Evidence map›Paper›PMID 38393501›Full record

ArticleInternal and emergency medicine2024

4C mortality score and COVID-19 mortality risk score: an analysis in four different age groups of an Italian population.

Lorenzo Pelagatti, Ginevra Fabiani, Anna De Paris, Alessia Lagomarsini, Elisa Paolucci, Francesco Pepe, Maurizio Villanti, Francesca Todde, Simona Matteini, Francesca Caldi and 2 more

Abstract read
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In one paragraph

Article in Internal and emergency medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.2field-weighted citation impact, top 25% of its field
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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

12 authors at 1 institution in 1 country.

Lorenzo PelagattiHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Ginevra FabianiHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Anna De ParisHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Alessia LagomarsiniHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Elisa PaolucciHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Francesco PepeHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Maurizio VillantiHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Francesca ToddeHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Simona MatteiniHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Francesca CaldiHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Riccardo PiniHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy.
Francesca InnocentiHigh-Dependency Unit, Department of Clinical and Experimental Medicine, Careggi University Hospital, Lg. Brambilla 3, 50134, Florence, Italy. innocenti.fra66@gmail.com.ORCID 0000-0002-7805-7619
Azienda Ospedaliero-Universitaria Careggi · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To evaluate the prognostic stratification ability of 4C Mortality Score and COVID-19 Mortality Risk Score in different age groups. Retrospective study, including all patients, presented to the Emergency Department of the University Hospital Careggi, between February, 2020 and May, 2021, and admitted for SARS-CoV2. Patients were divided into four subgroups based on the quartiles of age distribution: patients < 57 years (G1, n = 546), 57-71 years (G2, n = 508), 72-81 years (G3, n = 552), and > 82 years (G4, n = 578). We calculated the 4C Mortality Score and COVID-19 Mortality Risk Score. The end-point was in-hospital mortality. In the whole population (age 68 ± 16 years), the mortality rate was 19% (n = 424), and increased with increasing age (G1: 4%, G2: 11%, G3: 22%, and G4: 39%, p < 0.001). Both scores were higher among non-survivors than survivors in all subgroups (4C-MS, G1: 6 [3-7] vs 3 [2-5]; G2: 10 [7-11] vs 7 [5-8]; G3: 11 [10-14] vs 10 [8-11]; G4: 13 [12-15] vs 11 [10-13], all p < 0.001; COVID-19 MRS, G1: 8 [7-9] vs 9 [9-11], G2: 10 [8-11] vs 11 [10-12]; G3: 11 [10-12] vs 12 [11-13]; G4: 11 [10-13] vs 13 [12-14], all p < 0.01). The ability of both scores to identify patients at higher risk of in-hospital mortality, was similar in different age groups (4C-MS: G1 0.77, G2 0.76, G3 0.68, G4 0.72; COVID-19 MRS: G1 0.67, G2 0.69, G3 0.69, G4 0.72, all p for comparisons between subgroups = NS). Both scores confirmed their good performance in predicting in-hospital mortality in all age groups, despite their different mortality rate.

Indexed as

COVID-19Hospital MortalityAgedAged, 80 and overAge FactorsFemaleHumansItalyMaleMiddle AgedPrognosisRetrospective StudiesRisk AssessmentRisk FactorsCOVID-19Critical careEmergency care systemsPrognostic scores

Identifiers

PMID38393501
OpenAlexW4392110568

What OpenQuestion holds

Textmetadata
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.