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Wang Y, Zhu M, Chen X, Liu R, Ge J, Song Y, Yu G. The application of metaverse in healthcare. Front Public Health 2024; 12:1420367. [PMID: 39135928 PMCID: PMC11317258 DOI: 10.3389/fpubh.2024.1420367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Accepted: 07/10/2024] [Indexed: 08/15/2024] Open
Abstract
While metaverse is widely discussed, comprehension of its intricacies remains limited to a select few. Conceptually akin to a three-dimensional embodiment of the Internet, the metaverse facilitates simultaneous existence in both physical and virtual domains. Fundamentally, it embodies a visually immersive virtual environment, striving for authenticity, where individuals engage in real-world activities such as commerce, gaming, social interaction, and leisure pursuits. The global pandemic has accelerated digital innovations across diverse sectors. Beyond strides in telehealth, payment systems, remote monitoring, and secure data exchange, substantial advancements have been achieved in artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and blockchain technologies. Nevertheless, the metaverse, in its nascent stage, continues to evolve, harboring significant potential for revolutionizing healthcare. Through integration with the Internet of Medical Devices, quantum computing, and robotics, the metaverse stands poised to redefine healthcare systems, offering enhancements in surgical precision and therapeutic modalities, thus promising profound transformations within the industry.
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Affiliation(s)
- Yue Wang
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
| | - Mengying Zhu
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
- Department of Clinical Integration of Traditional Chinese and Western Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang, China
| | - Xi Chen
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
- Department of Clinical Integration of Traditional Chinese and Western Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang, China
| | - Ruibin Liu
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
- Department of Clinical Integration of Traditional Chinese and Western Medicine, Liaoning University of Traditional Chinese Medicine, Shenyang, China
| | - Jinnian Ge
- Department of General Surgery, First Hospital of China Medical University, Shenyang, China
| | - Yuxuan Song
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
- Pharmaceutical Science, China Medical University-The Queen’s University Belfast Joint College, Shenyang, China
| | - Guilin Yu
- Department of General Surgery, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang, China
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Obeagu EI, Obeagu GU. Management of diabetes mellitus patients with sickle cell anemia: Challenges and therapeutic approaches. Medicine (Baltimore) 2024; 103:e37941. [PMID: 38669382 PMCID: PMC11049766 DOI: 10.1097/md.0000000000037941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
The coexistence of diabetes mellitus (DM) and sickle cell anemia (SCA) poses significant challenges in clinical management due to the complex interactions and overlapping complications associated with both conditions. Managing diabetes in individuals with SCA requires a comprehensive approach that addresses the unique physiological and pathological aspects of both diseases. This paper reviews the challenges encountered in the management of DM in patients with SCA and explores therapeutic strategies and approaches to optimize patient care. Challenges in the management of DM in individuals with SCA stem from several factors, including the impact of hemoglobin variants on glycemic control assessment, increased susceptibility to infections, altered immune response, and complications associated with both diseases. Moreover, the coexistence of SCA and DM heightens the susceptibility to infections due to compromised immune function, emphasizing the need for vigilant preventive measures, including vaccinations and close monitoring for infectious complications. Close collaboration among healthcare providers specializing in diabetes, hematology, and other relevant fields is crucial for developing comprehensive care plans. Individualized treatment strategies that balance glycemic control, pain management, and preventive care are essential to mitigate complications and optimize the overall health outcomes of patients with both DM and SCA. In conclusion, managing diabetes in the context of SCA necessitates a nuanced and patient-centered approach. By addressing the challenges and employing tailored therapeutic strategies, healthcare providers can improve the quality of life and health outcomes for individuals affected by both conditions.
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3
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Pandit SV, Lampe JW, Silver AE. Recurrence of ventricular fibrillation in out-of-hospital cardiac arrest: Clinical evidence and underlying ionic mechanisms. J Physiol 2024. [PMID: 38661672 DOI: 10.1113/jp284621] [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: 11/21/2023] [Accepted: 03/08/2024] [Indexed: 04/26/2024] Open
Abstract
Defibrillation remains the optimal therapy for terminating ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OHCA) patients, with reported shock success rates of ∼90%. A key persistent challenge, however, is the high rate of VF recurrence (∼50-80%) seen during post-shock cardiopulmonary resuscitation (CPR). Studies have shown that the incidence and time spent in recurrent VF are negatively associated with neurologically-intact survival. Recurrent VF also results in the administration of extra shocks at escalating energy levels, which can cause cardiac dysfunction. Unfortunately, the mechanisms underlying recurrent VF remain poorly understood. In particular, the role of chest-compressions (CC) administered during CPR in mediating recurrent VF remains controversial. In this review, we first summarize the available clinical evidence for refibrillation occurring during CPR in OHCA patients, including the postulated contribution of CC and non-CC related pathways. Next, we examine experimental studies highlighting how CC can re-induce VF via direct mechano-electric feedback. We postulate the ionic mechanisms involved by comparison with similar phenomena seen in commotio cordis. Subsequently, the hypothesized contribution of partial cardiac reperfusion (either as a result of CC or CC independent organized rhythm) in re-initiating VF in a globally ischaemic heart is examined. An overview of the proposed ionic mechanisms contributing to VF recurrence in OHCA during CPR from a cellular level to the whole heart is outlined. Possible therapeutic implications of the proposed mechanistic theories for VF recurrence in OHCA are briefly discussed.
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Affiliation(s)
- Sandeep V Pandit
- University of Memphis, ZOLL Medical, Chelmsford, Massachusetts, USA
| | - Joshua W Lampe
- University of Pennsylvania, ZOLL Medical, Chelmsford, Massachusetts, USA
| | - Annemarie E Silver
- University of Colorado Boulder, ZOLL Medical, Chelmsford, Massachusetts, USA
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4
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Armoundas AA, Narayan SM, Arnett DK, Spector-Bagdady K, Bennett DA, Celi LA, Friedman PA, Gollob MH, Hall JL, Kwitek AE, Lett E, Menon BK, Sheehan KA, Al-Zaiti SS. Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association. Circulation 2024; 149:e1028-e1050. [PMID: 38415358 PMCID: PMC11042786 DOI: 10.1161/cir.0000000000001201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Abstract
A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.
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Golubnitschaja O, Polivka J, Potuznik P, Pesta M, Stetkarova I, Mazurakova A, Lackova L, Kubatka P, Kropp M, Thumann G, Erb C, Fröhlich H, Wang W, Baban B, Kapalla M, Shapira N, Richter K, Karabatsiakis A, Smokovski I, Schmeel LC, Gkika E, Paul F, Parini P, Polivka J. The paradigm change from reactive medical services to 3PM in ischemic stroke: a holistic approach utilising tear fluid multi-omics, mitochondria as a vital biosensor and AI-based multi-professional data interpretation. EPMA J 2024; 15:1-23. [PMID: 38463624 PMCID: PMC10923756 DOI: 10.1007/s13167-024-00356-6] [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: 02/01/2024] [Accepted: 02/08/2024] [Indexed: 03/12/2024]
Abstract
Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990-2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep-wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population-all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA.
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Affiliation(s)
- Olga Golubnitschaja
- Predictive, Preventive and Personalised (3P) Medicine, Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
| | - Jiri Polivka
- Department of Histology and Embryology, Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
- Biomedical Centre, Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
| | - Pavel Potuznik
- Department of Neurology, University Hospital Plzen and Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
| | - Martin Pesta
- Department of Biology, Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
| | - Ivana Stetkarova
- Department of Neurology, University Hospital Kralovske Vinohrady, Third Faculty of Medicine, Charles University, Prague, Czech Republic
| | - Alena Mazurakova
- Department of Anatomy, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia
| | - Lenka Lackova
- Department of Histology and Embryology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia
| | - Peter Kubatka
- Department of Histology and Embryology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia
| | - Martina Kropp
- Experimental Ophthalmology, University of Geneva, 1205 Geneva, Switzerland
- Ophthalmology Department, University Hospitals of Geneva, 1205 Geneva, Switzerland
| | - Gabriele Thumann
- Experimental Ophthalmology, University of Geneva, 1205 Geneva, Switzerland
- Ophthalmology Department, University Hospitals of Geneva, 1205 Geneva, Switzerland
| | - Carl Erb
- Private Institute of Applied Ophthalmology, Berlin, Germany
| | - Holger Fröhlich
- Artificial Intelligence & Data Science Group, Fraunhofer SCAI, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT (B-It), University of Bonn, 53115 Bonn, Germany
| | - Wei Wang
- Edith Cowan University, Perth, Australia
- Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
| | - Babak Baban
- The Dental College of Georgia, Departments of Neurology and Surgery, The Medical College of Georgia, Augusta University, Augusta, USA
| | - Marko Kapalla
- Negentropic Systems, Ružomberok, Slovakia
- PPPM Centre, s.r.o., Ruzomberok, Slovakia
| | - Niva Shapira
- Department of Nutrition, School of Health Sciences, Ashkelon Academic College, Ashkelon, Israel
| | - Kneginja Richter
- CuraMed Tagesklinik Nürnberg GmbH, Nuremberg, Germany
- Technische Hochschule Nürnberg GSO, Nuremberg, Germany
- University Clinic for Psychiatry and Psychotherapy, Paracelsus Medical University, Nuremberg, Germany
| | - Alexander Karabatsiakis
- Department of Psychology, Clinical Psychology II, University of Innsbruck, Innsbruck, Austria
| | - Ivica Smokovski
- University Clinic of Endocrinology, Diabetes and Metabolic Disorders Skopje, University Goce Delcev, Faculty of Medical Sciences, Stip, North Macedonia
| | - Leonard Christopher Schmeel
- Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
| | - Eleni Gkika
- Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
| | | | - Paolo Parini
- Cardio Metabolic Unit, Department of Medicine Huddinge, and Department of Laboratory Medicine, Karolinska Institutet, and Medicine Unit of Endocrinology, Theme Inflammation and Ageing, Karolinska University Hospital, Stockholm, Sweden
| | - Jiri Polivka
- Department of Neurology, University Hospital Plzen and Faculty of Medicine in Plzen, Charles University, Prague, Czech Republic
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Garg M, Gupta M, Patel NN, Bansal K, Lam PH, Sheikh FH. Predictors and Outcomes of Sudden Cardiac Arrest in Heart Failure With Preserved Ejection Fraction: A Nationwide Inpatient Sample Analysis. Am J Cardiol 2023; 206:277-284. [PMID: 37725853 DOI: 10.1016/j.amjcard.2023.08.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/21/2023] [Accepted: 08/21/2023] [Indexed: 09/21/2023]
Abstract
Sudden cardiac arrest (SCA) is the leading cause of cardiovascular mortality in heart failure with preserved ejection fraction (HFpEF), contributing to around 25% of deaths observed in pivotal HFpEF trials. However, predictors and outcomes of in-hospital SCA in HFpEF have not been well characterized. We queried the Nationwide Inpatient Sample (2016 to 2017) to identify adult hospitalizations with a diagnosis of HFpEF. Patients with acute or chronic conditions associated with SCA (e.g., acute myocardial infarction, acute pulmonary embolism, sarcoidosis) were excluded. We ascertained whether SCA occurred during these hospitalizations, identified predictors of SCA using multivariate logistic regression, and determined outcomes of SCA in HFpEF. Of 2,909,134 hospitalizations, SCA occurred in 1.48% (43,105). The mean age of the SCA group was 72.3 ± 12.4 years, 55.8% were women, and 66.4% were White. Presence of third-degree atrioventricular block (odds ratio [OR] 5.95, 95% confidence interval [CI] 5.31 to 6.67), left bundle branch block (OR 1.96, 95% CI 1.72 to 2.25), and liver disease (OR 1.87, 95% CI 1.73 to 2.02) were the leading predictors of SCA in HFpEF. After excluding patients with do-not-resuscitate status, the SCA group versus those without SCA had higher mortality (25.9% vs 1.6%), major bleeding complications (4.1% vs 1.7%), increased use of percutaneous coronary intervention (2.5% vs 0.7%), and mechanical circulatory assist device (1.2% vs 0.1%). These observational inpatient data suggest identifiable risk factors for SCA in HFpEF including cardiac arrhythmias. Further research is warranted to identify the best tools to risk-stratify patients with HFpEF to implement targeted SCA prevention strategies.
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Affiliation(s)
- Mohil Garg
- Department of Internal Medicine, MedStar Washington Hospital Center, Washington, District of Columbia
| | - Mohak Gupta
- Department of Internal Medicine, Cleveland Clinic, Cleveland, Ohio
| | - Neel N Patel
- Department of Internal Medicine, New York Medical College, Landmark Medical Center, Woonsocket, Rhode Island
| | - Kannu Bansal
- Department of Internal Medicine, Saint Vincent Hospital, Worcester, Massachusetts
| | - Phillip H Lam
- Advanced Heart Failure Program, MedStar Heart and Vascular Institute, MedStar Washington Hospital Center, MedStar Georgetown University Hospital, Washington, District of Columbia
| | - Farooq H Sheikh
- Advanced Heart Failure Program, MedStar Heart and Vascular Institute, MedStar Washington Hospital Center, MedStar Georgetown University Hospital, Washington, District of Columbia.
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Li K, Zhu Z, Sun X, Zhao L, Liu Z, Xing J. Harnessing the therapeutic potential of mesenchymal stem cell-derived exosomes in cardiac arrest: Current advances and future perspectives. Biomed Pharmacother 2023; 165:115201. [PMID: 37480828 DOI: 10.1016/j.biopha.2023.115201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/16/2023] [Accepted: 07/18/2023] [Indexed: 07/24/2023] Open
Abstract
BACKGROUND Cardiac arrest (CA), characterized by sudden onset and high mortality rates, is one of the leading causes of death globally, with a survival rate of approximately 6-24%. Studies suggest that the restoration of spontaneous circulation (ROSC) hardly improved the mortality rate and prognosis of patients diagnosed with CA, largely due to ischemia-reperfusion injury. MAIN BODY Mesenchymal stem cells (MSCs) exhibit self-renewal and strong potential for multilineage differentiation. Their effects are largely mediated by extracellular vesicles (EVs). Exosomes are the most extensively studied subgroup of EVs. EVs mainly mediate intercellular communication by transferring vesicular proteins, lipids, nucleic acids, and other substances to regulate multiple processes, such as cytokine production, cell proliferation, apoptosis, and metabolism. Thus, exosomes exhibit significant potential for therapeutic application in wound repair, tissue reconstruction, inflammatory reaction, and ischemic diseases. CONCLUSION Based on similar pathological mechanisms underlying post-cardiac arrest syndrome involving various tissues and organs in many diseases, the review summarizes the therapeutic effects of MSC-derived exosomes and explores the prospects for their application in the treatment of CA.
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Affiliation(s)
- Ke Li
- Department of Emergency Medicine, The First Hospital of Jilin University, Changchun 130021, China.
| | - Zhu Zhu
- Department of Breast Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun 130021, China.
| | - Xiumei Sun
- Department of Emergency Medicine, The First Hospital of Jilin University, Changchun 130021, China.
| | - Linhong Zhao
- Northeast Normal University, Changchun 130022, China.
| | - Zuolong Liu
- Department of Emergency Medicine, The First Hospital of Jilin University, Changchun 130021, China.
| | - Jihong Xing
- Department of Emergency Medicine, The First Hospital of Jilin University, Changchun 130021, China.
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Garcia R, Warming PE, Narayanan K, Defaye P, Guedon-Moreau L, Blangy H, Piot O, Leclercq C, Marijon E. Dynamic changes in nocturnal heart rate predict short-term cardiovascular events in patients using the wearable cardioverter-defibrillator: from the WEARIT-France cohort study. Europace 2023; 25:euad062. [PMID: 37021342 PMCID: PMC10227653 DOI: 10.1093/europace/euad062] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/31/2023] [Indexed: 04/07/2023] Open
Abstract
AIMS While elevated resting heart rate measured at a single point of time has been associated with cardiovascular outcomes, utility of continuous monitoring of nocturnal heart rate (NHR) has never been evaluated. We hypothesized that dynamic NHR changes may predict, at short term, impending cardiovascular events in patients equipped with a wearable cardioverter-defibrillator (WCD). METHODS AND RESULTS The WEARIT-France prospective cohort study enrolled heart failure patients with WCD between 2014 and 2018. Night-time was defined as midnight to 7 a.m. NHR initial trajectories were classified into four categories based on mean NHR in the first week (High/Low) and NHR evolution over the second week (Up/Down) of WCD use. The primary endpoint was a composite of cardiovascular death and heart failure hospitalization. A total of 1013 [61 (interquartile range, IQR 53-68) years, 16% women, left ventricular ejection fraction 26% (IQR 22-30)] were included. During a median WCD wear duration of 68 (IQR 44-90) days, 58 patients (6%) experienced 69 events. After considering potential confounders, High-Up NHR trajectory was significantly associated with the primary endpoint compared to Low-Down [adjusted hazard ratio (HR) 6.08, 95% confidence interval (CI) 2.56-14.45, P < 0.001]. Additionally, a rise of >5 bpm in weekly average NHR from the preceding week was associated with 2.5 higher composite event risk (HR 2.51, 95% CI 1.22-5.18, P = 0.012) as well as total mortality (HR 11.21, 95% CI 3.55-35.37, P < 0.001) and cardiovascular hospitalization (HR 2.70, 95% CI 1.51-4.82, P < 0.001). CONCLUSION Dynamic monitoring of NHR may allow timely identification of impending cardiovascular events, with the potential for 'pre-emptive' action. REGISTRATION NUMBER Clinical Trials.gov Identifier: NCT03319160.
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Affiliation(s)
- Rodrigue Garcia
- Department of Cardiology, University Hospital of Poitiers, 86021 Poitiers, France
- Centre d'Investigation Clinique CIC1402, CHU Poitiers, 86000, Poitiers, France
| | - Peder Emil Warming
- Department of Cardiology, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Kumar Narayanan
- Department of Cardiology, Medicover Hospitals, Hyderabad, Telangana 500081, India
- Université Paris Cité, Inserm, PARCC, F-75015 Paris, France
| | - Pascal Defaye
- Department of Cardiology, University Hospital Grenoble Alpes, Grenoble 38043, France
| | | | - Hugues Blangy
- Department of Cardiology, University Hospital of Nancy, Vandoeuvre-Lès-Nancy 54500, France
| | - Olivier Piot
- Department of Cardiology, Cardiology Center of Nord, Saint Denis 93200, France
| | - Christophe Leclercq
- Department of Cardiology, University Hospital Pontchaillou, Rennes 35000, France
| | - Eloi Marijon
- Department of Cardiology, European Georges Pompidou Hospital, Paris Cedex 15, 75908, France
- Université Paris Cité, Inserm, PARCC, F-75015 Paris, France
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Javaid A, Zghyer F, Kim C, Spaulding EM, Isakadze N, Ding J, Kargillis D, Gao Y, Rahman F, Brown DE, Saria S, Martin SS, Kramer CM, Blumenthal RS, Marvel FA. Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology. Am J Prev Cardiol 2022; 12:100379. [PMID: 36090536 PMCID: PMC9460561 DOI: 10.1016/j.ajpc.2022.100379] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/21/2022] [Accepted: 08/28/2022] [Indexed: 11/30/2022] Open
Abstract
Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.
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Affiliation(s)
- Aamir Javaid
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Fawzi Zghyer
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Chang Kim
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Erin M. Spaulding
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Nino Isakadze
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Jie Ding
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Daniel Kargillis
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Yumin Gao
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Faisal Rahman
- Division of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Donald E. Brown
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Suchi Saria
- Machine Learning and Healthcare Laboratory, Departments of Computer Science, Statistics, and Health Policy, Malone Center for Engineering in Healthcare, and Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, MD, USA
| | - Seth S. Martin
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Christopher M. Kramer
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, VA, USA
| | - Roger S. Blumenthal
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Francoise A. Marvel
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
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10
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Ramakrishna S, Salazar JW, Olgin JE, Moffatt E, Tseng ZH. Heart Failure Burden by Autopsy, Guideline-Directed Medical Therapy, and ICD Utilization Among Sudden Deaths. JACC Clin Electrophysiol 2022; 9:403-413. [PMID: 36752450 DOI: 10.1016/j.jacep.2022.10.018] [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: 06/13/2022] [Revised: 09/14/2022] [Accepted: 10/09/2022] [Indexed: 12/03/2022]
Abstract
BACKGROUND Studies of heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF) report high sudden cardiac death (SCD) rates but presume cardiac cause. Underlying causes, guideline-directed medical therapy (GDMT), and implantable cardioverter-defibrillator (ICD) use in community sudden deaths with heart failure (HF) are unknown. OBJECTIVES This study aims to assess the burden of HF, GDMT, and ICD use among autopsied sudden deaths in the POST SCD (Postmortem Systematic Investigation of Sudden Cardiac Death) study, a countywide postmortem study of all presumed SCDs. METHODS Incident WHO-defined (presumed) SCDs for individuals of ages 18 to 90 years were autopsied via prospective surveillance of consecutive out-of-hospital deaths in San Francisco County from February 1, 2011, to March 1, 2014. Sudden arrhythmic deaths (SADs) had no identifiable nonarrhythmic cause (eg, pulmonary embolism), and are thus considered potentially rescuable with ICD. RESULTS Of 525 presumed SCDs, 100 (19%) had HF. There were 85 patients with known HF (31 HFpEF, 54 HFrEF) and 15 with subclinical HF (postmortem evidence of cardiomyopathy and pulmonary edema without HF diagnosis). SADs comprised 56% (293 of 525) of all presumed SCDs, and 69% (69 of 100) of HF SCDs. The rates were similar in HFrEF (40 of 54 [74%]) and HFpEF (19 of 31 [61%], P = 0.45). Four SAD patients (4%) had ICDs, 3 of which experienced device failure. Twenty-eight SCDs had ejection fraction ≤35%: 22 (79%) with arrhythmic and 6 (21%) with noncardiac causes. Of the 22 SAD patients, 8 (36%) had no identifiable barrier to ICD referral. Complete use of GDMT in HFrEF was 6%. CONCLUSIONS One in 5 community sudden deaths had HF; two-thirds had autopsy-confirmed arrhythmic causes. ICD prevention criteria captured only 8% (22 of 293) of all SAD cases countywide; GDMT and ICD use remain important targets for HF sudden death prevention.
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Affiliation(s)
- Satvik Ramakrishna
- Division of Cardiovascular Medicine, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - James W Salazar
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Internal Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Jeffrey E Olgin
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Internal Medicine, University of California-San Francisco, San Francisco, California, USA
| | - Ellen Moffatt
- Office of the Chief Medical Examiner, City and County of San Francisco, San Francisco, California, USA
| | - Zian H Tseng
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Internal Medicine, University of California-San Francisco, San Francisco, California, USA.
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11
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Paratz ED, van Heusden A, Smith K, Brennan A, Dinh D, Ball J, Lefkovits J, Kaye DM, Nicholls S, Pflaumer A, La Gerche A, Stub D. Factors predicting cardiac arrest in acute coronary syndrome patients under 50: a state-wide angiographic and forensic evaluation of outcomes. Resuscitation 2022; 179:124-130. [PMID: 36031075 DOI: 10.1016/j.resuscitation.2022.08.016] [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: 07/07/2022] [Revised: 08/16/2022] [Accepted: 08/21/2022] [Indexed: 10/15/2022]
Abstract
BACKGROUND An uncertain proportion of patients with acute coronary syndrome (ACS) also experience out-of-hospital cardiac arrest (OHCA). Predictors of OHCA in ACS remain unclear and vulnerable to selection bias as pre-hospital deceased patients are usually not included. METHODS Data on patients aged 18-50 years from a percutaneous coronary intervention (PCI) and OHCA registry were combined to identify all patients experiencing OHCA due to ACS (not including those managed medically or who proceeded to cardiac surgery). Clinical, angiographic and forensic details were collated. In-hospital and post-discharge outcomes were compared between OHCA survivors and non-OHCA ACS patients. RESULTS OHCA occurred in 6.0% of ACS patients transported to hospital and 10.0% of all ACS patients. Clinical predictors were non-diabetic status (p=0.015), non-obesity (p=0.004), ST-elevation myocardial infarction (p<0.0001) and left main (p<0.0002) or left anterior descending (LAD) coronary artery (p<0.0001) as culprit vessel. OHCA patients had poorer in-hospital clinical outcomes, including longer length of stay and higher pre-procedural intubation, cardiogenic shock, major adverse cardiovascular events, bleeding, and mortality (p<0.0001 for all). At 30 days, OHCA survivors had equivalent cardiac function and return to premorbid independence but higher rates of anxiety/depression (p=0.029). CONCLUSION OHCA complicates approximately 10% of ACS in the young. Predictors of OHCA are being non-diabetic, non-obese, having a STEMI presentation, and left main or LAD coronary culprit lesion. For OHCA patients surviving to PCI, higher rates of in-hospital complications are observed. Despite this, recovery of pre-morbid physical and cardiac function is equivalent to non-OHCA patients, apart from higher rates of anxiety/depression.
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Affiliation(s)
- Elizabeth D Paratz
- Baker Heart and Diabetes Institute, 75 Commercial Rd Prahran VIC 3181; Alfred Hospital, 55 Commercial Rd Prahran VIC 3181; St Vincent's Hospital Melbourne, 41 Victoria Pde Fitzroy VIC 3065.
| | | | - Karen Smith
- Ambulance Victoria, 375 Manningham Rd Doncaster VIC 3108; Department of Paramedicine, Monash University, Melbourne VIC; Department of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd Melbourne 3004
| | - Angela Brennan
- Department of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd Melbourne 3004
| | - Diem Dinh
- Department of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd Melbourne 3004
| | - Jocasta Ball
- Department of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd Melbourne 3004
| | - Jeff Lefkovits
- Department of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd Melbourne 3004
| | - David M Kaye
- Alfred Hospital, 55 Commercial Rd Prahran VIC 3181
| | - Steve Nicholls
- Department of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd Melbourne 3004
| | - Andreas Pflaumer
- Royal Children's Hospital, 50 Flemington Rd Parkville Melbourne VIC 3052; Department of Paediatrics, Melbourne University, Parkville VIC 3010; Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Rd Parkville VIC 3052
| | - Andre La Gerche
- Baker Heart and Diabetes Institute, 75 Commercial Rd Prahran VIC 3181; Alfred Hospital, 55 Commercial Rd Prahran VIC 3181; St Vincent's Hospital Melbourne, 41 Victoria Pde Fitzroy VIC 3065
| | - Dion Stub
- Baker Heart and Diabetes Institute, 75 Commercial Rd Prahran VIC 3181; Alfred Hospital, 55 Commercial Rd Prahran VIC 3181; Ambulance Victoria, 375 Manningham Rd Doncaster VIC 3108; Department of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd Melbourne 3004
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12
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Suri JS, Bhagawati M, Paul S, Protogerou AD, Sfikakis PP, Kitas GD, Khanna NN, Ruzsa Z, Sharma AM, Saxena S, Faa G, Laird JR, Johri AM, Kalra MK, Paraskevas KI, Saba L. A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review. Diagnostics (Basel) 2022; 12:722. [PMID: 35328275 PMCID: PMC8947682 DOI: 10.3390/diagnostics12030722] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/10/2022] [Accepted: 03/13/2022] [Indexed: 12/16/2022] Open
Abstract
Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong 793022, India; (M.B.); (S.P.)
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong 793022, India; (M.B.); (S.P.)
| | - Athanasios D. Protogerou
- Research Unit Clinic, Laboratory of Pathophysiology, Department of Cardiovascular Prevention, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 11527 Athens, Greece;
| | - George D. Kitas
- Arthritis Research UK Centre for Epidemiology, Manchester University, Manchester 46962, UK;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110020, India;
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary;
| | - Aditya M. Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22903, USA;
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India;
| | - Gavino Faa
- Department of Pathology, A.O.U., di Cagliari-Polo di Monserrato s.s., 09045 Cagliari, Italy;
| | - John R. Laird
- Cardiology Department, St. Helena Hospital, St. Helena, CA 94574, USA;
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, N. Iraklio, 14122 Athens, Greece;
| | - Luca Saba
- Department of Radiology, A.O.U., di Cagliari-Polo di Monserrato s.s., 09045 Cagliari, Italy;
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13
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Epinephrine versus norepinephrine in cardiac arrest patients with post-resuscitation shock. Intensive Care Med 2022; 48:300-310. [PMID: 35129643 DOI: 10.1007/s00134-021-06608-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Accepted: 12/21/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE Whether epinephrine or norepinephrine is preferable as the continuous intravenous vasopressor used to treat post-resuscitation shock is unclear. We assessed outcomes of patients with post-resuscitation shock after out-of-hospital cardiac arrest according to whether the continuous intravenous vasopressor used was epinephrine or norepinephrine. METHODS We conducted an observational multicenter study of consecutive patients managed in 2011-2018 for post-resuscitation shock. The primary outcome was all-cause hospital mortality, and secondary outcomes were cardiovascular hospital mortality and unfavorable neurological outcome (Cerebral Performance Category 3-5). A multivariate regression analysis and a propensity score analysis were performed, as well as several sensitivity analyses. RESULTS Of the 766 patients included in five hospitals, 285 (37%) received epinephrine and 481 (63%) norepinephrine. All-cause hospital mortality was significantly higher in the epinephrine group (OR 2.6; 95%CI 1.4-4.7; P = 0.002). Cardiovascular hospital mortality was also higher with epinephrine (aOR 5.5; 95%CI 3.0-10.3; P < 0.001), as was the proportion of patients with CPC of 3-5 at hospital discharge. Sensitivity analyses produced consistent results. The analysis involving adjustment on a propensity score to control for confounders showed similar findings (aOR 2.1; 95%CI 1.1-4.0; P = 0.02). CONCLUSION Among patients with post-resuscitation shock after out-of-hospital cardiac arrest, use of epinephrine was associated with higher all-cause and cardiovascular-specific mortality, compared with norepinephrine infusion. Until additional data become available, intensivists may want to choose norepinephrine rather than epinephrine for the treatment of post-resuscitation shock after OHCA.
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14
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Ha ACT, Doumouras BS, Wang CN, Tranmer J, Lee DS. Prediction of sudden cardiac arrest in the general population: Review of traditional and emerging risk factors. Can J Cardiol 2022; 38:465-478. [PMID: 35041932 DOI: 10.1016/j.cjca.2022.01.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 01/08/2022] [Accepted: 01/09/2022] [Indexed: 12/28/2022] Open
Abstract
Sudden cardiac death (SCD) is the most common and devastating outcome of sudden cardiac arrest (SCA), defined as an abrupt and unexpected cessation of cardiovascular function leading to circulatory collapse. The incidence of SCD is relatively infrequent for individuals in the general population, in the range of 0.03-0.10% per year. Yet, the absolute number of cases around the world is high due to the sheer size of the population at risk, making SCA/SCD a major global health issue. Based on conservative estimates, there are at least 2 million cases of SCA occurring worldwide on a yearly basis. As such, identification of risk factors associated with SCA in the general population is an important objective from a clinical and public health standpoint. This review will provide an in-depth discussion of established and emerging factors predictive of SCA/SCD in the general population beyond coronary artery disease and impaired left ventricular ejection fraction. Contemporary studies evaluating the association between age, sex, race, socioeconomic status and the emerging contribution of diabetes and obesity to SCD risk beyond their role as atherosclerotic risk factors will be reviewed. In addition, the role of biomarkers, particularly electrocardiographic ones, on SCA/SCD risk prediction in the general population will be discussed. Finally, the use of machine learning as a tool to facilitate SCA/SCD risk prediction will be examined.
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Affiliation(s)
- Andrew C T Ha
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
| | - Barbara S Doumouras
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
| | - Chang Nancy Wang
- Department of Medicine, Queen's University, Kingston, Ontario, Canada; ICES Central, Toronto, Ontario, Canada
| | - Joan Tranmer
- School of Nursing, Queen's University, Kingston, Ontario, Canada; ICES Queens, Queen's University, Kingston, Ontario, Canada
| | - Douglas S Lee
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada; ICES Central, Toronto, Ontario, Canada; Ted Rogers Centre for Heart Research, Toronto, Ontario, Canada.
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15
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OUP accepted manuscript. Eur Heart J 2022; 43:2103-2115. [DOI: 10.1093/eurheartj/ehac104] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 02/22/2022] [Accepted: 01/03/2022] [Indexed: 11/14/2022] Open
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16
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Evolution of Incidence, Management, and Outcomes Over Time in Sports-Related Sudden Cardiac Arrest. J Am Coll Cardiol 2022; 79:238-246. [DOI: 10.1016/j.jacc.2021.11.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 10/28/2021] [Accepted: 11/01/2021] [Indexed: 12/11/2022]
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17
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Guryanov MI, Pusev RS, Guryanova NM, Kharitonova EA, Yablonsky PK. Organized Structure of Ventricular Fibrillation during Prolonged Heart Perfusion in Dogs. Sovrem Tekhnologii Med 2021; 12:26-30. [PMID: 34795976 PMCID: PMC8596255 DOI: 10.17691/stm2020.12.3.03] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Indexed: 11/14/2022] Open
Abstract
The aim of the study was to identify the organized ventricular fibrillation (VF) activity in the dog heart and characterize its quantitative parameters during prolonged heart perfusion.
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Affiliation(s)
- M I Guryanov
- Professor, Department of Basic and Specific Medical Sciences, Faculty of Medicine; Saint Petersburg State University, 79 Universitetskaya Naberezhnaya, Saint Petersburg, 199034, Russia
| | - R S Pusev
- Associate Professor, Department of Informatics, Saint Petersburg School of Physics, Mathematics, and Computer Science; National Research University Higher School of Economics, 16 Soyuza Pechatnikov St., Saint Petersburg, 199008, Russia
| | - N M Guryanova
- PhD Student, Department of Pharmacology, Faculty of Medicine; Saint Petersburg State University, 79 Universitetskaya Naberezhnaya, Saint Petersburg, 199034, Russia
| | - E A Kharitonova
- Associate Professor, Department of Basic and Specific Medical Sciences, Faculty of Medicine; Saint Petersburg State University, 79 Universitetskaya Naberezhnaya, Saint Petersburg, 199034, Russia
| | - P K Yablonsky
- Professor, Director; Saint Petersburg Research Institute of Phthisiopulmonology, Ministry of Health the Russian Federation, 24 Ligovsky Avenue, Saint Petersburg, 191036, Russia; Head of the Department of Hospital Surgery; Saint Petersburg State University, 7-9 Universitetskaya Naberezhnaya, Saint Petersburg, 199034, Russia; Dean of the Faculty of Medicine; Saint Petersburg State University, 79 Universitetskaya Naberezhnaya, Saint Petersburg, 199034, Russia
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18
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Mir T, Qureshi WT, Uddin M, Soubani A, Saydain G, Rab T, Kakouros N. Predictors and outcomes of cardiac arrest in the emergency department and in-patient settings in the United States (2016-2018). Resuscitation 2021; 170:100-106. [PMID: 34801637 DOI: 10.1016/j.resuscitation.2021.11.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 11/06/2021] [Accepted: 11/11/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND Outcomes of cardiac arrest (CA) remain dismal despite therapeutic advances. Literature is limited regarding outcomes of CA in emergency departments (ED). OBJECTIVE To study the possible causes, predictors, and outcomes of CA in ED and in-patient settings throughout the United States (US). METHODS Data from the US national emergency department sample (NEDS) was analyzed for the episodes of CA for 2016-2018. In-hospital CA was divided into in-patient (IPCA) and in the ED (EDCA). Only patients who had cardiopulmonary resuscitation (CPR) within the hospital were included in the study (out-of-hospital were excluded). RESULTS A total of 1,068,847 CA (mean age 63.7 ± 19.4 years, 24%females), of whom 325,062 (30.4%) EDCA and 177,104 (16.6%) IPCA were included in the study. Patients without CPR, 743,785 (69.6%), were excluded. Survival was higher among IPCA 55,821 (31.6%) than the EDCA 32,516 (10%). IPCA encounters had multifactorial associated etiologies including respiratory failure (73%), acidosis (38.7%) sepsis (36.8%) and ST-elevated myocardial infarction (STEMI) (7.3%). Majority of ED arrests (67.1%) had no possible identifiable cause. The predominant known causes include intoxication (7.5%), trauma (6.4%), respiratory failure (5%), and STEMI (2.7%). Cardiovascular interventions had significant survival benefits in IPCA on univariate logistic regression after coarsened exact matching for comorbidities. IPCA had higher intervention rates than EDCA. For all live discharges, a total of 40% of patients were discharged to hospice. CONCLUSION Survival remains dismal among CA patients especially those occurring in the ED. Given that there are considerable variations in the etiology between the two studied cohorts, more research is required to improve the understanding of these factors, which may improve survival outcomes.
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Affiliation(s)
- Tanveer Mir
- Internal Medicine, Detroit Medical Center Wayne State University, Detroit, MI, USA
| | - Waqas T Qureshi
- Division of Cardiology, University of Massachusetts School of Medicine, Worcester, MA, USA.
| | - Mohammed Uddin
- Division of Cardiology, Emory University, Atlanta, GA, USA
| | - Ayman Soubani
- Internal Medicine, Detroit Medical Center Wayne State University, Detroit, MI, USA
| | - Ghulam Saydain
- Division of Cardiology, Emory University, Atlanta, GA, USA
| | - Tanveer Rab
- Division of Cardiology, Emory University, Atlanta, GA, USA
| | - Nikolaos Kakouros
- Division of Cardiology, University of Massachusetts School of Medicine, Worcester, MA, USA
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19
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Sung E, Etoz S, Zhang Y, Trayanova NA. Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications. BIOPHYSICS REVIEWS 2021; 2:031304. [PMID: 36281224 PMCID: PMC9588428 DOI: 10.1063/5.0058050] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Ventricular arrhythmias are the primary cause of sudden cardiac death and one of the leading causes of mortality worldwide. Whole-heart computational modeling offers a unique approach for studying ventricular arrhythmias, offering vast potential for developing both a mechanistic understanding of ventricular arrhythmias and clinical applications for treatment. In this review, the fundamentals of whole-heart ventricular modeling and current methods of personalizing models using clinical data are presented. From this foundation, the authors summarize recent advances in whole-heart ventricular arrhythmia modeling. Efforts in gaining mechanistic insights into ventricular arrhythmias are discussed, in addition to other applications of models such as the assessment of novel therapeutics. The review emphasizes the unique benefits of computational modeling that allow for insights that are not obtainable by contemporary experimental or clinical means. Additionally, the clinical impact of modeling is explored, demonstrating how patient care is influenced by the information gained from ventricular arrhythmia models. The authors conclude with future perspectives about the direction of whole-heart ventricular arrhythmia modeling, outlining how advances in neural network methodologies hold the potential to reduce computational expense and permit for efficient whole-heart modeling.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Sevde Etoz
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Yingnan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Author to whom correspondence should be addressed: . Tel.: 410-516-4375
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20
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Koshy AN, Gow PJ, Han HC, Teh AW, Jones R, Testro A, Lim HS, McCaughan G, Jeffrey GP, Crawford M, Macdonald G, Fawcett J, Wigg A, Chen JWC, Gane EJ, Munn SR, Clark DJ, Yudi MB, Farouque O. Cardiovascular mortality following liver transplantation: predictors and temporal trends over 30 years. EUROPEAN HEART JOURNAL. QUALITY OF CARE & CLINICAL OUTCOMES 2021; 6:243-253. [PMID: 32011663 DOI: 10.1093/ehjqcco/qcaa009] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/21/2020] [Accepted: 01/24/2020] [Indexed: 12/13/2022]
Abstract
AIMS There has been significant evolution in operative and post-transplant therapies following liver transplantation (LT). We sought to study their impact on cardiovascular (CV) mortality, particularly in the longer term. METHODS AND RESULTS A retrospective cohort study was conducted of all adult LTs in Australia and New Zealand across three 11-year eras from 1985 to assess prevalence, modes, and predictors of early (≤30 days) and late (>30 days) CV mortality. A total of 4265 patients were followed-up for 37 409 person-years. Overall, 1328 patients died, and CV mortality accounted for 228 (17.2%) deaths. Both early and late CV mortality fell significantly across the eras (P < 0.001). However, CV aetiologies were consistently the leading cause of early mortality and accounted for ∼40% of early deaths in the contemporary era. Cardiovascular deaths occurred significantly later than non-cardiac aetiologies (8.8 vs. 5.2 years, P < 0.001). On multivariable Cox regression, coronary artery disease [hazard ratio (HR) 4.6, 95% confidence interval (CI) 1.2-21.6; P = 0.04] and era of transplantation (HR 0.44; 95% CI 0.28-0.70; P = 0.01) were predictors of early CV mortality, while advancing age (HR 1.05, 95% CI 1.02-1.10; P = 0.005) was an independent predictors of late CV mortality. Most common modes of CV death were cardiac arrest, cerebrovascular events, and myocardial infarction. CONCLUSION Despite reductions in CV mortality post-LT over 30 years, they still account for a substantial proportion of early and late deaths. The late occurrence of CV deaths highlights the importance of longitudinal follow-up to study the efficacy of targeted risk-reduction strategies in this unique patient population.
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Affiliation(s)
- Anoop N Koshy
- Department of Cardiology, Austin Health, Melbourne, Victoria, Australia.,The University of Melbourne, Parkville, Victoria, Australia
| | - Paul J Gow
- The University of Melbourne, Parkville, Victoria, Australia.,Victorian Liver Transplant Unit, Austin Hospital, Melbourne, Victoria, Australia
| | - Hui-Chen Han
- Department of Cardiology, Austin Health, Melbourne, Victoria, Australia.,The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew W Teh
- Department of Cardiology, Austin Health, Melbourne, Victoria, Australia.,The University of Melbourne, Parkville, Victoria, Australia
| | - Robert Jones
- The University of Melbourne, Parkville, Victoria, Australia.,Victorian Liver Transplant Unit, Austin Hospital, Melbourne, Victoria, Australia
| | - Adam Testro
- The University of Melbourne, Parkville, Victoria, Australia.,Victorian Liver Transplant Unit, Austin Hospital, Melbourne, Victoria, Australia
| | - Han S Lim
- Department of Cardiology, Austin Health, Melbourne, Victoria, Australia.,The University of Melbourne, Parkville, Victoria, Australia
| | - Geoffrey McCaughan
- Department of Liver Transplantation, Royal Prince Alfred Hospital, Sydney, Australia.,University of Sydney, Sydney, Australia
| | - Gary P Jeffrey
- Department of Liver Transplantation, Sir Charles Gardiner Hospital, Perth, Australia.,School of Medicine, University of Western Australia, Nedlands, Australia
| | - Michael Crawford
- Department of Liver Transplantation, Royal Prince Alfred Hospital, Sydney, Australia.,University of Sydney, Sydney, Australia
| | - Graeme Macdonald
- Department of Liver Transplantation, Princess Alexandra Hospital, Brisbane, Australia.,School of Medicine, The University of Queensland, Brisbane, Australia
| | - Jonathan Fawcett
- Department of Liver Transplantation, Princess Alexandra Hospital, Brisbane, Australia.,School of Medicine, The University of Queensland, Brisbane, Australia
| | - Alan Wigg
- Department of Liver Transplantation, Flinders Medical Centre, Adelaide, Australia
| | - John W C Chen
- Department of Liver Transplantation, Flinders Medical Centre, Adelaide, Australia
| | | | | | - David J Clark
- Department of Cardiology, Austin Health, Melbourne, Victoria, Australia.,The University of Melbourne, Parkville, Victoria, Australia
| | - Matias B Yudi
- Department of Cardiology, Austin Health, Melbourne, Victoria, Australia.,The University of Melbourne, Parkville, Victoria, Australia
| | - Omar Farouque
- Department of Cardiology, Austin Health, Melbourne, Victoria, Australia.,The University of Melbourne, Parkville, Victoria, Australia
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21
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Ricceri S, Salazar JW, Vu AA, Vittinghoff E, Moffatt E, Tseng ZH. Factors Predisposing to Survival After Resuscitation for Sudden Cardiac Arrest. J Am Coll Cardiol 2021; 77:2353-2362. [PMID: 33985679 DOI: 10.1016/j.jacc.2021.03.299] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 03/12/2021] [Accepted: 03/21/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND In the POST SCD study, the authors autopsied all World Health Organization (WHO)-defined sudden cardiac deaths (SCDs) and found that only 56% had an arrhythmic cause; resuscitated sudden cardiac arrests (SCAs) were excluded because they did not die suddenly. They hypothesized that causes underlying resuscitated SCAs would be similarly heterogeneous. OBJECTIVES The aim of this study was to determine the causes and outcomes of resuscitated SCAs. METHODS The authors identified all out-of-hospital cardiac arrests (OHCAs) from February 1, 2011, to January 1, 2015, of patients aged 18 to 90 years in San Francisco County. Resuscitated SCAs were OHCAs surviving to hospitalization and meeting WHO criteria for suddenness. Underlying cause was determined by comprehensive record review. RESULTS The authors identified 734 OHCAs over 48 months; 239 met SCA criteria, 133 (55.6%) were resuscitated to hospitalization, and 47 (19.7%) survived to discharge. Arrhythmic causes accounted for significantly more resuscitated SCAs overall (92 of 133, 69.1%), particularly among survivors (43 of 47, 91.5%), than WHO-defined SCDs in POST SCD (293 of 525, 55.8%; p < 0.004 for both). Among resuscitated SCAs, arrhythmic cause, ventricular tachycardia/fibrillation initial rhythm, and white race were independent predictors of survival. None of the resuscitated SCAs due to neurologic causes survived. CONCLUSIONS In this 4-year countywide study of OHCAs, only one-third were sudden, of which one-half were resuscitated to hospitalization and 1 in 5 survived to discharge. Arrhythmic cause predicted survival and nearly one-half of nonsurvivors had nonarrhythmic causes, suggesting that SCA survivors are not equivalent to SCDs. Early identification of nonarrhythmic SCAs, such as neurologic emergencies, may be a target to improve OHCA survival.
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Affiliation(s)
- Santo Ricceri
- Division of Cardiology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA. https://twitter.com/SantoRicceri
| | - James W Salazar
- Department of Medicine, University of California-San Francisco, San Francisco, California, USA. https://twitter.com/JamesSalazarMD
| | - Andrew A Vu
- Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Eric Vittinghoff
- Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, California, USA
| | - Ellen Moffatt
- Office of Chief Medical Examiner, City and County of San Francisco, San Francisco, California, USA
| | - Zian H Tseng
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, University of California-San Francisco, San Francisco, California, USA.
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22
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Rajagopalan B, Shen WK, Patton K, Kutyifa V, Di Biase L, Al-Ahmad A, Natale A, Gopinathannair R, Lakkireddy D. Surviving sudden cardiac arrest-successes, challenges, and opportunities. J Interv Card Electrophysiol 2021; 64:567-571. [PMID: 33909223 DOI: 10.1007/s10840-021-00969-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 02/18/2021] [Indexed: 11/29/2022]
Abstract
Sudden cardiac arrest (SCA) is the most common cause of death in the world. This manuscript highlights the various challenges in prevention and early management of SCA and also discusses the current state of SCA awareness. The manuscript also outlines the various national and international initiatives in improving SCA awareness and their impact on improving outcomes in SCA. Various campaigns have strived for widespread dissemination of cardiopulmonary resuscitation training and advocated for broader public access defibrillator availability. Finally, the manuscript describes future directions including harnessing technology with voice command and artificial intelligence to allow lay person deliver effective CPR, to improve EMS response times, and to allow wider CPR knowledge dissemination in schools and places of employment. Future research should be focused on optimizing SCA outcomes among vulnerable populations and minorities. Advancements in resuscitation science and use of big data for improvement of EMS services will improve outcomes in SCA.
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Affiliation(s)
| | | | - Kristin Patton
- University of Washington Medical Center, Seattle, WA, USA
| | | | | | | | | | - Rakesh Gopinathannair
- The Kansas City Heart Rhythm Institute & Research Foundation, Overland Park, KS, USA
| | - Dhanunjaya Lakkireddy
- The Kansas City Heart Rhythm Institute & Research Foundation, Overland Park, KS, USA.
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23
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Daubert JP, Lee JS, Narayan SM. Prognostication for Sudden Cardiac Arrest Patients Achieving ROSC. J Am Coll Cardiol 2021; 77:372-374. [PMID: 33509393 PMCID: PMC10950326 DOI: 10.1016/j.jacc.2020.11.052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Accepted: 11/30/2020] [Indexed: 11/20/2022]
Affiliation(s)
- James P Daubert
- Electrophysiology Section, Division of Cardiology, Department of Medicine, Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA.
| | - Joshua S Lee
- Electrophysiology Section, Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, North Carolina, USA
| | - Sanjiv M Narayan
- Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
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24
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Chuan L, Zhang L, Fu H, Yang Y, Wang Q, Jiang X, Li Z, Ni K, Ding L. Metformin prevents brain injury after cardiopulmonary resuscitation by inhibiting the endoplasmic reticulum stress response and activating AMPK-mediated autophagy. Scott Med J 2021; 66:16-22. [PMID: 32990500 DOI: 10.1177/0036933020961543] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND AND AIMS The neurological damage caused by cardiac arrest (CA) can seriously affect quality of life. We investigated the effect of metformin pretreatment on brain injury and survival in a rat CA/cardiopulmonary resuscitation (CPR) model. METHODS AND RESULTS After 14 days of pretreatment with metformin, rats underwent 9 minutes of asphyxia CA/CPR. Survival was evaluated 7 days after restoration of spontaneous circulation; neurological deficit scale (NDS) score was evaluated at days 1, 3, and 7. Proteins related to the endoplasmic reticulum (ER) stress response and autophagy were measured using immunoblotting. Seven-day survival was significantly improved and NDS score was significantly improved in rats pretreated with metformin. Metformin enhanced AMPK-induced autophagy activation. AMPK and autophagy inhibitors removed the metformin neuroprotective effect. Although metformin inhibited the ER stress response, its inhibitory effect was weaker than 4-PBA. CONCLUSION In a CA/CPR rat model, 14-day pretreatment with metformin has a neuroprotective effect. This effect is closely related to the activation of AMPK-induced autophagy and inhibition of the ER stress response. Long-term use of metformin can reduce brain damage following CA/CPR.
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Affiliation(s)
- Libo Chuan
- Attending Physician, Faculty of Life Science and Biotechnology, Kunming University of Science and Technology, P.R. China
| | - Lei Zhang
- Associate Chief Physician, Department of Neurology, The Affiliated Hospital of Kunming University of Science and Technology, P.R. China
| | - Hao Fu
- Attending Physician, Department of Neurology, The Affiliated Hospital of Kunming University of Science and Technology, P.R. China
| | - Ying Yang
- Attending Physician, Department of Neurology, The Affiliated Hospital of Kunming University of Science and Technology, P.R. China
| | - Quanyu Wang
- Attending Physician, Department of Neurology, The Affiliated Hospital of Kunming University of Science and Technology, P.R. China
| | - Xingpeng Jiang
- Attending Physician, ICU, The Affiliated Hospital of Kunming University of Science and Technology, P.R. China
| | - Zhengchao Li
- Resident Physician, ICU, The Affiliated Hospital of Kunming University of Science and Technology, P.R. China
| | - Kuang Ni
- Resident Physician, ICU, The Affiliated Hospital of Kunming University of Science and Technology, P.R. China
| | - Li Ding
- Chief Physician, Department of Neurology, The Affiliated Hospital of Kunming University of Science and Technology, P.R. China
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25
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Rogers AJ, Selvalingam A, Alhusseini MI, Krummen DE, Corrado C, Abuzaid F, Baykaner T, Meyer C, Clopton P, Giles W, Bailis P, Niederer S, Wang PJ, Rappel WJ, Zaharia M, Narayan SM. Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death. Circ Res 2020; 128:172-184. [PMID: 33167779 DOI: 10.1161/circresaha.120.317345] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
RATIONALE Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside. OBJECTIVE To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes. METHODS AND RESULTS We recorded 5706 ventricular MAPs in 42 patients with coronary artery disease and left ventricular ejection fraction ≤40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines and convolutional neural networks were trained to 2 end points: (1) sustained VT/VF or (2) mortality at 3 years. Support vector machines provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each end point. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI, 0.76-1.00) and 0.91 for mortality (95% CI, 0.83-1.00) and were the most significant multivariate predictors. Interpreting trained support vector machine revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium-calcium exchanger as predominant phenotypes for VT/VF. CONCLUSIONS Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.
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Affiliation(s)
- Albert J Rogers
- Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford University
| | - Anojan Selvalingam
- Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford University.,Department of Cardiology, University Medical Center Hamburg-Eppendorf, Germany (A.S., C.M.)
| | - Mahmood I Alhusseini
- Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford University
| | - David E Krummen
- Department of Medicine (D.E.K.), University of California, San Diego
| | - Cesare Corrado
- Department of Biomedical Engineering, King's College London, United Kingdom (C.C., S.N.)
| | - Firas Abuzaid
- Department of Computer Sciences (F.A., M.Z., P.B.), Stanford University
| | - Tina Baykaner
- Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford University
| | - Christian Meyer
- Department of Cardiology, University Medical Center Hamburg-Eppendorf, Germany (A.S., C.M.)
| | - Paul Clopton
- Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford University
| | - Wayne Giles
- Department of Physiology and Pharmacology, University of Calgary, Canada (W.G.)
| | - Peter Bailis
- Department of Computer Sciences (F.A., M.Z., P.B.), Stanford University
| | - Steven Niederer
- Department of Biomedical Engineering, King's College London, United Kingdom (C.C., S.N.)
| | - Paul J Wang
- Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford University
| | - Wouter-Jan Rappel
- Department of Physics (W.-J.R.), University of California, San Diego
| | - Matei Zaharia
- Department of Computer Sciences (F.A., M.Z., P.B.), Stanford University
| | - Sanjiv M Narayan
- Department of Medicine and Cardiovascular Institute (A.J.R., A.S., M.I.A., T.B., P.C., P.J.W., S.M.N.), Stanford University
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26
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Torrisi M, Pennisi G, Russo I, Amico F, Esposito M, Liberto A, Cocimano G, Salerno M, Li Rosi G, Di Nunno N, Montana A. Sudden Cardiac Death in Anabolic-Androgenic Steroid Users: A Literature Review. ACTA ACUST UNITED AC 2020; 56:medicina56110587. [PMID: 33158202 PMCID: PMC7694262 DOI: 10.3390/medicina56110587] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/29/2020] [Accepted: 11/02/2020] [Indexed: 02/06/2023]
Abstract
Background and objectives: Anabolic-androgenic steroids (AASs) are a group of synthetic molecules derived from testosterone and its related precursors. AASs are widely used illicitly by adolescents and athletes, especially by bodybuilders, both for aesthetic uses and as performance enhancers to increase muscle growth and lean body mass. When used illicitly they can damage health and cause disorders affecting several functions. Sudden cardiac death (SCD) is the most common medical cause of death in athletes. SCD in athletes has also been associated with the use of performance-enhancing drugs. This review aimed to focus on deaths related to AAS abuse to investigate the cardiac pathophysiological mechanism that underlies this type of death, which still needs to be fully investigated. Materials and Methods: This review was conducted using PubMed Central and Google Scholar databases, until 21 July 2020, using the following key terms: “((Sudden cardiac death) OR (Sudden death)) AND ((androgenic anabolic steroid) OR (androgenic anabolic steroids) OR (anabolic-androgenic steroids) OR (anabolic-androgenic steroid))”. Thirteen articles met the inclusion and exclusion criteria, for a total of 33 reported cases. Results: Of the 33 cases, 31 (93.9%) were males while only 2 (61%) were females. Mean age was 29.79 and, among sportsmen, the most represented sports activity was bodybuilding. In all cases there was a history of AAS abuse or a physical phenotype suggesting AAS use; the total usage period was unspecified in most cases. In 24 cases the results of the toxicological analysis were reported. The most detected AASs were nandrolone, testosterone, and stanozolol. The most frequently reported macroscopic alterations were cardiomegaly and left ventricular hypertrophy, while the histological alterations were foci of fibrosis and necrosis of the myocardial tissue. Conclusions: Four principal mechanisms responsible for SCD have been proposed in AAS abusers: the atherogenic model, the thrombosis model, the model of vasospasm induced by the release of nitric oxide, and the direct myocardial injury model. Hypertrophy, fibrosis, and necrosis represent a substrate for arrhythmias, especially when combined with exercise. Indeed, AAS use has been shown to change physiological cardiac remodeling of athletes to pathophysiological cardiac hypertrophy with an increased risk of life-threatening arrhythmias.
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Affiliation(s)
- Marco Torrisi
- Legal Medicine, Department of Medical, Surgical and Advanced Technologies, “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy; (M.T.); (G.P.); (I.R.); (F.A.); (M.E.); (A.L.); (G.C.); (M.S.)
| | - Giuliana Pennisi
- Legal Medicine, Department of Medical, Surgical and Advanced Technologies, “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy; (M.T.); (G.P.); (I.R.); (F.A.); (M.E.); (A.L.); (G.C.); (M.S.)
| | - Ilenia Russo
- Legal Medicine, Department of Medical, Surgical and Advanced Technologies, “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy; (M.T.); (G.P.); (I.R.); (F.A.); (M.E.); (A.L.); (G.C.); (M.S.)
| | - Francesco Amico
- Legal Medicine, Department of Medical, Surgical and Advanced Technologies, “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy; (M.T.); (G.P.); (I.R.); (F.A.); (M.E.); (A.L.); (G.C.); (M.S.)
| | - Massimiliano Esposito
- Legal Medicine, Department of Medical, Surgical and Advanced Technologies, “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy; (M.T.); (G.P.); (I.R.); (F.A.); (M.E.); (A.L.); (G.C.); (M.S.)
| | - Aldo Liberto
- Legal Medicine, Department of Medical, Surgical and Advanced Technologies, “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy; (M.T.); (G.P.); (I.R.); (F.A.); (M.E.); (A.L.); (G.C.); (M.S.)
| | - Giuseppe Cocimano
- Legal Medicine, Department of Medical, Surgical and Advanced Technologies, “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy; (M.T.); (G.P.); (I.R.); (F.A.); (M.E.); (A.L.); (G.C.); (M.S.)
| | - Monica Salerno
- Legal Medicine, Department of Medical, Surgical and Advanced Technologies, “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy; (M.T.); (G.P.); (I.R.); (F.A.); (M.E.); (A.L.); (G.C.); (M.S.)
| | - Giuseppe Li Rosi
- Department of Law, Criminology, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy;
| | - Nunzio Di Nunno
- Department of History, Society and Studies on Humanity, University of Salento, 73100 Lecce, Italy;
| | - Angelo Montana
- Legal Medicine, Department of Medical, Surgical and Advanced Technologies, “G.F. Ingrassia”, University of Catania, 95123 Catania, Italy; (M.T.); (G.P.); (I.R.); (F.A.); (M.E.); (A.L.); (G.C.); (M.S.)
- Correspondence: ; Tel.: +39-3287655428
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27
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Loza A, del Nogal F, Macías D, León C, Socías L, Herrera L, Yuste L, Ferrero J, Vidal B, Sánchez J, Zabalegui A, Saavedra P, Lesmes A. Predictors of mortality and neurological function in ICU patients recovering from cardiac arrest: A Spanish nationwide prospective cohort study. Med Intensiva 2020; 44:463-474. [DOI: 10.1016/j.medin.2020.02.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 01/24/2020] [Accepted: 02/09/2020] [Indexed: 12/24/2022]
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Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management. Nat Rev Cardiol 2020; 18:75-91. [PMID: 33037325 PMCID: PMC7545156 DOI: 10.1038/s41569-020-00445-9] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/01/2020] [Indexed: 01/19/2023]
Abstract
Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. These tools are increasingly recognized by regulatory agencies and professional medical societies, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring. Advances in cardiovascular monitoring technologies have resulted in an influx of consumer-targeted wearable sensors that have the potential to detect numerous heart conditions. In this Review, Krittanawong and colleagues describe processes involved in biosignal acquisition and analysis of cardiovascular monitors, as well as their associated ethical, regulatory and legal challenges. Advances in the use of cardiovascular monitoring technologies, such as the development of novel portable sensors and machine learning algorithms that can provide near-real-time diagnosis, have the potential to provide personalized care. Wearable sensor technologies can detect numerous biosignals, such as cardiac output, blood-pressure levels and heart rhythm, and can integrate multiple modalities. The use of novel biosignals for diagnosis raises concerns regarding accuracy and actionability within clinical guidelines, in addition to medical, legal and ethical issues. Machine learning-based interpretation of biosensor data can facilitate rapid evaluation of the haemodynamic consequences of heart failure or arrhythmias, but is limited by the presence of noise and training data that might not be representative of the real-world clinical setting. The use of data derived from cardiovascular monitoring devices is associated with numerous challenges, such as data security, accessibility and ownership, in addition to other ethical and regulatory concerns.
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29
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Sun CLF, Karlsson L, Morrison LJ, Brooks SC, Folke F, Chan TCY. Effect of Optimized Versus Guidelines-Based Automated External Defibrillator Placement on Out-of-Hospital Cardiac Arrest Coverage: An In Silico Trial. J Am Heart Assoc 2020; 9:e016701. [PMID: 32814479 PMCID: PMC7660789 DOI: 10.1161/jaha.120.016701] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Background Mathematical optimization of automated external defibrillator (AED) placement may improve AED accessibility and out‐of‐hospital cardiac arrest (OHCA) outcomes compared with American Heart Association (AHA) and European Resuscitation Council (ERC) placement guidelines. We conducted an in silico trial (simulated prospective cohort study) comparing mathematically optimized placements with placements derived from current AHA and ERC guidelines, which recommend placement in locations where OHCAs are usually witnessed. Methods and Results We identified all public OHCAs of presumed cardiac cause from 2008 to 2016 in Copenhagen, Denmark. For the control, we computationally simulated placing 24/7‐accessible AEDs at every unique, public, witnessed OHCA location at monthly intervals over the study period. The intervention consisted of an equal number of simulated AEDs placements, deployed monthly, at mathematically optimized locations, using a model that analyzed historical OHCAs before that month. For each approach, we calculated the number of OHCAs in the study period that occurred within a 100‐m route distance based on Copenhagen’s road network of an available AED after it was placed (“OHCA coverage”). Estimated impact on bystander defibrillation and 30‐day survival was calculated by multivariate logistic regression. The control scenario involved 393 AEDs at historical, public, witnessed OHCA locations, covering 15.8% of the 653 public OHCAs from 2008 to 2016. The optimized locations provided significantly higher coverage (24.2%; P<0.001). Estimated bystander defibrillation and 30‐day survival rates increased from 15.6% to 18.2% (P<0.05) and from 32.6% to 34.0% (P<0.05), respectively. As a baseline, the 1573 real AEDs in Copenhagen covered 14.4% of the OHCAs. Conclusions Mathematical optimization can significantly improve OHCA coverage and estimated clinical outcomes compared with a guidelines‐based approach to AED placement.
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Affiliation(s)
- Christopher L F Sun
- Sloan School of Management Massachusetts Institute of Technology Cambridge MA.,Healthcare Systems Engineering Massachusetts General Hospital Boston MA
| | - Lena Karlsson
- Department of Cardiology Copenhagen University Hospital Herlev and Gentofte Copenhagen Denmark.,Copenhagen Emergency Medical Services University of Copenhagen Denmark
| | - Laurie J Morrison
- Division of Emergency Medicine Department of Medicine University of Toronto Canada.,Rescu Li Ka Shing Knowledge Institute St. Michael's Hospital Toronto Canada
| | - Steven C Brooks
- Rescu Li Ka Shing Knowledge Institute St. Michael's Hospital Toronto Canada.,Departments of Emergency Medicine and Public Health Sciences Queen's University Kingston Canada
| | - Fredrik Folke
- Healthcare Systems Engineering Massachusetts General Hospital Boston MA.,Department of Cardiology Copenhagen University Hospital Herlev and Gentofte Copenhagen Denmark
| | - Timothy C Y Chan
- Rescu Li Ka Shing Knowledge Institute St. Michael's Hospital Toronto Canada.,Department of Mechanical and Industrial Engineering University of Toronto Canada
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30
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Feng K, Mei B, Chen Z, Fu X. Exploring the Rescue Strategy for Cardiac Arrest in Makeshift (FangCang) Hospital Workers during the Pneumonia Outbreak Associated with COVID-19. IRANIAN JOURNAL OF PUBLIC HEALTH 2020; 49:76-81. [PMID: 34268208 PMCID: PMC8266017 DOI: 10.18502/ijph.v49is1.3672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 03/19/2020] [Indexed: 11/24/2022]
Abstract
Background: Beginning in Dec 2019, a novel coronavirus, designated SARS-CoV-2, has caused an international outbreak of respiratory illness termed COVID-19. The workers in the FangCang hospital have to work for more than 8 h and the work is high strength. Furthermore, to protect health and prevent serious cross-infection, they need to wear isolation equipment when working in FangCang hospital. These characteristics increase the risk of cardiac arrest (CA), which seriously endangers the lives of workers. Methods: We participated in the rescue of the patient and workers at first-line in FangCang hospital, and summarized the rescue strategies for workers rescuing. Results: Workers with CA were rescued in time according our guideline and showed zero dead in FangCang hospital. Conclusion: This study establishes the strategy for the CA of workers including the establishment of an in-FangCang resuscitation team, the establishment of a dedicated rescue room, and the formulation of rescue measures and procedures for CA of workers in the FangCang hospital. Therefore, we aimed to provide a strategy for the rescue of workers with CA in the FangCang hospital and share the success in rescuing with the world.
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Affiliation(s)
- Ke Feng
- Department of Emergency, General Hospital of Ningxia Medical University, Yinchuan, Ningixa 750004, China
| | - Bin Mei
- Department of Neurology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei 430071, China
| | - Zhongwei Chen
- Department of Emergency, General Hospital of Ningxia Medical University, Yinchuan, Ningixa 750004, China
| | - Xufeng Fu
- Key Laboratory of Fertility Preservation and Maintenance of Ministry of Education, Ningxia Medical University, Yinchuan 750004, China
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31
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Detection of sudden cardiac death by a comparative study of heart rate variability in normal and abnormal heart conditions. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.06.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Alba AC, Gaztañaga J, Foroutan F, Thavendiranathan P, Merlo M, Alonso-Rodriguez D, Vallejo-García V, Vidal-Perez R, Corros-Vicente C, Barreiro-Pérez M, Pazos-López P, Perez-David E, Dykstra S, Flewitt J, Pérez-Rivera JÁ, Vazquez-Caamaño M, Katz SD, Sinagra G, Køber L, Poole J, Ross H, Farkouh ME, White JA. Prognostic Value of Late Gadolinium Enhancement for the Prediction of Cardiovascular Outcomes in Dilated Cardiomyopathy: An International, Multi-Institutional Study of the MINICOR Group. Circ Cardiovasc Imaging 2020; 13:e010105. [PMID: 32312112 DOI: 10.1161/circimaging.119.010105] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Dilated cardiomyopathy is associated with increased risk of major cardiovascular events. Late gadolinium enhancement (LGE) cardiac magnetic resonance imaging is a unique tissue-based marker that, in single-center studies, suggests strong prognostic value. We retrospectively studied associations between LGE presence and adverse cardiovascular events in patients with dilated cardiomyopathy in a multicenter setting as part of an emerging global consortium (MINICOR [Multi-Modal International Cardiovascular Outcomes Registry]). METHODS Consecutive patients with dilated cardiomyopathy referred for cardiac magnetic resonance (2000-2017) at 12 institutions in 4 countries were studied. Using multivariable Cox proportional hazard and semiparametric Fine and Gray models, we evaluated the association between LGE and the composite primary end point of all-cause mortality, heart transplantation, or left ventricular assist device implant and a secondary arrhythmic end point of sudden cardiac death or appropriate implantable cardioverter-defibrillator shock. RESULTS We studied 1672 patients, mean age 56±14 years (29% female), left ventricular ejection fraction 33±11%, and 25% having New York Heart Association class III to IV; 650 patients (39%) had LGE. During 2.3 years (interquartile range, 1.0-4.3) follow-up, 160 patients experienced the primary end point, and 88 experienced the arrhythmic end point. In multivariable analyses, LGE was associated with 1.5-fold (hazard ratio, 1.45 [95% CI, 1.03-2.04]) risk of the primary end point and 1.8-fold (hazard ratio, 1.82 [95% CI, 1.20-3.06]) risk of the arrhythmic end point. Primary end point risk was increased in patients with multiple LGE patterns, although arrhythmic risk was higher among patients receiving primary prevention implantable cardioverter-defibrillator and widening QRS. CONCLUSIONS In this large multinational study of patients with dilated cardiomyopathy, the presence of LGE showed strong prognostic value for identification of high-risk patients. Randomized controlled trials evaluating LGE-based care management strategies are warranted.
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Affiliation(s)
- Ana Carolina Alba
- Peter Munk Cardiac Centre, Department of Medicine, Toronto General Hospital, University Health Network, Ontario, Canada (A.C.A., F.F. P.T., H.R., M.E.F.)
| | - Juan Gaztañaga
- Division of Cardiology, Department of Medicine, NYU Winthrop Hospital, Mineola, NY (J.G.)
| | - Farid Foroutan
- Peter Munk Cardiac Centre, Department of Medicine, Toronto General Hospital, University Health Network, Ontario, Canada (A.C.A., F.F. P.T., H.R., M.E.F.)
| | - Paaladinesh Thavendiranathan
- Peter Munk Cardiac Centre, Department of Medicine, Toronto General Hospital, University Health Network, Ontario, Canada (A.C.A., F.F. P.T., H.R., M.E.F.)
| | - Marco Merlo
- Cardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), Universita Degli Studi di Trieste, Trieste, Italy (M.M., G.S.)
| | | | - Victor Vallejo-García
- Department of Cardiology, Hospital Clínico Universitario de Salamanca, Spain (V.V.-G., M.B.-P.)
| | - Rafael Vidal-Perez
- Department of Cardiology, Hospital Universitario Lucus Augusti, Lugo, Spain (R.V.-P.)
| | - Cecilia Corros-Vicente
- Department of Cardiology, Hospital Universitario Central de Asturias, Oviedo, Spain (C.C.-V.)
| | - Manuel Barreiro-Pérez
- Department of Cardiology, Hospital Clínico Universitario de Salamanca, Spain (V.V.-G., M.B.-P.)
| | - Pablo Pazos-López
- Department of Cardiology, Complejo Hospitalario Universitario de Vigo, Spain (P.P.-L.)
| | - Esther Perez-David
- Department of Cardiology, Hospital General Universitario Gregorio Marañon, Madrid, Spain (E.P.-D.)
| | - Steven Dykstra
- Departments of Cardiac Sciences and Diagnostic Imaging, Libin Cardiovascular Institute of Alberta, Calgary, Canada (S.D., J.F., J.A.W.)
| | - Jacqueline Flewitt
- Departments of Cardiac Sciences and Diagnostic Imaging, Libin Cardiovascular Institute of Alberta, Calgary, Canada (S.D., J.F., J.A.W.)
| | | | | | - Stuart D Katz
- NYU Langone Health, Leon H. Charney Division of Cardiology, NY (S.D.K.)
| | - Gianfranco Sinagra
- Cardiovascular Department, Azienda Sanitaria Universitaria Giuliano Isontina (ASUGI), Universita Degli Studi di Trieste, Trieste, Italy (M.M., G.S.)
| | - Lars Køber
- Rigshospitalet, Copenhagen University Hospital, Denmark (L.K.)
| | - Jeanne Poole
- Division of Cardiology, Department of Medicine, University of Washington, Seattle, WA (J.P.)
| | - Heather Ross
- Peter Munk Cardiac Centre, Department of Medicine, Toronto General Hospital, University Health Network, Ontario, Canada (A.C.A., F.F. P.T., H.R., M.E.F.)
| | - Michael E Farkouh
- Peter Munk Cardiac Centre, Department of Medicine, Toronto General Hospital, University Health Network, Ontario, Canada (A.C.A., F.F. P.T., H.R., M.E.F.)
| | - James A White
- Departments of Cardiac Sciences and Diagnostic Imaging, Libin Cardiovascular Institute of Alberta, Calgary, Canada (S.D., J.F., J.A.W.)
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Abstract
PURPOSE OF REVIEW There is a need for an early assessment of outcome in patients with return of spontaneous circulation after cardiac arrest. During the last decade, several models were developed in order to identify predictive factors that may facilitate prognostication and stratification of outcome. RECENT FINDINGS In addition to prognostication tools that are used in intensive care, at least five scores were recently developed using large datasets, based on simple and immediately available parameters, such as circumstances of arrest and early in-hospital indicators. Regarding neurological outcome, predictive performance of these models is good and even excellent for some of them. These scores perform very well for identifying patients at high-risk of unfavorable outcome. The most important limitation of these scores remains the lack of replication in different communities. In addition, these scores were not developed for individual decision- making, but they could instead be useful for the description and comparison of different cohorts, and also to design trials targeting specific categories of patients regarding outcome. Finally, the recent development of big data allows extension of research in epidemiology of cardiac arrest, including the identification of new prognostic factors and the improvement of prediction according to the profile of populations. SUMMARY In addition to the development of artificial intelligence, the prediction approach based on adequate scores will further increase the knowledge in prognostication after cardiac arrest. This strategy may help to develop treatment strategies according to the predicted severity of the outcome.
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Giorgetti R, Chiricolo G, Melniker L, Calaf C, Gaeta T. RESCUE transesophageal echocardiography for monitoring of mechanical chest compressions and guidance for extracorporeal cardiopulmonary resuscitation cannulation in refractory cardiac arrest. JOURNAL OF CLINICAL ULTRASOUND : JCU 2020; 48:184-187. [PMID: 31820822 DOI: 10.1002/jcu.22788] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 10/01/2019] [Accepted: 10/24/2019] [Indexed: 06/10/2023]
Abstract
There is a growing interest in using point-of-care transesophageal echocardiography (TEE) during cardiac arrest. TEE is effective at identifying the etiology of sudden cardiovascular collapse and guiding management during the resuscitation. In selected patients with refractory cardiac arrest, extracorporeal cardiopulmonary resuscitation (ECPR) can be considered. ECPR requires percutaneous vascular access for the implantation of veno-arterial extracorporeal membrane oxygenation circuit. We present a case of prolonged cardiac arrest in which rescue TEE was pivotal in narrowing the differential diagnosis, monitoring of mechanical chest compression performance, and guiding cannulation for ECPR.
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Affiliation(s)
- Ryan Giorgetti
- Department of Emergency Medicine, NewYork-Presbyterian Brooklyn Methodist Hospital, Brooklyn, New York
| | - Gerardo Chiricolo
- Department of Emergency Medicine, NewYork-Presbyterian Brooklyn Methodist Hospital, Brooklyn, New York
| | - Lawrence Melniker
- Department of Emergency Medicine, NewYork-Presbyterian Brooklyn Methodist Hospital, Brooklyn, New York
| | - Carlos Calaf
- Department of Emergency Medicine, NewYork-Presbyterian Brooklyn Methodist Hospital, Brooklyn, New York
| | - Theodore Gaeta
- Department of Emergency Medicine, NewYork-Presbyterian Brooklyn Methodist Hospital, Brooklyn, New York
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Burch AE, D'Souza B, Gimbel JR, Rohrer U, Masuda T, Sears S, Scherr D. Physical activity is reduced prior to ventricular arrhythmias in patients with a wearable cardioverter defibrillator. Clin Cardiol 2019; 43:60-65. [PMID: 31710766 PMCID: PMC6954377 DOI: 10.1002/clc.23288] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Revised: 10/20/2019] [Accepted: 10/23/2019] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION The utility of accelerometer-based activity data to identify patients at risk of sustained ventricular tachycardia (VT) or ventricular fibrillation (VF) has not previously been investigated. The aim of the current study was to determine whether physical activity is associated with manifesting spontaneous sustained VT/VF requiring emergent defibrillation in patients with an ejection fraction of ≤35%. METHODS Patients consecutively prescribed a wearable cardioverter defibrillator (WCD) from April 2015 to May 2018 were included. Shock data and 4 weeks of physical activity data, beginning with the first week of WCD wear, were analyzed. RESULTS Based on the ROC curve outcome generated from 4057 patients, average daily step count during the first week accurately predicted those patients with sustained VT/VF compared to those without (shocked (n = 81) vs nonshocked (n = 3976) area under the curve, c-index = 0.71, 95% CI = 0.65-0.77, P < .001). An average cutoff of 3637 daily steps during week 1 separated the groups. Patients who averaged fewer than 3637 steps per day during the first week of WCD use were 4.3 times more likely to experience a shock than those who walked more than 3637 steps per day (OR = 4.29, 95% CI = 2.58-7.15, P < .001). DISCUSSION Average daily step counts are lower in WCD patients who manifest spontaneous VT/VF. Whether these findings represent a causal or correlational relationship, future studies to encourage a minimum daily step count in high-risk patients may impact the incidence of sustained VT/VF.
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Affiliation(s)
- Ashley E Burch
- East Carolina Heart Institute, Greenville, North Carolina
| | | | | | - Ursula Rohrer
- Department of Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria
| | | | - Samuel Sears
- East Carolina University, Greenville, North Carolina
| | - Daniel Scherr
- Department of Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria
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Bong J, Yasin O, Vaidya VR, Park J, Attia ZI, Padmanabhan D, Cho SJ, Asirvatham R, Schneider N, Lee J, Kim EM, Friedman PA, Ma Z. Injectable Flexible Subcutaneous Electrode Array Technology for Electrocardiogram Monitoring Device. ACS Biomater Sci Eng 2019; 6:2652-2658. [DOI: 10.1021/acsbiomaterials.9b01102] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Jihye Bong
- Department of Electrical and Computer Engineering, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - Omar Yasin
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Vaibhav R. Vaidya
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Jeongpil Park
- Department of Electrical and Computer Engineering, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - Zachi I. Attia
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Deepak Padmanabhan
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Sang June Cho
- Department of Electrical and Computer Engineering, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - Roshini Asirvatham
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Noah Schneider
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Juhwan Lee
- Department of Electrical and Computer Engineering, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
| | - Eun Mee Kim
- Department of Emergency Medical Technology, Korea Nazarene University, Cheonan 31172, South Korea
| | - Paul A. Friedman
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Zhenqiang Ma
- Department of Electrical and Computer Engineering, University of Wisconsin−Madison, Madison, Wisconsin 53706, United States
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Balan P, Hsi B, Thangam M, Zhao Y, Monlezun D, Arain S, Charitakis K, Dhoble A, Johnson N, Anderson HV, Persse D, Warner M, Ostermayer D, Prater S, Wang H, Doshi P. The cardiac arrest survival score: A predictive algorithm for in-hospital mortality after out-of-hospital cardiac arrest. Resuscitation 2019; 144:46-53. [PMID: 31539610 DOI: 10.1016/j.resuscitation.2019.09.009] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2019] [Revised: 08/21/2019] [Accepted: 09/06/2019] [Indexed: 12/20/2022]
Abstract
BACKGROUND Out-of-hospital cardiac arrest (OHCA) is associated with high mortality. Current methods for predicting mortality post-arrest require data unavailable at the time of initial medical contact. We created and validated a risk prediction model for patients experiencing OHCA who achieved return of spontaneous circulation (ROSC) which relies only on objective information routinely obtained at first medical contact. METHODS We performed a retrospective evaluation of 14,892 OHCA patients in a large metropolitan cardiac arrest registry, of which 3952 patients had usable data. This population was divided into a derivation cohort (n = 2,635) and a verification cohort (n = 1,317) in a 2:1 ratio. Backward stepwise logistic regression was used to identify baseline factors independently associated with death after sustained ROSC in the derivation cohort. The cardiac arrest survival score (CASS) was created from the model and its association with in-hospital mortality was examined in both the derivation and verification cohorts. RESULTS Baseline characteristics of the derivation and verification cohorts were not different. The final CASS model included age >75 years (odds ratio [OR] = 1.61, confidence interval [CI][1.30-1.99], p < 0.001), unwitnessed arrest (OR = 1.95, CI[1.58-2.40], p < 0.001), home arrest (OR = 1.28, CI[1.07-1.53], p = 0.008), absence of bystander CPR (OR = 1.35, CI[1.12-1.64], p = 0.003), and non-shockable initial rhythm (OR = 3.81, CI[3.19-4.56], p < 0.001). The area under the curve for the model derivation and model verification cohorts were 0.7172 and 0.7081, respectively. CONCLUSION CASS accurately predicts mortality in OHCA patients. The model uses only binary, objective clinical data routinely obtained at first medical contact. Early risk stratification may allow identification of more patients in whom timely and aggressive invasive management may improve outcomes.
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Affiliation(s)
- Prakash Balan
- Department of Internal Medicine, Division of Cardiology McGovern Medical School at The University of Texas Health Science Center Houston, United States.
| | - Brian Hsi
- Department of Internal Medicine, Division of Cardiology Houston Methodist Hospital, Weill Cornell Medical College, United States
| | - Manoj Thangam
- Department of Internal Medicine, Division of Cardiovascular Medicine Washington University School of Medicine St. Louis, United States
| | - Yelin Zhao
- Department of Internal Medicine, Division of Cardiology McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - Dominique Monlezun
- Department of Internal Medicine, Division of Cardiology McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - Salman Arain
- Department of Internal Medicine, Division of Cardiology McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - Konstantinos Charitakis
- Department of Internal Medicine, Division of Cardiology McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - Abhijeet Dhoble
- Department of Internal Medicine, Division of Cardiology McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - Nils Johnson
- Department of Internal Medicine, Division of Cardiology McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - H Vernon Anderson
- Department of Internal Medicine, Division of Cardiology McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - David Persse
- Physician Director of Emergency Medical Services City of Houston, United States
| | - Mark Warner
- Department of Internal Medicine, Division of Pulmonary/Critical Care Medicine McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - Daniel Ostermayer
- Department of Emergency Medicine McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - Samuel Prater
- Department of Emergency Medicine McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - Henry Wang
- Department of Emergency Medicine McGovern Medical School at The University of Texas Health Science Center Houston, United States
| | - Pratik Doshi
- Department of Internal Medicine, Division of Pulmonary/Critical Care Medicine McGovern Medical School at The University of Texas Health Science Center Houston, United States; Department of Emergency Medicine McGovern Medical School at The University of Texas Health Science Center Houston, United States
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