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Background: Interstitial lung diseases (ILDs) are an heterogeneous group of infiltrating lung pathologies, for which prompt diagnosis and continuous assessment are of paramount importance. While chest CT is an established diagnostic tool for ILDs, there are no formal guidelines on the follow-up regimen, leaving the frequency and modality of follow-up largely at the clinician’s discretion. Methods: The study retrospectively evaluated the indication of chest CT in a cohort of 129 ILD patients selected from the ambulatory care polyclinic at University Hospital of Liège. The aim was to determine whether the imagining acquisition had a true impact on clinical course and follow-up. We accepted three different situations for justifying the indication of the CTs: clinical deterioration, a decrease in pulmonary function tests (at least a 10% drop in a parameter), and monitoring for oncological purposes. The other indications, mainly routine follow-up, were classified as “non-justified”. Radiation dose output was evaluated with Computed Tomography Dose Index (CTDI) and Dose Length Product (DLP). Results: The mean number of CT scans per patient per year was 1.7 ± 0.4, determining irradiation in CTDI (mGy)/year of 34.9 ± 64.9 and DLP in (mGy*cm)/year of 1095 ± 1971. The percentage of justified CT scans was 57 ± 32%, while the scans justified a posteriori were 60 ± 34%. Around 40% of the prescribed monitoring CT scans had no impact on the management of ILD and direct patient care. Conclusions: Our study identifies a trend of overuse in chest CT scans at follow-up (up to 40%), outside those performed for clinical exacerbation or oncological investigation. In the particular case of ILD exacerbation, CT scan value remains high, underlying the benefit of this strategy.

Pulmonary vascular diseases such as pulmonary embolism and pulmonary hypertension are important and frequently under-recognised conditions. This article provides an overview of key highlights in pulmonary vascular diseases from the European Respiratory Society International Congress 2023. This includes insights into disease modification in pulmonary arterial hypertension and novel therapies such as sotatercept and seralutinib. Exciting developments in our understanding of the mechanisms underpinning pulmonary hypertension associated with interstitial lung disease are also explored. A comprehensive overview of the complex relationship between acute pulmonary embolism and chronic thromboembolic pulmonary hypertension (CTEPH) is provided along with our current understanding of the molecular determinants of CTEPH. The importance of multidisciplinary and holistic care cannot be understated, and this article also addresses advances beyond medication, with a special focus on exercise training and rehabilitation.

Diagnosing COVID-19 and treating its complications remains a challenge. This review reflects the perspective of some of the Dragon (IMI 2-call 21, #101005122) research consortium collaborators on the utility of bronchoalveolar lavage (BAL) in COVID-19. BAL has been proposed as a potentially useful diagnostic tool to increase COVID-19 diagnosis sensitivity. In both critically ill and non-critically ill COVID-19 patients, BAL has a relevant role in detecting other infections or supporting alternative diagnoses and can change management decisions in up to two-thirds of patients. BAL is used to guide steroid and immunosuppressive treatment and to narrow or discontinue antibiotic treatment, reducing the use of unnecessary broad antibiotics. Moreover, cellular analysis and novel multi-omics techniques on BAL are of critical importance for understanding the microenvironment and interaction between epithelial cells and immunity, revealing novel potential prognostic and therapeutic targets. The BAL technique has been described as safe for both patients and healthcare workers in more than a thousand procedures reported to date in the literature. Based on these preliminary studies, we recognize that BAL is a feasible procedure in COVID-19 known or suspected cases, useful to properly guide patient management, and has great potential for research.

Abstract The paper deals with the evaluation of the performance of an existing and previously validated CT based radiomic signature, developed in oropharyngeal cancer to predict human papillomavirus (HPV) status, in the context of anal cancer. For the validation in anal cancer, a dataset of 59 patients coming from two different centers was collected. The primary endpoint was HPV status according to p16 immunohistochemistry. Predefined statistical tests were performed to evaluate the performance of the model. The AUC obtained here in anal cancer is 0.68 [95% CI (0.32–1.00)] with F1 score of 0.78. This signature is TRIPOD level 4 (57%) with an RQS of 61%. This study provides proof of concept that this radiomic signature has the potential to identify a clinically relevant molecular phenotype (i.e., the HPV-ness) across multiple cancers and demonstrates potential for this radiomic signature as a CT imaging biomarker of p16 status.

Abstract Background Pulmonary fibrosis is an emerging complication of SARS-CoV-2 infection. In this study, we speculate that patients with COVID-19 and idiopathic pulmonary fibrosis (IPF) may share aberrant expressed microRNAs (miRNAs) associated to the progression of lung fibrosis. Objective To identify miRNAs presenting similar alteration in COVID-19 and IPF, and describe their impact on fibrogenesis. Methods A systematic review of the literature published between 2010 and January 2022 (PROSPERO, CRD42022341016) was conducted using the key words (COVID-19 OR SARS-CoV-2) AND (microRNA OR miRNA) or (idiopathic pulmonary fibrosis OR IPF) AND (microRNA OR miRNA) in Title/Abstract. Results Of the 1988 references considered, 70 original articles were appropriate for data extraction: 27 studies focused on miRNAs in COVID-19, and 43 on miRNAs in IPF. 34 miRNAs were overlapping in COVID-19 and IPF, 7 miRNAs presenting an upregulation (miR-19a-3p, miR-200c-3p, miR-21-5p, miR-145-5p, miR-199a-5p, miR-23b and miR-424) and 9 miRNAs a downregulation (miR-17-5p, miR-20a-5p, miR-92a-3p, miR-141-3p, miR-16-5p, miR-142-5p, miR-486-5p, miR-708-3p and miR-150-5p). Conclusion Several studies reported elevated levels of profibrotic miRNAs in COVID-19 context. In addition, the balance of antifibrotic miRNAs responsible of the modulation of fibrotic processes is impaired in COVID-19. This evidence suggests that the deregulation of fibrotic-related miRNAs participates in the development of fibrotic lesions in the lung of post-COVID-19 patients.

ObjectivesIn our study, we explored the specific subgroup of patients with rheumatoid arthritis (RA) suffering from obstructive lung disease (OLD) and its impact on morbi-mortality.MethodsOur retrospective study included 309 patients suffering from RA with either obstructive (O-RA) or non-obstructive patterns (non-O-RA). OLD was defined based on the Tiffeneau index at the first available pulmonary functional test (PFT). Survival was then calculated and represented by a Kaplan–Meier curve. The comparison between the populations considered was performed by the Log-Rank test.ResultsOut of the 309 RA patients, 102 (33%) had airway obstruction. The overall survival time was significantly lower in the O-RA group than in the non-O-RA group (n = 207) (p

Abstract Background Patients suffering from combined obstructive and interstitial lung disease (O-ILD) represent a pathological entity which still has to be well clinically described. The aim of this descriptive and explorative study was to describe the phenotype and functional characteristics of a cohort of patients suffering from functional obstruction in a population of ILD patients in order to raise the need of dedicated prospective observational studies and the evaluation of the impact of anti-fibrotic therapies. Methods The current authors conducted a retrospective study including 557 ILD patients, with either obstructive (O-ILD, n = 82) or non-obstructive (non O-ILD, n = 475) pattern. Patients included were mainly males (54%) with a mean age of 62 years. Results Patients with O-ILD exhibited a characteristic functional profile with reduced percent predicted forced expired volume in 1 s (FEV1) [65% (53–77) vs 83% (71–96), p

BackgroundRecently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).ObjectivesTo develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.MethodsThe Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.ResultsA total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.ConclusionWe developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

IMPORTANCE:. At present, it is not clear if critically ill COVID-19 survivors have different needs in terms of follow-up compared with other critically ill survivors, and thus if duplicated post-ICU trajectories are mandatory. OBJECTIVES:. To compare the post-intensive care syndrome (PICS) of COVID-19 acute respiratory distress syndrome and non-COVID-19 (NC) survivors referred to a follow-up clinic at 3 months (M3) after ICU discharge. DESIGN, SETTING, AND PARTICIPANTS:. Adults who survived an ICU stay greater than or equal to 7 days and attended the M3 consultation were included in this observational study performed in a post-ICU follow-up clinic of a single tertiary hospital. MAIN OUTCOMES AND MEASURES:. Patients underwent a standardized assessment, addressing health-related quality of life (3-level version of EQ-5D), sleep disorders (Pittsburgh Sleep Quality Index [PSQI]), physical status (Barthel index, handgrip and quadriceps strengths), mental health disorders (Hospital Anxiety and Depression Scale and Impact of Event Scale-Revised [IES-R]), and cognitive impairment (Montreal Cognitive Assessment [MoCA]). RESULTS:. A total of 143 survivors (86 COVID and 57 NC) attended the M3 consultation. Their median age and severity scores were similar. NC patients had a shorter ICU stay (10 d [8–17.2 d]) compared with COVID group (18 d [10.8–30 d]) (p = 0.001). M3 outcomes were similar in the two groups, except for a higher PSQI (p = 0.038) in the COVID group (6 [3–9.5]) versus NC group (4 [2–7]), and a slightly lower Barthel index in the NC group (100 [100–100]) than in the COVID group (100 [85–100]) (p = 0.026). However, the proportion of patients with abnormal values at each score was similar in the two groups. Health-related quality of life was similar in the two groups. The three MoCA (≥ 26), IES-R (

Background and objectiveRheumatoid arthritis associated-interstitial lung disease (RA-ILD) is the most common pulmonary manifestation of rheumatoid arthritis (RA) and an important cause of mortality. In patients suffering from interstitial lung diseases (ILD) from different etiologies (including RA-ILD), a significant proportion is exhibiting a fibrotic progression despite immunosuppressive therapies, defined as progressive fibrosing interstitial lung disease (PF-ILD). Here, we report the frequency of RA-ILD and PF-ILD in all RA patients’ cohort at University Hospital of Liège and compare their characteristics and outcomes.MethodsPatients were retrospectively recruited from 2010 to 2020. PF-ILD was defined based on functional, clinical and/or iconographic progression criteria within 24 months despite specific anti-RA treatment.ResultsOut of 1,500 RA patients, about one third had high-resolution computed tomography (HRCT) performed, 89 showed RA-ILD and 48 PF-ILD. RA-ILD patients were significantly older than other RA patients (71 old of median age vs. 65, p < 0.0001), with a greater proportion of men (46.1 vs. 27.7%, p < 0.0001) and of smoking history. Non-specific interstitial pneumonia pattern was more frequent than usual interstitial pneumonia among RA-ILD (60.7 vs. 27.0%) and PF-ILD groups (60.4 vs. 31.2%). The risk of death was 2 times higher in RA-ILD patients [hazard ratio 2.03 (95% confidence interval 1.15–3.57), p < 0.01] compared to RA.ConclusionWe identified a prevalence of PF-ILD of 3% in a general RA population. The PF-ILD cohort did not seem to be different in terms of demographic characteristics and mortality compared to RA-ILD patients who did not exhibit the progressive phenotype yet.

ABSTRACT: Objectives: Various symptoms and considerable organ dysfunction persist following infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Uncertainty remains about the potential mid- and long-term health sequelae. This prospective study of patients hospitalized with coronavirus disease 2019 (COVID-19) in Liège University Hospital, Belgium aimed to determine the persistent consequences of COVID-19. Methods: Patients admitted to the University Hospital of Liège with moderate-to-severe confirmed COVID-19, discharged between 2 March and 1 October 2020, were recruited prospectively. Follow-up at 3 and 6 months after hospital discharge included demographic and clinical data, biological data, pulmonary function tests (PFTs) and high-resolution computed tomography (CT) scans of the chest. Results: In total, 199 individuals were included in the analysis. Most patients received oxygen supplementation (80.4%). Six months after discharge, 47% and 32% of patients still had exertional dyspnoea and fatigue. PFTs at 3-month follow-up revealed a reduced diffusion capacity of carbon monoxide (mean 71.6 ± 18.6%), and this increased significantly at 6-month follow-up (P

Abstract Face masks and personal respirators are used to curb the transmission of SARS-CoV-2 in respiratory droplets; filters embedded in some personal protective equipment could be used as a non-invasive sample source for applications, including at-home testing, but information is needed about whether filters are suited to capture viral particles for SARS-CoV-2 detection. In this study, we generated inactivated virus-laden aerosols of 0.3–2 microns in diameter (0.9 µm mean diameter by mass) and dispersed the aerosolized viral particles onto electrostatic face mask filters. The limit of detection for inactivated coronaviruses SARS-CoV-2 and HCoV-NL63 extracted from filters was between 10 to 100 copies/filter for both viruses. Testing for SARS-CoV-2, using face mask filters and nasopharyngeal swabs collected from hospitalized COVID-19-patients, showed that filter samples offered reduced sensitivity (8.5% compared to nasopharyngeal swabs). The low concordance of SARS-CoV-2 detection between filters and nasopharyngeal swabs indicated that number of viral particles collected on the face mask filter was below the limit of detection for all patients but those with the highest viral loads. This indicated face masks are unsuitable to replace diagnostic nasopharyngeal swabs in COVID-19 diagnosis. The ability to detect nucleic acids on face mask filters may, however, find other uses worth future investigation.

The pandemic of COVID-19 led to a dramatic situation in hospitals, where staff had to deal with a huge number of patients in respiratory distress. To alleviate the workload of radiologists, we implemented an artificial intelligence (AI) - based analysis named CACOVID-CT, to automatically assess disease severity on chest CT scans obtained from those patients. We retrospectively studied CT scans obtained from 476 patients admitted at the University Hospital of Liege with a COVID-19 disease. We quantified the percentage of COVID-19 affected lung area (% AA) and the CT severity score (total CT-SS). These quantitative measurements were used to investigate the overall prognosis and patient outcome: hospital length of stay (LOS), ICU admission, ICU LOS, mechanical ventilation, and in-hospital death. Both CT-SS and % AA were highly correlated with the hospital LOS, the risk of ICU admission, the risk of mechanical ventilation and the risk of in-hospital death. Thus, CAD4COVID-CT analysis proved to be a useful tool in detecting patients with higher hospitalization severity risk. It will help for management of the patients flow. The software measured the extent of lung damage with great efficiency, thus relieving the workload of radiologists.

PurposeTo develop handcrafted radiomics (HCR) and deep learning (DL) based automated diagnostic tools that can differentiate between idiopathic pulmonary fibrosis (IPF) and non-IPF interstitial lung diseases (ILDs) in patients using high-resolution computed tomography (HRCT) scans.Material and MethodsIn this retrospective study, 474 HRCT scans were included (mean age, 64.10 years ± 9.57 [SD]). Five-fold cross-validation was performed on 365 HRCT scans. Furthermore, an external dataset comprising 109 patients was used as a test set. An HCR model, a DL model, and an ensemble of HCR and DL model were developed. A virtual in-silico trial was conducted with two radiologists and one pulmonologist on the same external test set for performance comparison. The performance was compared using DeLong method and McNemar test. Shapley Additive exPlanations (SHAP) plots and Grad-CAM heatmaps were used for the post-hoc interpretability of HCR and DL models, respectively.ResultsIn five-fold cross-validation, the HCR model, DL model, and the ensemble of HCR and DL models achieved accuracies of 76.2 ± 6.8, 77.9 ± 4.6, and 85.2 ± 2.7%, respectively. For the diagnosis of IPF and non-IPF ILDs on the external test set, the HCR, DL, and the ensemble of HCR and DL models achieved accuracies of 76.1, 77.9, and 85.3%, respectively. The ensemble model outperformed the diagnostic performance of clinicians who achieved a mean accuracy of 66.3 ± 6.7% (p < 0.05) during the in-silico trial. The area under the receiver operating characteristic curve (AUC) for the ensemble model on the test set was 0.917 which was significantly higher than the HCR model (0.817, p = 0.02) and the DL model (0.823, p = 0.005). The agreement between HCR and DL models was 61.4%, and the accuracy and specificity for the predictions when both the models agree were 93 and 97%, respectively. SHAP analysis showed the texture features as the most important features for IPF diagnosis and Grad-CAM showed that the model focused on the clinically relevant part of the image.ConclusionDeep learning and HCR models can complement each other and serve as useful clinical aids for the diagnosis of IPF and non-IPF ILDs.

Abstract Tumour‐derived extracellular vesicles (EVs) participate in tumour progression by deregulating various physiological processes including angiogenesis and inflammation. Here we report that EVs released by endothelial cells in a mammary tumour environment participate in the recruitment of macrophages within the tumour, leading to an immunomodulatory phenotype permissive for tumour growth. Using RNA‐Seq approaches, we identified several microRNAs (miRNAs) found in endothelial EVs sharing common targets involved in the regulation of the immune system. To further study the impact of these miRNAs in a mouse tumour model, we focused on three miRNAs that are conserved between humans and mouse, that is, miR‐142‐5p, miR‐183‐5p and miR‐222‐3p. These miRNAs are released from endothelial cells in a tumour microenvironment and are transferred via EVs to macrophages. In mouse mammary tumour models, treatment with EVs enriched in these miRNAs leads to a polarization of macrophages toward an M2‐like phenotype, which in turn promotes tumour growth.

Abstract Systemic sclerosis (SSc) is a rare connective tissue disease associated with rapid evolving interstitial lung disease (ILD), driving its mortality. Specific biomarkers associated with the progression of this lung disease are highly needed. We aimed to identify specific biomarkers of SSc-ILD to predict the evolution of the disease. For this, we compared prospectively serum levels of several biomarkers associated with lung fibrosis in SSc patients (n = 102), among which SSc-no ILD (n = 63) and SSc-ILD (n = 39), compared to healthy subjects (HS) (n = 39). We also performed a longitudinal study in a subgroup of 28 patients analyzing biomarkers variations and pulmonary function tests over a period of 2 years. Serum level of IGFBP-2 was significantly increased in SSc patients compared to HS, and negatively correlated with pulmonary function (assessed by carbon monoxide transfer coefficient (KCO)) (r = − 0.29, p 105 ng/ml was associated with a poor outcome (KCO

Purpose In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza.

BackgroundThe global coronavirus disease 2019 (COVID-19) has presented significant challenges and created concerns worldwide. Besides, patients who have experienced a SARS-CoV-2 infection could present post-viral complications that can ultimately lead to pulmonary fibrosis. Serum levels of Krebs von den Lungen 6 (KL-6), high molecular weight human MUC1 mucin, are increased in the most patients with various interstitial lung damage. Since its production is raised during epithelial damages, KL-6 could be a helpful non-invasive marker to monitor COVID-19 infection and predict post-infection sequelae.MethodsWe retrospectively evaluated KL-6 levels of 222 COVID-19 infected patients and 70 healthy control. Serum KL-6, fibrinogen, lactate dehydrogenase (LDH), platelet-lymphocytes ratio (PLR) levels and other biological parameters were analyzed. This retrospective study also characterized the relationships between serum KL-6 levels and pulmonary function variables.ResultsOur results showed that serum KL-6 levels in COVID-19 patients were increased compared to healthy subjects (470 U/ml vs 254 U/ml, P 453.5 U/ml was associated with COVID-19 (AUC = 0.8415, P < 0.0001). KL-6 level was positively correlated with other indicators of disease severity such as fibrinogen level (r = 0.1475, P = 0.0287), LDH level (r = 0,31, P = 0,004) and PLR level (r = 0.23, P = 0.0005). However, KL-6 levels were not correlated with pulmonary function tests (r = 0.04, P = 0.69).ConclusionsKL-6 expression was correlated with several disease severity indicators. However, the association between mortality and long-term follow-up outcomes needs further investigation. More extensive trials are required to prove that KL-6 could be a marker of disease severity in COVID-19 infection.

The PERSEIDS study aimed to estimate incidence/prevalence of interstitial lung diseases (ILDs), fibrosing interstitial lung diseases (F-ILDs), idiopathic pulmonary fibrosis (IPF), systemic sclerosis-associated ILD (SSc-ILD), other non-IPF F-ILDs and their progressive-fibrosing (PF) forms in six European countries, as current data are scarce. This retrospective, two-phase study used aggregate data (2014–2018). In Phase 1, incident/prevalent cases of ILDs above were identified from clinical databases through an algorithm based on codes/keywords, and incidence/prevalence was estimated. For non-IPF F-ILDs, the relative percentage of subtypes was also determined. In Phase 2, a subset of non-IPF F-ILD cases was manually reviewed to determine the percentage of PF behaviour and usual interstitial pneumonia-like (UIP-like) pattern. A weighted mean percentage of progression was calculated for each country and used to extrapolate incidence/prevalence of progressive-fibrosing ILDs (PF-ILDs). In 2018, incidence/105 person-years ranged between 9.4 and 83.6 (ILDs), 7.7 and 76.2 (F-ILDs), 0.4 and 10.3 (IPF), 6.6 and 71.7 (non-IPF F-ILDs), and 0.3 and 1.5 (SSc-ILD); and prevalence/105 persons ranged between 33.6 and 247.4 (ILDs), 26.7 and 236.8 (F-ILDs), 2.8 and 31.0 (IPF), 22.3 and 205.8 (non-IPF F-ILDs), and 1.4 and 10.1 (SSc-ILD). Among non-IPF F-ILDs, sarcoidosis was the most frequent subtype. PF behaviour and UIP-like pattern were present in a third of non-IPF F-ILD cases each and hypersensitivity pneumonitis showed the highest percentage of progressive behaviour. Incidence of PF-ILDs ranged between 2.1 and 14.5/105 person-years, and prevalence between 6.9 and 78.0/105 persons. To our knowledge, PERSEIDS is the first study assessing incidence, prevalence and rate of progression of ILDs across several European countries. Still below the threshold for orphan diseases, the estimates obtained were higher and more variable than reported in previous studies, but differences in study design/population must be considered.

Teaching point: The use of dual-energy instead of conventional single-energy computed tomography pulmonary angiogram can provide additional value concerning the diagnosis of COVID-19 and its complications, especially in the detection of small pulmonary embolism.

Acute respiratory distress syndrome (ARDS) is a diffuse, acute, inflammatory lung disease characterized by a severe respiratory failure. Recognizing and promptly treating ARDS is critical to combat the high mortality associated with the disease. Despite a significant progress in the treatment of ARDS, our ability to identify early patients and predict outcomes remains limited. The development of novel biomarkers is crucial. In this study, we profiled microRNA (miRNA) expression of plasma-derived exosomes in ARDS disease by small RNA sequencing. Sequencing of 8 ARDS patients and 10 healthy subjects (HSs) allowed to identify 12 differentially expressed exosomal miRNAs (adjusted p < 0.05). Pathway analysis of their predicted targets revealed enrichment in several biological processes in agreement with ARDS pathophysiology, such as inflammation, immune cell activation, and fibrosis. By quantitative RT-PCR, we validated the alteration of nine exosomal miRNAs in an independent cohort of 15 ARDS patients and 20 HSs, among which seven present high capability in discriminating ARDS patients from HSs (area under the curve > 0.8) (miR-130a-3p, miR-221-3p, miR-24-3p, miR-98-3p, Let-7d-3p, miR-1273a, and miR-193a-5p). These findings highlight exosomal miRNA dysregulation in the plasma of ARDS patients which provide promising diagnostic biomarkers and open new perspectives for the development of therapeutics.

OBJECTIVES:. To investigate exercise capacity at 3 and 6 months after a prolonged ICU stay. DESIGN:. Observational monocentric study. SETTING:. A post-ICU follow-up clinic in a tertiary university hospital in Liège, Belgium. PATIENTS:. Patients surviving an ICU stay greater than or equal to 7 days for a severe coronavirus disease 2019 pneumonia and attending our post-ICU follow-up clinic. MEASUREMENTS AND MAIN RESULTS:. Cardiopulmonary and metabolic variables provided by a cardiopulmonary exercise testing on a cycle ergometer were collected at rest, at peak exercise, and during recovery. Fourteen patients (10 males, 59 yr [52–62 yr], all obese with body mass index > 27 kg/m2) were included after a hospital stay of 40 days (35–53 d). At rest, respiratory quotient was abnormally high at both 3 and 6 months (0.9 [0.83–0.96] and 0.94 [0.86–0.97], respectively). Oxygen uptake was also abnormally increased at 3 months (8.24 mL/min/kg [5.38–10.54 mL/min/kg]) but significantly decreased at 6 months (p = 0.013). At 3 months, at the maximum workload (67% [55–89%] of predicted workload), oxygen uptake peaked at 81% (64–104%) of predicted maximum oxygen uptake, with oxygen pulse and heart rate reaching respectively 110% (76–140%) and 71% (64–81%) of predicted maximum values. Ventilatory equivalent for carbon dioxide remains within normal ranges. The 50% decrease in oxygen uptake after maximum effort was delayed, at 130 seconds (115–142 s). Recovery was incomplete with a persistent anaerobic metabolism. At 6 months, no significant improvement was observed, excepting an increase in heart rate reaching 79% (72–95%) (p = 0.008). CONCLUSIONS:. Prolonged reduced exercise capacity was observed up to 6 months in critically ill coronavirus disease 2019 survivors. This disability did not result from residual pulmonary or cardiac dysfunction but rather from a metabolic disorder characterized by a sustained hypermetabolism and an impaired oxygen utilization.

Retrospective studies showed a relationship between vitamin D status and COVID-19 severity and mortality, with an inverse relation between SARS-CoV-2 positivity and circulating calcifediol levels. The objective of this pilot study was to investigate the effect of vitamin D supplementation on the length of hospital stay and clinical improvement in patients with vitamin D deficiency hospitalized with COVID-19. The study was randomized, double blind and placebo controlled. A total of 50 subjects were enrolled and received, in addition to the best available COVID therapy, either vitamin D (25,000 IU per day over 4 consecutive days, followed by 25,000 IU per week up to 6 weeks) or placebo. The length of hospital stay decreased significantly in the vitamin D group compared to the placebo group (4 days vs. 8 days; p = 0.003). At Day 7, a significantly lower percentage of patients were still hospitalized in the vitamin D group compared to the placebo group (19% vs. 54%; p = 0.0161), and none of the patients treated with vitamin D were hospitalized after 21 days compared to 14% of the patients treated with placebo. Vitamin D significantly reduced the duration of supplemental oxygen among the patients who needed it (4 days vs. 7 days in the placebo group; p = 0.012) and significantly improved the clinical recovery of the patients, as assessed by the WHO scale (p = 0.0048). In conclusion, this study demonstrated that the clinical outcome of COVID-19 patients requiring hospitalization was improved by administration of vitamin D.

Early diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects.

During the COVID-19 pandemic induced by the SARS-CoV-2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non-COVID-19 chest pathologies discovered de novo. The fortuitous discovery of de novo non-COVID-19 lesions was generally not detected by the automated systems for COVID-19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT by the radiologist. The objective is to use the study of the occurrence of non-COVID-19-related chest abnormalities (known and unknown) in a large cohort of patients having suffered from confirmed COVID-19 infection and statistically correlate the clinical data and the occurrence of these abnormalities in order to assess the potential of increased early detection of lesions/alterations. This study was performed on a group of 362 COVID-19-positive patients who were prescribed a CT scan in order to diagnose and predict COVID-19-associated lung disease. Statistical analysis using mean, standard deviation (SD) or median and interquartile range (IQR), logistic regression models and linear regression models were used for data analysis. Results were considered significant at the 5% critical level (p < 0.05). These de novo non-COVID-19 thoracic lesions detected on chest CT showed a significant prevalence in cardiovascular pathologies, with calcifying atheromatous anomalies approaching nearly 35.4% in patients over 65 years of age. The detection of non-COVID-19 pathologies was mostly already known, except for suspicious nodule, thyroid goiter and the ascending thoracic aortic aneurysm. The presence of vertebral compression or signs of pulmonary fibrosis has shown a significant impact on inpatient length of stay. The characteristics of the patients in this sample, both from a demographic and a tomodensitometric point of view on non-COVID-19 pathologies, influenced the length of hospital stay as well as the risk of intra-hospital death. This retrospective study showed that the potential importance of the detection of these non-COVID-19 lesions by the radiologist was essential in the management and the intra-hospital course of the patients.

The severity of coronavirus disease 2019 (COVID-19) varies significantly with cases spanning from asymptomatic to lethal with a subset of individuals developing Severe Acute Respiratory Syndrome (SARS) and death from respiratory failure. To determine whether global nucleosome and citrullinated nucleosome levels were elevated in COVID-19 patients, we tested two independent cohorts of COVID-19 positive patients with quantitative nucleosome immunoassays and found that nucleosomes were highly elevated in plasma of COVID-19 patients with a severe course of the disease relative to healthy controls and that both histone 3.1 variant and citrullinated nucleosomes increase with disease severity. Elevated citrullination of circulating nucleosomes is indicative of neutrophil extracellular trap formation, neutrophil activation and NETosis in severely affected individuals. Importantly, using hospital setting (outpatient, inpatient or ICU) as a proxy for disease severity, nucleosome levels increased with disease severity and may serve as a guiding biomarker for treatment. Owing to the limited availability of mechanical ventilators and extracorporal membrane oxygenation (ECMO) equipment, there is an urgent need for effective tools to rapidly assess disease severity and guide treatment selection. Based on our studies of two independent cohorts of COVID-19 patients from Belgium and Germany, we suggest further investigation of circulating nucleosomes and citrullination as biomarkers for clinical triage, treatment allocation and clinical drug discovery.

ObjectiveTo establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies.MethodsThe training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model.ResultsIn the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77.ConclusionWhen applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.

This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851–0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Within the non-coding genome landscape, long non-coding RNAs (lncRNAs) and their secretion within exosomes are a window that could further explain the regulation, the sustaining, and the spread of lung diseases. We present here a compilation of the current knowledge on lncRNAs commonly found in Chronic Obstructive Pulmonary Disease (COPD), asthma, Idiopathic Pulmonary Fibrosis (IPF), or lung cancers. We built interaction networks describing the mechanisms of action for COPD, asthma, and IPF, as well as private networks for H19, MALAT1, MEG3, FENDRR, CDKN2B-AS1, TUG1, HOTAIR, and GAS5 lncRNAs in lung cancers. We identified five signaling pathways targeted by these eight lncRNAs over the lung diseases mentioned above. These lncRNAs were involved in ten treatment resistances in lung cancers, with HOTAIR being itself described in seven resistances. Besides, five of them were previously described as promising biomarkers for the diagnosis and prognosis of asthma, COPD, and lung cancers. Additionally, we describe the exosomal-based studies on H19, MALAT1, HOTAIR, GAS5, UCA1, lnc-MMP2-2, GAPLINC, TBILA, AGAP2-AS1, and SOX2-OT. This review concludes on the need for additional studies describing the lncRNA mechanisms of action and confirming their potential as biomarkers, as well as their involvement in resistance to treatment, especially in non-cancerous lung diseases.

This review focuses on the role of the lung microbiome in idiopathic pulmonary fibrosis. Although historically considered sterile, bacterial communities have now been well documented in lungs both in healthy and pathological conditions. Studies in idiopathic pulmonary fibrosis (IPF) suggest that increased bacterial burden and/or abundance of potentially pathogenic bacteria may drive disease progression, acute exacerbations, and mortality. More recent work has highlighted the interaction between the lung microbiome and the innate immune system in IPF, strengthening the argument for the role of both host and environment interaction in disease pathogenesis. Existing published data suggesting that the lung microbiome may represent a therapeutic target, via antibiotic administration, immunization against pathogenic organisms, or treatment directed at gastroesophageal reflux. Taken altogether, published literature suggests that the lung microbiome might serve in the future as a prognostic biomarker, a therapeutic target, and/or provide an explanation for disease pathogenesis in IPF.