COVID outcome prediction in the emergency department (COPE): Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19

David Van Klaveren; Alexandros Rekkas; Jelmer Alsma; Rob J.C.G. Verdonschot; Dick T.J.J. Koning; Marlijn J.A. Kamps; Tom Dormans; Stassen Robert; Sebastiaan Weijer; Klaas Sierk Arnold; Benjamin Tomlow; Hilde R.H. De Geus; Rozemarijn L. Van Bruchem-Visser; Jelle R. Miedema; Annelies Verbon; Els Van Nood; David M. Kent; Stephanie C.E. Schuit; Hester Lingsma

doi:10.1136/bmjopen-2021-051468

COVID outcome prediction in the emergency department (COPE): Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19

David Van Klaveren^*, Alexandros Rekkas, Jelmer Alsma, Rob J.C.G. Verdonschot, Dick T.J.J. Koning, Marlijn J.A. Kamps, Tom Dormans, Stassen Robert, Sebastiaan Weijer, Klaas Sierk Arnold, Benjamin Tomlow, Hilde R.H. De Geus, Rozemarijn L. Van Bruchem-Visser, Jelle R. Miedema, Annelies Verbon, Els Van Nood, David M. Kent, Stephanie C.E. Schuit, Hester Lingsma

^*Corresponding author for this work

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Abstract

Objectives Develop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19. Design Retrospective. Setting Secondary care in four large Dutch hospitals. Participants Patients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation. Outcome measures We developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots. Results Of 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model - COVID outcome prediction in the emergency department (COPE) - with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86); 0.82 (95% CI 0.74 to 0.90); 0.79 (95% CI 0.70 to 0.88); 0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90); 0.81 (95% CI 0.66 to 0.95)). Conclusions COPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

Original language	English
Article number	e051468
Journal	BMJ Open
Volume	11
Issue number	9
DOIs	https://doi.org/10.1136/bmjopen-2021-051468
Publication status	Published - 16 Sept 2021

Bibliographical note

Funding Information:
Contributors DvK, HL, JA, RJCGV, HRHdG, RLvB-V, JRM, AV, EvN, DK and SCES conceived and designed the study. JA, RJCGV, DTJJK, MJAK, TD, RS, SW, K-SA and BT were responsible for collecting the data. DvK analysed the data and wrote the first draft of the paper. AR implemented the models into a web application. All authors contributed to writing the paper and approved the final version. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. The corresponding author has the right to grant on behalf of all authors and does grant on behalf of all authors, a worldwide licence to the publishers and its licensees in perpetuity, in all forms, formats and media (whether known now or created in the future), to (1) publish, reproduce, distribute, display and store the Contribution, (2) translate the contribution into other languages, create adaptations, reprints, include within collections and create summaries, extracts and/or, abstracts of the Contribution, (3) create any other derivative work(s) based on the contribution, (4) to exploit all subsidiary rights in the contribution, (5) the inclusion of electronic links from the Contribution to third party material where-ever it may be located; and, (6) licence any third party to do any or all of the above.' Funding This work was supported by ZonMw (project number 10430 01 201 0019: Clinical prediction models for COVID-19: development, international validation and use) and the Patient-Centred Outcomes Research Institute (PCORI grant number ME-1606–35555: How Well Do Clinical Prediction Models (CPMs) Validate? A Large-Scale Evaluation of Cardiovascular Clinical Prediction Models).

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© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

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Van Klaveren, D., Rekkas, A., Alsma, J., Verdonschot, R. J. C. G., Koning, D. T. J. J., Kamps, M. J. A., Dormans, T., Robert, S., Weijer, S., Arnold, K. S., Tomlow, B., De Geus, H. R. H., Van Bruchem-Visser, R. L., Miedema, J. R., Verbon, A., Van Nood, E., Kent, D. M., Schuit, S. C. E., & Lingsma, H. (2021). COVID outcome prediction in the emergency department (COPE): Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19. BMJ Open, 11(9), Article e051468. https://doi.org/10.1136/bmjopen-2021-051468

Van Klaveren, David ; Rekkas, Alexandros ; Alsma, Jelmer et al. / COVID outcome prediction in the emergency department (COPE) : Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19. In: BMJ Open. 2021 ; Vol. 11, No. 9.

@article{d38d65bb21c7486284806531e14175b5,

title = "COVID outcome prediction in the emergency department (COPE): Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19",

abstract = "Objectives Develop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19. Design Retrospective. Setting Secondary care in four large Dutch hospitals. Participants Patients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation. Outcome measures We developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots. Results Of 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model - COVID outcome prediction in the emergency department (COPE) - with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86); 0.82 (95% CI 0.74 to 0.90); 0.79 (95% CI 0.70 to 0.88); 0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90); 0.81 (95% CI 0.66 to 0.95)). Conclusions COPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.",

author = "{Van Klaveren}, David and Alexandros Rekkas and Jelmer Alsma and Verdonschot, {Rob J.C.G.} and Koning, {Dick T.J.J.} and Kamps, {Marlijn J.A.} and Tom Dormans and Stassen Robert and Sebastiaan Weijer and Arnold, {Klaas Sierk} and Benjamin Tomlow and {De Geus}, {Hilde R.H.} and {Van Bruchem-Visser}, {Rozemarijn L.} and Miedema, {Jelle R.} and Annelies Verbon and {Van Nood}, Els and Kent, {David M.} and Schuit, {Stephanie C.E.} and Hester Lingsma",

note = "Funding Information: Contributors DvK, HL, JA, RJCGV, HRHdG, RLvB-V, JRM, AV, EvN, DK and SCES conceived and designed the study. JA, RJCGV, DTJJK, MJAK, TD, RS, SW, K-SA and BT were responsible for collecting the data. DvK analysed the data and wrote the first draft of the paper. AR implemented the models into a web application. All authors contributed to writing the paper and approved the final version. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. The corresponding author has the right to grant on behalf of all authors and does grant on behalf of all authors, a worldwide licence to the publishers and its licensees in perpetuity, in all forms, formats and media (whether known now or created in the future), to (1) publish, reproduce, distribute, display and store the Contribution, (2) translate the contribution into other languages, create adaptations, reprints, include within collections and create summaries, extracts and/or, abstracts of the Contribution, (3) create any other derivative work(s) based on the contribution, (4) to exploit all subsidiary rights in the contribution, (5) the inclusion of electronic links from the Contribution to third party material where-ever it may be located; and, (6) licence any third party to do any or all of the above.' Funding This work was supported by ZonMw (project number 10430 01 201 0019: Clinical prediction models for COVID-19: development, international validation and use) and the Patient-Centred Outcomes Research Institute (PCORI grant number ME-1606–35555: How Well Do Clinical Prediction Models (CPMs) Validate? A Large-Scale Evaluation of Cardiovascular Clinical Prediction Models). Publisher Copyright: {\textcopyright} Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.",

year = "2021",

month = sep,

day = "16",

doi = "10.1136/bmjopen-2021-051468",

language = "English",

volume = "11",

journal = "BMJ Open",

issn = "2044-6055",

publisher = "BMJ Publishing Group",

number = "9",

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Van Klaveren, D, Rekkas, A, Alsma, J , Verdonschot, RJCG, Koning, DTJJ, Kamps, MJA, Dormans, T, Robert, S, Weijer, S, Arnold, KS, Tomlow, B, De Geus, HRH , Van Bruchem-Visser, RL , Miedema, JR , Verbon, A , Van Nood, E, Kent, DM, Schuit, SCE & Lingsma, H 2021, 'COVID outcome prediction in the emergency department (COPE): Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19', BMJ Open, vol. 11, no. 9, e051468. https://doi.org/10.1136/bmjopen-2021-051468

COVID outcome prediction in the emergency department (COPE): Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19. / Van Klaveren, David; Rekkas, Alexandros; Alsma, Jelmer et al.
In: BMJ Open, Vol. 11, No. 9, e051468, 16.09.2021.

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

TY - JOUR

T1 - COVID outcome prediction in the emergency department (COPE)

T2 - Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19

AU - Van Klaveren, David

AU - Rekkas, Alexandros

AU - Alsma, Jelmer

AU - Verdonschot, Rob J.C.G.

AU - Koning, Dick T.J.J.

AU - Kamps, Marlijn J.A.

AU - Dormans, Tom

AU - Robert, Stassen

AU - Weijer, Sebastiaan

AU - Arnold, Klaas Sierk

AU - Tomlow, Benjamin

AU - De Geus, Hilde R.H.

AU - Van Bruchem-Visser, Rozemarijn L.

AU - Miedema, Jelle R.

AU - Verbon, Annelies

AU - Van Nood, Els

AU - Kent, David M.

AU - Schuit, Stephanie C.E.

AU - Lingsma, Hester

N1 - Funding Information: Contributors DvK, HL, JA, RJCGV, HRHdG, RLvB-V, JRM, AV, EvN, DK and SCES conceived and designed the study. JA, RJCGV, DTJJK, MJAK, TD, RS, SW, K-SA and BT were responsible for collecting the data. DvK analysed the data and wrote the first draft of the paper. AR implemented the models into a web application. All authors contributed to writing the paper and approved the final version. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. The corresponding author has the right to grant on behalf of all authors and does grant on behalf of all authors, a worldwide licence to the publishers and its licensees in perpetuity, in all forms, formats and media (whether known now or created in the future), to (1) publish, reproduce, distribute, display and store the Contribution, (2) translate the contribution into other languages, create adaptations, reprints, include within collections and create summaries, extracts and/or, abstracts of the Contribution, (3) create any other derivative work(s) based on the contribution, (4) to exploit all subsidiary rights in the contribution, (5) the inclusion of electronic links from the Contribution to third party material where-ever it may be located; and, (6) licence any third party to do any or all of the above.' Funding This work was supported by ZonMw (project number 10430 01 201 0019: Clinical prediction models for COVID-19: development, international validation and use) and the Patient-Centred Outcomes Research Institute (PCORI grant number ME-1606–35555: How Well Do Clinical Prediction Models (CPMs) Validate? A Large-Scale Evaluation of Cardiovascular Clinical Prediction Models). Publisher Copyright: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

PY - 2021/9/16

Y1 - 2021/9/16

N2 - Objectives Develop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19. Design Retrospective. Setting Secondary care in four large Dutch hospitals. Participants Patients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation. Outcome measures We developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots. Results Of 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model - COVID outcome prediction in the emergency department (COPE) - with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86); 0.82 (95% CI 0.74 to 0.90); 0.79 (95% CI 0.70 to 0.88); 0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90); 0.81 (95% CI 0.66 to 0.95)). Conclusions COPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

AB - Objectives Develop simple and valid models for predicting mortality and need for intensive care unit (ICU) admission in patients who present at the emergency department (ED) with suspected COVID-19. Design Retrospective. Setting Secondary care in four large Dutch hospitals. Participants Patients who presented at the ED and were admitted to hospital with suspected COVID-19. We used 5831 first-wave patients who presented between March and August 2020 for model development and 3252 second-wave patients who presented between September and December 2020 for model validation. Outcome measures We developed separate logistic regression models for in-hospital death and for need for ICU admission, both within 28 days after hospital admission. Based on prior literature, we considered quickly and objectively obtainable patient characteristics, vital parameters and blood test values as predictors. We assessed model performance by the area under the receiver operating characteristic curve (AUC) and by calibration plots. Results Of 5831 first-wave patients, 629 (10.8%) died within 28 days after admission. ICU admission was fully recorded for 2633 first-wave patients in 2 hospitals, with 214 (8.1%) ICU admissions within 28 days. A simple model - COVID outcome prediction in the emergency department (COPE) - with age, respiratory rate, C reactive protein, lactate dehydrogenase, albumin and urea captured most of the ability to predict death. COPE was well calibrated and showed good discrimination for mortality in second-wave patients (AUC in four hospitals: 0.82 (95% CI 0.78 to 0.86); 0.82 (95% CI 0.74 to 0.90); 0.79 (95% CI 0.70 to 0.88); 0.83 (95% CI 0.79 to 0.86)). COPE was also able to identify patients at high risk of needing ICU admission in second-wave patients (AUC in two hospitals: 0.84 (95% CI 0.78 to 0.90); 0.81 (95% CI 0.66 to 0.95)). Conclusions COPE is a simple tool that is well able to predict mortality and need for ICU admission in patients who present to the ED with suspected COVID-19 and may help patients and doctors in decision making.

UR - http://www.scopus.com/inward/record.url?scp=85115323321&partnerID=8YFLogxK

U2 - 10.1136/bmjopen-2021-051468

DO - 10.1136/bmjopen-2021-051468

M3 - Article

C2 - 34531219

AN - SCOPUS:85115323321

SN - 2044-6055

VL - 11

JO - BMJ Open

JF - BMJ Open

IS - 9

M1 - e051468

ER -

Van Klaveren D, Rekkas A, Alsma J , Verdonschot RJCG, Koning DTJJ, Kamps MJA et al. COVID outcome prediction in the emergency department (COPE): Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19. BMJ Open. 2021 Sept 16;11(9):e051468. doi: 10.1136/bmjopen-2021-051468

COVID outcome prediction in the emergency department (COPE): Using retrospective Dutch hospital data to develop simple and valid models for predicting mortality and need for intensive care unit admission in patients who present at the emergency department with suspected COVID-19

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