Background: Acute appendicitis is the most common non-gynecological emergency during pregnancy. The diagnosis of appendicitis during pregnancy is challenging due to changes in both physiological and laboratory variables. Guidelines suggest patients with suspected acute appendicitis should be stratified based on clinical scoring systems, to optimize the use of diagnostic imaging and prevent unnecessary surgery. Surgeons require additional information beyond that provided by imaging studies before deciding upon exploratory laparoscopy in patients with a high suspicion of appendicitis. Various scoring methods have been evaluated for the diagnosis of acute appendicitis. However, there is no consensus on a method to use during pregnancy, and a detailed comparison of existing scoring methods for this purpose has not yet been conducted. The purpose of this study was to evaluate the efficacy of the most popular scoring systems applied to diagnose acute appendicitis during pregnancy. Methods: This single-center retrospective study included 79 pregnant patients who were admitted to the emergency department with abdominal pain between May 2014 and May 2019. The patients were diagnosed with acute appendicitis and underwent an appendectomy. As a control group, the study also included 79 non-pregnant patients who underwent appendectomy within the last 1.5 years. To ensure that the groups were similar, women in the case group were stratified according to age, and the proportions of women in the strata were determined. The women in the control group were similarly stratified. Women were randomly selected from the strata to prevent bias. Both laboratory and examination findings required for each scoring method were obtained and assessed separately for each patient. Negative appendectomy rates were evaluated according to pathology results. Categorical variables were compared using the chi-square test. A p value < 0.05 was considered to indicate significance. Receiver operator characteristic curve analysis was used to identify the best threshold value and to assess the performance of the test scores in terms of diagnosing appendicitis. Results: Among all scoring systems, the Tzanakis score was most efficacious at predicting appendicitis in non-pregnant women. The positive predictive value (PPV) of the Tzanakis score was 90.6%, whereas the negative predictive value (NPV) was 46.7%. The RIPASA score performed the best among the scoring systems in pregnant women. It was associated with a PPV of 94.40%, NPV of 44%, and sensitivity and specificity of 78.46% and 78.57%, respectively. Conclusion: Although the RIPASA score can be used to efficaciously diagnose acute appendicitis in pregnant women, a specific scoring system is needed for diagnosis during the gestation period.

Background: The incidence of the highly morbid and potentially lethal gangrenous cholecystitis was reportedly increased during the COVID-19 pandemic. The aim of the ChoCO-W study was to compare the clinical findings and outcomes of acute cholecystitis in patients who had COVID-19 disease with those who did not. Methods: Data were prospectively collected over 6 months (October 1, 2020, to April 30, 2021) with 1-month follow-up. In October 2020, Delta variant of SARS CoV-2 was isolated for the first time. Demographic and clinical data were analyzed and reported according to the STROBE guidelines. Baseline characteristics and clinical outcomes of patients who had COVID-19 were compared with those who did not. Results: A total of 2893 patients, from 42 countries, 218 centers, involved, with a median age of 61.3 (SD: 17.39) years were prospectively enrolled in this study; 1481 (51%) patients were males. One hundred and eighty (6.9%) patients were COVID-19 positive, while 2412 (93.1%) were negative. Concomitant preexisting diseases including cardiovascular diseases (p < 0.0001), diabetes (p < 0.0001), and severe chronic obstructive airway disease (p = 0.005) were significantly more frequent in the COVID-19 group. Markers of sepsis severity including ARDS (p < 0.0001), PIPAS score (p < 0.0001), WSES sepsis score (p < 0.0001), qSOFA (p < 0.0001), and Tokyo classification of severity of acute cholecystitis (p < 0.0001) were significantly higher in the COVID-19 group. The COVID-19 group had significantly higher postoperative complications (32.2% compared with 11.7%, p < 0.0001), longer mean hospital stay (13.21 compared with 6.51 days, p < 0.0001), and mortality rate (13.4% compared with 1.7%, p < 0.0001). The incidence of gangrenous cholecystitis was doubled in the COVID-19 group (40.7% compared with 22.3%). The mean wall thickness of the gallbladder was significantly higher in the COVID-19 group [6.32 (SD: 2.44) mm compared with 5.4 (SD: 3.45) mm; p < 0.0001]. Conclusions: The incidence of gangrenous cholecystitis is higher in COVID patients compared with non-COVID patients admitted to the emergency department with acute cholecystitis. Gangrenous cholecystitis in COVID patients is associated with high-grade Clavien-Dindo postoperative complications, longer hospital stay and higher mortality rate. The open cholecystectomy rate is higher in COVID compared with non -COVID patients. It is recommended to delay the surgical treatment in COVID patients, when it is possible, to decrease morbidity and mortality rates. COVID-19 infection and gangrenous cholecystistis are not absolute contraindications to perform laparoscopic cholecystectomy, in a case by case evaluation, in expert hands. Graphical

Background. Many scoring systems have been developed for acute appendicitis, which is the most common emergent disorder in surgical practice. Considering the physiological changes and chronic diseases occurring with advancing age, an applied scoring system may not produce the same score in similar patients in all age groups. Objectives. We aimed to compare the predictive values of scoring systems in different age groups. Methods. In this prospective study, the patients operated on in our clinic with a prediagnosis of acute appendicitis between March 2020 and March 2021 were included. We divided them into three age groups as 1845 years (group 1), 4665 years (group 2), and >65 years (group 3). We compared the scores of the nine acute appendicitis scoring systems most commonly used in the literature for these age groups. Results. A total of 203 patients were included in our study. The Alvarado scoring system yielded the most accurate results for group 1, whereas the FenyoLinberg scoring system was the most accurate system for group 2 and the Eskelinen scoring system for group 3. Conclusion. Age should be considered as a major parameter during the selection of the scoring system to be applied for patients with prediagnosis of acute appendicitis. Our study revealed the Alvarado and the FenyoLindberg scoring systems as the most accurate systems for the differential diagnosis of appendicitis in the 1845 and 4665 years age groups, respectively. Although we found the Eskelinen scoring system as the most accurate one in the >65 years age group, the confidence intervals indicated that it may not be appropriate for use alone in this group.

Indroduction: Machine learning is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. Aim: Our aim is to predict acute appendicitis, which is the most common indication for emergency surgery, using machine learning algorithms with an easy and inexpensive method. Materials and methods: Patients who were treated surgically with a prediagnosis of acute appendicitis in a single center between 2011 and 2021 were analyzed. Patients with right lower quadrant pain were selected. A total of 189 positive and 156 negative appendectomies were found. Gender and hemogram were used as features. Machine learning algorithms and data analysis were made in Python (3.7) programming language. Results: Negative appendectomies were found in 62% (n = 97) of the women and in 38% (n = 59) of the men. Positive appendectomies were present in 38% (n = 72) of the women and 62% (n = 117) of the men. The accuracy in the test data was 82.7% in logistic regression, 68.9% in support vector machines, 78.1% in k-nearest neighbors, and 83.9% in neural networks. The accuracy in the voting classifier created with logistic regression, k-nearest neighbor, support vector machines, and artificial neural networks was 86.2%. In the voting classifier, the sensitivity was 83.7% and the specificity was 88.6%. Conclusions: The results of our study show that machine learning is an effective method for diagnosing acute appendicitis. This study presents a practical, easy, fast, and inexpensive method to predict the diagnosis of acute appendicitis.