fibrillation and dilated cardiomyopathy who developed new-onset hemoptysis and ground-glass opacities on imaging, months after his ablation procedure. Differential diagnoses included vasculitis, infection, and interstitial lung disease, all of which were ruled out. A dedicated pulmonary vein protocol computed tomography scan confirmed the diagnosis of high-grade stenosis of the left upper pulmonary vein. Due to the patient's asymptomatic status posthemoptysis, conservative management was pursued. This case underscores the importance of considering pulmonary vein stenosis in patients with similar presentations and highlights the role of interdisciplinary management in select cases.

Objectives: Prediction of lung function deficits following pulmonary infection is challenging and suffers from inaccuracy. We sought to develop machine-learning models for prediction of post-inflammatory lung changes based on COVID-19 recovery data. Methods: In the prospective CovILD study (n = 420 longitudinal observations from n = 140 COVID-19 survivors), data on lung function testing (LFT), chest CT including severity scoring by a human radiologist and density measurement by artificial intelligence, demography, and persistent symptoms were collected. This information was used to develop models of numeric readouts and abnormalities of LFT with four machine learning algorithms (Random Forest, gradient boosted machines, neural network, and support vector machines). Results: Reduced DLCO (diffusion capacity for carbon monoxide <80% of reference) was found in 94 (22%) observations. Those observations were modeled with a cross-validated accuracy of 82–85%, AUC of 0.87–0.9, and Cohen’s κ of 0.45–0.5. No reliable models could be established for FEV1 or FVC. For DLCO as a continuous variable, three machine learning algorithms yielded meaningful models with cross-validated mean absolute errors of 11.6–12.5% and R2 of 0.26–0.34. CT-derived features such as opacity, high opacity, and CT severity score were among the most influential predictors of DLCO impairment. Conclusions: Multi-parameter machine learning trained with demographic, clinical, and artificial intelligence chest CT data reliably and reproducibly predicts LFT deficits and outperforms single markers of lung pathology and human radiologist’s assessment. It may improve diagnostic and foster personalized treatment.

A hyperinflammatory state with highly elevated concentrations of inflammatory biomarkers such as C-reactive protein (CRP) is a characteristic feature of severe coronavirus disease 2019 (COVID-19). To examine a potential role of common genetic factors that may influence COVID-19 outcomes, we investigated whether individuals with a polygenic predisposition for a pro-inflammatory response (in the form of Polygenic Scores) are more likely to develop severe COVID-19. The innovative approach of polygenic scores to investigate genetic factors in COVID-19 severity should provide a comprehensive approach beyond single-gene studies. In our cohort of 156 patients of European ancestry, two overlapping Polygenic Scores (PGS) predicting a genetic predisposition to basal CRP concentrations were significantly different between non-severe and severe COVID-19 cases and were associated with less severe COVID-19 outcomes. Furthermore, specific single nucleotide polymorphisms (SNPs) that contribute to either of the two Polygenic Scores predicting basal CRP levels are associated with different traits that represent risk factors for COVID-19 disease initiation (ACE2 receptor, viral replication) and progression (CRP). We suggest that genetically determined enforced CRP formation may contribute to strengthening of innate immune responses and better initial pathogen control thereby reducing the risk of subsequent hyperinflammation and adverse course of COVID-19.

Research into the molecular basis of disease trajectory and Long-COVID is important to get insights toward underlying pathophysiological processes. The objective of this study was to investigate inflammation-mediated changes of metabolism in patients with acute COVID-19 infection and throughout a one-year follow up period. The study enrolled 34 patients with moderate to severe COVID-19 infection admitted to the University Clinic of Innsbruck in early 2020. The dynamics of multiple laboratory parameters (including inflammatory markers [C-reactive protein (CRP), interleukin-6 (IL-6), neopterin] as well as amino acids [tryptophan (Trp), phenylalanine (Phe) and tyrosine (Tyr)], and parameters of iron and vitamin B metabolism) was related to disease severity and patients’ physical performance. Also, symptom load during acute illness and at approximately 60 days (FU1), and one year after symptom onset (FU2) were monitored and related with changes of the investigated laboratory parameters: During acute infection many investigated laboratory parameters were elevated (e.g., inflammatory markers, ferritin, kynurenine, phenylalanine) and enhanced tryptophan catabolism and phenylalanine accumulation were found. At FU2 nearly all laboratory markers had declined back to reference ranges. However, kynurenine/tryptophan ratio (Kyn/Trp) and the phenylalanine/tyrosine ratio (Phe/Tyr) were still exceeding the 95th percentile of healthy controls in about two thirds of our cohort at FU2. Lower tryptophan concentrations were associated with B vitamin availability (during acute infection and at FU1), patients with lower vitamin B12 levels at FU1 had a prolonged and more severe impairment of their physical functioning ability. Patients who had fully recovered (ECOG 0) presented with higher concentrations of iron parameters (ferritin, hepcidin, transferrin) and amino acids (phenylalanine, tyrosine) at FU2 compared to patients with restricted ability to work. Persistent symptoms at FU2 were tendentially associated with IFN-γ related parameters. Women were affected by long-term symptoms more frequently. Conclusively, inflammation-mediated biochemical changes appear to be related to symptoms of patients with acute and Long Covid.

IntroductionAirway epithelial cells play a central role in the innate immune response to invading bacteria, yet adequate human infection models are lacking.MethodsWe utilized mucociliary-differentiated human airway organoids with direct access to the apical side of epithelial cells to model the initial phase of Pseudomonas aeruginosa respiratory tract infection.ResultsImmunofluorescence of infected organoids revealed that Pseudomonas aeruginosa invades the epithelial barrier and subsequently proliferates within the epithelial space. RNA sequencing analysis demonstrated that Pseudomonas infection stimulated innate antimicrobial immune responses, but specifically enhanced the expression of genes of the nitric oxide metabolic pathway. We demonstrated that activation of inducible nitric oxide synthase (iNOS) in airway organoids exposed bacteria to nitrosative stress, effectively inhibiting intra-epithelial pathogen proliferation. Pharmacological inhibition of iNOS resulted in expansion of bacterial proliferation whereas a NO producing drug reduced bacterial numbers. iNOS expression was mainly localized to ciliated epithelial cells of infected airway organoids, which was confirmed in primary human lung tissue during Pseudomonas pneumonia.DiscussionOur findings highlight the critical role of epithelial-derived iNOS in host defence against Pseudomonas aeruginosa infection. Furthermore, we describe a human tissue model that accurately mimics the airway epithelium, providing a valuable framework for systemically studying host-pathogen interactions in respiratory infections.

OBJECTIVE:Long-term consequences after COVID-19 include physical complaints, which may impair physical recovery and quality of life. DESIGN:We assessed body composition and physical ability in patients 12 months after COVID-19. Consecutively recruited patients recovering from mild to severe COVID-19 were assessed using bioelectrical impedance analysis, 6-min-walk test, additional scales for physical performance and health-related quality of life. RESULTS:Overall physical recovery was good (i.e., Glasgow Outcome Scale-Extended ≥7 in 96%, Modified Rankin Scale ≤1 in 87%, Eastern Cooperative Oncology Group ≤1 in 99%). Forty-four percent of the 69 patients experienced a significant body mass index increase in the year after COVID-19 (≥1 kg/m 2 ), whereas skeletal muscle mass index was reduced in only 12%. Patients requiring intensive care treatment ( n = 15, 22%) during acute COVID-19 more often had a body mass index increase ( P = 0.002), worse 6-min-walk test-performance ( P = 0.044), and higher body fat mass ( P = 0.030) at the 1-yr follow-up when compared with patients with mild ( n = 22, 32%) and moderate ( n = 32, 46%) acute COVID-19. Body mass index increase was also more frequent in patients who had no professional rehabilitation ( P = 0.014). CONCLUSIONS:Although patients with severe COVID-19 had increased body mass index and body fat and performed worse in physical outcome measures 1 yr after COVID-19, overall physical recovery was satisfying. Translating these findings to variants beyond the Alpha strain of severe acute respiratory syndrome coronavirus 2 virus needs further studies.

Accurate risk stratification in pulmonary arterial hypertension (PAH), a devastating cardiopulmonary disease, is essential to guide successful therapy. Machine learning may improve risk management and harness clinical variability in PAH. We conducted a long-term retrospective observational study (median follow-up: 67 months) including 183 PAH patients from three Austrian PAH expert centers. Clinical, cardiopulmonary function, laboratory, imaging, and hemodynamic parameters were assessed. Cox proportional hazard Elastic Net and partitioning around medoid clustering were applied to establish a multi-parameter PAH mortality risk signature and investigate PAH phenotypes. Seven parameters identified by Elastic Net modeling, namely age, six-minute walking distance, red blood cell distribution width, cardiac index, pulmonary vascular resistance, N-terminal pro-brain natriuretic peptide and right atrial area, constituted a highly predictive mortality risk signature (training cohort: concordance index = 0.82 [95

OBJECTIVE:Subjective illness perception (IP) can differ from physician's clinical assessment results. Herein, we explored patient's IP during coronavirus disease 2019 (COVID-19) recovery. METHODS:Participants of the prospective observation CovILD study (ClinicalTrials.gov: NCT04416100) with persistent somatic symptoms or cardiopulmonary findings one year after COVID-19 were analyzed (n = 74). Explanatory variables included demographic and comorbidity, COVID-19 course and one-year follow-up data of persistent somatic symptoms, physical performance, lung function testing, chest computed tomography and trans-thoracic echocardiography. Factors affecting IP (Brief Illness Perception Questionnaire) one year after COVID-19 were identified by regularized modeling and unsupervised clustering. RESULTS:In modeling, 33% of overall IP variance (R2) was attributed to fatigue intensity, reduced physical performance and persistent somatic symptom count. Overall IP was largely independent of lung and heart findings revealed by imaging and function testing. In clustering, persistent somatic symptom count (Kruskal-Wallis test: η2 = 0.31, p < .001), fatigue (η2 = 0.34, p < .001), diminished physical performance (χ2 test, Cramer V effect size statistic: V = 0.51, p < .001), dyspnea (V = 0.37, p = .006), hair loss (V = 0.57, p < .001) and sleep problems (V = 0.36, p = .008) were strongly associated with the concern, emotional representation, complaints, disease timeline and consequences IP dimensions. CONCLUSION:Persistent somatic symptoms rather than abnormalities in cardiopulmonary testing influence IP one year after COVID-19. Modifying IP represents a promising innovative approach to treatment of post-COVID-19 condition. Besides COVID-19 severity, individual IP should guide rehabilitation and psychological therapy decisions.

Background:Fatigue, sleep disturbance, and neurological symptoms during and after COVID-19 are common and might be associated with inflammation-induced changes in tryptophan (Trp) and phenylalanine (Phe) metabolism. Aim:This pilot study investigated interferon gamma inducible biochemical pathways (namely Trp catabolism, neopterin, tyrosine [Tyr], and nitrite formation) during acute COVID-19 and reconvalescence. Patients and methods:Thirty one patients with moderate to severe COVID-19 admitted to the University Hospital of Innsbruck in early 2020 (March-May) were followed up. Neurotransmitter precursors Trp, Phe, Tyr as well as kynurenine (Kyn), neopterin, nitrite, and routine laboratory parameters were analyzed during acute infection and at a follow-up (FU) 60 days thereafter. Clinical symptoms of patients (neurological symptoms, fatigue, sleep disturbance) were recorded and associations with concentrations of laboratory parameters investigated. Results and conclusion:Almost half of the patients suffered from neurological symptoms (48.4%), the majority of patients experienced sleep difficulties (56.7%) during acute COVID-19. Fatigue was present in nearly all patients. C-reactive protein (CRP), interleukin-6 (IL-6), neopterin, Kyn, Phe concentrations were significantly increased, and Trp levels depleted during acute COVID-19. Patients with sleep impairment and neurological symptoms during acute illness presented with increased CRP and IL-6 concentrations, Trp levels were lower in patients with sleep disturbance. In general, inflammatory markers declined during reconvalescence. A high percentage of patients suffered from persistent symptoms at FU (neurological symptoms: 17.2%, fatigue: 51.7%, sleeping disturbance: 34.5%) and had higher CRP concentrations. Nitrite and Phe levels were lower in patients with sleeping difficulties at FU and Kyn/Trp ratio, as indicator of IDO activity, was significantly lower in patients with neurological symptoms compared to patients without them at FU. In summary, inflammation induced alterations of amino acid metabolism might be related to acute and persisting symptoms of COVID-19.

ABSTRACT Introduction COVID-19 is caused by infection with the severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2). As the respiratory tract is the primary site of infection and host-mediated inflammatory responses, pathologies and dysfunction of the respiratory system characterize the severe disease and are typically associated with the need for oxygen supply or even ventilator support. In survivors of severe COVID-19, computed tomography follow-up frequently reveals structural lung abnormalities, and one-third of individuals who were hospitalized during acute COVID-19 demonstrate persisting lung abnormalities for at least 12 months after disease onset. Areas covered This review summarizes current evidence on pulmonary recovery after COVID-19, focusing on adult patients who suffered from COVID-19 pneumonia. Expert opinion Severe COVID-19 is associated with a high frequency of persisting lung abnormalities at follow-up. The long-term consequences of these findings remain elusive and urge further evaluation to identify individuals at risk for COVID-19 long-term consequences.

Background Recovery trajectories from coronavirus disease 2019 (COVID-19) call for longitudinal investigation. We aimed to characterise the kinetics and status of clinical, cardiopulmonary and mental health recovery up to 1 year following COVID-19. Methods Clinical evaluation, lung function testing (LFT), chest computed tomography (CT) and transthoracic echocardiography were conducted at 2, 3, 6 and 12 months after disease onset. Submaximal exercise capacity, mental health status and quality of life were assessed at 12 months. Recovery kinetics and patterns were investigated by mixed-effect logistic modelling, correlation and clustering analyses. Risk of persistent symptoms and cardiopulmonary abnormalities at the 1-year follow-up were modelled by logistic regression. Findings Out of 145 CovILD study participants, 108 (74.5%) completed the 1-year follow-up (median age 56.5 years; 59.3% male; 24% intensive care unit patients). Comorbidities were present in 75% (n=81). Key outcome measures plateaued after 180 days. At 12 months, persistent symptoms were found in 65% of participants; 33% suffered from LFT impairment; 51% showed CT abnormalities; and 63% had low-grade diastolic dysfunction. Main risk factors for cardiopulmonary impairment included pro-inflammatory and immunological biomarkers at early visits. In addition, we deciphered three recovery clusters separating almost complete recovery from patients with post-acute inflammatory profile and an enrichment in cardiopulmonary residuals from a female-dominated post-COVID-19 syndrome with reduced mental health status. Conclusion 1 year after COVID-19, the burden of persistent symptoms, impaired lung function, radiological abnormalities remains high in our study population. Yet, three recovery trajectories are emerging, ranging from almost complete recovery to post-COVID-19 syndrome with impaired mental health.

The severity of coronavirus disease 2019 (COVID-19) is related to the presence of comorbidities including metabolic diseases. We herein present data from the longitudinal prospective CovILD trial, and investigate the recovery from COVID-19 in individuals with dysglycemia and dyslipidemia. A total of 145 COVID-19 patients were prospectively followed and a comprehensive clinical, laboratory and imaging assessment was performed at 60, 100, 180, and 360 days after the onset of COVID-19. The severity of acute COVID-19 and outcome at early post-acute follow-up were significantly related to the presence of dysglycemia and dyslipidemia. Still, at long-term follow-up, metabolic disorders were not associated with an adverse pulmonary outcome, as reflected by a good recovery of structural lung abnormalities in both, patients with and without metabolic diseases. To conclude, dyslipidemia and dysglycemia are associated with a more severe course of acute COVID-19 as well as delayed early recovery but do not impair long-term pulmonary recovery.