ABSTRACT
Background: The novel coronavirus disease 2019 (COVID-19) has rapidly spread worldwide since the outbreak in Wuhan, China, in 2019, becoming a major threat to public health. The most common symptoms are fever, dry cough, shortness of breath, but subjects with COVID-19 may also manifest gastrointestinal symptoms, and in a few cases an involvement of the gallbladder has been observed. Case report: Here we present a case of 50-year-old male with SARS-CoV-2 infection who had abdominal pain, vomiting and diarrhea without respiratory symptoms and was finally diagnosed as acute acalculous cholecystitis (AAC). Laparoscopic cholecystectomy was performed and found a gangrenous gallbladder; the real-time reverse transcription polymerase chain reaction SARS-CoV-2 nucleic acid assay of the bile was negative. We also made a review of the literature and try to understand the hypothetic role of SARS-CoV-2 in the pathogenesis of AAC. Conclusions: We highlighted that it is noteworthy to look at gastrointestinal symptoms in patients with SARS-CoV-2 infection and take into account AAC as a possible complication of COVID-19. Although more evidence is needed to better elucidate the role of the pathogenic mechanisms of the SARS-CoV-2 in AAC, it is conceivable that the hepatobiliary system could be a potential target of SARS-CoV-2.
Subject(s)
Acalculous Cholecystitis , COVID-19 , Cholecystectomy, Laparoscopic , Acalculous Cholecystitis/diagnosis , Acalculous Cholecystitis/etiology , COVID-19/complications , Humans , Male , Middle Aged , Public Health , SARS-CoV-2ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan-Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548-49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000-3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141-2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895-2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.