Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.2
2.9
Journals
JCM
Special Issues
New Insights into Critical Care
jcm-logo
Submit to JCM Review for JCM Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

New Insights into Critical Care

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Emergency Medicine".

Deadline for manuscript submissions: closed (31 August 2025) | Viewed by 3409

Special Issue Editors

Dr. Francesca Rubulotta

E-Mail
Guest Editor
Department of General Surgery and Medical-Surgical Specialties, University of Catania, Catania, Italy
Interests: gender medicine; anesthesia; critical care medicine; mechanical ventilation; artificial intelligence; innovation
Dr. Luigi La Via

E-Mail Website
Guest Editor
Department of Anesthesia and Intensive Care, Azienda Ospedaliero Universitaria Policlinico “G.Rodolico-San Marco”, 95123 Catania, Italy
Interests: loco-regional anesthesia; pain medicine; ERAS
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue entitled “New Insights into Critical Care” aims to address several cutting-edge topics of great relevance in the current landscape of critical care medicine.

Potential topics for submission include the following:

  1. Gender medicine in critical care: An exploration of how gender disparities impact treatment outcomes and the importance of integrating gender-sensitive approaches in critical care practices, research, and reporting (SAGER guidelines).
  2. Artificial intelligence related to the treatment of SEPSIS, the prediction and recognition of ARDS, and the need for mechanical ventilation: A discussion on the role of AI technologies in optimizing mechanical ventilation strategies, enhancing patient management, and improving outcomes in critically ill patients.
  3. Robotic anesthesia with the Kepler Intubator: An examination of advancements in robotic-assisted anesthesia techniques, focusing on the Kepler Intubator system and its potential impact on patient safety and procedural efficiency.
  4. The importance of the preoperative evaluation and optimization of patients undergoing anesthesia for any kind of surgery.
  5. Closed-loop anesthesia with MCSleepy: Insights into the use of closed-loop anesthesia systems, such as MCSleepy, and how they enhance the monitoring and administration of anesthetic agents, ensuring optimal depth of anesthesia and improving patient outcomes.
  6. Innovations in diagnostic and treatment modalities: Highlighting recent advancements in diagnostic tools and treatment protocols that can significantly improve patient care in intensive care settings, such as ultrasound in emergency and critical care.

This Special Issue will synthesize recent findings and insights related to the above topics, providing a comprehensive overview of the current state and future directions in critical care medicine.

We believe that the insights presented will resonate well with the journal’s readership and underscore the journal's commitment to advancing critical care medicine. We look forward to the possibility of contributing to this important topic.

Dr. Francesca Rubulotta
Dr. Luigi La Via
Guest Editors

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. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly 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

  • critical care
  • gender medicine
  • artificial intelligence
  • mechanical ventilation
  • robotic anesthesia
  • Kepler Intubator
  • closed-loop anesthesia
  • diagnostic innovations
  • treatment advances
  • DEI

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 (3 papers)

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

Research

Jump to: Review

19 pages, 2585 KB  
Article
Interpretable Machine Learning Model Integrating Electrocardiographic and Acute Physiology Metrics for Mortality Prediction in Critical Ill Patients
by Qiuyu Wang, Bin Wang, Bo Chen, Qing Li, Yutong Zhao, Tianshan Dong, Yifei Wang and Ping Zhang
J. Clin. Med. 2025, 14(20), 7163; https://doi.org/10.3390/jcm14207163 (registering DOI) - 11 Oct 2025
Viewed by 57
Abstract
Background: Critically ill patients in the intensive care unit (ICU) are characterized by complex comorbidities and a high risk of short-term mortality. Traditional severity scoring systems rely on physiological and laboratory variables but lack direct integration of electrocardiogram (ECG) data. This study [...] Read more.
Background: Critically ill patients in the intensive care unit (ICU) are characterized by complex comorbidities and a high risk of short-term mortality. Traditional severity scoring systems rely on physiological and laboratory variables but lack direct integration of electrocardiogram (ECG) data. This study aimed to construct an interpretable machine learning (ML) model combining ECG-derived and clinical variables to predict 28-day mortality in ICU patients. Methods: A retrospective cohort analysis was performed with data from the MIMIC-IV v2.2 database. The primary outcome was 28-day mortality. An ECG-based risk score was generated from the first ECG after ICU admission using a deep residual convolutional neural network. Feature selection was guided by XGBoost importance ranking, SHapley Additive exPlanations, and clinical relevance. A three-variable model comprising ECG score, APS-III score, and age (termed the E3A score) was developed and evaluated across four ML algorithms. We evaluated model performance by calculating the AUC of ROC curves, examining calibration, and applying decision curve analysis. Results: A total of 18,256 ICU patients were included, with 2412 deaths within 28 days. The ECG score was significantly higher in non-survivors than in survivors (median [IQR]: 24.4 [15.6–33.4] vs. 13.5 [7.2–22.1], p < 0.001). Logistic regression demonstrated the best discrimination for the E3A score, achieving an AUC of 0.806 (95% CI: 0.784–0.826) for the test set and 0.804 (95% CI: 0.772–0.835) for the validation set. Conclusions: Integrating ECG-derived features with clinical variables improves prognostic accuracy for 28-day mortality prediction in ICU patients, supporting early risk stratification in critical care. Full article
(This article belongs to the Special Issue New Insights into Critical Care)
►▼ Show Figures

Figure 1

Review

Jump to: Research

19 pages, 800 KB  
Review
Artificial Intelligence in Anesthesia: Enhancing Precision, Safety, and Global Access Through Data-Driven Systems
by Rakshita Giri, Shaik Huma Firdhos and Thomas A. Vida
J. Clin. Med. 2025, 14(19), 6900; https://doi.org/10.3390/jcm14196900 - 29 Sep 2025
Viewed by 824
Abstract
Artificial intelligence (AI) enhances anesthesiology by introducing adaptive systems that improve clinical precision, safety, and responsiveness. This review examines the integration of AI in anesthetic practice, with a focus on closed-loop systems that exemplify autonomous control. These platforms integrate continuous physiologic inputs, such [...] Read more.
Artificial intelligence (AI) enhances anesthesiology by introducing adaptive systems that improve clinical precision, safety, and responsiveness. This review examines the integration of AI in anesthetic practice, with a focus on closed-loop systems that exemplify autonomous control. These platforms integrate continuous physiologic inputs, such as BIS, EEG, heart rate, and blood pressure, to titrate anesthetic agents in real time, providing more consistent and responsive management than manual methods. Predictive algorithms reduce intraoperative hypotension by up to 40%, and systems such as McSleepy demonstrate greater accuracy in maintaining anesthetic depth and shortening recovery times. In critical care, AI supports sedation management, reduces clinician cognitive load, and standardizes care delivery during high-acuity procedures. The review also addresses the ethical, legal, and logistical challenges to widespread adoption of AI. Key concerns include algorithmic bias, explainability, and accountability for machine-generated decisions and disparities in access due to infrastructure demands. Regulatory frameworks, such as HIPAA and GDPR, are discussed in the context of securing patient data and ensuring its ethical deployment. Additionally, AI may play a transformative role in global health through remote anesthesia delivery and telemonitoring, helping address anesthesiologist shortages in resource-limited settings. Ultimately, AI-guided closed-loop systems do not replace clinicians; instead, they extend their capacity to deliver safe, responsive, and personalized anesthesia. These technologies signal a shift toward robotic anesthesia, where machine autonomy complements human oversight. Continued interdisciplinary development and rigorous clinical validation will determine how AI integrates into both operating rooms and intensive care units. Full article
(This article belongs to the Special Issue New Insights into Critical Care)
►▼ Show Figures

Figure 1

19 pages, 1560 KB  
Review
The Burden of Sepsis and Septic Shock in the Intensive Care Unit
by Luigi La Via, Antonino Maniaci, Mario Lentini, Giuseppe Cuttone, Salvatore Ronsivalle, Simona Tutino, Francesca Maria Rubulotta, Giuseppe Nunnari and Andrea Marino
J. Clin. Med. 2025, 14(19), 6691; https://doi.org/10.3390/jcm14196691 - 23 Sep 2025
Viewed by 1454
Abstract
This narrative review synthesizes our current understanding of sepsis and septic shock burden in intensive care units (ICUs) worldwide. Based on a comprehensive but non-systematic literature search from 2000 to 2025, this review synthesizes our current understanding across eight key domains: epidemiology, pathophysiology, [...] Read more.
This narrative review synthesizes our current understanding of sepsis and septic shock burden in intensive care units (ICUs) worldwide. Based on a comprehensive but non-systematic literature search from 2000 to 2025, this review synthesizes our current understanding across eight key domains: epidemiology, pathophysiology, diagnostics, management strategies, long-term outcomes, disparities, and future directions. The global burden of sepsis, especially in the developed and developing world, is great: over 48 million cases per year, with mortality rates at the ICU level in the range of 30 to 50%, depending on geography and resources. The pathophysiological progression from an initial hyper-inflammatory state to immune paralysis underlies organ failure and complicates therapeutic targeting. Diagnostic approaches, including clinical scoring systems, biomarkers (e.g., procalcitonin, MR-proADM), and emerging AI tools, offer improved early detection but face challenges in reliability and accessibility. Management in the ICU remains anchored in timely antimicrobial administration, hemodynamic stabilization with balanced fluids and vasopressors, source control, and organ support, including lung-protective ventilation and kidney replacement therapy. Novel adjuncts, such as immunomodulators and extracorporeal therapies, show promise but demand further validation. Importantly, survivors face significant long-term sequelae—post-intensive care syndrome (PICS)—encompassing physical, cognitive, and psychological impairments, which require structured rehabilitation and follow-up. The future of sepsis care lies in integrating precision medicine—through molecular diagnostics, individualized immunotherapy, and AI-supported monitoring—with scalable, equitable implementation strategies that bridge the gap between high- and low-income settings. Addressing disparities and expanding rehabilitation services are essential to improving survival and long-term quality of life in sepsis survivors. Full article
(This article belongs to the Special Issue New Insights into Critical Care)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop