Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
2.4
Journals
Medicina
Special Issues
Approaches to Ventilation in Intensive Care Medicine
share announcement

Approaches to Ventilation in Intensive Care Medicine

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Intensive Care/ Anesthesiology".

Deadline for manuscript submissions: 30 May 2026 | Viewed by 436

Special Issue Editors

Dr. Jasminka Peršec

E-Mail Website
Guest Editor
School of Dental Medicine, University of Zagreb, Gundulićeva 5, 10000 Zagreb, Croatia
Interests: anesthesiology; intensive care
Dr. Andrej Šribar

E-Mail Website
Guest Editor Assistant
School of Dental Medicine, University of Zagreb, Gundulićeva 5, 10000 Zagreb, Croatia
Interests: anesthesiology; intensive care

Special Issue Information

Dear Colleagues,

This Special Issue, delving into the intricacies of mechanical ventilation in intensive care units (ICUs), offers a comprehensive exploration of the latest advancements, techniques, and challenges in this critical area of medicine.

Various types of manuscripts including research articles, expert reviews, and clinical practice guidelines aimed at enhancing patient outcomes and optimizing ventilatory support are welcome.

Key topics encompass the evolution of ventilatory strategies—from airway management, traditional volume control ventilation, advances in ventilation monitoring methods, to advanced modes such as closed-loop ventilation, airway pressure release ventilation (APRV) and extracorporeal membrane oxygenation (ECMO).

The issue also addresses the physiological and pathological mechanisms underlying acute respiratory distress syndrome (ARDS) and other conditions necessitating mechanical ventilation, providing insights into personalized treatment approaches.

Prominent researchers and clinicians are invited to contribute their perspectives on weaning protocols, sedation practices, and the impact of mechanical ventilation on long-term patient recovery. Additionally, the issue examines the role of artificial intelligence and machine learning in predicting ventilatory needs and improving decision-making in ICU.

Overall, this special journal issue serves as a valuable resource for intensivists, pulmonologists, and other healthcare professionals dedicated to advancing the science and practice of mechanical ventilation in critically ill patients.

Dr. Jasminka Peršec
Guest Editor

Dr. Andrej Šribar
Guest Editor Assistant

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. Medicina 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 2200 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

  • ICU
  • mechanical ventilation
  • machine learning
  • ECMO
  • sedation
  • weaning

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

12 pages, 568 KiB  
Article
Assessment of the Rapid Shallow Breathing Index, Integrative Weaning Index, and Dead Space to Tidal Volume Ratio by Respiratory Failure Type in Successfully Weaned Emergency Department Patients
by Murtaza Kaya, Harun Yildirim, Ali Halici, Abdil Coskun, Mehmed Ulu, Mehmet Toprak and Sami Eksert
Medicina 2025, 61(8), 1438; https://doi.org/10.3390/medicina61081438 - 10 Aug 2025
Viewed by 270
Abstract
Background/Objectives: Mechanical ventilation is essential in the management of acute respiratory failure (RF); however, prolonged use increases the risk of complications. Accurate predictors are therefore needed to guide timely weaning. The Rapid Shallow Breathing Index (RSBI), the dead space to tidal volume [...] Read more.
Background/Objectives: Mechanical ventilation is essential in the management of acute respiratory failure (RF); however, prolonged use increases the risk of complications. Accurate predictors are therefore needed to guide timely weaning. The Rapid Shallow Breathing Index (RSBI), the dead space to tidal volume ratio (VD/VT), and the Integrative Weaning Index (IWI) are among the key indices used to assess weaning readiness. This study aimed to examine whether these indices differ between patients with Type 1 (hypoxemic) and Type 2 (hypercapnic) respiratory failure who were successfully extubated in the emergency department, in order to explore their physiologic variability across respiratory failure phenotypes. Methods: This cross-sectional study included 35 adult patients (23 with Type 1 RF, 12 with Type 2 RF) who were successfully weaned from mechanical ventilation in the Emergency Department of a tertiary care hospital between 2022 and 2024. RSBI, VD/VT, IWI, and arterial blood gas parameters were recorded. Descriptive and comparative statistical analyses were performed, with significance set at p < 0.05. Results: There were no significant differences in age, gender, or comorbidities between the groups. Type 2 RF patients had higher FiO2 requirements (37.5% vs. 30.0%; p = 0.03) and PaCO2 levels (49.1 ± 9.65 mmHg vs. 40.3 ± 4.49 mmHg; p < 0.001). The PaO2/FiO2 ratio was lower in the Type 2 group (169 ± 49.6) compared to the Type 1 group (244 ± 95.6; p = 0.017). VD/VT ratios were significantly higher in Type 2 RF patients (0.37 ± 0.04 vs. 0.29 ± 0.13; p = 0.046). RSBI values were identical between groups (40.0 in both; p = 1.00), and IWI values showed no significant difference (70.8 ± 30.7 vs. 79.3 ± 32.5; p = 0.45). Conclusions: Although RSBI and IWI values were similar across respiratory failure types, patients with Type 2 RF demonstrated higher VD/VT ratios and lower PaO2/FiO2, indicating reduced gas exchange and alveolar ventilation efficiency. These findings suggest that VD/VT may be a more useful parameter for assessing weaning readiness in hypercapnic patients. Full article
(This article belongs to the Special Issue Approaches to Ventilation in Intensive Care Medicine)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop