Acid base disorders in patients with COVID-19.

PURPOSE: Acid-base derangement has been poorly described in patients with coronavirus disease 2019 (COVID-19). Considering the high prevalence of pneumonia and kidneys injury in COVID-19, frequent acid-base alterations are expected in patients admitted with SARS-Cov-2 infection. The study aimed to assess the prevalence of acid-base disorders in symptomatic patients with a diagnosis of COVID-19. METHODS: The retrospective study enrolled COVID-19 patients hospitalized at the University Hospital of Modena from 4 March to 20 June 2020. Baseline arterial blood gas (ABG) analysis was collected in 211 patients. In subjects with multiple ABG analysis, we selected only the first measurement. A pH of less than 7.37 was categorized as acidemia and a pH of more than 7.43 was categorized as alkalemia. RESULTS: ABG analyses revealed a low arterial partial pressure of oxygen (PO2, 70.2 ± 25.1 mmHg), oxygen saturation (SO2, 92%) and a mild reduction of PO2/FiO2 ratio (231 ± 129). Acid-base alterations were found in 79.7% of the patient. Metabolic alkalosis (33.6%) was the main alteration followed by respiratory alkalosis (30.3%), combined alkalosis (9.4%), respiratory acidosis (3.3%), metabolic acidosis (2.8%) and other compensated acid-base disturbances (3.6%). All six patients with metabolic acidosis died at the end of the follow-up. CONCLUSION: Variations of pH occurred in the majority (79.7%) of patients admitted with COVID-19. The patients experienced all the type of acid-base disorders, notably metabolic and respiratory alkalosis were the most common alterations in this group of patients.

Similar Rates of AKI during the First Two Waves of COVID-19 in Northern Italy: a single-center study (preprint)

Introduction Two waves of COVID-19 cases have overwhelmed most European countries during 2020. It is unclear if the incidence of acute kidney injury (AKI) has changed during the COVID-19 outbreaks. This study aims to evaluate the differences in incidence, risk factors and outcome of AKI in patients with SARS-CoV-2 infection during the first and second wave of COVID-19. Method We reviewed the health medical records of 792 consecutive patients with COVID-19 hospitalized at the University Hospital of Modena, Italy, from February 25 to December 14, 2020. Results AKI was diagnosed in 122 (15.4%) patients. Incidence of AKI remained steady rate during wave-1 (15.9%) and wave-2 (14.7%) (P=0.89). AKI patients were older (P=<0.001) and had a more severe respiratory impairment (PO2/FO2) (P=≤0.001) than their non-AKI counterparts. AKI led to a longer hospital stay (P=0.001), complicated with a higher rate of ICU admission. COVID-19-related AKI was associate with 59.7% of deaths during wave-1 and 70.6% during wave-2. At the end of the period of observation, 24% (wave-1) and 46.7% (wave-2) of survivors were discharged with a not fully recovered kidney function. Risk factors for AKI in patients with COVID-19 were diuretics (HR=5.3; 95%CI, 1.2-23.3; P=0.025) and cardiovascular disease (HR, 2.23; 95%CI, 1.05-5.1; P=0.036). Conclusion The incidence of AKI (about 15%) remained unchanged during 2020, regardless of the trend of COVID-19. AKI occurred in patients with severe COVID-19 symptoms and was associated with a higher incidence of deaths than non-AKI patients. The risk factors of COVID-19-related AKI were diuretic therapy and cardiovascular disease.

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.

Incidence, risk factors and mortality outcome in patients with acute kidney injury in COVID-19: a single-center observational study (preprint)

BackgroundAcute kidney injury (AKI) is a recently recognized complication of coronavirus disease-2019 (COVID-19). This study aims to evaluate the incidence, risk factors and case-fatality rate of AKI in patients with documented COVID-19. MethodsWe reviewed the health medical records of 307 consecutive patients hospitalized for symptoms of COVID-19 at the University Hospital of Modena, Italy. ResultsAKI was diagnosed in 69 out of 307 (22.4%) patients. The stages of AKI were stage 1 in 57.9%, stage 2 in 24.6% and stage 3 in 17.3%. Hemodialysis was performed in 7.2% of the subjects. AKI patients had a mean age of 74.7{+/-}9.9 years and higher serum levels of the main marker of inflammation and organ involvement (lung, liver, hearth and liver) than non-AKI patients. AKI events were more frequent in subjects with severe lung comprise. Two peaks of AKI events coincided with in-hospital admission and death of the patients. Kidney injury was associate with a higher rate of urinary abnormalities including proteinuria (0.448{+/-}0.85 vs 0.18{+/-}0.29; P=<0.0001) and hematuria (P=0.032) compared to non-AKI patients. At the end of follow-up, 65.2% of the patients did not recover their renal function after AKI. Risk factors for kidney injury were age, male sex, CKD and non-renal SOFA. Adjusted Cox regression analysis revealed that AKI was independently associated with in-hospital death (hazard ratio [HR]=3.74; CI 95%, 1.34-10.46) compared to non-AKI patients. Groups of patients with AKI stage 2-3 and failure to recover kidney function were associated with the highest risk of in-hospital mortality. Lastly, long-hospitalization was positively associated with a decrease of serum creatinine, likely due to muscle depletion occurred with prolonged bed rest. ConclusionsAKI was a dire consequence of patients with COVID-19. Identification of patients at high-risk for AKI and prevention of kidney injury by avoiding dehydration and nephrotoxic agents is imperative in this vulnerable cohort of patients.

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.