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Martone AM, Parrini I, Ciciarello F, Galluzzo V, Cacciatore S, Massaro C, Giordano R, Giani T, Landi G, Gulizia MM, Colivicchi F, Gabrielli D, Oliva F, Zuccalà G. Recent Advances and Future Directions in Syncope Management: A Comprehensive Narrative Review. J Clin Med 2024; 13:727. [PMID: 38337421 PMCID: PMC10856004 DOI: 10.3390/jcm13030727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 01/21/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
Syncope is a highly prevalent clinical condition characterized by a rapid, complete, and brief loss of consciousness, followed by full recovery caused by cerebral hypoperfusion. This symptom carries significance, as its potential underlying causes may involve the heart, blood pressure, or brain, leading to a spectrum of consequences, from sudden death to compromised quality of life. Various factors contribute to syncope, and adhering to a precise diagnostic pathway can enhance diagnostic accuracy and treatment effectiveness. A standardized initial assessment, risk stratification, and appropriate test identification facilitate determining the underlying cause in the majority of cases. New technologies, including artificial intelligence and smart devices, may have the potential to reshape syncope management into a proactive, personalized, and data-centric model, ultimately enhancing patient outcomes and quality of life. This review addresses key aspects of syncope management, including pathogenesis, current diagnostic testing options, treatments, and considerations in the geriatric population.
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Affiliation(s)
- Anna Maria Martone
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy; (A.M.M.); (F.C.); (V.G.); (G.L.); (G.Z.)
- Department of Geriatrics, Orthopedics, and Rheumatology, Università Cattolica del Sacro Cuore, L.go F. Vito 1, 00168 Rome, Italy; (C.M.); (R.G.); (T.G.)
| | - Iris Parrini
- Department of Cardiology, Mauriziano Hospital, Largo Filippo Turati, 62, 10128 Turin, Italy
| | - Francesca Ciciarello
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy; (A.M.M.); (F.C.); (V.G.); (G.L.); (G.Z.)
| | - Vincenzo Galluzzo
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy; (A.M.M.); (F.C.); (V.G.); (G.L.); (G.Z.)
| | - Stefano Cacciatore
- Department of Geriatrics, Orthopedics, and Rheumatology, Università Cattolica del Sacro Cuore, L.go F. Vito 1, 00168 Rome, Italy; (C.M.); (R.G.); (T.G.)
| | - Claudia Massaro
- Department of Geriatrics, Orthopedics, and Rheumatology, Università Cattolica del Sacro Cuore, L.go F. Vito 1, 00168 Rome, Italy; (C.M.); (R.G.); (T.G.)
| | - Rossella Giordano
- Department of Geriatrics, Orthopedics, and Rheumatology, Università Cattolica del Sacro Cuore, L.go F. Vito 1, 00168 Rome, Italy; (C.M.); (R.G.); (T.G.)
| | - Tommaso Giani
- Department of Geriatrics, Orthopedics, and Rheumatology, Università Cattolica del Sacro Cuore, L.go F. Vito 1, 00168 Rome, Italy; (C.M.); (R.G.); (T.G.)
| | - Giovanni Landi
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy; (A.M.M.); (F.C.); (V.G.); (G.L.); (G.Z.)
| | | | - Furio Colivicchi
- Division of Cardiology, San Filippo Neri Hospital-ASL Roma 1, Via Giovanni Martinotti, 20, 00135 Rome, Italy;
| | - Domenico Gabrielli
- Department of Cardio-Thoracic and Vascular Medicine and Surgery, Division of Cardiology, S. Camillo-Forlanini Hospital, Circonvallazione Gianicolense, 87, 00152 Rome, Italy;
| | - Fabrizio Oliva
- “A. De Gasperis” Cardiovascular Department, Division of Cardiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza dell’Ospedale Maggiore, 3, 20162 Milan, Italy;
| | - Giuseppe Zuccalà
- Fondazione Policlinico Universitario “A. Gemelli” IRCCS, L.go A. Gemelli 8, 00168 Rome, Italy; (A.M.M.); (F.C.); (V.G.); (G.L.); (G.Z.)
- Department of Geriatrics, Orthopedics, and Rheumatology, Università Cattolica del Sacro Cuore, L.go F. Vito 1, 00168 Rome, Italy; (C.M.); (R.G.); (T.G.)
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Aamir A, Jamil Y, Bilal M, Diwan M, Nashwan AJ, Ullah I. Artificial Intelligence in Enhancing Syncope Management - An Update. Curr Probl Cardiol 2024; 49:102079. [PMID: 37716544 DOI: 10.1016/j.cpcardiol.2023.102079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
This review looks into the use of Artificial Intelligence (AI) in the management of syncope, a condition characterized by a brief loss of consciousness caused by cerebral hypoperfusion. With rising prevalence, high costs, and difficulty in diagnosis and risk stratification, syncope poses significant healthcare challenges. AI has the potential to improve symptom differentiation, risk assessment, and patient management. Machine learning, specifically Artificial Neural Networks, has shown promise in accurate risk stratification. AI-powered clinical decision support tools can improve patient evaluation and resource utilization. While AI holds great promise for syncope management, challenges such as data quality, class imbalance, and defining risk categories remain. Ethical concerns about patient privacy, as well as the need for human empathy, complicate AI integration. Collaboration among data scientists, clinicians, and ethics experts is critical for the successful implementation of AI, which has the potential to improve patient outcomes and healthcare efficiency in syncope management.
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Affiliation(s)
- Alifiya Aamir
- Dow University of Health Sciences, Karachi, Pakistan
| | - Yumna Jamil
- Dow University of Health Sciences, Karachi, Pakistan
| | - Maham Bilal
- Dow University of Health Sciences, Karachi, Pakistan
| | | | | | - Irfan Ullah
- Kabir Medical College, Gandhara University, Peshawar, Pakistan; Department of Internal Medicine, Khyber Teaching Hospital, Peshawar, Pakistan
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Dipaola F, Gatti M, Menè R, Shiffer D, Giaj Levra A, Solbiati M, Villa P, Costantino G, Furlan R. A Hybrid Model for 30-Day Syncope Prognosis Prediction in the Emergency Department. J Pers Med 2023; 14:4. [PMID: 38276219 PMCID: PMC10817569 DOI: 10.3390/jpm14010004] [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: 10/25/2023] [Revised: 12/06/2023] [Accepted: 12/11/2023] [Indexed: 01/27/2024] Open
Abstract
Syncope is a challenging problem in the emergency department (ED) as the available risk prediction tools have suboptimal predictive performances. Predictive models based on machine learning (ML) are promising tools whose application in the context of syncope remains underexplored. The aim of the present study was to develop and compare the performance of ML-based models in predicting the risk of clinically significant outcomes in patients presenting to the ED for syncope. We enrolled 266 consecutive patients (age 73, IQR 58-83; 52% males) admitted for syncope at three tertiary centers. We collected demographic and clinical information as well as the occurrence of clinically significant outcomes at a 30-day telephone follow-up. We implemented an XGBoost model based on the best-performing candidate predictors. Subsequently, we integrated the XGboost predictors with knowledge-based rules. The obtained hybrid model outperformed the XGboost model (AUC = 0.81 vs. 0.73, p < 0.001) with acceptable calibration. In conclusion, we developed an ML-based model characterized by a commendable capability to predict adverse events within 30 days post-syncope evaluation in the ED. This model relies solely on clinical data routinely collected during a patient's initial syncope evaluation, thus obviating the need for laboratory tests or syncope experienced clinical judgment.
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Affiliation(s)
- Franca Dipaola
- Internal Medicine, Syncope Unit, IRCCS Humanitas Research Hospital, 20089 Milan, Italy;
| | | | - Roberto Menè
- Department of Medicine and Surgery, University of Milano-Bicocca, 20100 Milan, Italy;
| | - Dana Shiffer
- Emergency Department, IRCCS Humanitas Research Hospital, 20089 Milan, Italy;
- Department of Biomedical Sciences, Humanitas University, 20072 Milan, Italy;
| | | | - Monica Solbiati
- Emergency Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Università Degli Studi Di Milano, 20100 Milan, Italy; (M.S.); (G.C.)
| | - Paolo Villa
- Emergency Medicine Unit, Luigi Sacco Hospital, ASST Fatebenefratelli Sacco, 20100 Milan, Italy;
| | - Giorgio Costantino
- Emergency Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Università Degli Studi Di Milano, 20100 Milan, Italy; (M.S.); (G.C.)
| | - Raffaello Furlan
- Internal Medicine, Syncope Unit, IRCCS Humanitas Research Hospital, 20089 Milan, Italy;
- Department of Biomedical Sciences, Humanitas University, 20072 Milan, Italy;
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Wojcik GM, Shriki O, Kwasniewicz L, Kawiak A, Ben-Horin Y, Furman S, Wróbel K, Bartosik B, Panas E. Investigating brain cortical activity in patients with post-COVID-19 brain fog. Front Neurosci 2023; 17:1019778. [PMID: 36845422 PMCID: PMC9947499 DOI: 10.3389/fnins.2023.1019778] [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: 08/15/2022] [Accepted: 01/12/2023] [Indexed: 02/11/2023] Open
Abstract
Brain fog is a kind of mental problem, similar to chronic fatigue syndrome, and appears about 3 months after the infection with COVID-19 and lasts up to 9 months. The maximum magnitude of the third wave of COVID-19 in Poland was in April 2021. The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis of the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B), and the control group that had no COVID-19 and no symptoms (sub-cohort C). The aim of this article was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and, if possible differentiate and classify them using the machine-learning tools. he dense array electroencephalographic amplifier with 256 electrodes was used for recordings. The event-related potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: face recognition, digit span, and task switching. These potentials were plotted for all three patients' sub-cohorts and all three experiments. The cross-correlation method was used to find differences, and, in fact, such differences manifested themselves in the shape of event-related potentials on the cognitive electrodes. The discussion of such differences will be presented; however, an explanation of such differences would require the recruitment of a much larger cohort. In the classification problem, the avalanche analysis for feature extractions from the resting state signal and linear discriminant analysis for classification were used. The differences between sub-cohorts in such signals were expected to be found. Machine-learning tools were used, as finding the differences with eyes seemed impossible. Indeed, the A&B vs. C, B&C vs. A, A vs. B, A vs. C, and B vs. C classification tasks were performed, and the efficiency of around 60-70% was achieved. In future, probably there will be pandemics again due to the imbalance in the natural environment, resulting in the decreasing number of species, temperature increase, and climate change-generated migrations. The research can help to predict brain fog after the COVID-19 recovery and prepare the patients for better convalescence. Shortening the time of brain fog recovery will be beneficial not only for the patients but also for social conditions.
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Affiliation(s)
- Grzegorz M. Wojcik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland,*Correspondence: Grzegorz M. Wojcik ✉
| | - Oren Shriki
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel,Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Lukasz Kwasniewicz
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Andrzej Kawiak
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Yarden Ben-Horin
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Sagi Furman
- Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - Krzysztof Wróbel
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Bernadetta Bartosik
- Department of Neuroinformatics and Biomedical Engineering, Institute of Computer Science, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
| | - Ewelina Panas
- Department of International Relations, Faculty of Political Science and Journalism, Maria Curie-Sklodowska University in Lublin, Lublin, Poland
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Lee S, Reddy Mudireddy A, Kumar Pasupula D, Adhaduk M, Barsotti EJ, Sonka M, Statz GM, Bullis T, Johnston SL, Evans AZ, Olshansky B, Gebska MA. Novel Machine Learning Approach to Predict and Personalize Length of Stay for Patients Admitted with Syncope from the Emergency Department. J Pers Med 2022; 13:jpm13010007. [PMID: 36675668 PMCID: PMC9864075 DOI: 10.3390/jpm13010007] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/25/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Background: Syncope, a common problem encountered in the emergency department (ED), has a multitude of causes ranging from benign to life-threatening. Hospitalization may be required, but the management can vary substantially depending on specific clinical characteristics. Models predicting admission and hospitalization length of stay (LoS) are lacking. The purpose of this study was to design an effective, exploratory model using machine learning (ML) technology to predict LoS for patients presenting with syncope. Methods: This was a retrospective analysis using over 4 million patients from the National Emergency Department Sample (NEDS) database presenting to the ED with syncope between 2016−2019. A multilayer perceptron neural network with one hidden layer was trained and validated on this data set. Results: Receiver Operator Characteristics (ROC) were determined for each of the five ANN models with varying cutoffs for LoS. A fair area under the curve (AUC of 0.78) to good (AUC of 0.88) prediction performance was achieved based on sequential analysis at different cutoff points, starting from the same day discharge and ending at the longest analyzed cutoff LoS ≤7 days versus >7 days, accordingly. The ML algorithm showed significant sensitivity and specificity in predicting short (≤48 h) versus long (>48 h) LoS, with an AUC of 0.81. Conclusions: Using variables available to triaging ED clinicians, ML shows promise in predicting hospital LoS with fair to good performance for patients presenting with syncope.
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Affiliation(s)
- Sangil Lee
- Department of Emergency Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
| | - Avinash Reddy Mudireddy
- The Iowa Initiative of Artificial Intelligence, University of Iowa, 103 South Capitol Street, Iowa City, IA 52242, USA;
| | - Deepak Kumar Pasupula
- Division of Cardiology, Mercy One North Iowa Heart Center, 250 S Crescent Dr, Mason City, IA 50401, USA;
| | - Mehul Adhaduk
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (M.A.); (T.B.); (A.Z.E.)
| | - E. John Barsotti
- Department of Epidemiology, College of Public Health, University of Iowa, 145 N. Riverside Drive, Iowa City, IA 52242, USA;
| | - Milan Sonka
- The Iowa Initiative of Artificial Intelligence, University of Iowa, 103 South Capitol Street, Iowa City, IA 52242, USA;
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
| | - Giselle M. Statz
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
| | - Tyler Bullis
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (M.A.); (T.B.); (A.Z.E.)
| | - Samuel L. Johnston
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
| | - Aron Z. Evans
- Department of Internal Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (M.A.); (T.B.); (A.Z.E.)
| | - Brian Olshansky
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
| | - Milena A. Gebska
- Division of Cardiovascular Medicine, Carver College of Medicine, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, USA; (G.M.S.); (S.L.J.)
- Correspondence: (S.L.); (M.S.); (B.O.); (M.A.G.)
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