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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; Verdonschot, Rob J C G; Koning, Dick T J J; Kamps, Marlijn J A; Dormans, Tom; Stassen, Robert; Weijer, Sebastiaan; Arnold, Klaas-Sierk; Tomlow, Benjamin; de Geus, Hilde R H; van Bruchem-Visser, Rozemarijn L; Miedema, Jelle R; Verbon, Annelies; van Nood, Els; Kent, David M; Schuit, Stephanie C E; Lingsma, Hester.
Afiliación
  • van Klaveren D; Department of Public Health, Erasmus MC, Rotterdam, The Netherlands d.vanklaveren@erasmusmc.nl.
  • Rekkas A; Predictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA.
  • Alsma J; Department of Medical Informatics, Erasmus MC, Rotterdam, The Netherlands.
  • Verdonschot RJCG; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • Koning DTJJ; Emergency Department, Erasmus MC, Rotterdam, The Netherlands.
  • Kamps MJA; Department of Intensive Care, Catharina Hospital, Eindhoven, The Netherlands.
  • Dormans T; Department of Intensive Care, Catharina Hospital, Eindhoven, The Netherlands.
  • Stassen R; Department of Intensive Care, Zuyderland Medical Centre Heerlen, Heerlen, The Netherlands.
  • Weijer S; Department of Traumatology, Maastricht University Medical Centre+, Maastricht, The Netherlands.
  • Arnold KS; Department of Internal Medicine, Antonius Hospital Sneek, Sneek, The Netherlands.
  • Tomlow B; Department of Intensive Care, Antonius Hospital Sneek, Sneek, The Netherlands.
  • de Geus HRH; Department of Pulmonary Medicine, Isala Hospitals, Zwolle, The Netherlands.
  • van Bruchem-Visser RL; Department of Intensive Care, Erasmus MC, Rotterdam, The Netherlands.
  • Miedema JR; Department of Internal Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • Verbon A; Department of Pulmonary Medicine, Erasmus MC, Rotterdam, The Netherlands.
  • van Nood E; Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, The Netherlands.
  • Kent DM; Department of Internal Medicine, Department of Medical Microbiology and Infectious Diseases, Erasmus MC, Rotterdam, The Netherlands.
  • Schuit SCE; Predictive Analytics and Comparative Effectiveness Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, Massachusetts, USA.
  • Lingsma H; Executive Board, UMCG, Groningen, The Netherlands.
BMJ Open ; 11(9): e051468, 2021 09 16.
Article en En | MEDLINE | ID: mdl-34531219
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.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMJ Open Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Revista: BMJ Open Año: 2021 Tipo del documento: Article País de afiliación: Países Bajos