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Feng WS, Chen WC, Lin JY, Tseng HY, Chen CL, Chou CY, Cho DY, Lin YB. Design and Implementation of an Intensive Care Unit Command Center for Medical Data Fusion. SENSORS (BASEL, SWITZERLAND) 2024; 24:3929. [PMID: 38931713 PMCID: PMC11207609 DOI: 10.3390/s24123929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Revised: 06/08/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024]
Abstract
The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare sector, especially as the world approaches an aging society which will be reached by 2050. This paper presents an innovative AIoT-enabled data fusion system implemented at the CMUH Respiratory Intensive Care Unit (RICU) to address the high incidence of medical errors in ICUs, which are among the top three causes of mortality in healthcare facilities. ICU patients are particularly vulnerable to medical errors due to the complexity of their conditions and the critical nature of their care. We introduce a four-layer AIoT architecture designed to manage and deliver both real-time and non-real-time medical data within the CMUH-RICU. Our system demonstrates the capability to handle 22 TB of medical data annually with an average delay of 1.72 ms and a bandwidth of 65.66 Mbps. Additionally, we ensure the uninterrupted operation of the CMUH-RICU with a three-node streaming cluster (called Kafka), provided a failed node is repaired within 9 h, assuming a one-year node lifespan. A case study is presented where the AI application of acute respiratory distress syndrome (ARDS), leveraging our AIoT data fusion approach, significantly improved the medical diagnosis rate from 52.2% to 93.3% and reduced mortality from 56.5% to 39.5%. The results underscore the potential of AIoT in enhancing patient outcomes and operational efficiency in the ICU setting.
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Affiliation(s)
- Wen-Sheng Feng
- China Medical University Hospital (CMUH), Taichung 404327, Taiwan; (W.-S.F.); (W.-C.C.); (J.-Y.L.); (H.-Y.T.); (C.-L.C.); (C.-Y.C.); (D.-Y.C.)
| | - Wei-Cheng Chen
- China Medical University Hospital (CMUH), Taichung 404327, Taiwan; (W.-S.F.); (W.-C.C.); (J.-Y.L.); (H.-Y.T.); (C.-L.C.); (C.-Y.C.); (D.-Y.C.)
| | - Jiun-Yi Lin
- China Medical University Hospital (CMUH), Taichung 404327, Taiwan; (W.-S.F.); (W.-C.C.); (J.-Y.L.); (H.-Y.T.); (C.-L.C.); (C.-Y.C.); (D.-Y.C.)
| | - How-Yang Tseng
- China Medical University Hospital (CMUH), Taichung 404327, Taiwan; (W.-S.F.); (W.-C.C.); (J.-Y.L.); (H.-Y.T.); (C.-L.C.); (C.-Y.C.); (D.-Y.C.)
| | - Chieh-Lung Chen
- China Medical University Hospital (CMUH), Taichung 404327, Taiwan; (W.-S.F.); (W.-C.C.); (J.-Y.L.); (H.-Y.T.); (C.-L.C.); (C.-Y.C.); (D.-Y.C.)
| | - Ching-Yao Chou
- China Medical University Hospital (CMUH), Taichung 404327, Taiwan; (W.-S.F.); (W.-C.C.); (J.-Y.L.); (H.-Y.T.); (C.-L.C.); (C.-Y.C.); (D.-Y.C.)
| | - Der-Yang Cho
- China Medical University Hospital (CMUH), Taichung 404327, Taiwan; (W.-S.F.); (W.-C.C.); (J.-Y.L.); (H.-Y.T.); (C.-L.C.); (C.-Y.C.); (D.-Y.C.)
| | - Yi-Bing Lin
- China Medical University Hospital (CMUH), Taichung 404327, Taiwan; (W.-S.F.); (W.-C.C.); (J.-Y.L.); (H.-Y.T.); (C.-L.C.); (C.-Y.C.); (D.-Y.C.)
- Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
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Alrawashdeh A, Alqahtani S, Alkhatib ZI, Kheirallah K, Melhem NY, Alwidyan M, Al-Dekah AM, Alshammari T, Nehme Z. Applications and Performance of Machine Learning Algorithms in Emergency Medical Services: A Scoping Review. Prehosp Disaster Med 2024:1-11. [PMID: 38757150 DOI: 10.1017/s1049023x24000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
OBJECTIVE The aim of this study was to summarize the literature on the applications of machine learning (ML) and their performance in Emergency Medical Services (EMS). METHODS Four relevant electronic databases were searched (from inception through January 2024) for all original studies that employed EMS-guided ML algorithms to enhance the clinical and operational performance of EMS. Two reviewers screened the retrieved studies and extracted relevant data from the included studies. The characteristics of included studies, employed ML algorithms, and their performance were quantitively described across primary domains and subdomains. RESULTS This review included a total of 164 studies published from 2005 through 2024. Of those, 125 were clinical domain focused and 39 were operational. The characteristics of ML algorithms such as sample size, number and type of input features, and performance varied between and within domains and subdomains of applications. Clinical applications of ML algorithms involved triage or diagnosis classification (n = 62), treatment prediction (n = 12), or clinical outcome prediction (n = 50), mainly for out-of-hospital cardiac arrest/OHCA (n = 62), cardiovascular diseases/CVDs (n = 19), and trauma (n = 24). The performance of these ML algorithms varied, with a median area under the receiver operating characteristic curve (AUC) of 85.6%, accuracy of 88.1%, sensitivity of 86.05%, and specificity of 86.5%. Within the operational studies, the operational task of most ML algorithms was ambulance allocation (n = 21), followed by ambulance detection (n = 5), ambulance deployment (n = 5), route optimization (n = 5), and quality assurance (n = 3). The performance of all operational ML algorithms varied and had a median AUC of 96.1%, accuracy of 90.0%, sensitivity of 94.4%, and specificity of 87.7%. Generally, neural network and ensemble algorithms, to some degree, out-performed other ML algorithms. CONCLUSION Triaging and managing different prehospital medical conditions and augmenting ambulance performance can be improved by ML algorithms. Future reports should focus on a specific clinical condition or operational task to improve the precision of the performance metrics of ML models.
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Affiliation(s)
- Ahmad Alrawashdeh
- Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Saeed Alqahtani
- Department of Emergency Medical Services, Prince Sultan Military College for Health Sciences, Dhahran, Saudi Arabia
| | - Zaid I Alkhatib
- Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | - Khalid Kheirallah
- Department of Public Health and Family Medicine, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan
| | - Nebras Y Melhem
- Department of Anatomy, Physiology and Biochemistry, Faculty of Medicine, The Hashemite University, Zarqa, Jordan
| | - Mahmoud Alwidyan
- Department of Allied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan
| | | | - Talal Alshammari
- Department of Emergency Medical Care, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia
| | - Ziad Nehme
- Ambulance Victoria, Doncaster, Victoria, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
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Tan S, Mills G. Designing Chinese hospital emergency departments to leverage artificial intelligence-a systematic literature review on the challenges and opportunities. FRONTIERS IN MEDICAL TECHNOLOGY 2024; 6:1307625. [PMID: 38577009 PMCID: PMC10991761 DOI: 10.3389/fmedt.2024.1307625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 03/08/2024] [Indexed: 04/06/2024] Open
Abstract
Artificial intelligence (AI) has witnessed rapid advances in the healthcare domain in recent years, especially in the emergency field, where AI is likely to radically reshape medical service delivery. Although AI has substantial potential to enhance diagnostic accuracy and operational efficiency in hospitals, research on its applications in Emergency Department building design remains relatively scarce. Therefore, this study aims to investigate Emergency Department facility design by identifying the challenges and opportunities of using AI. Two systematic literature reviews are combined, one in AI and the other in sensors, to explore their potential application to support decision-making, resource optimisation and patient monitoring. These reviews have then informed a discussion on integrating AI sensors in contemporary Emergency Department designs for use in China to support the evidence base on resuscitation units, emergency operating rooms and Emergency Department Intensive Care Unit (ED-ICU) design. We hope to inform the strategic implementation of AI sensors and how they might transform Emergency Department design to support medical staff and enhance the patient experience.
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Affiliation(s)
- Sijie Tan
- Bartlett School of Sustainable Construction, Bartlett Faculty of the Built Environment, University College London, London, United Kingdom
| | - Grant Mills
- Bartlett School of Sustainable Construction, Bartlett Faculty of the Built Environment, University College London, London, United Kingdom
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Di Costanzo A, Spaccarotella CAM, Esposito G, Indolfi C. An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review. J Clin Med 2024; 13:1033. [PMID: 38398346 PMCID: PMC10889404 DOI: 10.3390/jcm13041033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 02/04/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.
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Affiliation(s)
- Assunta Di Costanzo
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy
| | - Carmen Anna Maria Spaccarotella
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80126 Naples, Italy; (C.A.M.S.)
| | - Giovanni Esposito
- Division of Cardiology, Department of Advanced Biomedical Sciences, University of Naples Federico II, 80126 Naples, Italy; (C.A.M.S.)
| | - Ciro Indolfi
- Division of Cardiology, Cardiovascular Research Center, University Magna Graecia Catanzaro, 88100 Catanzaro, Italy
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Goebel M, Westafer LM, Ayala SA, Ragone E, Chapman SJ, Mohammed MR, Cohen MR, Niemann JT, Eckstein M, Sanko S, Bosson N. A Novel Algorithm for Improving the Prehospital Diagnostic Accuracy of ST-Segment Elevation Myocardial Infarction. Prehosp Disaster Med 2024; 39:37-44. [PMID: 38047380 PMCID: PMC10922545 DOI: 10.1017/s1049023x23006635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
INTRODUCTION Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting. METHODS A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics. RESULTS There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r2 = 0.90) and specificity (r2 = 0.85) with increasing number of criteria. CONCLUSIONS Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.
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Affiliation(s)
- Mat Goebel
- University of Massachusetts Chan Medical School – Baystate, Department of Emergency Medicine, Springfield, Massachusetts USA
| | - Lauren M. Westafer
- University of Massachusetts Chan Medical School – Baystate, Department of Emergency Medicine, Springfield, Massachusetts USA
| | - Stephanie A. Ayala
- University of Massachusetts Chan Medical School – Baystate, Department of Emergency Medicine, Springfield, Massachusetts USA
| | - El Ragone
- Fairview Hospital, Emergency Department, Barrington, Massachusetts USA
| | - Scott J. Chapman
- Belchertown Fire Rescue, Belchertown, Massachusetts USA
- Greenfield Community College, Greenfield, Massachusetts USA
| | | | - Marc R. Cohen
- Los Angeles City Fire Department, Emergency Medical Services Bureau, Los Angeles, California USA
| | - James T. Niemann
- University of California Los Angeles, Los Angeles, California USA
- Harbor-UCLA Medical Center, Department of Emergency Medicine, Torrance, California USA
- The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California USA
| | - Marc Eckstein
- Los Angeles City Fire Department, Emergency Medical Services Bureau, Los Angeles, California USA
- Keck School of Medicine of the University of Southern California, Department of Emergency Medicine, Los Angeles, California USA
| | - Stephen Sanko
- Keck School of Medicine of the University of Southern California, Department of Emergency Medicine, Los Angeles, California USA
- Los Angeles County EMS Agency, Los Angeles, California USA
| | - Nichole Bosson
- University of California Los Angeles, Los Angeles, California USA
- Harbor-UCLA Medical Center, Department of Emergency Medicine, Torrance, California USA
- Los Angeles County EMS Agency, Los Angeles, California USA
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Aleksandra S, Robert K, Klaudia K, Dawid L, Mariusz S. Artificial Intelligence in Optimizing the Functioning of Emergency Departments; a Systematic Review of Current Solutions. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2024; 12:e22. [PMID: 38572221 PMCID: PMC10988184 DOI: 10.22037/aaem.v12i1.2110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Introduction The burgeoning burden on emergency departments is a global challenge that we have been confronting for many years. Emerging artificial intelligence (AI)-based solutions may constitute a critical component in the optimization of these units. This systematic review was conducted to thoroughly examine and summarize the currently available AI solutions, assess potential benefits from their implementation, and identify anticipated directions of further development in this fascinating and rapidly evolving field. Methods This systematic review utilized data compiled from three key scientific databases: PubMed (2045 publications), Scopus (877 publications), and Web of Science (2495 publications). After meticulous removal of duplicates, we conducted a detailed analysis of 2052 articles, including 147 full-text papers. From these, we selected 51 of the most pertinent and representative publications for the review. Results Overall the present research indicates that due to high accuracy and sensitivity of machine learning (ML) models it's reasonable to use AI in support of doctors as it can show them the potential diagnosis, which could save time and resources. However, AI-generated diagnoses should be verified by a doctor as AI is not infallible. Conclusions Currently available AI algorithms are capable of analysing complex medical data with unprecedented precision and speed. Despite AI's vast potential, it is still a nascent technology that is often perceived as complicated and challenging to implement. We propose that a pivotal point in effectively harnessing this technology is the close collaboration between medical professionals and AI experts. Future research should focus on further refining AI algorithms, performing comprehensive validation, and introducing suitable legal regulations and standard procedures, thereby fully leveraging the potential of AI to enhance the quality and efficiency of healthcare delivery.
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Affiliation(s)
- Szymczyk Aleksandra
- Department of Emergency Medicine, Medical University of Gdansk, Smoluchowskiego 17, 80-214 Gdansk, Poland
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van de Leur RR, van Sleuwen MTGM, Zwetsloot PPM, van der Harst P, Doevendans PA, Hassink RJ, van Es R. Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:89-96. [PMID: 38264701 PMCID: PMC10802816 DOI: 10.1093/ehjdh/ztad070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 10/10/2023] [Accepted: 11/07/2023] [Indexed: 01/25/2024]
Abstract
Aims Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated. Methods and results Adult patients at non-cardiology departments who underwent ECG testing as a part of routine clinical care were included in this prospective cohort study. DELTAnet was used to classify 12-lead ECGs into one of the following triage classes: normal, abnormal not acute, subacute, and acute. Performance was compared with triage classes based on the final clinical diagnosis. Moreover, the associations between predicted classes and clinical outcomes were investigated. A total of 1061 patients and ECGs were included. Performance was good with a mean concordance statistic of 0.96 (95% confidence interval 0.95-0.97) when comparing DELTAnet with the clinical triage classes. Moreover, zero ECGs that required a change in policy or referral to the cardiologist were missed and there was a limited number of cases predicted as acute that did not require follow-up (2.6%). Conclusion This study is the first to prospectively investigate the impact of clinical implementation of an ECG-based AI triage algorithm. It shows that DELTAnet is efficacious and safe to be used in clinical practice for triage of 12-lead ECGs in non-cardiology hospital departments.
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Affiliation(s)
- Rutger R van de Leur
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Meike T G M van Sleuwen
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Peter-Paul M Zwetsloot
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Pim van der Harst
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - Pieter A Doevendans
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
- Netherlands Heart Institute, Utrecht, The Netherlands
- Central Military Hospital, Utrecht, The Netherlands
| | - Rutger J Hassink
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
| | - René van Es
- Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands
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Cimino J, Braun C. Clinical Research in Prehospital Care: Current and Future Challenges. Clin Pract 2023; 13:1266-1285. [PMID: 37887090 PMCID: PMC10605888 DOI: 10.3390/clinpract13050114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/08/2023] [Accepted: 10/19/2023] [Indexed: 10/28/2023] Open
Abstract
Prehospital care plays a critical role in improving patient outcomes, particularly in cases of time-sensitive emergencies such as trauma, cardiac failure, stroke, bleeding, breathing difficulties, systemic infections, etc. In recent years, there has been a growing interest in clinical research in prehospital care, and several challenges and opportunities have emerged. There is an urgent need to adapt clinical research methodology to a context of prehospital care. At the same time, there are many barriers in prehospital research due to the complex context, posing unique challenges for research, development, and evaluation. Among these, this review allows the highlighting of limited resources and infrastructure, ethical and regulatory considerations, time constraints, privacy, safety concerns, data collection and analysis, selection of a homogeneous study group, etc. The analysis of the literature also highlights solutions such as strong collaboration between emergency medical services (EMS) and hospital care, use of (mobile) health technologies and artificial intelligence, use of standardized protocols and guidelines, etc. Overall, the purpose of this narrative review is to examine the current state of clinical research in prehospital care and identify gaps in knowledge, including the challenges and opportunities for future research.
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Affiliation(s)
- Jonathan Cimino
- Clinical Research Unit, Fondation Hôpitaux Robert Schuman, 44 Rue d’Anvers, 1130 Luxembourg, Luxembourg
- Hôpitaux Robert Schuman, 9 Rue Edward Steichen, 2540 Luxembourg, Luxembourg
| | - Claude Braun
- Clinical Research Unit, Fondation Hôpitaux Robert Schuman, 44 Rue d’Anvers, 1130 Luxembourg, Luxembourg
- Hôpitaux Robert Schuman, 9 Rue Edward Steichen, 2540 Luxembourg, Luxembourg
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Martín Domínguez C, Aboal Viñas J, Loma-Osorio Rincón P, Herrera Martínez B, Agudelo Montañez V, Brugada Terradellas R. STEMI code cancelation after telematic assessment: patient characteristics and prognosis. REVISTA ESPANOLA DE CARDIOLOGIA (ENGLISH ED.) 2023; 76:828-831. [PMID: 37506971 DOI: 10.1016/j.rec.2023.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 04/21/2023] [Indexed: 07/30/2023]
Affiliation(s)
| | - Jaime Aboal Viñas
- Servicio de Cardiología, Hospital Universitario Josep Trueta, Girona, Spain; Instituto de Investigación Biomédica de Girona (idIBGi), Girona, Spain
| | | | | | | | - Ramón Brugada Terradellas
- Servicio de Cardiología, Hospital Universitario Josep Trueta, Girona, Spain; Instituto de Investigación Biomédica de Girona (idIBGi), Girona, Spain
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Tern PJW, Vaswani A, Yeo KK. Identifying and Solving Gaps in Pre- and In-Hospital Acute Myocardial Infarction Care in Asia-Pacific Countries. Korean Circ J 2023; 53:594-605. [PMID: 37653695 PMCID: PMC10475691 DOI: 10.4070/kcj.2023.0169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 06/25/2023] [Indexed: 09/02/2023] Open
Abstract
Acute myocardial infarction (AMI) is a major cause of morbidity and mortality in the Asia-Pacific region, and mortality rates differ between countries in the region. Systems of care have been shown to play a major role in determining AMI outcomes, and this review aims to highlight pre-hospital and in-hospital system deficiencies and suggest possible improvements to enhance quality of care, focusing on Korea, Japan, Singapore and Malaysia as representative countries. Time to first medical contact can be shortened by improving patient awareness of AMI symptoms and the need to activate emergency medical services (EMS), as well as by developing robust, well-coordinated and centralized EMS systems. Additionally, performing and transmitting pre-hospital electrocardiograms, algorithmically identifying patients with high risk AMI and developing hospital networks that appropriately divert such patients to percutaneous coronary intervention-capable hospitals have been shown to be beneficial. Within the hospital environment, developing and following clinical practice guidelines ensures that treatment plans can be standardised, whilst integrated care pathways can aid in coordinating care within the healthcare institution and can guide care even after discharge. Prescription of guideline directed medical therapy for secondary prevention and patient compliance to medications can be further optimised. Finally, the authors advocate for the establishment of more regional, national and international AMI registries for the formal collection of data to facilitate audit and clinical improvement.
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Affiliation(s)
- Paul Jie Wen Tern
- Department of Cardiology, National Heart Centre Singapore, Singapore
| | - Amar Vaswani
- Department of Cardiology, National Heart Centre Singapore, Singapore
| | - Khung Keong Yeo
- Department of Cardiology, National Heart Centre Singapore, Singapore.
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Martínez-Sellés M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis 2023; 10:jcdd10040175. [PMID: 37103054 PMCID: PMC10145690 DOI: 10.3390/jcdd10040175] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 04/28/2023] Open
Abstract
Artificial intelligence (AI) is increasingly used in electrocardiography (ECG) to assist in diagnosis, stratification, and management. AI algorithms can help clinicians in the following areas: (1) interpretation and detection of arrhythmias, ST-segment changes, QT prolongation, and other ECG abnormalities; (2) risk prediction integrated with or without clinical variables (to predict arrhythmias, sudden cardiac death, stroke, and other cardiovascular events); (3) monitoring ECG signals from cardiac implantable electronic devices and wearable devices in real time and alerting clinicians or patients when significant changes occur according to timing, duration, and situation; (4) signal processing, improving ECG quality and accuracy by removing noise/artifacts/interference, and extracting features not visible to the human eye (heart rate variability, beat-to-beat intervals, wavelet transforms, sample-level resolution, etc.); (5) therapy guidance, assisting in patient selection, optimizing treatments, improving symptom-to-treatment times, and cost effectiveness (earlier activation of code infarction in patients with ST-segment elevation, predicting the response to antiarrhythmic drugs or cardiac implantable devices therapies, reducing the risk of cardiac toxicity, etc.); (6) facilitating the integration of ECG data with other modalities (imaging, genomics, proteomics, biomarkers, etc.). In the future, AI is expected to play an increasingly important role in ECG diagnosis and management, as more data become available and more sophisticated algorithms are developed.
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Affiliation(s)
- Manuel Martínez-Sellés
- Cardiology Department, Hospital General Universitario Gregorio Marañón, Calle Doctor Esquerdo, 46, 28007 Madrid, Spain
- Centro de Investigación Biomédica en Red-Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Facultad de Ciencias de la Salud, Universidad Europea, Villaviciosa de Odón, 28670 Madrid, Spain
- Facultad de Medicina, Universidad Complutense, 28040 Madrid, Spain
| | - Manuel Marina-Breysse
- Centro de Investigación Biomédica en Red-Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, 28029 Madrid, Spain
- IDOVEN Research, 28013 Madrid, Spain
- Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC), Myocardial Pathophysiology Area, 28029 Madrid, Spain
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Saqib M, Iftikhar M, Neha F, Karishma F, Mumtaz H. Artificial intelligence in critical illness and its impact on patient care: a comprehensive review. Front Med (Lausanne) 2023; 10:1176192. [PMID: 37153088 PMCID: PMC10158493 DOI: 10.3389/fmed.2023.1176192] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 04/04/2023] [Indexed: 05/09/2023] Open
Abstract
Artificial intelligence (AI) has great potential to improve the field of critical care and enhance patient outcomes. This paper provides an overview of current and future applications of AI in critical illness and its impact on patient care, including its use in perceiving disease, predicting changes in pathological processes, and assisting in clinical decision-making. To achieve this, it is important to ensure that the reasoning behind AI-generated recommendations is comprehensible and transparent and that AI systems are designed to be reliable and robust in the care of critically ill patients. These challenges must be addressed through research and the development of quality control measures to ensure that AI is used in a safe and effective manner. In conclusion, this paper highlights the numerous opportunities and potential applications of AI in critical care and provides guidance for future research and development in this field. By enabling the perception of disease, predicting changes in pathological processes, and assisting in the resolution of clinical decisions, AI has the potential to revolutionize patient care for critically ill patients and improve the efficiency of health systems.
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Affiliation(s)
- Muhammad Saqib
- Khyber Medical College, Peshawar, Khyber Pakhtunkhwa, Pakistan
| | | | - Fnu Neha
- Ghulam Muhammad Mahar Medical College, Sukkur, Sindh, Pakistan
| | - Fnu Karishma
- Jinnah Sindh Medical University, Karachi, Sindh, Pakistan
| | - Hassan Mumtaz
- Health Services Academy, Islamabad, Pakistan
- *Correspondence: Hassan Mumtaz,
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