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JCM
Special Issues
New Trends and Challenges in Critical Care Management
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New Trends and Challenges in Critical Care Management

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 939

Special Issue Editor

Dr. Anna Wrzosek

E-Mail Website
Guest Editor
Department of Interdisciplinary Intensive Care, Medical College, Jagiellonian University, 31-501 Krakow, Poland
Interests: critical care; blood purification; hemoadsorption; fluid therapy; sepsis; endothelial disfunction

Special Issue Information

Dear Colleagues,

Over the past decade, significant advancements have been made in the field of critical care, particularly through the integration of innovative technologies that offer new opportunities for patient treatment. AI-driven tools have opened up new possibilities for optimizing treatment strategies and personalizing care for critically ill patients. Notably, there has been substantial progress in the technology and availability of Extracorporeal Organ Support Systems (ECOSs), including ECMO, blood purification, liver dialysis, and the combined use of various techniques. Technological progress has also driven advancements in ventilation modalities, leading to improved lung-protective strategies. Alongside these technological advancements, new pharmacological therapies and diagnostic methods have emerged. Furthermore, increasing attention is being paid to enhancing patient well-being and comfort during critical care, as well as post-ICU recovery and functioning. However, these advancements have also been accompanied by new challenges, such as rising antimicrobial resistance, an increase in thrombotic and cardiovascular events in the global population following the COVID-19 pandemic, the emergence of new viruses, and the need for strategic resource allocation in preparation for potential future pandemics.

In this Special Issue, we focus on exploring the latest opportunities and innovations in the treatment of critically ill patients, along with the emerging challenges that have arisen in recent years. We will also discuss future perspectives and the potential impact of these advancements on critical care practices.

Dr. Anna Wrzosek
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. 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
  • intensive care
  • extracorporeal organ support
  • mechanical ventilation
  • personalized medicine
  • artificial intelligence
  • infections
  • antimicrobial resistance
  • COVID-19

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Published Papers (1 paper)

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Research

15 pages, 1305 KiB  
Article
Combining Predictive Models of Mortality and Time-to-Discharge for Improved Outcome Assessment in Intensive Care Units
by Àlex Pardo, Josep Gómez, Julen Berrueta, Alejandro García, Sara Manrique, Alejandro Rodríguez and María Bodí
J. Clin. Med. 2025, 14(13), 4515; https://doi.org/10.3390/jcm14134515 - 25 Jun 2025
Viewed by 477
Abstract
Background: The Patient Outcome Assessment and Decision Support (PADS) model is a real-time framework designed to predict both mortality and the likelihood of discharge within 48 h in critically ill patients. By combining these predictions, PADS enables clinically meaningful stratification of patient trajectories, [...] Read more.
Background: The Patient Outcome Assessment and Decision Support (PADS) model is a real-time framework designed to predict both mortality and the likelihood of discharge within 48 h in critically ill patients. By combining these predictions, PADS enables clinically meaningful stratification of patient trajectories, supporting bedside decision-making and the planning of critical care resources such as nursing allocation and surgical scheduling. Methods: PADS integrates routinely collected clinical data: SOFA variables, age, gender, admission type, and comorbidities. It consists of two Long Short-Term Memory (LSTM) neural networks—one predicting the probability of death and the other the probability of discharge within 48 h. The combination places each patient into one of four states: alive/discharged within 48 h, alive/not discharged, dead within 48 h, or dead later. The model was trained using MIMIC-IV data, emphasizing ease of implementation in units with electronic health records. Out of the 76,540 stays present in MIMIC-IV (53,150 patients), 32,875 (25,555 patients) were used after excluding those with short stays (<48 h) or life support treatment limitations. The code is open, well-documented, and designed for reproducibility and external validation. Results: The model achieved strong performance: AUCROC of 0.94 (±0.03) for mortality and 0.89 (±0.07) for discharge on training data, and 0.87 (±0.02) and 0.88 (±0.03), respectively, on the test set. As a comparison, benchmark models obtain worse accuracy (−13.4% for APS III, −19% for OASIS, and −7.4% for SAPS II). Predictions are visualized in an intuitive format to support clinical interpretation. Conclusions: PADS offers a transparent, reproducible, and practical tool that supports both individual patient care and the strategic organization of intensive care resources by anticipating short-term outcomes. Full article
(This article belongs to the Special Issue New Trends and Challenges in Critical Care Management)
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