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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.

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.

Introduction Treatment with biologics for severe asthma is informed by international and national guidelines and defined by national regulating bodies, but how these drugs are used in real-life is unknown. Materials and methods The European Respiratory Society (ERS) SHARP Clinical Research Collaboration conducted a three-step survey collecting information on asthma biologics use in Europe. Five geographically distant countries defined the survey questions, focusing on seven end-points: biologics availability and financial issues, prescription and administration modalities, inclusion criteria, continuation criteria, switching biologics, combining biologics and evaluation of corticosteroid toxicity. The survey was then sent to SHARP National Leads of 28 European countries. Finally, selected questions were submitted to a broad group of 263 asthma experts identified by national societies. Results Availability of biologics varied between countries, with 17 out of 28 countries having all five existing biologics. Authorised prescribers (pulmonologists and other specialists) also differed. In-hospital administration was the preferred deliverance modality. While exacerbation rate was used as an inclusion criterion in all countries, forced expiratory volume in 1 s was used in 46%. Blood eosinophils were an inclusion criterion in all countries for interleukin-5 (IL-5)-targeted and IL-4/IL-13-targeted biologics, with varying thresholds. There were no formally established criteria for continuing biologics. Reduction in exacerbations represented the most important benchmark, followed by improvement in asthma control and quality of life. Only 73% (191 out of 263) of surveyed clinicians assessed their patients for corticosteroid-induced toxicity. Conclusion Our study reveals important heterogeneity in the use of asthma biologics across Europe. To what extent this impacts on clinical outcomes relevant to patients and healthcare services needs further 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.

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.