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Citarella BW, Kartsonaki C, Ibáñez-Prada ED, Gonçalves BP, Baruch J, Escher M, Pritchard MG, Wei J, Philippy F, Dagens A, Hall M, Lee J, Kutsogiannis DJ, Wils EJ, Fernandes MA, Tirupakuzhi Vijayaraghavan BK, Panda PK, Martin-Loeches I, Ohshimo S, Fatoni AZ, Horby P, Dunning J, Rello J, Merson L, Rojek A, Vaillant M, Olliaro P, Reyes LF. Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms. Heliyon 2024; 10:e29591. [PMID: 38779000 PMCID: PMC11109728 DOI: 10.1016/j.heliyon.2024.e29591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 05/25/2024] Open
Abstract
Background COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83-0.93]). Conclusion Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders.
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Affiliation(s)
| | - Christiana Kartsonaki
- MRC Population Health Research Unit, Clinical Trials Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Elsa D. Ibáñez-Prada
- Universidad de La Sabana, Chía, Colombia
- Clínica Universidad de La Sabana, Chía, Colombia
| | | | - Joaquin Baruch
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Martina Escher
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Mark G. Pritchard
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Jia Wei
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Fred Philippy
- Competence Centre for Methodology and Statistics, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Andrew Dagens
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Matthew Hall
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - James Lee
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Demetrios James Kutsogiannis
- Department of Critical Care Medicine, Faculty of Medicine and Dentistry, The University of Alberta, Edmonton, Alberta, Canada
| | - Evert-Jan Wils
- Department of Intensive Care, Franciscus Gasthuis & Vlietland, Rotterdam, the Netherlands
| | - Marília Andreia Fernandes
- Department of Internal Medicine, Hospital Curry Cabral, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal
| | | | | | - Ignacio Martin-Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James' Hospital, Dublin, Ireland
| | - Shinichiro Ohshimo
- Department of Emergency and Critical Care Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Arie Zainul Fatoni
- Department of Anesthesiology and Intensive Therapy, Saiful Anwar General Hospital, Brawijaya University, Malang, East Java, Indonesia
| | - Peter Horby
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Jake Dunning
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Jordi Rello
- Vall d'Hebrón Institute Research, Barcelona, Spain
- CHU Nîmes, Nîmes, France
| | - Laura Merson
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Amanda Rojek
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
- Royal Melbourne Hospital, Melbourne, Australia
| | - Michel Vaillant
- Competence Centre for Methodology and Statistics, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Piero Olliaro
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
| | - Luis Felipe Reyes
- ISARIC, Pandemic Sciences Institute, University of Oxford, Oxford, UK
- Universidad de La Sabana, Chía, Colombia
- Clínica Universidad de La Sabana, Chía, Colombia
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Cresswell K, Rigby M, Magrabi F, Scott P, Brender J, Craven CK, Wong ZSY, Kukhareva P, Ammenwerth E, Georgiou A, Medlock S, De Keizer NF, Nykänen P, Prgomet M, Williams R. The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision. Health Policy 2023; 136:104889. [PMID: 37579545 DOI: 10.1016/j.healthpol.2023.104889] [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: 07/27/2023] [Accepted: 08/04/2023] [Indexed: 08/16/2023]
Abstract
Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology. We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes. Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.
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Affiliation(s)
- Kathrin Cresswell
- The University of Edinburgh, Usher Institute, Edinburgh, United Kingdom.
| | - Michael Rigby
- Keele University, School of Social, Political and Global Studies and School of Primary, Community and Social Care, Keele, United Kingdom
| | - Farah Magrabi
- Macquarie University, Australian Institute of Health Innovation, Sydney, Australia
| | - Philip Scott
- University of Wales Trinity Saint David, Swansea, United Kingdom
| | - Jytte Brender
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Catherine K Craven
- University of Texas Health Science Center at San Antonio, San Antonio, TX, United States
| | - Zoie Shui-Yee Wong
- St. Luke's International University, Graduate School of Public Health, Tokyo, Japan
| | - Polina Kukhareva
- Department of Biomedical Informatics, University of Utah, United States of America
| | - Elske Ammenwerth
- UMIT TIROL, Private University for Health Sciences and Health Informatics, Institute of Medical Informatics, Hall in Tirol, Austria
| | - Andrew Georgiou
- Macquarie University, Australian Institute of Health Innovation, Sydney, Australia
| | - Stephanie Medlock
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health research institute, Digital Health and Quality of Care Amsterdam, the Netherlands
| | - Nicolette F De Keizer
- Amsterdam UMC location University of Amsterdam, Department of Medical Informatics, Meibergdreef 9, Amsterdam, the Netherlands; Amsterdam Public Health research institute, Digital Health and Quality of Care Amsterdam, the Netherlands
| | - Pirkko Nykänen
- Tampere University, Faculty for Information Technology and Communication Sciences, Finland
| | - Mirela Prgomet
- Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
| | - Robin Williams
- The University of Edinburgh, Institute for the Study of Science, Technology and Innovation, Edinburgh, United Kingdom
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Clinical and Laboratory Characteristics of Pediatric COVID-19 Population—A Bibliometric Analysis. J Clin Med 2022; 11:jcm11205987. [PMID: 36294306 PMCID: PMC9605229 DOI: 10.3390/jcm11205987] [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: 08/01/2022] [Revised: 09/17/2022] [Accepted: 10/09/2022] [Indexed: 11/16/2022] Open
Abstract
The literature on the COVID-19 landscape has rapidly expanded in the pandemic period. The current study undertakes a bibliometric analysis of research in the topic of the clinical and laboratory characteristics of pediatric COVID-19 cases. Our aim is to perform a comprehensive bibliometric review of current research trends and patterns of this research domain. Publications retrieved from the Web of Science Core Collection and VOSviewer were used for analysis and network visualization. We analyzed geographical distribution and temporal trends, collaboration and citation patterns of authors, institutions, and countries, and core research themes from co-occurrence of keywords and terms. The analysis showed that contributions in the research field were from 302 publications, 1104 institutions, 62 countries, and 172 journals. Many publications were authored by American and Chinese authors, and many were published in the Pediatric Infectious Disease Journal, Pediatric Pulmonology, and Frontiers in Pediatrics. The top cited and co-cited journals were the New England Journal of Medicine, Nature, JAMA, Lancet Infectious Diseases, and BMJ. The network visualization maps of keywords and terms offered a global overview of the clinical and laboratory characteristics of pediatric COVID-19 patients. The bibliometric profile of the researched domain, based on analyzing a large collection of publications/data, could (i) enrich the researchers and non-researchers understanding of the field existing patterns and trends, and (ii) be useful in clinical practice (diagnostic and management) and public health policy.
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Leiner J, Pellissier V, Nitsche A, König S, Hohenstein S, Nachtigall I, Hindricks G, Kutschker C, Rolinski B, Gebauer J, Prantz A, Schubert J, Patzschke J, Bollmann A, Wolz M. SARS-CoV-2 rapid antigen testing in the healthcare sector: A clinical prediction model for identifying false negative results. Int J Infect Dis 2021; 112:117-123. [PMID: 34517045 PMCID: PMC8431843 DOI: 10.1016/j.ijid.2021.09.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/30/2021] [Accepted: 09/07/2021] [Indexed: 01/08/2023] Open
Abstract
OBJECTIVES SARS-CoV-2 rapid antigen tests (RAT) provide fast identification of infectious patients when RT-PCR results are not immediately available. We aimed to develop a prediction model for identification of false negative (FN) RAT results. METHODS In this multicenter trial, patients with documented paired results of RAT and RT-PCR between October 1st 2020 and January 31st 2021 were retrospectively analyzed regarding clinical findings. Variables included demographics, laboratory values and specific symptoms. Three different models were evaluated using Bayesian logistic regression. RESULTS The initial dataset contained 4,076 patients. Overall sensitivity and specificity of RAT was 62.3% and 97.6%. 2,997 cases with negative RAT results (FN: 120; true negative: 2,877; reference: RT-PCR) underwent further evaluation after removal of cases with missing data. The best-performing model for predicting FN RAT results containing 10 variables yielded an area under the curve of 0.971. Sensitivity, specificity, PPV and NPV for 0.09 as cut-off value (probability for FN RAT) were 0.85, 0.99, 0.7 and 0.99. CONCLUSION FN RAT results can be accurately identified through ten routinely available variables. Implementation of a prediction model in addition to RAT testing in clinical care can provide decision guidance for initiating appropriate hygiene measures and therefore helps avoiding nosocomial infections.
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Affiliation(s)
- Johannes Leiner
- Leipzig Heart Institute, Leipzig, Germany; Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Leipzig, Germany.
| | - Vincent Pellissier
- Leipzig Heart Institute, Leipzig, Germany; Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Leipzig, Germany
| | - Anne Nitsche
- Leipzig Heart Institute, Leipzig, Germany; Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Leipzig, Germany
| | - Sebastian König
- Leipzig Heart Institute, Leipzig, Germany; Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Leipzig, Germany
| | - Sven Hohenstein
- Leipzig Heart Institute, Leipzig, Germany; Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Leipzig, Germany
| | | | - Gerhard Hindricks
- Leipzig Heart Institute, Leipzig, Germany; Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Leipzig, Germany
| | | | | | | | - Anja Prantz
- Elblandkliniken, Department of laboratory medicine
| | | | | | - Andreas Bollmann
- Leipzig Heart Institute, Leipzig, Germany; Heart Center Leipzig at University of Leipzig, Department of Electrophysiology, Leipzig, Germany
| | - Martin Wolz
- Elblandklinikum Meissen, Department of Neurology and Geriatrics
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