Έκβαση βαρέως πασχόντων COVID-19 ασθενών που λαμβάνουν υψηλή και χαμηλή δόση δεξαμεθαζόνης σε συνδυασμό με anti-IL6 μονοκλωνικό αντίσωμα (Tocilizumab) στη Μονάδα Εντατικής Θεραπείας

Εισαγωγή: Στη σοβαρή νόσο COVID -19 έχει καθιερωθεί η χορήγηση δεξαμεθαζόνης. Η χορήγηση υψηλότερης δόσης κορτικοστεροειδών ή/και Tocilizumab είναι υπό διερεύνηση. Σκοπός: Να αξιολογηθούν οι διαφορές ως προς την έκβαση και τις επιπλοκές των διαφορετικών δόσεων κορτικοστεροειδών (υψηλή δόση vs συνήθους δόσης) συν/πλην Tocilizumab. Υλικό και Μέθοδος: Πραγματοποιήθηκε μία αναδρομική μελέτη στη ΜΕΘ της Α’ Πανεπιστημιακής Πνευμονολογικής της Ιατρικής Σχολής ΕΚΠΑ το διάστημα 1/9/2020-1/9/2021 σε βαρέως πάσχοντες COVID -19 ασθενείς. Έγινε καταγραφή επιδημιολογικών στοιχείων, συννοσηροτήτων, χαρακτηριστικών της νόσησης, της έκβασης, καθώς και της λήψης κορτικοστεροειδών συν / πλην Tocilizumab. Ως συνήθης δόση ορίστηκε η δεξαμεθαζόνη 6mg και ως υψηλή οποιοδήποτε σχήμα μεγαλύτερης δόσης ημερησίως. Αποτελέσματα: Συμπεριελήφθησαν 300 ασθενείς. Από τη μονοπαραγοντική ανάλυση, οι ασθενείς που έλαβαν υψηλή δόση δεξαμεθαζόνης είχαν στατιστικά σημαντική αύξηση της πιθανότητας διασωλήνωσης στη ΜΕΘ, αύξηση των επιπλοκών (λοιμώξεις, βαρότραυμα, θρομβοεμβολικό επεισόδιο), καθώς και αυξημένη διάρκεια υποστήριξης από μηχανικό αερισμό και νοσηλείας στη ΜΕΘ και το νοσοκομείο. Από τις καμπύλες Kaplan Meier οι ασθενείς που έλαβαν υψηλή δόση δεξαμεθαζόνης είχαν καλύτερη επιβίωση στις 28 ημέρες ενώ στη συνολική επιβίωση δεν υπήρχε διαφορά. Η μη διαφορά της συνολικής επιβίωσης διατηρείται στην πολυπαραγοντική ανάλυση. Επίσης, από την πολυπαραγοντική ανάλυση δεν παρατηρήθηκαν στατιστικά σημαντικές διαφορές ως προς τις ημέρες μηχανικού αερισμού. Από τη σύγκριση της ομάδας των ασθενών που έλαβαν υψηλή δόση δεξαμεθαζόνης με Tocilizumab με την ομάδα των ασθενών που έλαβαν υψηλή δόση κορτικοστεροειδών χωρίς Tocilizumab δε διαπιστώθηκαν διαφορές ως προς την έκβαση (συνολική επιβίωση, ημέρες μηχανικού αερισμού), αλλά παρατηρήθηκε αύξηση των λοιμώξεων στην πρώτη ομάδα. Συμπεράσματα: H υψηλή δόση δεξαμεθαζόνης φαίνεται ότι έχει πιθανώς θετική επίδραση στην επιβίωση των ασθενών, αλλά αρνητική στην ανάπτυξη επιπλοκών. Ως προς τη συγχορήγηση Tocilizumab ή όχι, δε διαπιστώθηκε στατιστικά σημαντική διαφορά στην έκβαση.Introduction: Dexamethasone is the mainstay of treatment for severe COVID -19. Higher dosage of dexamethasone and/or Tocilizumab are still under investigation. Aim: To assess differences in outcome and complications of different doses of corticosteroids (high vs standard dose) and/or Tocilizumab. Material and Methods: A retrospective study was carried out on COVID-19 patients hospitalized in the ICU of the 1st Respiratory Medicine Department of National and Kapodistrian University of Athens during the period 1/9/2020-1/9/2021. Patient and disease characteristics, comorbidities, outcomes, corticosteroid dosage and Tocilizumab administration were recorded. Standard dose was defined as 6mg of dexamethasone for 10 days and high dose as any higher dose regimen per day. Results: 300 patients were recruited in the study. On univariate analysis, patients who received high dose dexamethasone had a statistically significant increase in the probability of intubation in the ICU, complications (infections, barotrauma, thromboembolic events) as well as increased length of mechanical ventilation and increased length of stay in ICU and hospital. Kaplan Meier curves showed that patients who received high-dose dexamethasone had better survival at 28 days, while overall survival did not differ at Kaplan Meier curves or multivariate analysis. Also, multivariate analysis showed no statistically significant differences in terms of days of mechanical ventilation between the groups. Patients receiving high-dose dexamethasone with Tocilizumab and patients receiving high-dose corticosteroids without Tocilizumab had no statistically significant differences in outcome (overall survival, days of mechanical ventilation), but the first group had an increased incidence of infections. Conclusion: High-dose dexamethasone seems to have a possibly positive effect on patient survival, but a negative effect on the development of complications. Regarding the co-administration of Tocilizumab or not, no statistically significant difference on the outcome was found

Fungal Infections in the ICU during the COVID-19 Era: Descriptive and Comparative Analysis of 178 Patients

Background: COVID-19-associated fungal infections seem to be a concerning issue. The aim of this study was to assess the incidence of fungal infections, the possible risk factors, and their effect on outcomes of critically ill patients with COVID-19. Methods: A retrospective observational study was conducted in the COVID-19 ICU of the First Respiratory Department of National and Kapodistrian University of Athens in Sotiria Chest Diseases Hospital between 27 August 2020 and 10 November 2021. Results: Here, 178 patients were included in the study. Nineteen patients (10.7%) developed fungal infection, of which five had COVID-19 associated candidemia, thirteen had COVID-19 associated pulmonary aspergillosis, and one had both. Patients with fungal infection were younger, had a lower Charlson Comorbidity Index, and had a lower PaO2/FiO2 ratio upon admission. Regarding health-care factors, patients with fungal infections were treated more frequently with Tocilizumab, a high regimen of dexamethasone, continuous renal replacement treatment, and were supported more with ECMO. They also had more complications, especially infections, and subsequently developed septic shock more frequently. Finally, patients with fungal infections had a longer length of ICU stay, as well as length of mechanical ventilation, although no statistically significant difference was reported on 28-day and 90-day mortality. Conclusions: Fungal infections seem to have a high incidence in COVID-19 critically ill patients and specific risk factors are identified. However, fungal infections do not seem to burden on mortality

The Real Impact of Age on Mortality in Critically Ill COVID-19 Patients

Objective: The impact of severe infection from COVID-19 and the resulting need for life support in an ICU environment is a fact that caused immense pressure in healthcare systems around the globe. Accordingly, elderly people faced multiple challenges, especially after admission to the ICU. On this basis, we performed this study to assess the impact of age on COVID-19 mortality in critically ill patients. Materials and Methods: In this retrospective study, we collected data from 300 patients who were hospitalized in the ICU of a Greek respiratory hospital. We split patients into two age groups using a threshold of 65 years old. The primary objective of the study was the survival of patients in a follow up period of 60 days after their admission to the ICU. Secondary objectives were to determine whether mortality is affected by other factors, including sepsis and clinical and laboratory factors, Charlson Comorbidity Index (CCI), APACHE II and d-dimers, CRP, etc. Results: The survival of all patients in the ICU was 75.7%. Those in the p-value p-value p-value = 0.320). Conclusions: Age alone as a simple number is not capable of predicting mortality in patients with severe COVID-19 in the ICU. We must use more composite clinical markers that may better reflect the biological age of patients, such as CCI. Moreover, the effective control of infections in the ICU is of utmost importance for the survival of patients, since avoiding septic complications can drastically improve the prognosis of all patients, regardless of age

A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19

Background: Although several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU scoring index using dynamically associated biological markers. Methods: We propose a multimodal approach which combines explainable AI models with dynamic modeling methods to shed light into the clinical features of COVID-19. Dynamic Bayesian networks were used to seek associations among cytokines across four time intervals after hospitalization. Explainable gradient boosting trees were trained to predict the risk for ICU admission and mortality towards the development of an ICU scoring index. Results: Our results highlight LDH, IL-6, IL-8, Cr, number of monocytes, lymphocyte count, TNF as risk predictors for ICU admission and survival along with LDH, age, CRP, Cr, WBC, lymphocyte count for mortality in the ICU, with prediction accuracy 0.79 and 0.81, respectively. These risk factors were combined with dynamically associated biological markers to develop an ICU scoring index with accuracy 0.9. Conclusions: to our knowledge, this is the first multimodal and explainable AI model which quantifies the risk of intensive care with accuracy up to 0.9 across multiple timepoints

A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19

Background: Although several studies have been launched towards the prediction of risk factors for mortality and admission in the intensive care unit (ICU) in COVID-19, none of them focuses on the development of explainable AI models to define an ICU scoring index using dynamically associated biological markers. Methods: We propose a multimodal approach which combines explainable AI models with dynamic modeling methods to shed light into the clinical features of COVID-19. Dynamic Bayesian networks were used to seek associations among cytokines across four time intervals after hospitalization. Explainable gradient boosting trees were trained to predict the risk for ICU admission and mortality towards the development of an ICU scoring index. Results: Our results highlight LDH, IL-6, IL-8, Cr, number of monocytes, lymphocyte count, TNF as risk predictors for ICU admission and survival along with LDH, age, CRP, Cr, WBC, lymphocyte count for mortality in the ICU, with prediction accuracy 0.79 and 0.81, respectively. These risk factors were combined with dynamically associated biological markers to develop an ICU scoring index with accuracy 0.9. Conclusions: to our knowledge, this is the first multimodal and explainable AI model which quantifies the risk of intensive care with accuracy up to 0.9 across multiple timepoints

Safety and Effectiveness of Mycophenolate Mofetil in Interstitial Lung Diseases: Insights from a Machine Learning Radiographic Model

Introduction: Treatment of interstitial lung diseases (ILDs) other than idiopathic pulmonary fibrosis (IPF) often includes systemic corticosteroids. Use of steroid-sparing agents is amenable to avoid potential side effects. Methods: Functional indices and high-resolution computed tomography (HRCT) patterns of patients with non-IPF ILDs receiving mycophenolate mofetil (MMF) with a minimum follow-up of 1 year were analyzed. Two independent radiologists and a machine learning software system (Imbio 1.4.2.) evaluated HRCT patterns. Results: Fifty-five (n = 55) patients were included in the analysis (male: 30 [55%], median age: 65.0 [95% CI: 59.7-70.0], mean forced vital capacity %predicted [FVC %pred.] +/- standard deviation [SD]: 69.4 +/- 18.3, mean diffusing capacity of lung for carbon monoxide %pred. +/- SD: 40.8 +/- 14.3, hypersensitivity pneumonitis: 26, connective tissue disease-ILDs [CTD-ILDs]: 22, other ILDs: 7). There was no significant difference in mean FVC %pred. post-6 months (1.59 +/- 2.04) and 1 year (-0.39 +/- 2.49) of treatment compared to baseline. Radiographic evaluation showed no significant difference between baseline and post-1 year %ground glass opacities (20.0 [95% CI: 14.4-30.0] vs. 20.0 [95% CI: 14.4-25.6]) and %reticulation (5.0 [95% CI: 2.0-15.6] vs. 7.5 [95% CI: 2.0-17.5]). A similar performance between expert radiologists and Imbio software analysis was observed in assessing ground glass opacities (intraclass correlation coefficient [ICC] = 0.73) and reticulation (ICC = 0.88). Fourteen patients (25.5%) reported at least one side effect and 8 patients (14.5%) switched to antifibrotics due to disease progression. Conclusion: Our data suggest that MMF is a safe and effective steroid-sparing agent leading to disease stabilization in a proportion of patients with non-IPF ILDs. Machine learning software systems may exhibit similar performance to specialist radiologists and represent fruitful diagnostic and prognostic tools

Patients Hospitalized for COVID-19 in the Periods of Delta and Omicron Variant Dominance in Greece: Determinants of Severity and Mortality

Background: Coronavirus disease 2019 (COVID-19) has been a pandemic since 2020, and depending on the SARS-CoV-2 mutation, different pandemic waves have been observed. The aim of this study was to compare the baseline characteristics of patients in two phases of the pandemic and evaluate possible predictors of mortality. Methods: This is a retrospective multicenter observational study that included patients with COVID-19 in 4 different centers in Greece. Patients were divided into two groups depending on the period during which they were infected during the Delta and Omicron variant predominance. Results: A total of 979 patients (433 Delta, 546 Omicron) were included in the study (median age 67 years (54, 81); 452 [46.2%] female). Compared to the Omicron period, the patients during the Delta period were younger (median age [IQR] 65 [51, 77] vs. 70 [55, 83] years, p p = 0.001), had higher procalcitonin levels (ng/mL): 0.08 [0.05, 0.17] vs. 0.06 [0.02, 0.16], p = 0.005, ferritin levels (ng/mL): 301 [159, 644] vs. 239 [128, 473], p = 0.002, C- reactive protein levels (mg/L): 40.4 [16.7, 98.5] vs. 31.8 [11.9, 81.7], p = 0.003, and lactate dehydrogenase levels (U/L): 277 [221, 375] vs. 255 [205, 329], p p p 2/FiO2 ratio on admission were identified as independent predictors of mortality for patients in the Omicron period. Conclusions: In the Omicron wave, patients were older with a higher number of comorbidities, but patients with the Delta variant had more severe disease and a longer duration of hospitalization

The risk of COVID-19 death is much greater and age dependent with type I IFN autoantibodies

International audienceSignificance There is growing evidence that preexisting autoantibodies neutralizing type I interferons (IFNs) are strong determinants of life-threatening COVID-19 pneumonia. It is important to estimate their quantitative impact on COVID-19 mortality upon SARS-CoV-2 infection, by age and sex, as both the prevalence of these autoantibodies and the risk of COVID-19 death increase with age and are higher in men. Using an unvaccinated sample of 1,261 deceased patients and 34,159 individuals from the general population, we found that autoantibodies against type I IFNs strongly increased the SARS-CoV-2 infection fatality rate at all ages, in both men and women. Autoantibodies against type I IFNs are strong and common predictors of life-threatening COVID-19. Testing for these autoantibodies should be considered in the general population