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Innovations in Perioperative Anesthesia and Intensive Care

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

Deadline for manuscript submissions: closed (25 April 2026) | Viewed by 13358

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Editors

Dr. Ivan Gornik

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Guest Editor

Emergency Department, University Hospital Centre, Zagreb, Croatia
Interests: intensive care medicine; resuscitation; critical care medicine; airway management; artificial intelligence in critical care medicine
Special Issues, Collections and Topics in MDPI journals

Dr. Ileana Lulic

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Guest Editor

Department of Anesthesiology, Intensive Care and Pain Medicine, Clinical Hospital Merkur, Zajceva 19, 10 000, Zagreb, Croatia
Interests: perioperative anesthesia; resuscitation; critical care medicine; airway management; pain management; artificial intelligence in anesthesiology

Special Issue Information

Dear Colleagues,

Advancements in perioperative anesthesia and intensive care are transforming clinical practice, driven by artificial intelligence, precision medicine, and novel therapeutic strategies. However, challenges remain in optimizing patient safety, hemodynamic stability, and postoperative recovery. Traditional risk assessment models lack precision, and perioperative complications remain a leading cause of morbidity and mortality. This Special Issue aims to address these gaps by exploring innovations that refine perioperative management.

Key areas of focus include AI-driven predictive analytics, personalized anesthesia protocols, hemodynamic optimization, opioid-sparing analgesia, ventilation strategies, neuromonitoring, and advanced resuscitation techniques. Research is needed to integrate these advancements into daily practice, assess their clinical impact, and determine their limitations. Both successful and negative findings are crucial to refining best practices.

This Special Issue invites original research, systematic reviews, and clinical studies that provide evidence-based insights into perioperative care. We encourage submissions from anesthesiologists, intensivists, and perioperative medicine specialists to drive scientific collaboration and innovation. Through this initiative, we aim to mobilize the global research community to advance perioperative anesthesia and intensive care, ensuring safer and more effective patient management.

Dr. Ivan Gornik
Dr. Ileana Lulic
Guest Editors

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Keywords

perioperative anesthesia
intensive care
artificial intelligence in anesthesia
precision medicine in perioperative care
hemodynamic monitoring
multimodal analgesia
opioid-free anesthesia
ventilation strategies in ICU and surgery
neuromonitoring and sedation
perioperative resuscitation and ECMO

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Published Papers (6 papers)

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Research

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16 pages, 1371 KB

Open AccessArticle

Large Language Model-Assisted Point-in-Time Interpretation of Advanced Hemodynamics in Liver Transplant Recipients: A Pilot Evaluation of Content Quality and Safety

by Selma Kahyaoglu, Abdullah Kaygisiz, Izzet Alatli, Ayse Isik Boyaci, Emre Aray, Serkan Tulgar and Deniz Balci

J. Clin. Med. 2026, 15(2), 716; https://doi.org/10.3390/jcm15020716 - 15 Jan 2026

Cited by 2 | Viewed by 717

Abstract

Background: Large language models (LLMs) are increasingly used in clinical medicine, yet their ability to interpret advanced intraoperative hemodynamic monitoring—particularly in the context of liver transplantation—remains largely unexplored. In this proof-of-concept study, we evaluated ChatGPT’s capacity to interpret multimodal hemodynamic data derived from both standard anesthesia monitoring and the PiCCO system. The study also employed a structured assessment instrument (ARQuAT), adapted through a Delphi-based process to evaluate LLM-generated clinical interpretations. Methods: Ten key surgical–hemodynamic phases of liver transplantation were identified using a modified Delphi approach to capture the major physiological transitions of the procedure. Sequential screenshots representing these phases were obtained from five liver transplant recipients, yielding a total of 50 images. Each screenshot, along with standardized clinical background information, was submitted to ChatGPT. Five expert anesthesiologists independently assessed the model’s responses using the modified ARQuAT tool, which includes six content-quality domains (Accuracy, Up-to-dateness, Contextual Consistency, Clinical Usability, Trustworthiness, Clarity) and a separate catastrophic Risk item. Descriptive statistics were calculated for domain-level performance. Inter-rater reliability (Kendall’s W) and internal consistency (Cronbach’s alpha, McDonald’s omega) were also analyzed. All statistical analyses and visualizations were performed using NumIQO. Results: ChatGPT demonstrated consistently high performance across all content-quality domains, with median scores ranging from 4.6 to 4.8 and more than 90% of all ratings classified as satisfactory. Lower scores appeared only in a small subset of frames associated with abrupt hemodynamic changes and did not indicate a recurring weakness in any specific domain. Catastrophic Risk exhibited a pronounced floor effect, with 86% of ratings scored as 0 and only three isolated high-risk assessments across the dataset. Internal consistency of the six ARQuAT content domains was excellent, while inter-rater agreement was modest, reflecting ceiling effects and tied ratings among evaluators. Conclusions: ChatGPT generated clinically acceptable, contextually aligned interpretations of complex intraoperative hemodynamic data in liver transplant recipients, with minimal evidence of unsafe recommendations. These findings suggest preliminary promise for LLM-assisted interpretation of advanced monitoring, while underscoring the need for future studies involving larger datasets, dynamic physiological inputs, and expanded evaluator groups. The reliability characteristics observed also provide initial support for further refinement and broader validation of the Delphi-derived ARQuAT framework. Full article

(This article belongs to the Special Issue Innovations in Perioperative Anesthesia and Intensive Care)

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18 pages, 1862 KB

Open AccessArticle

Impact of Ventilation Discontinuation During Cardiopulmonary Bypass: A Prospective Observational Study

by Tatyana Li, Azhar Zhailauova, Iwan Wachruschew, Aidyn Kuanyshbek, Shaimurat Tulegenov, Perizat Bukirova, Bekaidar Zhakupbekov, Ilya Nikitin, Dauren Ayaganov, Timur Kapyshev, Robertas Samalavicius, Andrey L. Melnikov and Theodoros Aslanidis

J. Clin. Med. 2025, 14(22), 8215; https://doi.org/10.3390/jcm14228215 - 19 Nov 2025

Viewed by 1266

Abstract

Background: Discontinuing mechanical ventilation during cardiopulmonary bypass (CPB) is common but may adversely affect postoperative pulmonary function. This study aimed to evaluate the impact of stopping ventilation during CPB on postoperative gas exchange, radiographic findings, intensive care unit (ICU) length of stay (LOS), mortality, reintubation, re-exploration, and bleeding. Methods: A prospective observational study was performed involving adult patients scheduled for elective cardiac surgery requiring CPB. Participants were divided into ventilated and non-ventilated groups according to intraoperative strategy. Postoperative arterial carbon dioxide levels (PaCO₂), arterial partial pressure of oxygen (PaO₂), the PaO₂/FiO₂ ratio (P/F ratio), arterial oxygen saturation (SaO₂), and the ratio of PaCO₂ to minute ventilation (PaCO₂/MV) were measured before the induction of anesthesia (within 5 min after transportation into the operating room), postoperatively within 5–10 min after transportation to the ICU, and in a 24 h postoperative period. Chest X-ray data, mechanical ventilation time, LOS in ICU, re-exploration, reintubation, and bleeding parameters were documented. Analyses were also conducted with the estimation of the age effect and BMI. Results: Individuals in the non-ventilated group exhibited lower postoperative P/F ratios and elevated postoperative PaCO₂ and PaCO₂/MV ratios. The difference in gas exchange leveled off within 24 h. There was no difference in the incidence of atelectasis (postoperatively in a 24 h period), mechanical ventilation time, LOS in ICU, or mortality. However, the incidence of bleeding was higher in the non-ventilated group (χ² = 5.78, p = 0.016). Interestingly, postoperative PaCO₂ and PaCO₂/MV peaked in the 50-year age group. Conclusions: Continued mechanical ventilation during CPB correlates with better postoperative gas exchange, better CO₂ clearance, and fewer bleeding events. The results suggest that maintaining low tidal volume ventilation during CPB may provide benefits, especially for patients aged 50 years. Full article

(This article belongs to the Special Issue Innovations in Perioperative Anesthesia and Intensive Care)

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10 pages, 187 KB

Open AccessArticle

Correlation of Airway POCUS Measures with Screening and Severity Evaluation Tools in Obstructive Sleep Apnea: An Exploratory Study

by Sapna Ravindranath, Yatish S. Ranganath, Ethan Lemke, Matthew B Behrens, Anil A. Marian, Hari Kalagara, Nada Sadek, Melinda S. Seering, Linder Wendt, Patrick Ten Eyck and Rakesh V. Sondekoppam

J. Clin. Med. 2025, 14(14), 4858; https://doi.org/10.3390/jcm14144858 - 9 Jul 2025

Cited by 1 | Viewed by 1349

Abstract

Background: Obstructive Sleep Apnea (OSA) is a common occurrence in the perioperative patient population but is often undiagnosed. Point-of-Care Ultrasound (POCUS) has emerged as a promising tool for perioperative assessment; however, its effectiveness in detecting the presence or severity of OSA needs to be evaluated. Objective: We assessed the ability of airway POCUS as a screening and severity evaluation tool for OSA by examining its correlation with STOP-BANG scores and the Apnea–Hypopnea Index (AHI). Design: Cross-sectional observational study. Setting: A single-center study in a tertiary care hospital between June 2020 to May 2021. Patients: Adult patients aged 18–65 with prior Polysomnography (PSG) for OSA workup were screened. Interventions: The participants completed the STOP-BANG questionnaire and subsequently underwent POCUS examinations, either pre- or post-surgery. Ten different POCUS views previously used for evaluating OSA were acquired in a predefined sequence, with subsequent measurements of airway parameters. Outcome measures: Generalized linear modeling was used to explore and assess the relationships between the measured parameters, STOP-BANG, and AHI scores (modeled continuously and categorized into risk levels of STOP-BANG and AHI). Results: A total of 260 patients were screened, of which 142 were enrolled and 127 completed the scanning studies. The median AHI was 16.71, while the STOP-BANG scores were mostly between 5 and 6, indicating a moderate-to-high OSA risk in the study population. Notably, only neck circumference was significantly associated with AHI severity (p = 0.012), whereas none of the other POCUS measures were. Among the POCUS measures, significant associations with STOP-BANG scores were observed for the Tongue Cross-Sectional Area (T-CSA) (p = 0.002), Retro-Palatal Diameter (RPD) (p = 0.034), Distance Between Lingual Arteries (DLA) (p = 0.034), and Geniohyoid Muscle Thickness (GMT) (p = 0.040). Conclusions: Neck circumference is a more reliable predictor of OSA severity (AHI) compared to other POCUS measurements. Many of the POCUS measures had a good correlation with the STOP-BANG scores, highlighting the utility of POCUS as a screening tool for OSA rather than as a severity evaluation tool. Full article

(This article belongs to the Special Issue Innovations in Perioperative Anesthesia and Intensive Care)

18 pages, 2605 KB

Open AccessArticle

An Innovative Deep Learning Approach for Ventilator-Associated Pneumonia (VAP) Prediction in Intensive Care Units—Pneumonia Risk Evaluation and Diagnostic Intelligence via Computational Technology (PREDICT)

by Geoffray Agard, Christophe Roman, Christophe Guervilly, Jean-Marie Forel, Véronica Orléans, Damien Barrau, Pascal Auquier, Mustapha Ouladsine, Laurent Boyer and Sami Hraiech

J. Clin. Med. 2025, 14(10), 3380; https://doi.org/10.3390/jcm14103380 - 13 May 2025

Cited by 3 | Viewed by 3952

Abstract

Background: Ventilator-associated pneumonia (VAP) is a common and serious ICU complication, affecting up to 40% of mechanically ventilated patients. The diagnosis of VAP currently relies on retrospective clinical, radiological, and microbiological criteria, which often delays targeted treatment and promotes the overuse of broad-spectrum antibiotics. The early prediction of VAP is crucial to improve outcomes and guide antimicrobial use related to this disease. This study aimed to develop and validate PREDICT (Pneumonia Risk Evaluation and Diagnostic Intelligence via Computational Technology), a deep learning algorithm for early VAP prediction that is based solely on vital signs. Methods: We conducted a retrospective cohort study using the MIMIC-IV database, which includes ICU patients who were ventilated for at least 48 h. Five vital signs (respiratory rate, SpO₂, heart rate, temperature, and mean arterial pressure) were structured into 24 h temporal windows. The PREDICT model, based on a long short-term memory neural network, was trained to predict the onset of VAP 6, 12, and 24 h in the future. Its performance was compared to that of conventional machine learning models (random forest, XGBoost, logistic regression) using their AUPRC, sensitivity, specificity, and predictive values. Results: PREDICT achieved high predictive accuracy with AUPRC values of 96.0%, 94.1%, and 94.7% at 6, 12, and 24 h before the onset of VAP, respectively. Its sensitivity and positive predictive values exceeded 85% across all horizons. Traditional ML models showed a drop in performance over longer timeframes. Analysis of the model’s explainability highlighted the respiratory rate, SpO₂, and temperature as key predictive features. Conclusions: PREDICT is the first deep learning model specifically designed for early VAP prediction in ICUs. It represents a promising tool for timely clinical decision-making and improved antibiotic stewardship. Full article

(This article belongs to the Special Issue Innovations in Perioperative Anesthesia and Intensive Care)

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Review

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32 pages, 1547 KB

Open AccessReview

Artificial Intelligence in Post-Liver Transplantation: A Scoping Review of Comparative Model Performance

by Ileana Lulic, Ivan Gornik, Jadranka Pavicic Saric, Dunja Rogic, Alberto Gallego, Laura Karla Bozic, Nikola Prpic, Iva Bacak Kocman, Gorjana Erceg, Jelena Pegan, Iva Majurec, Damira Vukicevic Stironja, Lucija Ermacora, Lorka Tarnovski, Stipislav Jadrijevic, Danko Mikulic, Filip Jadrijevic, Lana Mihanovic and Dinka Lulic

J. Clin. Med. 2026, 15(4), 1491; https://doi.org/10.3390/jcm15041491 - 13 Feb 2026

Viewed by 877

Abstract

Objective: To map and characterize artificial intelligence (AI) applications in post-liver transplantation (LT) care, summarize comparative performance where available, and identify methodological and translational gaps. Methods: We conducted a scoping review in accordance with PRISMA-ScR. A comprehensive search of electronic databases was performed from inception through 1 April 2025. We included primary studies evaluating AI applications in the post-LT period (model development, validation, or implementation). Comparative studies were defined as those reporting head-to-head evaluation of at least two algorithmic models for the same task with quantitative performance metrics. Single-model studies were retained for evidence mapping but analyzed separately. Reviews and the other non-primary literature were included for contextual mapping. Results: The search yielded 3088 records. After deduplication, 2408 were screened, 191 full texts were assessed, and 65 studies were included. Of these, 52 reported primary outcome data. Clinical prediction studies (n = 43) focused on graft survival, rejection, fibrosis, oncologic recurrence, mortality, and composite outcomes. Operational studies (n = 3) evaluated early warning or bedside decision-support systems, and system-level studies (n = 6) examined benchmarking, donor–recipient matching, explainability, fairness, and cross-domain modeling. Most studies were retrospective and single-center, with internal validation commonly reported and external validation uncommon. Conclusions: AI research in post-LT care is expanding, with a predominant focus on clinical prediction. However, limited external validation, heterogeneous methods, and scarce real-world implementation constrain clinical readiness. Standardized evaluation and prospective integration are needed to determine whether AI tools can support decision-making and improve post-transplant outcomes. Full article

(This article belongs to the Special Issue Innovations in Perioperative Anesthesia and Intensive Care)

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Other

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22 pages, 1447 KB

Open AccessPerspective

Improving Sepsis Prediction in the ICU with Explainable Artificial Intelligence: The Promise of Bayesian Networks

by Geoffray Agard, Christophe Roman, Christophe Guervilly, Mustapha Ouladsine, Laurent Boyer and Sami Hraiech

J. Clin. Med. 2025, 14(18), 6463; https://doi.org/10.3390/jcm14186463 - 13 Sep 2025

Cited by 7 | Viewed by 4267

Abstract

Background/Objectives: Sepsis remains one of the leading causes of mortality worldwide, characterized by a complex and heterogeneous clinical presentation. Despite advances in patient monitoring and biomarkers, early detection of sepsis in the intensive care unit (ICU) is often hampered by incomplete data and diagnostic uncertainty. In recent years, machine learning models have been proposed as predictive tools, but many function as opaque “black boxes”, meaning that humans are unable to understand algorithmic reasoning, poorly suited to the uncertainty-laden clinical environment of critical care. Even when post-hoc interpretability methods are available for these algorithms, their explanations often remain difficult for non-expert clinicians to understand. Methods: In this clinical perspective, we explore the specific advantages of probabilistic graphical models, particularly Bayesian Networks (BNs) and their dynamic counterparts (DBNs), for sepsis prediction. Results: Recent applications of AI models in sepsis prediction have demonstrated encouraging results, such as DBNs achieving an AUROC of 0.94 in early detection, or causal probabilistic models in hospital admissions (AUROC 0.95). These models explicitly represent clinical reasoning under uncertainty, handle missing data natively, and offer interpretable, transparent decision paths. Drawing on recent studies, including real-time sepsis alert systems and treatment-effect modeling, we highlight concrete clinical applications and their current limitations. Conclusions: We argue that BNs present a great opportunity to bridge the gap between artificial intelligence and bedside care through human-in-the-loop collaboration, transparent inference, and integration into clinical information systems. As critical care continues to move toward data-driven decision-making, Bayesian models may offer not only technical performance but also the epistemic humility needed to support clinicians facing uncertain, high-stakes decisions. Full article

(This article belongs to the Special Issue Innovations in Perioperative Anesthesia and Intensive Care)

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