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Wallace W, Chan C, Chidambaram S, Hanna L, Acharya A, Daniels E, Normahani P, Matin RN, Markar SR, Sounderajah V, Liu X, Darzi A. Evaluating the diagnostic and triage performance of digital and online symptom checkers for the presentation of myocardial infarction; A retrospective cross-sectional study. PLOS DIGITAL HEALTH 2024; 3:e0000558. [PMID: 39102377 PMCID: PMC11299816 DOI: 10.1371/journal.pdig.0000558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/20/2024] [Indexed: 08/07/2024]
Abstract
Online symptom checkers are increasingly popular health technologies that enable patients to input their symptoms to produce diagnoses and triage advice. However, there is concern regarding the performance and safety of symptom checkers in diagnosing and triaging patients with life-threatening conditions. This retrospective cross-sectional study aimed to evaluate and compare commercially available symptom checkers for performance in diagnosing and triaging myocardial infarctions (MI). Symptoms and biodata of MI patients were inputted into 8 symptom checkers identified through a systematic search. Anonymised clinical data of 100 consecutive MI patients were collected from a tertiary coronary intervention centre between 1st January 2020 to 31st December 2020. Outcomes included (1) diagnostic sensitivity as defined by symptom checkers outputting MI as the primary diagnosis (D1), or one of the top three (D3), or top five diagnoses (D5); and (2) triage sensitivity as defined by symptom checkers outputting urgent treatment recommendations. Overall D1 sensitivity was 48±31% and varied between symptom checkers (range: 6-85%). Overall D3 and D5 sensitivity were 73±20% (34-92%) and 79±14% (63-94%), respectively. Overall triage sensitivity was 83±13% (55-91%). 24±16% of atypical cases had a correct D1 though for female atypical cases D1 sensitivity was only 10%. Atypical MI D3 and D5 sensitivity were 44±21% and 48±24% respectively and were significantly lower than typical MI cases (p<0.01). Atypical MI triage sensitivity was significantly lower than typical cases (53±20% versus 84±15%, p<0.01). Female atypical cases had significantly lower diagnostic and triage sensitivity than typical female MI cases (p<0.01).Given the severity of the pathology, the diagnostic performance of symptom checkers for correctly diagnosing an MI is concerningly low. Moreover, there is considerable inter-symptom checker performance variation. Patients presenting with atypical symptoms were under-diagnosed and under-triaged, especially if female. This study highlights the need for improved clinical performance, equity and transparency associated with these technologies.
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Affiliation(s)
- William Wallace
- Department of Surgery & Cancer, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Calvin Chan
- Department of Surgery & Cancer, Imperial College London, St. Mary’s Hospital, London, United Kingdom
- Institute of Global Health Innovation, Imperial College London, South Kensington Campus, London, United Kingdom
| | - Swathikan Chidambaram
- Department of Surgery & Cancer, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Lydia Hanna
- Department of Surgery & Cancer, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Amish Acharya
- Department of Surgery & Cancer, Imperial College London, St. Mary’s Hospital, London, United Kingdom
- Institute of Global Health Innovation, Imperial College London, South Kensington Campus, London, United Kingdom
| | - Elisabeth Daniels
- Department of Surgery & Cancer, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Pasha Normahani
- Department of Surgery & Cancer, Imperial College London, St. Mary’s Hospital, London, United Kingdom
| | - Rubeta N. Matin
- Department of Dermatology, Oxford University Hospitals NHS Foundation Trust, Oxford, United Kingdom
| | - Sheraz R. Markar
- Surgical Intervention Trials Unit, Nuffield Department of Surgery, University of Oxford, United Kingdom
| | - Viknesh Sounderajah
- Institute of Global Health Innovation, Imperial College London, South Kensington Campus, London, United Kingdom
- Google Health UK, London, United Kingdom
| | - Xiaoxuan Liu
- Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom
- University Hospitals Birmingham NHS Foundation Trust, Birmingham, United Kingdom
| | - Ara Darzi
- Department of Surgery & Cancer, Imperial College London, St. Mary’s Hospital, London, United Kingdom
- Institute of Global Health Innovation, Imperial College London, South Kensington Campus, London, United Kingdom
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Reyes-Pérez P, Hernández-Ledesma AL, Román-López TV, García-Vilchis B, Ramírez-González D, Lázaro-Figueroa A, Martinez D, Flores-Ocampo V, Espinosa-Méndez IM, Tinajero-Nieto L, Peña-Ayala A, Morelos-Figaredo E, Guerra-Galicia CM, Torres-Valdez E, Gordillo-Huerta MV, Gandarilla-Martínez NA, Salinas-Barboza K, Félix-Rodríguez G, Frontana-Vázquez G, Matuk-Pérez Y, Estrada-Bellmann I, Alpizar-Rodríguez D, Rodríguez-Violante M, Rentería ME, Ruíz-Contreras AE, Alcauter S, Medina-Rivera A. Building national patient registries in Mexico: insights from the MexOMICS Consortium. Front Digit Health 2024; 6:1344103. [PMID: 38895515 PMCID: PMC11183280 DOI: 10.3389/fdgth.2024.1344103] [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: 11/27/2023] [Accepted: 05/01/2024] [Indexed: 06/21/2024] Open
Abstract
Objective To introduce MexOMICS, a Mexican Consortium focused on establishing electronic databases to collect, cross-reference, and share health-related and omics data on the Mexican population. Methods Since 2019, the MexOMICS Consortium has established three electronic-based registries: the Mexican Twin Registry (TwinsMX), Mexican Lupus Registry (LupusRGMX), and the Mexican Parkinson's Research Network (MEX-PD), designed and implemented using the Research Electronic Data Capture web-based application. Participants were enrolled through voluntary participation and on-site engagement with medical specialists. We also acquired DNA samples and Magnetic Resonance Imaging scans in subsets of participants. Results The registries have successfully enrolled a large number of participants from a variety of regions within Mexico: TwinsMX (n = 2,915), LupusRGMX (n = 1,761) and MEX-PD (n = 750). In addition to sociodemographic, psychosocial, and clinical data, MexOMICS has collected DNA samples to study the genetic biomarkers across the three registries. Cognitive function has been assessed with the Montreal Cognitive Assessment in a subset of 376 MEX-PD participants. Furthermore, a subset of 267 twins have participated in cognitive evaluations with the Creyos platform and in MRI sessions acquiring structural, functional, and spectroscopy brain imaging; comparable evaluations are planned for LupusRGMX and MEX-PD. Conclusions The MexOMICS registries offer a valuable repository of information concerning the potential interplay of genetic and environmental factors in health conditions among the Mexican population.
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Affiliation(s)
- Paula Reyes-Pérez
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Ana Laura Hernández-Ledesma
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Talía V. Román-López
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Brisa García-Vilchis
- Laboratorio de Neurogenómica Cognitiva, Unidad de Investigación de Psicobiología y Neurociencias, Coordinación de Psicobiología y Neurociencias, Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | - Diego Ramírez-González
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Alejandra Lázaro-Figueroa
- Laboratorio de Neurogenómica Cognitiva, Unidad de Investigación de Psicobiología y Neurociencias, Coordinación de Psicobiología y Neurociencias, Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | - Domingo Martinez
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
- Unidad de Genómica Avanzada, Langebio, Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional, Irapuato, Mexico
- Escuela Nacional de Estudios Superiores, Unidad Juriquilla, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Victor Flores-Ocampo
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Ian M. Espinosa-Méndez
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Lizbet Tinajero-Nieto
- Hospital General Regional No. 1, Instituto Mexicano del Seguro Social, Querétaro, Santiago de Querétaro, Mexico
| | - Angélica Peña-Ayala
- Hospital General Regional No. 1, Instituto Mexicano del Seguro Social, Querétaro, Santiago de Querétaro, Mexico
- Instituto Nacional de Rehabilitación “Luis Guillermo Ibarra Ibarra”, Ciudad de México, Mexico
| | - Eugenia Morelos-Figaredo
- Hospital Regional, Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Morelia, Mexico
| | | | | | - María Vanessa Gordillo-Huerta
- Hospital General Querétaro, Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado, Santiago de Querétaro, Mexico
| | | | | | | | | | - Yamil Matuk-Pérez
- Facultad de Medicina, Universidad Autónoma de Querétaro. Unidad de Neurociencias, Hospital Angeles Centro Sur, Santiago de Querétaro, Mexico
| | - Ingrid Estrada-Bellmann
- Movement Disorders Clinic, Neurology Division, Internal Medicine Department, University Hospital “Dr. José E. González”, Universidad Autónoma de Nuevo León, Monterrey, Mexico
| | | | - Mayela Rodríguez-Violante
- Laboratorio Clínico de Enfermedades Neurodegenerativas, Instituto Nacional de Neurología y Neurocirugía Manuel Velasco Suárez, Mexico City, Mexico
| | - Miguel E. Rentería
- Mental Health and Neuroscience Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Alejandra E. Ruíz-Contreras
- Laboratorio de Neurogenómica Cognitiva, Unidad de Investigación de Psicobiología y Neurociencias, Coordinación de Psicobiología y Neurociencias, Facultad de Psicología, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
| | - Sarael Alcauter
- Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
| | - Alejandra Medina-Rivera
- Laboratorio Internacional de Investigación Sobre el Genoma Humano, Universidad Nacional Autónoma de México, Santiago de Querétaro, Mexico
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Vaidya YP, Shumway SJ. Artificial intelligence: The future of cardiothoracic surgery. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00371-4. [PMID: 38685465 DOI: 10.1016/j.jtcvs.2024.04.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 04/17/2024] [Indexed: 05/02/2024]
Affiliation(s)
- Yash Pradeep Vaidya
- Department of Cardiothoracic Surgery, University of Minnesota, Minneapolis, Minn.
| | - Sara Jane Shumway
- Department of Cardiothoracic Surgery, University of Minnesota, Minneapolis, Minn
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Nohara-Shitama Y, Ishii K, Morikawa N, Nohara S, Fukumoto Y. Annual Increase of Acute Inpatients with Both Cancer and Cardiovascular Diseases in Japan 2011-2015: Analysis From National Database of Health Insurance Claims and Specific Health Checkups of Japan. Kurume Med J 2023; 68:209-220. [PMID: 37544754 DOI: 10.2739/kurumemedj.ms6834012] [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] [Indexed: 08/08/2023]
Abstract
BACKGROUND Patients with cancer were able to live longer due to improvements in cancer treatment. Additionally, cardiovascular disease (CVD) is the second leading cause of mortality in cancer survivors. However, epidemiological data on onco-CVD have not been sufficiently provided. We aimed to investigate the clinical characteristics of cancer in CVD patients using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB). METHOD AND RESULTS The NDB sampling dataset used in this study was randomly sampled 10% from the whole Diagnosis Procedure Combination (DPC) records from every January, April, July, and October from 2011 to 2015. The significance of the increase trend in the percentage of records in each disease group to the total number of all DPC records from 2011 to 2015 was checked with Chi-square test with a Bonferroni correction. The percentage of records in cancer with the CVD group to the total number of all DPC records significantly increased over time, and their average age also increased since 2011. Their proportion over 75 years was approximately 56 % in 2015. There was no difference in the cancer sites. However, the prevalence of heart failure dramatically elevated. CONCLUSION We were able to assess the increase in cancer among CVD patients using DPC inpatient records obtained from the NDB. Both cardiologists and oncologists should be more aware of this phenomenon.
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Affiliation(s)
- Yume Nohara-Shitama
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kurume University School of Medicine
| | - Kazuo Ishii
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kurume University School of Medicine
- Department of Applied Information Engineering, Faculty of Engineering, Suwa University of Science
| | - Nagisa Morikawa
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kurume University School of Medicine
| | - Shoichiro Nohara
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kurume University School of Medicine
| | - Yoshihiro Fukumoto
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kurume University School of Medicine
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Palmieri V, Montisci A, Vietri MT, Colombo PC, Sala S, Maiello C, Coscioni E, Donatelli F, Napoli C. Artificial intelligence, big data and heart transplantation: Actualities. Int J Med Inform 2023; 176:105110. [PMID: 37285695 DOI: 10.1016/j.ijmedinf.2023.105110] [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: 03/05/2023] [Revised: 05/21/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND As diagnostic and prognostic models developed by traditional statistics perform poorly in real-world, artificial intelligence (AI) and Big Data (BD) may improve the supply chain of heart transplantation (HTx), allocation opportunities, correct treatments, and finally optimize HTx outcome. We explored available studies, and discussed opportunities and limits of medical application of AI to the field of HTx. METHOD A systematic overview of studies published up to December 31st, 2022, in English on peer-revied journals, have been identified through PUBMED-MEDLINE-WEB of Science, referring to HTx, AI, BD. Studies were grouped in 4 domains based on main studies' objectives and results: etiology, diagnosis, prognosis, treatment. A systematic attempt was made to evaluate studies by the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD). RESULTS Among the 27 publications selected, none used AI applied to BD. Of the selected studies, 4 fell in the domain of etiology, 6 in the domain of diagnosis, 3 in the domain of treatment, and 17 in that of prognosis, as AI was most frequently used for algorithmic prediction and discrimination of survival, but in retrospective cohorts and registries. AI-based algorithms appeared superior to probabilistic functions to predict patterns, but external validation was rarely employed. Indeed, based on PROBAST, selected studies showed, to some extent, significant risk of bias (especially in the domain of predictors and analysis). In addition, as example of applicability in the real-world, a free-use prediction algorithm developed through AI failed to predict 1-year mortality post-HTx in cases from our center. CONCLUSIONS While AI-based prognostic and diagnostic functions performed better than those developed by traditional statistics, risk of bias, lack of external validation, and relatively poor applicability, may affect AI-based tools. More unbiased research with high quality BD meant for AI, transparency and external validations, are needed to have medical AI as a systematic aid to clinical decision making in HTx.
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Affiliation(s)
- Vittorio Palmieri
- Azienda Ospedaliera dei Colli Monaldi-Cotugno-CTO, Department of Cardiac Surgery and Transplantation, Naples, Italy.
| | - Andrea Montisci
- Division of Cardiothoracic Intensive Care, Cardiothoracic Department, ASST Spedali Civili, Brescia, Italy
| | - Maria Teresa Vietri
- Department of Precision Medicine, "Luigi Vanvitelli" University of Campania School of Medicine, Naples, Italy
| | - Paolo C Colombo
- Milstein Division of Cardiology, Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Silvia Sala
- Chair of Anesthesia and Intensive Care, University of Brescia, Brescia, Italy
| | - Ciro Maiello
- Azienda Ospedaliera dei Colli Monaldi-Cotugno-CTO, Department of Cardiac Surgery and Transplantation, Naples, Italy
| | - Enrico Coscioni
- Department of Cardiac Surgery, AOU San Giovanni di Dio e Ruggi d'Aragona, Salerno, Italy
| | - Francesco Donatelli
- Department of Cardiac Surgery, Istituto Clinico Sant'Ambrogio, Milan, Italy; Chair of Cardiac Surgery, University of Milan, Milan, Italy
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), "Luigi Vanvitelli" University of Campania School of Medicine, Naples, Italy
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Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications. Diagnostics (Basel) 2022; 12:diagnostics12122964. [PMID: 36552971 PMCID: PMC9777312 DOI: 10.3390/diagnostics12122964] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 11/20/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.
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Using text mining and forest plots to identify similarities and differences between two spine-related journals based on medical subject headings (MeSH terms) and author-specified keywords in 100 top-cited articles. Scientometrics 2022. [DOI: 10.1007/s11192-022-04549-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Montisci A, Palmieri V, Vietri MT, Sala S, Maiello C, Donatelli F, Napoli C. Big Data in cardiac surgery: real world and perspectives. J Cardiothorac Surg 2022; 17:277. [PMID: 36309702 PMCID: PMC9617748 DOI: 10.1186/s13019-022-02025-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 10/14/2022] [Indexed: 11/10/2022] Open
Abstract
Big Data, and the derived analysis techniques, such as artificial intelligence and machine learning, have been considered a revolution in the modern practice of medicine. Big Data comes from multiple sources, encompassing electronic health records, clinical studies, imaging data, registries, administrative databases, patient-reported outcomes and OMICS profiles. The main objective of such analyses is to unveil hidden associations and patterns. In cardiac surgery, the main targets for the use of Big Data are the construction of predictive models to recognize patterns or associations better representing the individual risk or prognosis compared to classical surgical risk scores. The results of these studies contributed to kindle the interest for personalized medicine and contributed to recognize the limitations of randomized controlled trials in representing the real world. However, the main sources of evidence for guidelines and recommendations remain RCTs and meta-analysis. The extent of the revolution of Big Data and new analytical models in cardiac surgery is yet to be determined.
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Mikhail P, Howden N, Monjur M, Jeyaprakash P, Said C, Bland A, Collison D, McCartney P, Adamson C, Morrow A, Carrick D, McEntegart M, Ford TJ. Coronary perforation incidence, outcomes and temporal trends (COPIT): a systematic review and meta-analysis. Open Heart 2022; 9:openhrt-2022-002076. [PMID: 36270713 PMCID: PMC9594565 DOI: 10.1136/openhrt-2022-002076] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 09/06/2022] [Indexed: 11/13/2022] Open
Abstract
Coronary perforation is a potentially life-threatening complication of percutaneous coronary intervention (PCI). We studied incidence, outcomes and temporal trends following PCI-related coronary artery perforation (CAP). METHODS Prospective systematic review and meta-analysis including meta-regression using MEDLINE and EMBASE to November 2020. We included 'all-comer' PCI cohorts including large PCI registries and randomised controlled trials and excluding registries or trials limited to PCI in high-risk populations such as chronic total occlusion PCI or cohorts treated only with atheroablative devices. Regression analysis and corresponding correlation coefficients were performed comparing perforation incidence, mortality rate, tamponade rate and the rate of Ellis III perforations against the midpoint (year) of data collection to determine if a significant temporal relationship was present. RESULTS 3997 studies were screened for inclusion. 67 studies met eligibility criteria with a total of 5 568 191 PCIs included over a 38-year period (1982-2020). The overall pooled incidence of perforation was 0.39% (95% CI 0.34% to 0.45%) and remained similar throughout the study period. Around 1 in 5 coronary perforations led to tamponade (21.1%). Ellis III perforations are increasing in frequency and account for 43% of all perforations. Perforation mortality has trended lower over the years (7.5%; 95% CI 6.7% to 8.4%). Perforation risk factors derived using meta-regression were female sex, hypertension, chronic kidney disease and previous coronary bypass grafting. Coronary perforation was most frequently caused by distal wire exit (37%) followed by balloon dilation catheters (28%). Covered stents were used to treat 25% of perforations, with emergency cardiac surgery needed in 17%. CONCLUSION Coronary perforation complicates approximately 1 in 250 PCIs. Ellis III perforations are increasing in incidence although it is unclear whether this is due to reporting bias. Despite this, the overall perforation mortality rate (7.5%) has trended lower in recent years. Limitations of our findings include bias that may be introduced through analysis of multidesign studies and registries without pre-specified standardised perforation reporting CMore research into coronary perforation management including the optimal use of covered stents seems warranted. PROSPERO REGISTRATION NUMBER CRD42020207881.
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Affiliation(s)
- Philopatir Mikhail
- Department of Cardiology, Central Coast Local Health District, Gosford, New South Wales, Australia,Faculty of Medicine, The University of Newcastle, Newcastle, NSW, Australia
| | - Nicklas Howden
- Department of Cardiology, Central Coast Local Health District, Gosford, New South Wales, Australia,Faculty of Medicine, The University of Newcastle, Newcastle, NSW, Australia
| | - Mohammad Monjur
- Department of Cardiology, South Eastern Sydney Local Health District, Kogarah, New South Wales, Australia
| | - Prajith Jeyaprakash
- Department of Cardiology, Nepean Hospital, Penrith, New South Wales, Australia
| | - Christian Said
- Department of Cardiology, Central Coast Local Health District, Gosford, New South Wales, Australia
| | - Adam Bland
- Department of Cardiology, Central Coast Local Health District, Gosford, New South Wales, Australia,Faculty of Medicine, The University of Newcastle, Newcastle, NSW, Australia
| | - Damien Collison
- Department of Cardiology, Golden Jubilee Hospital, Clydebank, UK,Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Peter McCartney
- Department of Cardiology, Golden Jubilee Hospital, Clydebank, UK,Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Carly Adamson
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - Andrew Morrow
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
| | - David Carrick
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK,Department of Cardiology, University Hospital Hairmyres, East Kilbride, UK
| | | | - Thomas J Ford
- Department of Cardiology, Central Coast Local Health District, Gosford, New South Wales, Australia,Faculty of Medicine, The University of Newcastle, Newcastle, NSW, Australia,Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
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Wu JW, Yan YH, Chien TW, Chou W. Trend and prediction of citations on the topic of neuromuscular junctions in 100 top-cited articles since 2001 using a temporal bar graph: A bibliometric analysis. Medicine (Baltimore) 2022; 101:e30674. [PMID: 36221404 PMCID: PMC9542577 DOI: 10.1097/md.0000000000030674] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND A neuromuscular junction (NMJ) (or myoneural junction) is a chemical synapse between a motor neuron (MN) and a muscle fiber. Although numerous articles have been published, no such analyses on trend or prediction of citations in NMJ were characterized using the temporal bar graph (TBG). This study is to identify the most dominant entities in the 100 top-cited articles in NMJ (T100MNJ for short) since 2001; to verify the improved TBG that is viable for trend analysis; and to investigate whether medical subject headings (MeSH terms) can be used to predict article citations. METHODS We downloaded T100MNJ from the PubMed database by searching the string ("NMJ" [MeSH Major Topic] AND ("2001" [Date - Modification]: "2021" [Date - Modification])) and matching citations to each article. Cluster analysis of citations was performed to select the most cited entities (e.g., authors, research institutes, affiliated countries, journals, and MeSH terms) in T100MNJ using social network analysis. The trend analysis was displayed using TBG with two major features of burst spot and trend development. Next, we examined the MeSH prediction effect on article citations using its correlation coefficients (CC) when the mean citations in MeSH terms were collected in 100 top-cited articles related to NMJ (T100NMJs). RESULTS The most dominant entities (i.e., country, journal, MesH term, and article in T100NMJ) in citations were the US (with impact factor [IF] = 142.2 = 10237/72), neuron (with IF = 151.3 = 3630/24), metabolism (with IF = 133.02), and article authored by Wagh et al from Germany in 2006 (with 342 citing articles). The improved TBG was demonstrated to highlight the citation evolution using burst spots, trend development, and line-chart plots. MeSH terms were evident in the prediction power on the number of article citations (CC = 0.40, t = 4.34). CONCLUSION Two major breakthroughs were made by developing the improved TBG applied to bibliographical studies and the prediction of article citations using the impact factor of MeSH terms in T100NMJ. These visualizations of improved TBG and scatter plots in trend, and prediction analyses are recommended for future academic pursuits and applications in other disciplines.
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Affiliation(s)
- Jian-Wei Wu
- Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yu-Hua Yan
- Superintendent Office, Tainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), Tainan, Taiwan
- Department of Health Care Administration, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Tsair-Wei Chien
- Medical Research Department, Chi-Mei Medical Center, Tainan, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, Tainan, Taiwan
- Department of Physical Medicine and Rehabilitation, Chung San Medical University Hospital, Taichung, Taiwan
- *Correspondence: Tsair-Wei Chien, Department of Physical Medicine and Rehabilitation, Chi Mei Medical Center, 901 Chung Hwa Road, Yung Kung District, Tainan 710, Taiwan (e-mail: )
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11
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Timmermans MJC, Houterman S, Daeter ED, Danse PW, Li WW, Lipsic E, Roefs MM, van Veghel D. Using real-world data to monitor and improve quality of care in coronary artery disease: results from the Netherlands Heart Registration. Neth Heart J 2022; 30:546-556. [PMID: 35389133 PMCID: PMC8988537 DOI: 10.1007/s12471-022-01672-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/02/2022] [Indexed: 11/30/2022] Open
Abstract
Worldwide, quality registries for cardiovascular diseases enable the use of real-world data to monitor and improve the quality of cardiac care. In the Netherlands Heart Registration (NHR), cardiologists and cardiothoracic surgeons register baseline, procedural and outcome data across all invasive cardiac interventional, electrophysiological and surgical procedures. This paper provides insight into the governance and processes as organised by the NHR in collaboration with the hospitals. To clarify the processes, examples are given from the percutaneous coronary intervention and coronary artery bypass grafting registries. Physicians who are mandated by their hospital to instruct the NHR to process their data are united in registration committees. The committees determine standard sets of variables and periodically discuss the completeness and quality of data and patient-relevant outcomes. In the case of significant variation in outcomes, processes of healthcare delivery are discussed and good practices are shared in a non-competitive and safe setting. To create new insights for further improvement in patient-relevant outcomes, quality projects are initiated on, for example, multivessel disease treatment, cardiogenic shock and diagnostic intracoronary procedures. Moreover, possibilities are explored to expand the quality registries through additional relevant indicators, such as resource use before and after the procedure, by enriching NHR data with other existing data resources.
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Affiliation(s)
| | | | - Edgar D Daeter
- Department of Cardiothoracic Surgery, St Antonius Hospital, Nieuwegein, The Netherlands
| | - Peter W Danse
- Department of Cardiology, Rijnstate Hospital, Arnhem, The Netherlands
| | - Wilson W Li
- Department of Cardiothoracic Surgery, Radboud University Medical Centre, Nijmegen, The Netherlands
| | - Erik Lipsic
- Department of Cardiology, University Medical Centre Groningen, Groningen, The Netherlands
| | - Maaike M Roefs
- Netherlands Heart Registration, Utrecht, The Netherlands
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12
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Dai H, Younis A, Kong JD, Puce L, Jabbour G, Yuan H, Bragazzi NL. Big Data in Cardiology: State-of-Art and Future Prospects. Front Cardiovasc Med 2022; 9:844296. [PMID: 35433868 PMCID: PMC9010556 DOI: 10.3389/fcvm.2022.844296] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 02/24/2022] [Indexed: 11/23/2022] Open
Abstract
Cardiological disorders contribute to a significant portion of the global burden of disease. Cardiology can benefit from Big Data, which are generated and released by different sources and channels, like epidemiological surveys, national registries, electronic clinical records, claims-based databases (epidemiological Big Data), wet-lab, and next-generation sequencing (molecular Big Data), smartphones, smartwatches, and other mobile devices, sensors and wearable technologies, imaging techniques (computational Big Data), non-conventional data streams such as social networks, and web queries (digital Big Data), among others. Big Data is increasingly having a more and more relevant role, being highly ubiquitous and pervasive in contemporary society and paving the way for new, unprecedented perspectives in biomedicine, including cardiology. Big Data can be a real paradigm shift that revolutionizes cardiological practice and clinical research. However, some methodological issues should be properly addressed (like recording and association biases) and some ethical issues should be considered (such as privacy). Therefore, further research in the field is warranted.
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Affiliation(s)
- Haijiang Dai
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Arwa Younis
- Clinical Cardiovascular Research Center, University of Rochester Medical Center, Rochester, New York, NY, United States
| | - Jude Dzevela Kong
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
| | - Luca Puce
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Georges Jabbour
- Physical Education Department, College of Education, Qatar University, Doha, Qatar
| | - Hong Yuan
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
- Hong Yuan
| | - Nicola Luigi Bragazzi
- Laboratory for Industrial and Applied Mathematics (LIAM), Department of Mathematics and Statistics, York University, Toronto, ON, Canada
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Postgraduate School of Public Health, Department of Health Sciences, University of Genoa, Genoa, Italy
- Section of Musculoskeletal Disease, Leeds Institute of Molecular Medicine, NIHR Leeds Musculoskeletal Biomedical Research Unit, University of Leeds, Chapel Allerton Hospital, Leeds, United Kingdom
- *Correspondence: Nicola Luigi Bragazzi
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13
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Huellebrand M, Ivantsits M, Tautz L, Kelle S, Hennemuth A. A Collaborative Approach for the Development and Application of Machine Learning Solutions for CMR-Based Cardiac Disease Classification. Front Cardiovasc Med 2022; 9:829512. [PMID: 35360025 PMCID: PMC8960112 DOI: 10.3389/fcvm.2022.829512] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 02/07/2022] [Indexed: 01/22/2023] Open
Abstract
The quality and acceptance of machine learning (ML) approaches in cardiovascular data interpretation depends strongly on model design and training and the interaction with the clinical experts. We hypothesize that a software infrastructure for the training and application of ML models can support the improvement of the model training and provide relevant information for understanding the classification-relevant data features. The presented solution supports an iterative training, evaluation, and exploration of machine-learning-based multimodal data interpretation methods considering cardiac MRI data. Correction, annotation, and exploration of clinical data and interpretation of results are supported through dedicated interactive visual analytics tools. We test the presented concept with two use cases from the ACDC and EMIDEC cardiac MRI image analysis challenges. In both applications, pre-trained 2D U-Nets are used for segmentation, and classifiers are trained for diagnostic tasks using radiomics features of the segmented anatomical structures. The solution was successfully used to identify outliers in automatic segmentation and image acquisition. The targeted curation and addition of expert annotations improved the performance of the machine learning models. Clinical experts were supported in understanding specific anatomical and functional characteristics of the assigned disease classes.
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14
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Vilanilam G, Abraham L. Big data in clinical sciences-value, impact, and fallacies. ARCHIVES OF MEDICINE AND HEALTH SCIENCES 2022. [DOI: 10.4103/amhs.amhs_296_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
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15
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Caldarola P, Murrone A, Roncon L, Di Pasquale G, Tavazzi L, Amodeo V, Aspromonte N, Cipriani M, Di Lenarda A, Domenicucci S, Francese GM, Imazio M, di Uccio FS, Urbinati S, Valente S, Gulizia MM, Colivicchi F, Gabrielli D. ANMCO POSITION PAPER: The reorganization of cardiology in times of the SARS-CoV-2 pandemic. Eur Heart J Suppl 2021; 23:C154-C163. [PMID: 34456642 PMCID: PMC8387778 DOI: 10.1093/eurheartj/suab073] [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] [Indexed: 11/17/2022]
Abstract
The COVID-19 pandemic represents an unprecedented event that has brought deep changes in hospital facilities with reshaping of the health system organization, revealing inadequacies of current hospital and local health systems. When the COVID-19 emergency will end, further evaluation of the national health system, new organization of acute wards, and a further evolution of the entire health system will be needed to improve care during the chronic phase of disease. Therefore, new standards for healthcare personnel, more efficient organization of hospital facilities for patients with acute illnesses, improvement of technological approaches, and better integration between hospital and territorial services should be pursued. With experience derived from the COVID-19 pandemic,new models, paradigms, interventional approaches, values and priorities should be suggested and implemented.
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Affiliation(s)
| | - Adriano Murrone
- Cardiology-ICU Department, Ospedali di Città di Castello e di Gubbio-Gualdo Tadino, AUSL Umbria 1, Perugia, Italy
| | - Loris Roncon
- Cardiology Department, Ospedale Santa Maria della Misericordia, Rovigo, Italy
| | | | - Luigi Tavazzi
- Maria Cecilia Hospital, GVM Care & Research, Cotignola, Italy
| | - Vincenzo Amodeo
- Cardiology-ICU Department, Ospedale Santa Maria degli Ungheresi, Polistena, Italy
| | - Nadia Aspromonte
- Heart Failure Unit, Department of Cardiovascular and Thoracic Sciences, Fondazione Policlinico Universitario A. Gemelli IRCCS, Roma, Italy
| | - Manlio Cipriani
- Cardiology 2-Heart Failure and Transplants, Dipartimento Cardiotoracovascolare “A. De Gasperis”, ASST Grande Ospedale Metropolitano Niguarda, Milano, Italy
| | - Andrea Di Lenarda
- Cardiovascolular and Sports Medicine Department, Azienda Sanitaria Universitaria Giuliano Isontina-ASUGI, Trieste, Italy
| | | | - Giuseppina Maura Francese
- Cardiology Department, Azienda di Rilievo Nazionale e Alta Specializzazione “Garibaldi”, Catania, Italy
| | - Massimo Imazio
- Cardiology Department, P.O.U. Santa Maria della Misericordia, Udine, Italy
| | | | - Stefano Urbinati
- Cardiology Department, Ospedale Bellaria, Azienda USL di Bologna, Bologna, Italy
| | - Serafina Valente
- Clinical-Surgical Cardiology and ICU Department, A.O.U. Senese, Ospedale Santa Maria alle Scotte, Siena, Italy
| | - Michele Massimo Gulizia
- Cardiology Department, Azienda di Rilievo Nazionale e Alta Specializzazione “Garibaldi”, Catania, Italy
- Fondazione per il Tuo cuore—Heart Care Foundation, Firenze, Italy
| | - Furio Colivicchi
- Clinical and Rehabilitation Cardiology Department, Presidio Ospedaliero San Filippo Neri, ASL Roma 1, Roma, Italy
| | - Domenico Gabrielli
- Cardiology Unit, Cardiotoracovascular Department, Azienda Ospedaliera San Camillo Forlanini, Roma, Italy
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16
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Social Determinants of Health and Cardiovascular Disease: Current State and Future Directions Towards Healthcare Equity. Curr Atheroscler Rep 2021; 23:55. [PMID: 34308497 DOI: 10.1007/s11883-021-00949-w] [Citation(s) in RCA: 67] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/06/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE OF REVIEW We sought to examine the role of social and environmental conditions that determine an individual's behaviors and risk of disease-collectively known as social determinants of health (SDOH)-in shaping cardiovascular (CV) health of the population and giving rise to disparities in risk factors, outcomes, and clinical care for cardiovascular disease (CVD), the leading cause of death in the United States (US). RECENT FINDINGS Traditional CV risk factors have been extensively targeted in existing CVD prevention and management paradigms, often with little attention to SDOH. Limited evidence suggests an association between individual SDOH (e.g., income, education) and CVD. However, inequities in CVD care, risk factors, and outcomes have not been studied using a broad SDOH framework. We examined existing evidence of the association between SDOH-organized into 6 domains, including economic stability, education, food, neighborhood and physical environment, healthcare system, and community and social context-and CVD. Greater social adversity, defined by adverse SDOH, was linked to higher burden of CVD risk factors and poor outcomes, such as stroke, myocardial infarction (MI), coronary heart disease, heart failure, and mortality. Conversely, favorable social conditions had protective effects on CVD. Upstream SDOH interact across domains to produce cumulative downstream effects on CV health, via multiple physiologic and behavioral pathways. SDOH are major drivers of sociodemographic disparities in CVD, with a disproportionate impact on socially disadvantaged populations. Efforts to achieve health equity should take into account the structural, institutional, and environmental barriers to optimum CV health in marginalized populations. In this review, we highlight major knowledge gaps for each SDOH domain and propose a set of actionable recommendations to inform CVD care, ensure equitable distribution of healthcare resources, and reduce observed disparities.
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17
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Chew DS, Rennert-May E, Lu S, Parkins M, Miller RJ, Somayaji R. Sex differences in health resource utilization, costs and mortality during hospitalization for infective endocarditis in the United States. AMERICAN HEART JOURNAL PLUS : CARDIOLOGY RESEARCH AND PRACTICE 2021; 3:100014. [PMID: 38558928 PMCID: PMC10978108 DOI: 10.1016/j.ahjo.2021.100014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 05/01/2021] [Accepted: 05/01/2021] [Indexed: 04/04/2024]
Abstract
Background Few studies have assessed the association between sex and outcomes among patients with infective endocarditis. The aim of the study was to better understand the association between biologic sex, clinical outcomes and surgical treatment patterns among a contemporary cohort of patients admitted to hospital with infective endocarditis. Methods We used the National Inpatient Sample dataset from the Health Care Utilization Project to identify adult patients admitted for infective endocarditis between January and December 2016. We compared outcomes between men and women including inpatient hospital mortality, direct hospital costs, length of stay, and inpatient surgical treatment patterns. Multivariable analyses were performed with adjustment for age, socioeconomic status, and comorbidity burden. Results Among 18,702 patients with infective endocarditis, there were 8730 (46.7%) women and 1753 (8.4%) in-hospital deaths. In multivariable analysis, female sex was associated with a trend toward lower in-hospital mortality (adjusted odds ratio (OR) 0.90; 95% confidence interval (CI) 0.80 to 1.01, p = 0.06). Additionally, female sex was associated with significantly shorter hospital length of stay (-0.5 days; 95% CI -0.88 to -0.12, p = 0.009) and lower hospital costs (-$3035; 95% CI -$4277 to -$1792; p < 0.001). Notably, women were less likely to undergo surgical intervention (adjusted OR 0.59; 95% CI 0.52 to 0.67, p < 0.001). Conclusions In a contemporary, nationally representative cohort of patients admitted for IE in the United States, there were sex-specific differences in management and in-hospital outcomes. Possible sex-based bias in treatment patterns and access to inpatient surgical intervention for infective endocarditis warrants further study.
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Affiliation(s)
- Derek S. Chew
- Duke Clinical Research Institute, Duke University, Durham, NC, USA
| | - Elissa Rennert-May
- Department of Community Health Sciences, University of Calgary, Alberta, Canada
- Department of Medicine, University of Calgary, Alberta, Canada
- O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada
| | - Shengjie Lu
- O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada
| | - Michael Parkins
- Department of Medicine, University of Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada
| | - Robert J.H. Miller
- Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, Alberta, Canada
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Ranjani Somayaji
- Department of Community Health Sciences, University of Calgary, Alberta, Canada
- Department of Medicine, University of Calgary, Alberta, Canada
- O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada
- Snyder Institute for Chronic Diseases, University of Calgary, Calgary, Alberta, Canada
- Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, Alberta, Canada
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18
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Vlahou A, Hallinan D, Apweiler R, Argiles A, Beige J, Benigni A, Bischoff R, Black PC, Boehm F, Céraline J, Chrousos GP, Delles C, Evenepoel P, Fridolin I, Glorieux G, van Gool AJ, Heidegger I, Ioannidis JPA, Jankowski J, Jankowski V, Jeronimo C, Kamat AM, Masereeuw R, Mayer G, Mischak H, Ortiz A, Remuzzi G, Rossing P, Schanstra JP, Schmitz-Dräger BJ, Spasovski G, Staessen JA, Stamatialis D, Stenvinkel P, Wanner C, Williams SB, Zannad F, Zoccali C, Vanholder R. Data Sharing Under the General Data Protection Regulation: Time to Harmonize Law and Research Ethics? Hypertension 2021; 77:1029-1035. [PMID: 33583200 PMCID: PMC7968961 DOI: 10.1161/hypertensionaha.120.16340] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Supplemental Digital Content is available in the text. The General Data Protection Regulation (GDPR) became binding law in the European Union Member States in 2018, as a step toward harmonizing personal data protection legislation in the European Union. The Regulation governs almost all types of personal data processing, hence, also, those pertaining to biomedical research. The purpose of this article is to highlight the main practical issues related to data and biological sample sharing that biomedical researchers face regularly, and to specify how these are addressed in the context of GDPR, after consulting with ethics/legal experts. We identify areas in which clarifications of the GDPR are needed, particularly those related to consent requirements by study participants. Amendments should target the following: (1) restricting exceptions based on national laws and increasing harmonization, (2) confirming the concept of broad consent, and (3) defining a roadmap for secondary use of data. These changes will be achieved by acknowledged learned societies in the field taking the lead in preparing a document giving guidance for the optimal interpretation of the GDPR, which will be finalized following a period of commenting by a broad multistakeholder audience. In parallel, promoting engagement and education of the public in the relevant issues (such as different consent types or residual risk for re-identification), on both local/national and international levels, is considered critical for advancement. We hope that this article will open this broad discussion involving all major stakeholders, toward optimizing the GDPR and allowing a harmonized transnational research approach.
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Affiliation(s)
- Antonia Vlahou
- From the Systems Biology Center, Biomedical Research Foundation, Academy of Athens, Greece (A.V.)
| | - Dara Hallinan
- FIZ Karlsruhe - Leibniz-Institut für Informationsinfrastruktur, Eggenstein-Leopoldshafen, Germany (D.H., F.B.)
| | - Rolf Apweiler
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom (R.A.)
| | - Angel Argiles
- SAS RD-Néphrologie and Bio-Communication Cardio-Métabolique (BC2M) EA7288 and University Hospital Lapeyronie, University of Montpellier, France (A.A.)
| | - Joachim Beige
- KfH-Nierenzentrum und Klinikum St. Georg, Nephrologie, Leipzig, Germany (J.B.)
| | - Ariela Benigni
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy (A.B., G.R.)
| | - Rainer Bischoff
- Department of Analytical Biochemistry, University of Groningen, The Netherlands (R.B.)
| | - Peter C Black
- Vancouver Prostate Centre, Department of Urologic Sciences, University of British Columbia, Canada (P.C.B.)
| | - Franziska Boehm
- FIZ Karlsruhe - Leibniz-Institut für Informationsinfrastruktur, Eggenstein-Leopoldshafen, Germany (D.H., F.B.)
| | - Jocelyn Céraline
- Institute of Genetics and Molecular and Cellular Biology, Institut de cancérologie Strasbourg Europe, Université de Strasbourg, France (J.C.)
| | - George P Chrousos
- University Research Institute of Maternal and Child Health & Precision Medicine, National and Kapodistrian University of Athens, 'Aghia Sophia' Children's Hospital, Greece; (G.P.C.)
| | - Christian Delles
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, United Kingdom (C.D.)
| | - Pieter Evenepoel
- Laboratory of Nephrology, Department of Immunology and Microbiology, Leuven, Belgium (P.E.)
| | - Ivo Fridolin
- Department of Health Technologies, Tallinn University of Technology, Estonia (I.F.)
| | - Griet Glorieux
- Nephrology Section, Department of Internal Medicine and Pediatrics, Ghent University Hospital, Belgium (G.G., R.V.)
| | - Alain J van Gool
- Translational Metabolic Laboratory, Department of Laboratory Medicine, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands (A.J.v.G.)
| | - Isabel Heidegger
- Department of Urology, Medizinische Universität Innsbruck, Austria (I.H.)
| | - John P A Ioannidis
- Departments of Medicine and of Epidemiology and Population Health and Meta-Research Innovation Center at Stanford (METRICS), Stanford University (J.P.A.I.)
| | - Joachim Jankowski
- Institute of Cardiovascular Research, RWTH Aachen University, Germany (J.J., V.J.)
| | - Vera Jankowski
- Institute of Cardiovascular Research, RWTH Aachen University, Germany (J.J., V.J.)
| | - Carmen Jeronimo
- Cancer Biology and Epigenetics Group, Portuguese Oncology Institute of Porto and Abel Salazar Institute of Biomedical Sciences, University of Porto, Portugal (C.J.)
| | - Ashish M Kamat
- Division of Surgery, Department of Urology, The University of Texas MD Anderson Cancer Centre, Houston (A.K.)
| | - Rosalinde Masereeuw
- Div. Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, NL (R.M.)
| | - Gert Mayer
- Department of Internal Medicine IV (Nephrology and Hypertension), Medizinische Universität Innsbruck, Austria (G.M.)
| | - Harald Mischak
- Mosaiques Diagnostics and Therapeutics AG, Hannover, Germany (H.M.)
| | - Alberto Ortiz
- Department of Nephrology and Hypertension, IIS - Fundación Jiménez Díaz-UAM, Madrid, Spain (A.O.)
| | - Giuseppe Remuzzi
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Bergamo, Italy (A.B., G.R.)
| | - Peter Rossing
- Steno Diabetes Center, University of Copenhagen, Denmark (P.R.)
| | - Joost P Schanstra
- Institut National de la Santé et de la Recherche Médicale (INSERM), U1048, Institut of Cardiovascular and Metabolic Disease, Toulouse and Université Toulouse III Paul-Sabatier, France (J.P.S.)
| | - Bernd J Schmitz-Dräger
- Urologie 24, Nuremberg, and Department of Urology, Friedrich-Alexander University of Erlangen, Germany (B.J.S-D)
| | - Goce Spasovski
- Department of Nephrology, University Clinical Center Skopje, North Macedonia (G.S.)
| | - Jan A Staessen
- Research Institute Alliance for the Promotion of Preventive Medicine, Mechelen, Belgium, Biomedical Science Group, University of Leuven (J.A.S.)
| | - Dimitrios Stamatialis
- Bioartificial organs, Department of Biomaterials Science and Technology, Technical Medical Centre, University of Twente, Enschede, The Netherlands (D.S.)
| | - Peter Stenvinkel
- Department of Renal Medicine M99, Karolinska University Hospital, Stockholm, Sweden (P.S.)
| | - Christoph Wanner
- Department of Medicine, Division of Nephrology, University Hospital, Würzburg, Germany (C.W.)
| | - Stephen B Williams
- Department of Surgery, Division of Urology, The University of Texas Medical Branch, Galveston (S.B.W.)
| | - Faiez Zannad
- Centre d'Investigation Clinique Inserm and Université de Lorraine, CHU Nancy, France (F.Z.)
| | - Carmine Zoccali
- Clinical Epidemiology and Physiopathology of Renal Diseases and Hypertension of Reggio Calabria, National Council of Research, Institute of Clinical Physiology, Italy (C.Z.)
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Yan YH, Chien TW. The use of forest plot to identify article similarity and differences in characteristics between journals using medical subject headings terms: A protocol for bibliometric study. Medicine (Baltimore) 2021; 100:e24610. [PMID: 33578568 PMCID: PMC7886407 DOI: 10.1097/md.0000000000024610] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 01/07/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Comparison of similarity and difference in research types among journals are concerned in literature. However, to date, none display the methodology seen in selecting similar journals related to the target journal, as similar articles did to a given article. Authors need 1 effective method not only to find similar journals for their studies but also to know the difference in methods. This study (1) shows the similar journals for the target journal online displayed, and (2) identifies the effect of similarity odds ratio compared to the counterparts using the forest plots in Meta-analysis and the major medical subject headings (MeSH terms). METHODS We downloaded 1000 recent top 20 most similar articles related to the Respiratory Care journal from the PubMed library, plotted the clusters of related journals using social network analysis (SNA), and compared the MeSH terms in differences in an odds ratio unit using the forest plot relevant to Respiratory Care and the most similar journals. Q statistic and I-square (I2) index were used to evaluate the difference in the proportion of events. RESULTS This study found that (1) the journals related to Respiratory Care are easily presented on Google Maps; (2) 10 journal clusters were identified using SNA; (3) the top 3 MeSH terms are methods, therapy, and physiopathology, and (4) the odds ratios of MeSH terms between journals associated with the Respiratory Care showing different from Int J Chron Obstruct Pulmori Dis and similar to Curr Opin Endocrinol Diabetes Obes within heterogeneity with I2 = 70.5% (P < 0.001) and 0% (P = 0.803), respectively. CONCLUSIONS SNA and forest plots provide deep insight into the relationships between journals in MeSH terms. The results of this research can provide readers with a concept diagram that can be used for future submissions to a given journal.
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Affiliation(s)
- Yu-Hua Yan
- Department of Medical Research, Tainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation)
- Department of Hospital and Health Care Administration, Chia Nan University of Pharmacy and Science
| | - Tsair-Wei Chien
- Department of Medical Research, Chi-Mei Medical Center, Tainan City, Taiwan
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20
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Meltzer SN, Weintraub WS. The Role of National Registries in Improving Quality of Care and Outcomes for Cardiovascular Disease. Methodist Debakey Cardiovasc J 2020; 16:205-211. [PMID: 33133356 DOI: 10.14797/mdcj-16-3-205] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Cardiovascular registries play an integral role in providing real-world data on a number of cardiovascular conditions and allowing measurement of quality metrics across a large cohort of patients. Over the past 35 years, the number of cardiovascular registries has skyrocketed, and their use will only continue to grow as data on novel procedures and devices will need to be collected and analyzed. The American College of Cardiology and Society of Thoracic Surgeons Transcatheter Valve Therapy Registry is just one example of a modern registry that plays a crucial role in collecting data on patients undergoing transcatheter valvular procedures. Through public reporting registries, data can be shared on a hospital and provider level for many quality performance measures. There remains much work to be done on allowing automated data extraction from the electronic medical record directly into registries. No matter how sophisticated and complete a registry is, it can never overcome the problem of treatment selection bias that is inherent in observational data. This review discusses the growth, benefits, and limitations of national registries and their role in developing evidence for best clinical practice, measuring outcomes, providing feedback to clinicians, and improving quality of care.
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21
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Khan IH, Javaid M. Big Data Applications in Medical Field: A Literature Review. JOURNAL OF INDUSTRIAL INTEGRATION AND MANAGEMENT 2020. [DOI: 10.1142/s242486222030001x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Digital imaging and medical reporting have acquired an essential role in healthcare, but the main challenge is the storage of a high volume of patient data. Although newer technologies are already introduced in the medical sciences to save records size, Big Data provides advancements by storing a large amount of data to improve the efficiency and quality of patient treatment with better care. It provides intelligent automation capabilities to reduce errors than manual inputs. Large numbers of research papers on big data in the medical field are studied and analyzed for their impacts, benefits, and applications. Big data has great potential to support the digitalization of all medical and clinical records and then save the entire data regarding the medical history of an individual or a group. This paper discusses big data usage for various industries and sectors. Finally, 12 significant applications for the medical field by the implementation of big data are identified and studied with a brief description. This technology can be gainfully used to extract useful information from the available data by analyzing and managing them through a combination of hardware and software. With technological advancement, big data provides health-related information for millions of patient-related to life issues such as lab tests reporting, clinical narratives, demographics, prescription, medical diagnosis, and related documentation. Thus, Big Data is essential in developing a better yet efficient analysis and storage healthcare services. The demand for big data applications is increasing due to its capability of handling and analyzing massive data. Not only in the future but even now, Big Data is proving itself as an axiom of storing, developing, analyzing, and providing overall health information to the physicians.
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Affiliation(s)
- Ibrahim Haleem Khan
- School of Engineering Sciences and Technology, Jamia Hamdard, New Delhi, India
| | - Mohd Javaid
- Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
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Ang YS, Rajamani S, Haldar SM, Hüser J. A New Therapeutic Framework for Atrial Fibrillation Drug Development. Circ Res 2020; 127:184-201. [DOI: 10.1161/circresaha.120.316576] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Atrial fibrillation (AF) is a highly prevalent cardiac arrhythmia and cause of significant morbidity and mortality. Its increasing prevalence in aging societies constitutes a growing challenge to global healthcare systems. Despite substantial unmet needs in AF prevention and treatment, drug developments hitherto have been challenging, and the current pharmaceutical pipeline is nearly empty. In this review, we argue that current drugs for AF are inadequate because of an oversimplified system for patient classification and the development of drugs that do not interdict underlying disease mechanisms. We posit that an improved understanding of AF molecular pathophysiology related to the continuous identification of novel disease-modifying drug targets and an increased appreciation of patient heterogeneity provide a new framework to personalize AF drug development. Together with recent innovations in diagnostics, remote rhythm monitoring, and big data capabilities, we anticipate that adoption of a new framework for patient subsegmentation based on pathophysiological, genetic, and molecular subsets will improve success rates of clinical trials and advance drugs that reduce the individual patient and public health burden of AF.
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Affiliation(s)
- Yen-Sin Ang
- From Amgen Research, Cardiometabolic Disorders, South San Francisco, CA (Y.-S.A., S.R., S.M.H.)
| | - Sridharan Rajamani
- From Amgen Research, Cardiometabolic Disorders, South San Francisco, CA (Y.-S.A., S.R., S.M.H.)
| | - Saptarsi M. Haldar
- From Amgen Research, Cardiometabolic Disorders, South San Francisco, CA (Y.-S.A., S.R., S.M.H.)
- Gladstone Institutes, San Francisco, CA (S.M.H.)
- Department of Medicine, Cardiology Division, UCSF School of Medicine, San Francisco, CA (S.M.H.)
| | - Jörg Hüser
- Bayer AG, Pharma-RD-PCR TA Cardiovascular Disease, Wuppertal, Germany (J.H.)
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Weintraub WS. The Approach to Transcatheter Aortic Valve Peplacement Matures. CARDIOVASCULAR REVASCULARIZATION MEDICINE 2020; 21:971-972. [PMID: 32461048 DOI: 10.1016/j.carrev.2020.05.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 05/15/2020] [Indexed: 10/24/2022]
Affiliation(s)
- William S Weintraub
- MedStar Heart & Vascular Institute, Washington, DC, United States of America.
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