Bacterial and Fungal Co-Infections in Patients with COVID-19 Related Pneumonia: A Retrospective Cohort Study (preprint)

Background: The study analysed risk factors for bacterial and fungal co-infection in patients with COVID-19 and the impact on mortality.Methods: This is a single-center retrospective study conducted on 387 patients with confirmed COVID-19 pneumonia admitted to an Italian Tertiary-care hospital, between 21 February 2020 and 31 May 2020. Bacterial/fungal coinfection was determined by the presence of characteristic clinical features and positive culture results. Multivariable logistic regression was used to analyze risk factors for the development of bacterial/fungal co-infection after adjusting for demographic characteristics and comorbidities. Thirty-day survival of the patients with or without co-infections was analysed by Kaplan-Meier method.Results: In 53/387 (13.7%) patients with COVID-19 pneumonia, 67 episodes of bacterial/fungal co-infection occurred (14 presented >1 episode). Pneumonia was the most frequent co-infection (47.7%), followed by BSI (34.3%) and UTI (11.9%). S. aureus was responsible for 24 episodes (35.8%), E. coli for 7 (10.4%), P. aerugionsa and Enterococcus spp. for 5 episodes each (7.4%). Five (7.4%) pulmonary aspergillosis, 3 (4.4%) pneumocystosis and 5 (7.4%) invasive candidiases were observed. Multivariable analysis showed a higher risk of infection in patients with an age>65 years (csHR 2.680; 95%CI: 1.254 - 5.727; p=0.054), with cancer (csHR 5.243; 95%CI: 1.173-23.423; p=0.030), with a LOS>10 days (csHR 12.507; 95%CI: 2.659 – 58.830; p=0.001), early (within 48h) admitted in ICU (csHR 11.766; 95% CI: 4.353-31.804; p<0.001), and with a SOFA score>5 (csHR 3.397; 95% CI: 1.091 - 10.581; p=0.035). Estimated cumulative risk of developing at least 1 bacterial/fungal co-infection episode was of 15% and 27% after 15 and 30 days from admission, respectively. Kaplan-Meier estimated a higher cumulative probability of death in patients with bacterial/fungal co-infection (log-rank=0.031). Thirty-day mortality rate of patients with pneumonia was 38.7%, higher than those with BSI (30.4%).Conclusions: Bacterial and fungal infections are a serious complication affecting the survival of patients with COVID-19-related pneumonia. Some issues need to be investigated, such as the best empirical antibiotic therapy and the need for possible antifungal prophylaxis.

Hypokalemia in Patients with COVID-19 (preprint)

Patients with COVID-19 may experience multiple conditions (e.g., fever, hyperventilation, anorexia, gastroenteritis, acid-base disorder) that may cause electrolyte imbalances. Hypokalemia is a concerning electrolyte disorder that may increase the susceptibility to various kinds of arrhythmia. This study aimed to estimate prevalence, risk factors and outcome of hypokalemia in a cohort of non-critically ill patients. A retrospective analysis was conducted on 290 hospitalized patients with confirmed COVID-19 infection at the tertiary teaching hospital of Modena, Italy. Hypokalemia (<3.5 mEq/L) was detected in 119 patients (41%). The decrease of serum potassium level was of mild entity (3-3.4 mEq/L) and occurred in association with hypocalcemia (P=0.001) and lower level of serum magnesium (P=0.028) compared to normokaliemic patients. Urine K: creatinine ratio, measured in a small subset of patients (n=45; 36.1%), showed an increase of urinary potassium excretion in the majority of the cases (95.5%). Causes of kaliuria were diuretic therapy (53.4%) and corticosteroids (23.3%). In the remaining patients, urinary potassium loss was associated with normal serum magnesium, low sodium excretion (FENa< 1%) and metabolic alkalosis. Risk factors for hypokalemia were female gender (P=0.002; HR 0.41, 95%CI 0.23-0.73) and diuretic therapy (P=0.027; HR 1.94, 95%CI 1.08-3.48). Hypokalemia, adjusted for sex, age and SOFA score, resulted not associated with ICU admission (P=0.131, 95% CI 0.228-1.212) and in-hospital mortality (P=0.474; 95% CI 0,170-1,324) in our cohort of patients. Hypokalemia is a frequent disorder in COVID-19 patients and urinary potassium loss may be the main cause of hypokalemia. The disorder was mild in the majority of the patients and was unrelated to poor outcomes. Nevertheless, hypokalemic patients required potassium supplements to dampen the risk of arrhythmias.

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia - challenges, strengths, and opportunities in a global health emergency (preprint)

Background Machine learning can assist clinicians in forecasting patients with COVID-19 who develop respiratory failure requiring mechanical ventilation. This analysis aimed to determine a 48 hours prediction of moderate to severe respiratory failure, as assessed with PaO2/FiO2 < 150 mmHg, in hospitalized patients with COVID-19 pneumonia. Methods This was an observational study that comprised all consecutive adult patients with COVID-19 pneumonia admitted to the Infectious Diseases Clinic of the University Hospital of Modena, Italy from 21 February to 6 April 2020. COVID-19 was confirmed with PCR positive nasopharyngeal swabs while the presence of pneumonia was radiologically confirmed. Patients received standard of care according to national guidelines for clinical management of SARS-CoV-2 infection. The patients' full medical history, demographic and epidemiological features, clinical data, complete blood count, coagulation, inflammatory and biochemical markers were routinely collected and aggregated in a clinically-oriented logical framework in order to build different datasets. The dataset was used to train a learning framework relying on Microsoft LightGBM and leveraging a hybrid approach, where clinical expertise is applied alongside a data-driven analysis. Shapley Additive exPlanations (SHAP) values were used to quantify the positive or negative impact of each variable included in the model on the predicted outcome. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio < 150 mmHg ([≥] 13.3 kPa) in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Results A total of 198 patients contributed to generate 1068 valuable observations which allowed to build 3 prediction models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth boosted mixed model which included 20 variables was selected from the model 3, achieved the best predictive performance (AUC=0.84). Its clinical performance was applied in a narrative case report as an example. Conclusion This study developed a machine learning algorithm, with a 84% prediction accuracy, which is potentially able to assist clinicians in decision making process with therapeutic implications.

Marked T cell activation, senescence, exhaustion and skewing towards TH17 in patients with Covid-19 pneumonia (preprint)

We have deeply investigated T cell compartment, plasma cytokines and cells producing cytokines in patients affected by Covid-19. At admission, patients were lymphopenic; in all of them SARS-CoV-2 was detected in a nasopharyngeal swab specimen by real-time RT-PCR, and pneumonia was subsequently confirmed by X-rays.Detailed 18-parameter flow cytometry was performed in 21 patients and 13 controls. Coupling polychromatic cytometry with unsupervised data analysis, we found that patients show an increased amount of CD4+ T lymphocytes that were activated, exhausted, stem memory or Treg. Similar results concerning activation and exhaustion were found in the CD8+ T cell compartment, within which the differences were even greater.Measuring plasma level of 31 cytokines linked to inflammation revealed that Covid-19 showed a dramatic increase of several molecules, such as TH1 and TH2 cytokines, chemokines, galectins, pro- and anti-inflammatory mediators, confirming the importance of a massive immune activation causing the cytokine storm. Then, intracellular staining detecting the simultaneous production of different cytokines after a para-physiologic stimulus given by anti-CD3/CD28 mAbs revealed not only a high capacity to produce a variety of molecules, including TNF-a, IFN-g and IL-2, but also a significant skewing of CD4+ T cells towards the TH17 phenotype.A therapeutic approach now exists based on the administration of drugs that block IL-6 pathway, and is now consistently improving the course of the disease. IL-17 is crucial in recruiting and activating neutrophils, cells that can migrate to the lung and are heavily involved in the pathogenesis of Covid-19. We show here that a significant skewing of activated T cells towards TH17 functional phenotype exists in Covid-19 patients. Thus, we suggest that blocking IL-17 pathway by already available biological drugs that are used to treat different pathologies could be a novel, additional strategy to improve the health of patients infected by SARS-CoV-2.