Evidence map›Paper›PMID 35887924›Full record

ArticleJournal of clinical medicine2022

Development and Internal Validation of a New Prognostic Model Powered to Predict 28-Day All-Cause Mortality in ICU COVID-19 Patients-The COVID-SOFA Score.

Emanuel Moisa, Dan Corneci, Mihai Ionut Negutu, Cristina Raluca Filimon, Andreea Serbu, Mihai Popescu, Silvius Negoita, Ioana Marina Grintescu

Abstract read
In one paragraph

Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–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

10 citing papers in PubMed.

  1. COVID-19 andAntibiotics (Basel, Switzerland) · 2024
    Article
  2. Article
  3. Observational
  4. Lessons from the COVID-19 Pandemic.Journal of clinical medicine · 2023
    Article
  5. Observational
  6. Article
  7. Review
  8. Article
  9. Article
  10. COVID-19 outbreak: Impact on global economy.Frontiers in public health · 2022
    Review
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.

Emanuel MoisaDepartment of Anaesthesia and Intensive Care Medicine, Faculty of Medicine, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania.ORCID 0000-0001-8138-8622
Dan CorneciDepartment of Anaesthesia and Intensive Care Medicine, Faculty of Medicine, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania.
Mihai Ionut NegutuClinic of Anaesthesia and Intensive Care Medicine, Elias Emergency University Hospital, 011461 Bucharest, Romania.
Cristina Raluca FilimonClinic of Anaesthesia and Intensive Care Medicine, Dr. Carol Davila Central Military Emergency University Hospital, 010825 Bucharest, Romania.
Andreea SerbuClinic of Anaesthesia and Intensive Care Medicine, Dr. Carol Davila Central Military Emergency University Hospital, 010825 Bucharest, Romania.
Mihai PopescuDepartment of Anaesthesia and Intensive Care Medicine, Faculty of Medicine, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania.ORCID 0000-0002-2947-335X
Silvius NegoitaDepartment of Anaesthesia and Intensive Care Medicine, Faculty of Medicine, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania.
Ioana Marina GrintescuDepartment of Anaesthesia and Intensive Care Medicine, Faculty of Medicine, 'Carol Davila' University of Medicine and Pharmacy, 020021 Bucharest, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The sequential organ failure assessment (SOFA) score has poor discriminative ability for death in severely or critically ill patients with Coronavirus disease 2019 (COVID-19) requiring intensive care unit (ICU) admission. Our aim was to create a new score powered to predict 28-day mortality. Methods: Retrospective, observational, bicentric cohort study including 425 patients with COVID-19 pneumonia, acute respiratory failure and SOFA score ≥ 2 requiring ICU admission for ≥72 h. Factors with independent predictive value for 28-day mortality were identified after stepwise Cox proportional hazards (PH) regression. Based on the regression coefficients, an equation was computed representing the COVID-SOFA score. Discriminative ability was tested using receiver operating characteristic (ROC) analysis, concordance statistics and precision-recall curves. This score was internally validated. Results: Median (Q1−Q3) age for the whole sample was 64 [55−72], with 290 (68.2%) of patients being male. The 28-day mortality was 54.58%. After stepwise Cox PH regression, age, neutrophil-to-lymphocyte ratio (NLR) and SOFA score remained in the final model. The following equation was computed: COVID-SOFA score = 10 × [0.037 × Age + 0.347 × ln(NLR) + 0.16 × SOFA]. Harrell’s C-index for the COVID-SOFA score was higher than the SOFA score alone for 28-day mortality (0.697 [95% CI; 0.662−0.731] versus 0.639 [95% CI: 0.605−0.672]). Subsequently, the prediction error rate was improved up to 16.06%. Area under the ROC (AUROC) was significantly higher for the COVID-SOFA score compared with the SOFA score for 28-day mortality: 0.796 [95% CI: 0.755−0.833] versus 0.699 [95% CI: 0.653−0.742, p < 0.001]. Better predictive value was observed with repeated measurement at 48 h after ICU admission. Conclusions: The COVID-SOFA score is better than the SOFA score alone for 28-day mortality prediction. Improvement in predictive value seen with measurements at 48 h after ICU admission suggests that the COVID-SOFA score can be used in a repetitive manner. External validation is required to support these results.

Indexed as

ARDSCOVID-19ICUmortalityneutrophil-to-lymphocyte ratioNLRprognostic scoreSARS-CoV-2SOFA

Identifiers

PMID35887924
PMCPMC9323813

What OpenQuestion holds

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Registered trials

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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.