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Personalized Medicine in Anesthesia and Anesthetics
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Personalized Medicine in Anesthesia and Anesthetics

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Clinical Medicine, Cell, and Organism Physiology".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 4159

Special Issue Editor

Dr. Jowita Biernawska

E-Mail Website
Guest Editor
Department of Anaesthesiology and Intensive Therapy, Pomeranian Medical University in Szczecin, Szczecin, Poland
Interests: anaesthesiology; intensive care; artificial intelligence

Special Issue Information

Dear Colleagues,

As many of you already know, artificial intelligence (AI) is becoming an increasingly important part of medicine and perioperative care, with its use expanding every year.

AI stands to improve outcomes when focused on identifying at-risk patients, early detection of complications and implementation of proper personalized treatment.

With this Special Issue, we hope to discuss how can anesthesiologists use AI to improve perioperative outcomes.

Broad topics include general perioperative considerations: intraoperative hypotension prediction, acute kidney injury prediction based on intraabdominal pressure monitoring, analysis of facial features to determine the degree of airway difficulty, algorithms based on perioperative data which can detect early stage of heart failure, closed loop systems which enable precise titration of anaesthesia and analgesia, machine learning models which can predict events like mortality. All of these informations based on AI can improve patient safety by point-of-care guidance creation.

With this Special Issue, we hope to encourage submissions that discuss the current state-of-the-art, address ongoing knowledge gaps, and focus on pitfalls and advantages related to artificial intelligence. Original research and reviews are also welcome. 

I look forward to and welcome your participation in this Special Issue.

Dr. Jowita Biernawska
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 Personalized Medicine 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

  • artificial intelligence
  • machine learning models
  • outcomes
  • personalized treatment
  • perioperative care

Published Papers (4 papers)

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Research

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18 pages, 1912 KiB  
Article
Individualized Perioperative Hemodynamic Management Using Hypotension Prediction Index Software and the Dynamics of Troponin and NTproBNP Concentration Changes in Patients Undergoing Oncological Abdominal Surgery
by Jolanta Cylwik, Małgorzata Celińska-Spodar and Mariusz Dudzic
J. Pers. Med. 2024, 14(2), 211; https://doi.org/10.3390/jpm14020211 - 16 Feb 2024
Viewed by 1189
Abstract
Introduction: Abdominal oncologic surgeries pose significant risks due to the complexity of the surgery and patients’ often weakened health, multiple comorbidities, and increased perioperative hazards. Hypotension is a major risk factor for perioperative cardiovascular complications, necessitating individualized management in modern anesthesiology. Aim: This [...] Read more.
Introduction: Abdominal oncologic surgeries pose significant risks due to the complexity of the surgery and patients’ often weakened health, multiple comorbidities, and increased perioperative hazards. Hypotension is a major risk factor for perioperative cardiovascular complications, necessitating individualized management in modern anesthesiology. Aim: This study aimed to determine the dynamics of changes in troponin and NTproBNP levels during the first two postoperative days in patients undergoing major cancer abdominal surgery with advanced hemodynamic monitoring including The AcumenTM Hypotension Prediction Index software (HPI) (Edwards Lifesciences, Irvine, CA, USA) and their association with the occurrence of postoperative cardiovascular complications. Methods: A prospective study was conducted, including 50 patients scheduled for abdominal cancer surgery who, due to the overall risk of perioperative complications (ASA class 3 or 4), were monitored using the HPI software. Hypotension was qualified as at least one ≥ 1 min episode of a MAP < 65 mm Hg. Preoperatively and 24 and 48 h after the procedure, the levels of NTproBNP and troponin were measured, and an ECG was performed. Results: We analyzed data from 46 patients and found that 82% experienced at least one episode of low blood pressure (MAP < 65 mmHg). However, the quality indices of hypotension were low, with a median time-weighted average MAP < 65 mmHg of 0.085 (0.03–0.19) mmHg and a median of 2 (2–1.17) minutes spent below MAP < 65 mmHg. Although the incidence of perioperative myocardial injury was 10%, there was no evidence to suggest a relationship with hypotension. Acute kidney injury was seen in 23.9% of patients, and it was significantly associated with a number of episodes of MAP < 50 mmHg. Levels of NTproBNP were significantly higher on the first postoperative day compared to preoperative values (285.8 [IQR: 679.8] vs. 183.9 [IQR: 428.1] pg/mL, p < 0.001). However, they decreased on the second day (276.65 [IQR: 609.4] pg/mL, p = 0.154). The dynamics of NTproBNP were similar for patients with and without heart failure, although those with heart failure had significantly higher preoperative concentrations (435.9 [IQR: 711.15] vs. 87 [IQR: 232.2] pg/mL, p < 0.001). Patients undergoing laparoscopic surgery showed a statistically significant increase in NTproBNP. Conclusions: This study suggests that advanced HPI monitoring in abdominal cancer surgery effectively minimizes intraoperative hypotension with no significant NTproBNP or troponin perioperative dynamics, irrespective of preoperative heart failure. Full article
(This article belongs to the Special Issue Personalized Medicine in Anesthesia and Anesthetics)
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12 pages, 551 KiB  
Article
The Incidence of Perioperative Hypotension in Patients Undergoing Major Abdominal Surgery with the Use of Arterial Waveform Analysis and the Hypotension Prediction Index Hemodynamic Monitoring—A Retrospective Analysis
by Jakub Szrama, Agata Gradys, Tomasz Bartkowiak, Amadeusz Woźniak, Zuzanna Nowak, Krzysztof Zwoliński, Ashish Lohani, Natalia Jawień, Piotr Smuszkiewicz and Krzysztof Kusza
J. Pers. Med. 2024, 14(2), 174; https://doi.org/10.3390/jpm14020174 - 02 Feb 2024
Viewed by 827
Abstract
Intraoperative hypotension (IH) is common in patients receiving general anesthesia and can lead to serious complications such as kidney failure, myocardial injury and increased mortality. The Hypotension Prediction Index (HPI) algorithm is a machine learning system that analyzes the arterial pressure waveform and [...] Read more.
Intraoperative hypotension (IH) is common in patients receiving general anesthesia and can lead to serious complications such as kidney failure, myocardial injury and increased mortality. The Hypotension Prediction Index (HPI) algorithm is a machine learning system that analyzes the arterial pressure waveform and alerts the clinician of an impending hypotension event. The purpose of the study was to compare the frequency of perioperative hypotension in patients undergoing major abdominal surgery with different types of hemodynamic monitoring. The study included 61 patients who were monitored with the arterial pressure-based cardiac output (APCO) technology (FloTrac group) and 62 patients with the Hypotension Prediction Index algorithm (HPI group). Our primary outcome was the time-weighted average (TWA) of hypotension below < 65 mmHg. The median TWA of hypotension in the FloTrac group was 0.31 mmHg versus 0.09 mmHg in the HPI group (p = 0.000009). In the FloTrac group, the average time of hypotension was 27.9 min vs. 8.1 min in the HPI group (p = 0.000023). By applying the HPI algorithm in addition to an arterial waveform analysis alone, we were able to significantly decrease the frequency and duration of perioperative hypotension events in patients who underwent major abdominal surgery. Full article
(This article belongs to the Special Issue Personalized Medicine in Anesthesia and Anesthetics)
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Review

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19 pages, 10031 KiB  
Review
Artificial Intelligence-Supported Ultrasonography in Anesthesiology: Evaluation of a Patient in the Operating Theatre
by Sławomir Mika, Wojciech Gola, Monika Gil-Mika, Mateusz Wilk and Hanna Misiołek
J. Pers. Med. 2024, 14(3), 310; https://doi.org/10.3390/jpm14030310 - 15 Mar 2024
Viewed by 878
Abstract
Artificial intelligence has now changed regional anesthesia, facilitating, therefore, the application of the regional block under the USG guidance. Innovative technological solutions make it possible to highlight specific anatomical structures in the USG image in real time, as needed for regional block. This [...] Read more.
Artificial intelligence has now changed regional anesthesia, facilitating, therefore, the application of the regional block under the USG guidance. Innovative technological solutions make it possible to highlight specific anatomical structures in the USG image in real time, as needed for regional block. This contribution presents such technological solutions as U-Net architecture, BPSegData and Nerveblox and the basis for independent assisting systems in the use of regional blocks, e.g., ScanNav Anatomy PNB or the training system NeedleTrainer. The article describes also the systems integrated with the USG devices, such as Mindray SmartNerve or GE cNerve as well as the robotic system Magellan which substantially increases the patient’s safety, time needed for the regional block and quality of the procedure. All the solutions presented in this article facilitate the performance of regional blocks by less experienced physicians and appear as an excellent educational tool which, at the same time, improves the availability of the more and more popular regional anesthesia. Will, therefore, artificial intelligence replace physicians in regional block procedures? This seems unlikely. It will, however, assist them in a significant manner, contributing to better effectiveness and improved safety of the patient. Full article
(This article belongs to the Special Issue Personalized Medicine in Anesthesia and Anesthetics)
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15 pages, 14078 KiB  
Review
Ultrasonographic Applications of Novel Technologies and Artificial Intelligence in Critically Ill Patients
by Sławomir Mika, Wojciech Gola, Monika Gil-Mika, Mateusz Wilk and Hanna Misiolłek
J. Pers. Med. 2024, 14(3), 286; https://doi.org/10.3390/jpm14030286 - 07 Mar 2024
Viewed by 922
Abstract
The diagnostic process in Intensive Care Units has been revolutionized by ultrasonography and accelerated by artificial intelligence. Patients in critical condition are often sonoanatomically challenging, with time constraints being an additional stress factor. In this paper, we describe the technology behind the development [...] Read more.
The diagnostic process in Intensive Care Units has been revolutionized by ultrasonography and accelerated by artificial intelligence. Patients in critical condition are often sonoanatomically challenging, with time constraints being an additional stress factor. In this paper, we describe the technology behind the development of AI systems to support diagnostic ultrasound in intensive care units. Among the AI-based solutions, the focus was placed on systems supporting cardiac ultrasound, such as Smart-VTI, Auto-VTI, SmartEcho Vue, AutoEF, Us2.ai, and Real Time EF. Solutions to assist hemodynamic assessment based on the evaluation of the inferior vena cava, such as Smart-IVC or Auto-IVC, as well as to facilitate ultrasound assessment of the lungs, such as Smart B-line or Auto B-line, and to help in the estimation of gastric contents, such as Auto Gastric Antrum, were also discussed. All these solutions provide doctors with support by making it easier to obtain appropriate diagnostically correct ultrasound images by automatically performing time-consuming measurements and enabling real-time analysis of the obtained data. Artificial intelligence will most likely be used in the future to create advanced systems facilitating the diagnostic and therapeutic process in intensive care units. Full article
(This article belongs to the Special Issue Personalized Medicine in Anesthesia and Anesthetics)
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