Symptom‐based case definitions for COVID‐19: Time and geographical variations for detection at hospital admission among 260,000 patients

ISARIC Clinical Characterisation Group; Joaquin Baruch; Amanda Rojek; Christiana Kartsonaki; Bharath Kumar Tirupakuzhi Vijayaraghavan; Bronner P.  Gonçalves; Mark G Pritchard; Laura Merson; Jake Dunning; Matthew Hall; Louise Sigfrid; Barbara W. Citarella; Srinivas Murthy; Trokon O. Yeabah; Piero Olliaro

doi:10.1111/irv.13039

Symptom‐based case definitions for COVID‐19: Time and geographical variations for detection at hospital admission among 260,000 patients

ISARIC Clinical Characterisation Group, Joaquin Baruch^*, Amanda Rojek, Christiana Kartsonaki, Bharath Kumar Tirupakuzhi Vijayaraghavan, Bronner P. Gonçalves, Mark G Pritchard, Laura Merson, Jake Dunning, Matthew Hall, Louise Sigfrid, Barbara W. Citarella, Srinivas Murthy, Trokon O. Yeabah, Piero Olliaro

^*Corresponding author for this work

Chemical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

Introduction: Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions.

Methods: Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test result were excluded.

Results: A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe and Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years; geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did not meet the case definition, the CFR increased.

Conclusions: The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.

Original language	English
Pages (from-to)	1040-1050
Number of pages	11
Journal	Influenza and other respiratory viruses
Volume	16
Issue number	6
Early online date	5 Sept 2022
DOIs	https://doi.org/10.1111/irv.13039
Publication status	Published - Nov 2022

Bibliographical note

Funding information:
This work was made possible by the UK Foreign, Commonwealth and Development Office; Wellcome Trust (215091/Z/18/Z, 205228/Z/16/Z, 220757/Z/20/Z); Bill and Melinda Gates Foundation (OPP1209135); the philanthropic support of the donors to the University of Oxford's COVID-19 Research Response Fund (0009109); CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and the co-ordination in Canada by Sunnybrook Research Institute; endorsement of the Irish Critical Care-Clinical Trials Group, co-ordination in Ireland by the Irish Critical Care-Clinical Trials Network at University College Dublin and funding by the Health Research Board of Ireland (CTN-2014-12); the Rapid European COVID-19 Emergency Response research (RECOVER) (H2020 project 101003589) and European Clinical Research Alliance on Infectious Diseases (ECRAID) (965313); the COVID Clinical Management Team, AIIMS, Rishikesh, India; the COVID-19 Clinical Management Team, Manipal Hospital Whitefield, Bengaluru, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team; the Liverpool School of Tropical Medicine and the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 Study Team; the Research Council of Norway Grant No. 312780 and a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; Imperial NIHR Biomedical Research Centre; the Comprehensive Local Research Networks (CLRNs) of which PJMO is an NIHR Senior Investigator (NIHR201385); Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies, in-kind contribution; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funded by the REACTing (REsearch & ACtion emergING infectious diseases) Consortium and by a grant of the French Ministry of Health (PHRC No. 20-0424); Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; Italian Ministry of Health ‘Fondi Ricerca corrente–L1P6’ to IRCCS Ospedale Sacro Cuore–Don Calabria; Australian Department of Health grant (3273191); Gender Equity Strategic Fund at University of Queensland, Artificial Intelligence for Pandemics (A14PAN) at University of Queensland, the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009) and the Prince Charles Hospital Foundation, Australia; grants from Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship Number 303953/2018-7; the Firland Foundation, Shoreline, Washington, USA; a grant from foundation Bevordering Onderzoek Franciscus; the South Eastern Norway Health Authority and the Research Council of Norway; Institute for Clinical Research (ICR), National Institutes of Health (NIH) supported by the Ministry of Health Malaysia; and preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection.

This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The data used for this research were obtained from ISARIC4C. We are extremely grateful to the 2648 frontline NHS clinical and research staff and volunteer medical students who collected these data in challenging circumstances and the generosity of the patients and their families for their individual contributions in these difficult times. The COVID-19 Clinical Information Network (CO-CIN) data were collated by ISARIC4C Investigators. Data and Material provision was supported by grants from the National Institute for Health Research (NIHR; Award CO-CIN-01), the Medical Research Council (MRC; Grant MC_PC_19059) and the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (Award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (Award 200927), Liverpool Experimental Cancer Medicine Centre (Grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (Award ISBRC-1215-20013) and NIHR Clinical Research Network providing infrastructure support. We also acknowledge the support of Jeremy J. Farrar and Nahoko Shindo.

Keywords

case definitions
COVID-19
hospitalisation

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BaruchJ2022SymptomFinal published version, 5.08 MBLicence: Creative Commons: Attribution (CC BY)

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ISARIC Clinical Characterisation Group, Baruch, J., Rojek, A., Kartsonaki, C., Vijayaraghavan, B. K. T., Gonçalves, B. P., Pritchard, M. G., Merson, L., Dunning, J., Hall, M., Sigfrid, L., Citarella, B. W., Murthy, S., Yeabah, T. O., & Olliaro, P. (2022). Symptom‐based case definitions for COVID‐19: Time and geographical variations for detection at hospital admission among 260,000 patients. Influenza and other respiratory viruses, 16(6), 1040-1050. https://doi.org/10.1111/irv.13039

@article{ac45aab61f584e36a726b9ec292ee864,

title = "Symptom‐based case definitions for COVID‐19: Time and geographical variations for detection at hospital admission among 260,000 patients",

abstract = "Introduction: Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions.Methods: Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test result were excluded.Results: A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe and Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years; geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did not meet the case definition, the CFR increased.Conclusions: The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.",

keywords = "case definitions, COVID-19, hospitalisation",

author = "{ISARIC Clinical Characterisation Group} and Joaquin Baruch and Amanda Rojek and Christiana Kartsonaki and Vijayaraghavan, {Bharath Kumar Tirupakuzhi} and Gon{\c c}alves, {Bronner P.} and Pritchard, {Mark G} and Laura Merson and Jake Dunning and Matthew Hall and Louise Sigfrid and Citarella, {Barbara W.} and Srinivas Murthy and Yeabah, {Trokon O.} and Piero Olliaro and Christopher Green",

note = "Funding information: This work was made possible by the UK Foreign, Commonwealth and Development Office; Wellcome Trust (215091/Z/18/Z, 205228/Z/16/Z, 220757/Z/20/Z); Bill and Melinda Gates Foundation (OPP1209135); the philanthropic support of the donors to the University of Oxford's COVID-19 Research Response Fund (0009109); CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and the co-ordination in Canada by Sunnybrook Research Institute; endorsement of the Irish Critical Care-Clinical Trials Group, co-ordination in Ireland by the Irish Critical Care-Clinical Trials Network at University College Dublin and funding by the Health Research Board of Ireland (CTN-2014-12); the Rapid European COVID-19 Emergency Response research (RECOVER) (H2020 project 101003589) and European Clinical Research Alliance on Infectious Diseases (ECRAID) (965313); the COVID Clinical Management Team, AIIMS, Rishikesh, India; the COVID-19 Clinical Management Team, Manipal Hospital Whitefield, Bengaluru, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team; the Liverpool School of Tropical Medicine and the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 Study Team; the Research Council of Norway Grant No. 312780 and a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; Imperial NIHR Biomedical Research Centre; the Comprehensive Local Research Networks (CLRNs) of which PJMO is an NIHR Senior Investigator (NIHR201385); Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies, in-kind contribution; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funded by the REACTing (REsearch & ACtion emergING infectious diseases) Consortium and by a grant of the French Ministry of Health (PHRC No. 20-0424); Stiftungsfonds zur F{\"o}rderung der Bek{\"a}mpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; Italian Ministry of Health {\textquoteleft}Fondi Ricerca corrente–L1P6{\textquoteright} to IRCCS Ospedale Sacro Cuore–Don Calabria; Australian Department of Health grant (3273191); Gender Equity Strategic Fund at University of Queensland, Artificial Intelligence for Pandemics (A14PAN) at University of Queensland, the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009) and the Prince Charles Hospital Foundation, Australia; grants from Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship Number 303953/2018-7; the Firland Foundation, Shoreline, Washington, USA; a grant from foundation Bevordering Onderzoek Franciscus; the South Eastern Norway Health Authority and the Research Council of Norway; Institute for Clinical Research (ICR), National Institutes of Health (NIH) supported by the Ministry of Health Malaysia; and preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection. This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The data used for this research were obtained from ISARIC4C. We are extremely grateful to the 2648 frontline NHS clinical and research staff and volunteer medical students who collected these data in challenging circumstances and the generosity of the patients and their families for their individual contributions in these difficult times. The COVID-19 Clinical Information Network (CO-CIN) data were collated by ISARIC4C Investigators. Data and Material provision was supported by grants from the National Institute for Health Research (NIHR; Award CO-CIN-01), the Medical Research Council (MRC; Grant MC_PC_19059) and the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (Award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (Award 200927), Liverpool Experimental Cancer Medicine Centre (Grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (Award ISBRC-1215-20013) and NIHR Clinical Research Network providing infrastructure support. We also acknowledge the support of Jeremy J. Farrar and Nahoko Shindo.",

year = "2022",

month = nov,

doi = "10.1111/irv.13039",

language = "English",

volume = "16",

pages = "1040--1050",

journal = "Influenza and other respiratory viruses",

issn = "1750-2640",

publisher = "Wiley",

number = "6",

}

ISARIC Clinical Characterisation Group, Baruch, J, Rojek, A, Kartsonaki, C, Vijayaraghavan, BKT, Gonçalves, BP, Pritchard, MG, Merson, L, Dunning, J, Hall, M, Sigfrid, L, Citarella, BW, Murthy, S, Yeabah, TO & Olliaro, P 2022, 'Symptom‐based case definitions for COVID‐19: Time and geographical variations for detection at hospital admission among 260,000 patients', Influenza and other respiratory viruses, vol. 16, no. 6, pp. 1040-1050. https://doi.org/10.1111/irv.13039

Symptom‐based case definitions for COVID‐19: Time and geographical variations for detection at hospital admission among 260,000 patients. / ISARIC Clinical Characterisation Group; Baruch, Joaquin; Rojek, Amanda et al.
In: Influenza and other respiratory viruses, Vol. 16, No. 6, 11.2022, p. 1040-1050.

Research output: Contribution to journal › Article › peer-review

TY - JOUR

T1 - Symptom‐based case definitions for COVID‐19

T2 - Time and geographical variations for detection at hospital admission among 260,000 patients

AU - ISARIC Clinical Characterisation Group

AU - Baruch, Joaquin

AU - Rojek, Amanda

AU - Kartsonaki, Christiana

AU - Vijayaraghavan, Bharath Kumar Tirupakuzhi

AU - Gonçalves, Bronner P.

AU - Pritchard, Mark G

AU - Merson, Laura

AU - Dunning, Jake

AU - Hall, Matthew

AU - Sigfrid, Louise

AU - Citarella, Barbara W.

AU - Murthy, Srinivas

AU - Yeabah, Trokon O.

AU - Olliaro, Piero

AU - Green, Christopher

N1 - Funding information: This work was made possible by the UK Foreign, Commonwealth and Development Office; Wellcome Trust (215091/Z/18/Z, 205228/Z/16/Z, 220757/Z/20/Z); Bill and Melinda Gates Foundation (OPP1209135); the philanthropic support of the donors to the University of Oxford's COVID-19 Research Response Fund (0009109); CIHR Coronavirus Rapid Research Funding Opportunity OV2170359 and the co-ordination in Canada by Sunnybrook Research Institute; endorsement of the Irish Critical Care-Clinical Trials Group, co-ordination in Ireland by the Irish Critical Care-Clinical Trials Network at University College Dublin and funding by the Health Research Board of Ireland (CTN-2014-12); the Rapid European COVID-19 Emergency Response research (RECOVER) (H2020 project 101003589) and European Clinical Research Alliance on Infectious Diseases (ECRAID) (965313); the COVID Clinical Management Team, AIIMS, Rishikesh, India; the COVID-19 Clinical Management Team, Manipal Hospital Whitefield, Bengaluru, India; Cambridge NIHR Biomedical Research Centre; the dedication and hard work of the Groote Schuur Hospital Covid ICU Team; the Liverpool School of Tropical Medicine and the University of Oxford; the dedication and hard work of the Norwegian SARS-CoV-2 Study Team; the Research Council of Norway Grant No. 312780 and a philanthropic donation from Vivaldi Invest A/S owned by Jon Stephenson von Tetzchner; Imperial NIHR Biomedical Research Centre; the Comprehensive Local Research Networks (CLRNs) of which PJMO is an NIHR Senior Investigator (NIHR201385); Innovative Medicines Initiative Joint Undertaking under Grant Agreement No. 115523 COMBACTE, resources of which are composed of financial contribution from the European Union's Seventh Framework Programme (FP7/2007-2013) and EFPIA companies, in-kind contribution; the French COVID cohort (NCT04262921) is sponsored by INSERM and is funded by the REACTing (REsearch & ACtion emergING infectious diseases) Consortium and by a grant of the French Ministry of Health (PHRC No. 20-0424); Stiftungsfonds zur Förderung der Bekämpfung der Tuberkulose und anderer Lungenkrankheiten of the City of Vienna, Project Number: APCOV22BGM; Italian Ministry of Health ‘Fondi Ricerca corrente–L1P6’ to IRCCS Ospedale Sacro Cuore–Don Calabria; Australian Department of Health grant (3273191); Gender Equity Strategic Fund at University of Queensland, Artificial Intelligence for Pandemics (A14PAN) at University of Queensland, the Australian Research Council Centre of Excellence for Engineered Quantum Systems (EQUS, CE170100009) and the Prince Charles Hospital Foundation, Australia; grants from Instituto de Salud Carlos III, Ministerio de Ciencia, Spain; Brazil, National Council for Scientific and Technological Development Scholarship Number 303953/2018-7; the Firland Foundation, Shoreline, Washington, USA; a grant from foundation Bevordering Onderzoek Franciscus; the South Eastern Norway Health Authority and the Research Council of Norway; Institute for Clinical Research (ICR), National Institutes of Health (NIH) supported by the Ministry of Health Malaysia; and preparedness work conducted by the Short Period Incidence Study of Severe Acute Respiratory Infection. This work uses data provided by patients and collected by the NHS as part of their care and support #DataSavesLives. The data used for this research were obtained from ISARIC4C. We are extremely grateful to the 2648 frontline NHS clinical and research staff and volunteer medical students who collected these data in challenging circumstances and the generosity of the patients and their families for their individual contributions in these difficult times. The COVID-19 Clinical Information Network (CO-CIN) data were collated by ISARIC4C Investigators. Data and Material provision was supported by grants from the National Institute for Health Research (NIHR; Award CO-CIN-01), the Medical Research Council (MRC; Grant MC_PC_19059) and the NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England (PHE) (Award 200907), NIHR HPRU in Respiratory Infections at Imperial College London with PHE (Award 200927), Liverpool Experimental Cancer Medicine Centre (Grant C18616/A25153), NIHR Biomedical Research Centre at Imperial College London (Award ISBRC-1215-20013) and NIHR Clinical Research Network providing infrastructure support. We also acknowledge the support of Jeremy J. Farrar and Nahoko Shindo.

PY - 2022/11

Y1 - 2022/11

N2 - Introduction: Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions.Methods: Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test result were excluded.Results: A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe and Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years; geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did not meet the case definition, the CFR increased.Conclusions: The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.

AB - Introduction: Case definitions are used to guide clinical practice, surveillance and research protocols. However, how they identify COVID-19-hospitalised patients is not fully understood. We analysed the proportion of hospitalised patients with laboratory-confirmed COVID-19, in the ISARIC prospective cohort study database, meeting widely used case definitions.Methods: Patients were assessed using the Centers for Disease Control (CDC), European Centre for Disease Prevention and Control (ECDC), World Health Organization (WHO) and UK Health Security Agency (UKHSA) case definitions by age, region and time. Case fatality ratios (CFRs) and symptoms of those who did and who did not meet the case definitions were evaluated. Patients with incomplete data and non-laboratory-confirmed test result were excluded.Results: A total of 263,218 of the patients (42%) in the ISARIC database were included. Most patients (90.4%) were from Europe and Central Asia. The proportions of patients meeting the case definitions were 56.8% (WHO), 74.4% (UKHSA), 81.6% (ECDC) and 82.3% (CDC). For each case definition, patients at the extremes of age distribution met the criteria less frequently than those aged 30 to 70 years; geographical and time variations were also observed. Estimated CFRs were similar for the patients who met the case definitions. However, when more patients did not meet the case definition, the CFR increased.Conclusions: The performance of case definitions might be different in different regions and may change over time. Similarly concerning is the fact that older patients often did not meet case definitions, risking delayed medical care. While epidemiologists must balance their analytics with field applicability, ongoing revision of case definitions is necessary to improve patient care through early diagnosis and limit potential nosocomial spread.

KW - case definitions

KW - COVID-19

KW - hospitalisation

U2 - 10.1111/irv.13039

DO - 10.1111/irv.13039

M3 - Article

SN - 1750-2640

VL - 16

SP - 1040

EP - 1050

JO - Influenza and other respiratory viruses

JF - Influenza and other respiratory viruses

IS - 6

ER -

Symptom‐based case definitions for COVID‐19: Time and geographical variations for detection at hospital admission among 260,000 patients

Abstract

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Keywords

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