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      Clinical diagnosis of SARS-CoV-2 infection: An observational study of respiratory tract infection in primary care in the early phase of the pandemic

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          Abstract

          Background

          Early in the COVID-19 pandemic, GPs had to distinguish SARS-CoV-2 from other aetiologies in patients presenting with respiratory tract infection (RTI) symptoms on clinical grounds and adapt management accordingly.

          Objectives

          To test the diagnostic accuracy of GPs’ clinical diagnosis of a SARS-CoV-2 infection in a period when COVID-19 was a new disease. To describe GPs’ management of patients presenting with RTI for whom no confirmed diagnosis was available. To investigate associations between patient and clinical features with a SARS-CoV-2 infection.

          Methods

          In April 2020–March 2021, 876 patients (9 countries) were recruited when they contacted their GP with symptoms of an RTI of unknown aetiology. A swab was taken at baseline for later analysis. Aetiology (PCR), diagnostic accuracy of GPs’ clinical SARS-CoV-2 diagnosis, and patient management were explored. Factors related to SARS-CoV-2 infection were determined by logistic regression modelling.

          Results

          GPs suspected SARS-CoV-2 in 53% of patients whereas 27% of patients tested positive for SARS-CoV-2. True-positive patients (23%) were more intensively managed for follow-up, antiviral prescribing and advice than true-negatives (42%). False negatives (5%) were under-advised, particularly for social distancing and isolation. Older age (OR: 1.02 (1.01–1.03)), male sex (OR: 1.68 (1.16–2.41)), loss of taste/smell (OR: 5.8 (3.7–9)), fever (OR: 1.9 (1.3–2.8)), muscle aches (OR: 2.1 (1.5–3)), and a known risk factor for COVID-19 (travel, health care worker, contact with proven case; OR: 2.7 (1.8–4)) were predictive of SARS-CoV-2 infection. Absence of loss of taste/smell, fever, muscle aches and a known risk factor for COVID-19 correctly excluded SARS-CoV-2 in 92.3% of patients, whereas presence of 3, or 4 of these variables correctly classified SARS-CoV-2 in 57.7% and 87.1%.

          Conclusion

          Correct clinical diagnosis of SARS-CoV-2 infection, without POC-testing available, appeared to be complicated.

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          Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

          Laure Wynants, Ben Van Calster, Marc M. J. Bonten … (2020)
          Abstract Objective To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia. Design Rapid systematic review and critical appraisal. Data sources PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.
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            Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study

            Ash K. Clift, Carol Coupland, Ruth H. Keogh … (2020)
            Abstract Objective To derive and validate a risk prediction algorithm to estimate hospital admission and mortality outcomes from coronavirus disease 2019 (covid-19) in adults. Design Population based cohort study. Setting and participants QResearch database, comprising 1205 general practices in England with linkage to covid-19 test results, Hospital Episode Statistics, and death registry data. 6.08 million adults aged 19-100 years were included in the derivation dataset and 2.17 million in the validation dataset. The derivation and first validation cohort period was 24 January 2020 to 30 April 2020. The second temporal validation cohort covered the period 1 May 2020 to 30 June 2020. Main outcome measures The primary outcome was time to death from covid-19, defined as death due to confirmed or suspected covid-19 as per the death certification or death occurring in a person with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the period 24 January to 30 April 2020. The secondary outcome was time to hospital admission with confirmed SARS-CoV-2 infection. Models were fitted in the derivation cohort to derive risk equations using a range of predictor variables. Performance, including measures of discrimination and calibration, was evaluated in each validation time period. Results 4384 deaths from covid-19 occurred in the derivation cohort during follow-up and 1722 in the first validation cohort period and 621 in the second validation cohort period. The final risk algorithms included age, ethnicity, deprivation, body mass index, and a range of comorbidities. The algorithm had good calibration in the first validation cohort. For deaths from covid-19 in men, it explained 73.1% (95% confidence interval 71.9% to 74.3%) of the variation in time to death (R2); the D statistic was 3.37 (95% confidence interval 3.27 to 3.47), and Harrell’s C was 0.928 (0.919 to 0.938). Similar results were obtained for women, for both outcomes, and in both time periods. In the top 5% of patients with the highest predicted risks of death, the sensitivity for identifying deaths within 97 days was 75.7%. People in the top 20% of predicted risk of death accounted for 94% of all deaths from covid-19. Conclusion The QCOVID population based risk algorithm performed well, showing very high levels of discrimination for deaths and hospital admissions due to covid-19. The absolute risks presented, however, will change over time in line with the prevailing SARS-C0V-2 infection rate and the extent of social distancing measures in place, so they should be interpreted with caution. The model can be recalibrated for different time periods, however, and has the potential to be dynamically updated as the pandemic evolves.
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              Lessons on the COVID-19 pandemic, for and by primary care professionals worldwide

              Salman Rawaf, Luke N Allen, Florian Stigler … (2020)
              Abstract The COVID-19 pandemic has modified organisation and processes of primary care. In this paper, we aim to summarise experiences of international primary care systems. We explored personal accounts and findings in reporting on the early experiences from primary care during the pandemic, through the online Global Forum on Universal Health Coverage and Primary Health Care. During the early stage of the pandemic, primary care continued as the first point of contact to the health system but was poorly informed by policy makers on how to fulfil its role and ill equipped to provide care while protecting staff and patients against further spread of the infection. In many countries, the creativity and initiatives of local health professionals led to the introduction or extension of the use of telephone, e-mail and virtual consulting, and introduced triaging to separate ‘suspected’ COVID-19 from non-COVID-19 care. There were substantial concerns of collateral damage to the health of the population due to abandoned or postponed routine care. The pandemic presents important lessons to strengthen health systems through better connection between public health, primary care, and secondary care to cope better with future waves of this and other pandemics.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Eur J Gen Pract
                Journal ID (iso-abbrev): Eur J Gen Pract
                Title: The European Journal of General Practice
                Publisher: Taylor & Francis
                ISSN (Print): 1381-4788
                ISSN (Electronic): 1751-1402
                Publication date (Electronic, pub): 23 October 2023
                Publication date (Electronic, collection): 2023
                Publication date PMC-release: 23 October 2023
                Volume: 29
                Issue: 1
                Electronic Location Identifier: 2270707
                Affiliations
                [a ]Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht , Utrecht, The Netherlands
                [b ]Nuffield Department of Primary Care Health Sciences, University of Oxford , Oxford, UK
                [c ]Institute of General Practice, Rostock University Medical Center , Rostock, Germany
                [d ]Department of Family Medicine, Medical University of Bialystok , Bialystok, Poland
                [e ]Department of Family Medicine & Population Health, University of Antwerp , Antwerp, Belgium
                [f ]Institut Universitari d‘Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol) , Barcelona, Spain
                [g ]National Center for Disease Control and Public Health, Tbilisi and Arner Science Management LLC , Tbilisi, Georgia
                [h ]DRC Drug Research Centre , Balatonfüred, Hungary
                [i ]University Clinic of Primary Medical Assistance of State University of Medicine and Pharmacy “N. Testemițanu” , Chişinǎu, The Republic of Moldova
                [j ]Laboratory of Medical Microbiology, Vaccine & Infectious Disease Institute, University of Antwerp , Antwerp, Belgium
                [k ]School of Public Health, Physiotherapy and Sports Science, University College Dublin (UCD) , Dublin, Ireland
                Author notes
                CONTACT Alike W. van der Velden a.w.vandervelden@ 123456umcutrecht.nl Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht , Universiteitsweg 100, Utrecht, 3584 CG, The Netherlands.
                Author information
                https://orcid.org/0000-0002-9443-2837
                Article
                Publisher ID: 2270707
                DOI: 10.1080/13814788.2023.2270707
                PMC ID: 10990254
                PubMed ID: 37870070
                SO-VID: f3dc3e93-f14e-4052-b9e7-8519d78e0de3
                Copyright © © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

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                Page count
                Figures: 0, Tables: 5, Pages: 8, Words: 5255
                Categories
                Subject: Research Article
                Subject: Original Article

                ScienceOpen disciplines: Medicine
                Keywords: sars-cov-2,covid-19,respiratory tract infection,prediction,diagnostic accuracy
                Data availability:
                ScienceOpen disciplines: Medicine
                Keywords: sars-cov-2, covid-19, respiratory tract infection, prediction, diagnostic accuracy

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