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Life
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Advances in Anesthesia and Critical Care
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Advances in Anesthesia and Critical Care

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 849

Special Issue Editor

Dr. Gabriele Melegari

E-Mail Website
Guest Editor
Department of Anesthesia and Intensive Care, Azienda Ospedaliero Universitaria di Modena, Via del Pozzo 71, 41125 Modena, Italy
Interests: anesthesia; neurocritical care; neurosciences; biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite submissions for a Special Issue dedicated to exploring the transformative role of simulation, artificial intelligence (AI), and emerging technologies in anesthesia and intensive care. We encourage contributions that discuss how these tools can advance research and clinical practice.

The COVID-19 pandemic has profoundly impacted healthcare systems, exposing critical challenges such as ICU capacity constraints, physician shortages, and the need to maintain surgical activities during crises. In response, modern technologies have driven significant innovations in anesthesia and intensive care, enhancing efficiency, precision, and patient safety.

In this context, machine and deep learning present promising opportunities to optimize decision making, predict patient outcomes, and personalize treatments.

This Special Issue seeks contributions that explore the following topics:

  • The impact of AI and machine learning on anesthesia and intensive care;
  • New technologies and perspectives;
  • Emerging technologies improving perioperative care and ICU management;
  • Innovations in crisis preparedness and response post-COVID-19 or after a new pandemic event.

We welcome original research articles, systematic reviews, and expert perspectives that address these topics and help define the future of anesthesia and intensive care.

Dr. Gabriele Melegari
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • anesthesia
  • intensive care training
  • updates in anesthesia
  • machine learning

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

9 pages, 626 KiB  
Article
Potential Clinical Use of CytoSorb® for Ticagrelor and Rivaroxaban Elimination Prior to Emergency Orthopedic Surgery in Trauma Patients
by Gabriele Melegari, Fabio Gazzotti, Federica Arturi, Elisabetta Bertellini, Andrea Tognù, Domenico Pietro Santonastaso, Matteo Villani, Francesca Coppi, Fabrizio Fattorini, Fabio Catani and Alberto Barbieri
Life 2025, 15(7), 1065; https://doi.org/10.3390/life15071065 - 3 Jul 2025
Viewed by 306
Abstract
Background: Major orthopedic trauma in patients receiving anticoagulants such as ticagrelor or rivaroxaban poses a significant perioperative challenge, particularly in emergency contexts where bleeding risks are heightened and specific reversal agents may be unavailable. CytoSorb®, a hemoadsorption device, has demonstrated efficacy [...] Read more.
Background: Major orthopedic trauma in patients receiving anticoagulants such as ticagrelor or rivaroxaban poses a significant perioperative challenge, particularly in emergency contexts where bleeding risks are heightened and specific reversal agents may be unavailable. CytoSorb®, a hemoadsorption device, has demonstrated efficacy in cardiac surgery for drug removal. Its potential application in trauma surgery remains unexplored. Objective: This protocol describes a prospective clinical investigation assessing the feasibility and safety of CytoSorb® hemoadsorption for the preoperative removal of ticagrelor and rivaroxaban in trauma patients requiring urgent orthopedic surgery. Methods: The proposed intervention involves integrating CytoSorb® into a dedicated extracorporeal circuit under normothermic conditions (37 °C) with a blood flow of 150–200 mL/min for 300 min. Serial plasma samples will be collected at predefined intervals (0, 30, 60, 120, 240, 300 min) and drug concentrations. The primary outcome is the pharmacokinetic profile of drug clearance. Secondary endpoints include procedural safety, bleeding complications, and the feasibility of timely surgery. Expected Impact: The study aims to provide real-world data on the practical integration of CytoSorb® for anticoagulant removal in orthopedic trauma care, potentially facilitating earlier surgery and improving perioperative safety. Findings may inform future randomized trials and protocol standardization. Full article
(This article belongs to the Special Issue Advances in Anesthesia and Critical Care)
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26 pages, 691 KiB  
Article
Predicting ICU Delirium in Critically Ill COVID-19 Patients Using Demographic, Clinical, and Laboratory Admission Data: A Machine Learning Approach
by Ana Viegas, Cristiana P. Von Rekowski, Rúben Araújo, Miguel Viana-Baptista, Maria Paula Macedo and Luís Bento
Life 2025, 15(7), 1045; https://doi.org/10.3390/life15071045 - 30 Jun 2025
Viewed by 401
Abstract
Delirium is a common and underrecognized complication among critically ill patients, associated with prolonged ICU stays, cognitive dysfunction, and increased mortality. Its multifactorial causes and fluctuating course hinder early prediction, limiting timely management. Predictive models based on data available at ICU admission may [...] Read more.
Delirium is a common and underrecognized complication among critically ill patients, associated with prolonged ICU stays, cognitive dysfunction, and increased mortality. Its multifactorial causes and fluctuating course hinder early prediction, limiting timely management. Predictive models based on data available at ICU admission may help to identify high-risk patients and guide early interventions. This study evaluated machine learning models used to predict delirium in critically ill patients with SARS-CoV-2 infections using a prospective cohort of 426 patients. The dataset included demographic characteristics, clinical data (e.g., comorbidities, medication, reason for ICU admission, interventions), and routine lab test results. Five models—Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and Naïve Bayes—were developed using 112 features. Feature selection relied on Information Gain, and model performance was assessed via 10-fold cross-validation. The Naïve Bayes model showed moderate predictive performance and high interpretability, achieving an AUC of 0.717, accuracy of 65.3%, sensitivity of 62.4%, specificity of 68.1%, and precision of 66.2%. Key predictors included invasive mechanical ventilation, deep sedation with benzodiazepines, SARS-CoV-2 as the reason for ICU admission, ECMO use, constipation, and male sex. These findings support the use of interpretable models for early delirium risk stratification using routinely available ICU data. Full article
(This article belongs to the Special Issue Advances in Anesthesia and Critical Care)
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