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 (2024)

van Klaveren, D., Rekkas, A., Alsma, J., Verdonschot, R. J. C. G., Koning, D. T. J. J., Kamps, M. J. A., Dormans, T., Stassen, R., 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{46b68d5c02824f32b4ddbe36834ae129,

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.",

keywords = "COVID-19, public health, accident & emergency medicine, epidemiology, INDIVIDUAL PROGNOSIS, DIAGNOSIS TRIPOD, RISK, EXPLANATION",

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 Robert Stassen and Sebastiaan Weijer and Klaas-Sierk Arnold 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",

year = "2021",

month = sep,

day = "1",

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

language = "English",

volume = "11",

journal = "BMJ Open",

issn = "2044-6055",

publisher = "BMJ Publishing Group",

number = "9",

}

van Klaveren, D, Rekkas, A, Alsma, J, Verdonschot, RJCG, Koning, DTJJ, Kamps, MJA, Dormans, T, Stassen, R, Weijer, S, Arnold, K-S, 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, 01.09.2021.

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 - Stassen, Robert

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

PY - 2021/9/1

Y1 - 2021/9/1

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.

KW - COVID-19

KW - public health

KW - accident & emergency medicine

KW - epidemiology

KW - INDIVIDUAL PROGNOSIS

KW - DIAGNOSIS TRIPOD

KW - RISK

KW - EXPLANATION

U2 - 10.1136/bmjopen-2021-051468

DO - 10.1136/bmjopen-2021-051468

M3 - Article

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 1;11(9):e051468. doi: 10.1136/bmjopen-2021-051468