Evidence map›Paper›PMID 34255793›Full record

ArticlePloS one2021

Prediction of 28-day mortality in critically ill patients with COVID-19: Development and internal validation of a clinical prediction model.

Matteo Luigi Giuseppe Leoni, Luisa Lombardelli, Davide Colombi, Elena Giovanna Bignami, Benedetta Pergolotti, Francesca Repetti, Matteo Villani, Valentina Bellini, Tommaso Rossi, Geza Halasz and 3 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 1 pooled it
3.8field-weighted citation impact, top 5% 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

22 citing papers in PubMed, 1 synthesis or guideline pooled it, 61 citations in OpenAlex.

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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

13 authors at 3 institutions in 1 country.

Matteo Luigi Giuseppe LeoniDepartment of Anesthesia and Intensive Care Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy.ORCID 0000-0001-5228-3733
Luisa LombardelliUnit of Operating Room and Waiting Lists Management, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Davide ColombiDepartment of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Elena Giovanna BignamiUnit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy.
Benedetta PergolottiDepartment of Anesthesia and Intensive Care Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Francesca RepettiDepartment of Anesthesia and Intensive Care Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Matteo VillaniDepartment of Anesthesia and Intensive Care Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Valentina BelliniUnit of Anesthesiology, Division of Critical Care and Pain Medicine, Department of Medicine and Surgery, University of Parma, Parma, Italy.
Tommaso RossiDepartment of Anesthesia and Intensive Care Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Geza HalaszCardiology Department, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Serena CaprioliController and data management of Administrative Department, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Fabrizio MicheliDepartment of Anesthesia and Intensive Care Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Massimo NolliDepartment of Anesthesia and Intensive Care Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy.
Guglielmo da Saliceto Hospital · ITUniversity of Parma · ITUniversity of Ferrara · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCOVID-19 pandemic has rapidly required a high demand of hospitalization and an increased number of intensive care units (ICUs) admission. Therefore, it became mandatory to develop prognostic models to evaluate critical COVID-19 patients. MATERIALS AND

methodsWe retrospectively evaluate a cohort of consecutive COVID-19 critically ill patients admitted to ICU with a confirmed diagnosis of SARS-CoV-2 pneumonia. A multivariable Cox regression model including demographic, clinical and laboratory findings was developed to assess the predictive value of these variables. Internal validation was performed using the bootstrap resampling technique. The model's discriminatory ability was assessed with Harrell's C-statistic and the goodness-of-fit was evaluated with calibration plot.

results242 patients were included [median age, 64 years (56-71 IQR), 196 (81%) males]. Hypertension was the most common comorbidity (46.7%), followed by diabetes (15.3%) and heart disease (14.5%). Eighty-five patients (35.1%) died within 28 days after ICU admission and the median time from ICU admission to death was 11 days (IQR 6-18). In multivariable model after internal validation, age, obesity, procaltitonin, SOFA score and PaO2/FiO2 resulted as independent predictors of 28-day mortality. The C-statistic of the model showed a very good discriminatory capacity (0.82).

conclusionsWe present the results of a multivariable prediction model for mortality of critically ill COVID-19 patients admitted to ICU. After adjustment for other factors, age, obesity, procalcitonin, SOFA and PaO2/FiO2 were independently associated with 28-day mortality in critically ill COVID-19 patients. The calibration plot revealed good agreements between the observed and expected probability of death.

Indexed as

ComorbidityCOVID-19Diabetes MellitusFemaleHeart DiseasesHumansHypertensionIntensive Care UnitsMaleMiddle AgedModels, StatisticalMortalityObesity

Identifiers

PMID34255793
PMCPMC8277063
OpenAlexW3181871091

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.