Evidence map›Paper›PMID 33629918›Full record

ArticleAnnals of medicine2021

Performance of prediction models for short-term outcome in COVID-19 patients in the emergency department: a retrospective study.

Paul M E L van Dam, Noortje Zelis, Sander M J van Kuijk, Aimée E M J H Linkens, Renée A G Brüggemann, Bart Spaetgens, Iwan C C van der Horst, Patricia M Stassen

Abstract readComparative StudyValidation Study
In one paragraph

Article in Annals of medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Observational
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Paul M E L van DamDepartment of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Noortje ZelisDepartment of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Sander M J van KuijkDepartment of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, The Netherlands.
Aimée E M J H LinkensDepartment of Internal Medicine, Division of General Internal Medicine, Section Geriatric Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Renée A G BrüggemannDepartment of Internal Medicine, Division of General Internal Medicine, Section Geriatric Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Bart SpaetgensDepartment of Internal Medicine, Division of General Internal Medicine, Section Geriatric Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Iwan C C van der HorstDepartment of Intensive Care Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.
Patricia M StassenDepartment of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCoronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED).

methodsIn this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC).

resultsWe included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models.

conclusionThe RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources.

Indexed as

AgedCOVID-19Emergency Service, HospitalFeasibility StudiesFemaleHospital MortalityHumansLength of StayLogistic ModelsMaleMiddle AgedNetherlandsPrognosisRetrospective StudiesRisk AssessmentROC CurveCOVID-19emergency departmentmortalitypredictionprognosis

Identifiers

PMID33629918
PMCPMC7919920

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.