Pancreatic stiffness and anthropometric parameters in healthy volunteers.

PURPOSE: The aim of this study was to define the values of percutaneous ultrasound shear-wave and strain elastography of the pancreas in healthy volunteers. METHODS: This is a single-center prospective study conducted on volunteers who underwent examination of percutaneous point shear­wave elastography and strain elastography. Both the shear-wave speed (Vs) and strain histogram were measured 3 times and median values were evaluated. Relevant recorded clinical data were age, sex, and height. RESULTS:  From May 2020 to October 2021 a total of 90 patients (21 male, 69 female) were included in the study. Their average age was 26 years (from 22 to 65). The average SWM in kPa was 6.07 (2.58-17.29). The average value of SE was 134.44 (78.51-184.35). Most of the patients had BMI in the range of normal weight with an average value of 22.75 (17.5-28). The average depth of the localization of the pancreas was 4.5 cm. The effect of BMI on the strain histogram was significant (p < 0.05). We found a significant relationship between the strain histogram and the depth of localization of the pancreas (p < 0.01). CONCLUSION:  We described normal values for pancreatic stiffness using ultrasound elastography by 2D-SWE and strain elastography. Our results indicate changes in values depending on BMI and depth of the pancreas. (Tab. 1, Fig. 5, Ref. 19).

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.

Phenomenon of hypoglycemia unawareness in patients with insulinoma - single center experience.

INTRODUCTION: Insulinomas are rare neuroendocrine tumors of pancreas. Clinical manifestations include various symptoms of hypoglycemia, which is the result of insulin overproduction. Symptoms of hypoglycemia are heterogenous what most probably contributes to diagnostic delay. In this study we retrospectively evaluated clinical features of hypoglycemia. We discovered that a substantial number of patients suffered from hypoglycemia unawareness. MATERIALS AND METHODS: We performed retrospective analysis of prospectively collected patients with histologically confirmed insulinoma. We evaluated clinical features and signs of hypoglycemia and the duration of symptoms and performed thorough review of the patients' files in order to identify whether patients had been falsely diagnosed previously. Each patient underwent 72 hour fasting test during which levels of immunoreactive insulin (IRI), C-peptide and nadir blood glucose level were obtained. Based on the clinical findings and results of 72 hour fasting test we identified a subgroup of patients with hypoglycemia unawareness. These had an episode of clinically silent hypoglycemia. We compared IRI and C-peptide levels obtained at the time of the fasting test termination in the unawareness group and the group without hypoglycemia unawareness. RESULTS: Twenty two patients with insulinoma that had been hospitalized in our tertiary center were included in the analysis. Mean age was 51±16.7 years. The most common symptom reported by 63.6% of patients was fatigue, followed by increased appetite with consequent weight gain and the loss of consciousness, both reported by 40.9% of patients. Based on the review of clinical features and the results of the fasting test we identified a group of patients with hypoglycemia unawareness. We labeled the patient accordingly in case of the loss of consciousness in personal history as well as asymptomatic hypoglycemia or severe neuroglycopenic symptoms during the fasting test without any accompanying or preceding clinical signs. There were 7 patients with hypoglycemia unawareness in our cohort (31.8%). Patients with this phenomenon had significantly lower levels of both IRI (2.35±1.25 vs. 5.88±3.92ng/ml, p=0.01) and C-peptide (9.14±7.36 vs. 50±42.8 µU/ml, p=0.01) than the rest of the patients. Nadir blood glucose level during the fasting test showed no significant difference (9.4±8.2 vs. 12.2±8.2 months, p=0.28) in the unawareness group and the rest of the patients, respectively. CONCLUSION: We described the phenomenon of unawareness to hypoglycemia in the patients with insulinoma. This has not been recognized in insulinoma patients yet since available evidence mostly relates to type I diabetic patients. It might lead to higher morbidity and diagnostic delay. Further studies with prospective evaluation should be performed to further confirm relatively high prevalence in patients with insulinoma.