The Course and Surgical Treatment of Acute Appendicitis during the First and Second Wave of the COVID-19 Pandemic : A Retrospective Analysis in University Affiliated Hospital in Latvia

Background and Objectives: Acute appendicitis is the most common abdominal emergency requiring surgery and it has an estimated lifetime risk of 6.7 to 8.6%. The COVID-19 pandemic has transformed medical care worldwide, influencing diagnostic tactics, treatment modalities and outcomes. Our study aims to compare and analyze management of acute appendicitis before and during the first and second waves of the pandemic. Materials and Methods: Patients suffering acute appendicitis were enrolled retrospectively in a single-center study for a 10-month period before the pandemic (pre-COVID-19 period: 1 March to 31 December 2019) and during the pandemic (COVID-19 period: 1 March to 31 December 2020). The total number of patients, disease severity, diagnostic methods, complications, length of hospitalization and outcomes were analyzed. Results: A total number of 863 patients were included, 454 patients in the pre-COVID-19 period and 409 patients in the COVID-19 period. Compared to the pre-COVID-19 period, the number of complicated appendicitis increased in the COVID-19 period (24.4% to 37.2%; p < 0.001). The proportion of laparoscopic appendectomies increased during the COVID-19 period but did not show statistically significant differences between periods. In both time periods, we found that open technique was the chosen surgical approach more frequently in elderly patients (p < 0.001). Generalized peritonitis was significantly more common during the COVID-19 period (3.5% vs. 6.1%, p < 0.001). The postoperative course of patients was similar in the pre-COVID-19 period and during the COVID-19 period, with no significant differences in ICU admissions, overall hospital stay or morbidity. Conclusions: The COVID-19 pandemic has led to a significant increase in complicated forms of acute appendicitis; however, no significant impact was observed in terms of diagnostic or treatment approach.publishersversionPeer reviewe

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection

Funding Information: The development of the analysis approach and its evaluation and analysis were supported by a postdoctoral grant within the Activity 1.1.1.2 “Post-doctoral Research Aid” co-funded by the European Regional Development Fund (postdoctoral project numbers: 1.1.1.2/VIAA/2/18/270 and 1.1.1.2/VIAA/3/19/495). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.publishersversionPeer reviewe