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.

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 < 0.001) and required a longer duration of hospitalization (8 [6, 13] vs. 7 [5, 12] days, 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 < 0.001. The Charlson Comorbidity Index was lower (3 [0, 5] vs. 4 [1, 6], p < 0.001), and the extent of disease on computed tomography (CT) was greater during the Delta wave (p < 0.001). No evidence of a difference in risk of death or admission to the intensive care unit was found between the two groups. Age, cardiovascular events, acute kidney injury during hospitalization, extent of disease on chest CT, D-dimer, and neutrophil/lymphocyte ratio values were identified as independent predictors of mortality for patients in the Delta period. Cardiovascular events and acute liver injury during hospitalization and the PaO₂/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.

Background: Stewart’s approach is known to have better diagnostic accuracy for the identification of metabolic acid–base disturbances compared to traditional methods based either on plasma bicarbonate concentration ([HCO₃⁻]) and anion gap (AG) or on base excess/deficit (BE). This study aimed to identify metabolic acid–base disorders using either Stewart’s or traditional approaches in critically ill COVID-19 patients admitted to the ICU, to recognize potential hidden acid–base metabolic abnormalities and to assess the prognostic value of these abnormalities for patient outcome. Methods: This was a single-center retrospective study, in which we collected data from patients with severe COVID-19 admitted to the ICU. Electronical files were used to retrieve data for arterial blood gases, serum electrolytes, and proteins and to derive [HCO₃⁻], BE, anion gap (AG), AG adjusted for albumin (AG_adj), strong ion difference, strong ion gap (SIG), and SIG corrected for water excess/deficit (SIG_corr). The acid–base status was evaluated in each patient using the BE, [HCO₃⁻], and physicochemical approaches. Results: We included 185 patients. The physicochemical approach detected more individuals with metabolic acid–base abnormalities than the BE and [HCO₃⁻] approaches (p < 0.001), and at least one acid–base disorder was recognized in most patients. According to the physicochemical method, 170/185 patients (91.4%) had at least one disorder, as opposed to the number of patients identified using the BE 90/186 (48%) and HCO₃ 62/186 (33%) methods. Regarding the derived acid–base status variables, non-survivors had greater AG_adj, (p = 0.013) and SIG_corr (p = 0.035) compared to survivors. Conclusions: The identification of hidden acid–base disturbances may provide a detailed understanding of the underlying conditions in patients and of the possible pathophysiological mechanisms implicated. The association of these acid–base abnormalities with mortality provides the opportunity to recognize patients at increased risk of death and support them accordingly.

Hypoglycaemia in patients with type 1 diabetes mellitus (T1DM) remains a major clinical burden and, beyond its metabolic complications, can cause serious cardiac arrhythmias. Multiple mechanisms lead to different types of arrhythmias during hypoglycaemia. However, existing studies often involve mixed diabetes populations, small cohorts, or limited monitoring during nocturnal periods, leaving a critical gap in understanding the links between glucose fluctuations and arrhythmic events. This review provides an updated combination of experimental and clinical evidence describing how autonomic dysfunction and ionic imbalances lead to electrophysiological instability and structural remodelling of the myocardium during hypoglycaemia. Continuous glucose monitoring (CGM) combined with electrocardiographic or wearable rhythm tracking may enable early detection of glycemic and cardiac disturbances and help identify high-risk individuals. Future prospective studies using combined CGM–ECG monitoring, particularly during sleep, are essential to clarify the relationship between hypoglycaemia and arrhythmic events.

Introduction: Efficient clinical scores predicting the outcome of severe COVID-19 pneumonia may play a pivotal role in patients’ management. The aim of this study was to assess the modified Severe COvid Prediction Estimate score (mSCOPE) index as a predictor of mortality in patients admitted to the ICU due to severe COVID-19 pneumonia. Materials and methods: In this retrospective observational study, 268 critically ill COVID-19 patients were included. Demographic and laboratory characteristics, comorbidities, disease severity, and outcome were retrieved from the electronical medical files. The mSCOPE was also calculated. Results: An amount of 70 (26.1%) of patients died in the ICU. These patients had higher mSCOPE score compared to patients who survived (p < 0.001). mSCOPE correlated to disease severity (p < 0.001) and to the number and severity of comorbidities (p < 0.001). Furthermore, mSCOPE significantly correlated with days on mechanical ventilation (p < 0.001) and days of ICU stay (p = 0.003). mSCOPE was found to be an independent predictor of mortality (HR:1.219, 95% CI: 1.010–1.471, p = 0.039), with a value ≥ 6 predicting poor outcome with a sensitivity (95%CI) 88.6%, specificity 29.7%, a positive predictive value of 31.5%, and a negative predictive value of 87.7%. Conclusion: mSCOPE score could be proved useful in patients’ risk stratification, guiding clinical interventions in patients with severe COVID-19.