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.

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.