Evidence map›Paper›PMID 35867647›Full record

ArticlePloS one2022

The usefulness of D-dimer as a predictive marker for mortality in patients with COVID-19 hospitalized during the first wave in Italy.

Shermarke Hassan, Barbara Ferrari, Raffaella Rossio, Vincenzo la Mura, Andrea Artoni, Roberta Gualtierotti, Ida Martinelli, Alessandro Nobili, Alessandra Bandera, Andrea Gori and 7 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.2field-weighted citation impact, top 50% 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

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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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

17 authors at 4 institutions in 2 countries.

Shermarke HassanDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.ORCID 0000-0002-5045-636X
Barbara FerrariU.O.C. Medicina Generale Emostasi e Trombosi, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Raffaella RossioU.O.C. Medicina Generale Emostasi e Trombosi, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Vincenzo la MuraDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.ORCID 0000-0003-4685-7184
Andrea ArtoniAngelo Bianchi Bonomi Hemophilia and Thrombosis Centre, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Roberta GualtierottiDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
Ida MartinelliAngelo Bianchi Bonomi Hemophilia and Thrombosis Centre, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Alessandro NobiliDepartment of Neuroscience, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.
Alessandra BanderaDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
Andrea GoriDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
Francesco BlasiDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.ORCID 0000-0002-2285-9970
Valter MonzaniAcute Medical Unit, Department of Medicine, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
Giorgio CostantinoDepartment of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
Sergio HarariDepartment of Clinical Sciences and Community Health, University of Milan, Milan, Italy.
Frits Richard RosendaalDepartment of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
Flora PeyvandiDepartment of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
COVID-19 Network working group
University of Milan · ITFondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico · ITLeiden University Medical Center · NLMario Negri Institute for Pharmacological Research · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) presents an urgent threat to global health. Identification of predictors of poor outcomes will assist medical staff in treatment and allocating limited healthcare resources.

aimsThe primary aim was to study the value of D-dimer as a predictive marker for in-hospital mortality.

methodsThis was a cohort study. The study population consisted of hospitalized patients (age >18 years), who were diagnosed with COVID-19 based on real-time PCR at 9 hospitals during the first COVID-19 wave in Lombardy, Italy (Feb-May 2020). The primary endpoint was in-hospital mortality. Information was obtained from patient records. Statistical analyses were performed using a Fine-Gray competing risk survival model. Model discrimination was assessed using Harrell's C-index and model calibration was assessed using a calibration plot.

resultsOut of 1049 patients, 507 patients (46%) had evaluable data. Of these 507 patients, 96 died within 30 days. The cumulative incidence of in-hospital mortality within 30 days was 19% (95CI: 16%-23%), and the majority of deaths occurred within the first 10 days. A prediction model containing D-dimer as the only predictor had a C-index of 0.66 (95%CI: 0.61-0.71). Overall calibration of the model was very poor. The addition of D-dimer to a model containing age, sex and co-morbidities as predictors did not lead to any meaningful improvement in either the C-index or the calibration plot.

conclusionThe predictive value of D-dimer alone was moderate, and the addition of D-dimer to a simple model containing basic clinical characteristics did not lead to any improvement in model performance.

Indexed as

COVID-19AdolescentBiomarkersCohort StudiesFibrin Fibrinogen Degradation ProductsHospital MortalityHumansRetrospective StudiesSARS-CoV-2BiomarkersFibrin Fibrinogen Degradation Productsfibrin fragment D

Identifiers

PMID35867647
PMCPMC9307169
OpenAlexW4286587550

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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.