Gastrointestinal sequalae months after severe acute respiratory syndrome corona virus 2 infection: a prospective, observational study.

INTRODUCTION: Post-coronavirus disease (post-COVID) symptoms arise mostly from impaired function of respiratory tract although in many patients, the dysfunction of gastrointestinal tract and liver among other organ systems may persist. METHODS: Primary data collection was based on a short gastrointestinal symptom questionnaire at the initial screening. A brief telephone survey within the patient and control group was performed 5-8 months after the initial screening. R ver. 4.0.5 and imbalanced RandomForest (RF) machine-learning algorithm were used for data explorations and analyses. RESULTS: A total of 590 patients were included in the study. The general presence of gastrointestinal symptoms 208.2 days (153-230 days) after the initial acute severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) infection was 19% in patients with moderate-to-serious course of the disease and 7.3% in patients with mild course compared with 3.0% in SARS-CoV-2 negative controls (P < 0.001). Diarrhea and abdominal pain are the most prevalent post-COVID gastrointestinal symptoms. RF machine-learning algorithm identified acute diarrhea and antibiotics administration as the strongest predictors for gastrointestinal sequelae with area under curve of 0.68. Variable importance for acute diarrhea is 0.066 and 0.058 for antibiotics administration. CONCLUSION: The presence of gastrointestinal sequelae 7 months after the initial SARS-CoV-2 infection is significantly higher in patients with moderate-to-severe course of the acute COVID-19 compared with asymptomatic patients or those with mild course of the disease. The most prevalent post-COVID gastrointestinal symptoms are diarrhea and abdominal pain. The strongest predictors for persistence of these symptoms are antibiotics administration and acute diarrhea during the initial infection.

Activated CD8+CD38+ Cells Are Associated With Worse Clinical Outcome in Hospitalized COVID-19 Patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), that spread around the world during the past 2 years, has infected more than 260 million people worldwide and has imposed an important burden on the healthcare system. Several risk factors associated with unfavorable outcome were identified, including elderly age, selected comorbidities, immune suppression as well as laboratory markers. The role of immune system in the pathophysiology of SARS-CoV-2 infection is indisputable: while an appropriate function of the immune system is important for a rapid clearance of the virus, progression to the severe and critical phases of the disease is related to an exaggerated immune response associated with a cytokine storm. We analyzed differences and longitudinal changes in selected immune parameters in 823 adult COVID-19 patients hospitalized in the Martin University Hospital, Martin, Slovakia. Examined parameters included the differential blood cell counts, various parameters of cellular and humoral immunity (serum concentration of immunoglobulins, C4 and C3), lymphocyte subsets (CD3+, CD4+, CD8+, CD19+, NK cells, CD4+CD45RO+), expression of activation (HLA-DR, CD38) and inhibition markers (CD159/NKG2A). Besides already known changes in the differential blood cell counts and basic lymphocyte subsets, we found significantly higher proportion of CD8+CD38+ cells and significantly lower proportion of CD8+NKG2A+ and NK NKG2A+ cells on admission in non-survivors, compared to survivors; recovery in survivors was associated with a significant increase in the expression of HLA-DR and with a significant decrease of the proportion of CD8+CD38+cells. Furthermore, patients with fatal outcome had significantly lower concentrations of C3 and IgM on admission. However, none of the examined parameters had sufficient sensitivity or specificity to be considered a biomarker of fatal outcome. Understanding the dynamic changes in immune profile of COVID-19 patients may help us to better understand the pathophysiology of the disease, potentially improve management of hospitalized patients and enable proper timing and selection of immunomodulator drugs.

A machine learning approach for identification of gastrointestinal predictors for the risk of COVID-19 related hospitalization.

Background and aim: COVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak, several machine learning algorithms were implemented to assess new diagnostic and therapeutic methods for this disease. The aim of this study is to assess gastrointestinal and liver-related predictive factors for SARS-CoV-2 associated risk of hospitalization. Methods: Data collection was based on a questionnaire from the COVID-19 outpatient test center and from the emergency department at the University Hospital in combination with the data from internal hospital information system and from a mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls in three different SARS-CoV-2 positive patient groups (divided based on severity of the disease). The data were visualized and analyzed in R version 4.0.5. The Chi-squared or Fisher test was applied to test the null hypothesis of independence between the factors followed, where appropriate, by the multiple comparisons with the Benjamini Hochberg adjustment. The null hypothesis of the equality of the population medians of a continuous variable was tested by the Kruskal Wallis test, followed by the Dunn multiple comparisons test. In order to assess predictive power of the gastrointestinal parameters and other measured variables for predicting an outcome of the patient group the Random Forest machine learning algorithm was trained on the data. The predictive ability was quantified by the ROC curve, constructed from the Out-of-Bag data. Matthews correlation coefficient was used as a one-number summary of the quality of binary classification. The importance of the predictors was measured using the Variable Importance. A 2D representation of the data was obtained by means of Principal Component Analysis for mixed type of data. Findings with the p-value below 0.05 were considered statistically significant. Results: A total of 710 patients were enrolled in the study. The presence of diarrhea and nausea was significantly higher in the emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on the Random Forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance, although much lower than AST. Conclusion: SARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using the machine learning Random Forest algorithm, we have identified the elevated AST as the most important predictor for COVID-19 related hospitalizations.

Comparison of SARS-CoV-2 Detection by Rapid Antigen and by Three Commercial RT-qPCR Tests: A Study from Martin University Hospital in Slovakia.

The global pandemic of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is having a tremendous impact on the global economy, health care systems and the lives of almost all people in the world. The Central European country of Slovakia reached one of the highest daily mortality rates per 100,000 inhabitants in the first 3 months of 2021, despite implementing strong prophylactic measures, lockdowns and repeated nationwide antigen testing. The present study reports a comparison of the performance of the Standard Q COVID-19 antigen test (SD Biosensor) with three commercial RT-qPCR kits (vDetect COVID-19-MultiplexDX, gb SARS-CoV-2 Multiplex-GENERI BIOTECH Ltd. and Genvinset COVID-19 [E]-BDR Diagnostics) in the detection of infected individuals among employees of the Martin University Hospital in Slovakia. Health care providers, such as doctors and nurses, are classified as "critical infrastructure", and there is no doubt about the huge impact that incorrect results could have on patients. Out of 1231 samples, 14 were evaluated as positive for SARS-CoV-2 antigen presence, and all of them were confirmed by RT-qPCR kit 1 and kit 2. As another 26 samples had a signal in the E gene, these 40 samples were re-isolated and subsequently re-analysed using the three kits, which detected the virus in 22, 23 and 12 cases, respectively. The results point to a divergence not only between antigen and RT-qPCR tests, but also within the "gold standard" RT-qPCR testing. Performance analysis of the diagnostic antigen test showed the positive predictive value (PPV) to be 100% and negative predictive value (NPV) to be 98.10%, indicating that 1.90% of individuals with a negative result were, in fact, positive. If these data are extrapolated to the national level, where the mean daily number of antigen tests was 250,000 in April 2021, it points to over 4700 people per day being misinterpreted and posing a risk of virus shedding. While mean Ct values of the samples that were both antigen and RT-qPCR positive were about 20 (kit 1: 20.47 and 20.16 for Sarbeco E and RdRP, kit 2: 19.37 and 19.99 for Sarbeco E and RdRP and kit 3: 17.47 for ORF1b/RdRP), mean Ct values of the samples that were antigen-negative but RT-qPCR-positive were about 30 (kit 1: 30.67 and 30.00 for Sarbeco E and RdRP, kit 2: 29.86 and 31.01 for Sarbeco E and RdRP and kit 3: 27.47 for ORF1b/RdRP). It confirms the advantage of antigen test in detecting the most infectious individuals with a higher viral load. However, the reporting of Ct values is still a matter of ongoing debates and should not be conducted without normalisation to standardised controls of known concentration.

Immune Profile in Patients With COVID-19: Lymphocytes Exhaustion Markers in Relationship to Clinical Outcome.

The velocity of the COVID-19 pandemic spread and the variable severity of the disease course has forced scientists to search for potential predictors of the disease outcome. We examined various immune parameters including the markers of immune cells exhaustion and activation in 21 patients with COVID-19 disease hospitalised in our hospital during the first wave of the COVID-19 pandemic in Slovakia. The results showed significant progressive lymphopenia and depletion of lymphocyte subsets (CD3+, CD4+, CD8+ and CD19+) in correlation to the disease severity. Clinical recovery was associated with significant increase in CD3+ and CD3+CD4+ T-cells. Most of our patients had eosinopenia on admission, although no significant differences were seen among groups with different disease severity. Non-survivors, when compared to survivors, had significantly increased expression of PD-1 on CD4+ and CD8+ cells, but no significant difference in Tim-3 expression was observed, what suggests possible reversibility of immune paralysis in the most severe group of patients. During recovery, the expression of Tim-3 on both CD3+CD4+ and CD3+CD8+ cells significantly decreased. Moreover, patients with fatal outcome had significantly higher proportion of CD38+CD8+ cells and lower proportion of CD38+HLA-DR+CD8+ cells on admission. Clinical recovery was associated with significant decrease of proportion of CD38+CD8+ cells. The highest AUC values within univariate and multivariate logistic regression were achieved for expression of CD38 on CD8+ cells and expression of PD1 on CD4+ cells alone or combined, what suggests, that these parameters could be used as potential biomarkers of poor outcome. The assessment of immune markers could help in predicting outcome and disease severity in COVID-19 patients. Our observations suggest, that apart from the degree of depletion of total lymphocytes and lymphocytes subsets, increased expression of CD38 on CD3+CD8+ cells alone or combined with increased expression of PD-1 on CD3+CD4+ cells, should be regarded as a risk factor of an unfavourable outcome in COVID-19 patients. Increased expression of PD-1 in the absence of an increased expression of Tim-3 on CD3+CD4+ and CD3+CD8+ cells suggests potential reversibility of ongoing immune paralysis in patients with the most severe course of COVID-19.