Evidence map›Paper›PMID 33166904›Full record

Observational studyRespiratory medicine2020

Determining extent of COVID-19 pneumonia on CT based on biological variables.

Mickael Tordjman, Ahmed Mekki, Rahul D Mali, Hippolyte Monnier, Sophie Neveu, Guillaume Chassagnon, Fadila Mihoubi, Nicolas Carlier, Jonathan Marey, Laure Fournier and 3 more

Abstract readObservational Study
In one paragraph

Observational study in Respiratory medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

13 authors.

Mickael TordjmanDepartment of Radiology, Cochin Hospital, APHP, France. Electronic address: mickael_tordjman@hotmail.com.
Ahmed MekkiDepartment of Radiology, Ambroise Paré Hospital, APHP, France.
Rahul D MaliCenter for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA.
Hippolyte MonnierDepartment of Radiology, George Pompidou European Hospital, APHP, France.
Sophie NeveuDepartment of Radiology, Cochin Hospital, APHP, France.
Guillaume ChassagnonDepartment of Radiology, Cochin Hospital, APHP, France; Department of Radiology, Raymond Poincaré Hospital, APHP, France.
Fadila MihoubiDepartment of Radiology, Cochin Hospital, APHP, France.
Nicolas CarlierDepartment of Respiratory Medicine, Cochin Hospital, APHP, France.
Jonathan MareyDepartment of Respiratory Medicine, Cochin Hospital, APHP, France.
Laure FournierDepartment of Radiology, George Pompidou European Hospital, APHP, France; Université de Paris, France.
Robert-Yves CarlierDepartment of Radiology, Ambroise Paré Hospital, APHP, France; Department of Radiology, Raymond Poincaré Hospital, APHP, France.
Jean-Luc DrapéDepartment of Radiology, Cochin Hospital, APHP, France; Université de Paris, France.
Marie-Pierre RevelDepartment of Radiology, Cochin Hospital, APHP, France; Université de Paris, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCovid-19 pneumonia CT extent correlates well with outcome including mortality. However, CT is not widely available in many countries. This study aimed to explore the relationship between Covid-19 pneumonia CT extent and blood tests variations. The objective was to determine for the biological variables correlating with disease severity the cut-off values showing the best performance to predict the parenchymal extent of the pneumonia.

methodsBivariate correlations were calculated between biological variables and grade of disease extent on CT. Receiving Operating Characteristic curve analysis determined the best cutoffs for the strongest correlated biological variables. The performance of these variables to predict mild (<10%) or severe pneumonia (>50% of parenchyma involved) was evaluated.

resultsCorrelations between biological variables and disease extent was evaluated in 168 patients included in this study. LDH, lymphocyte count and CRP showed the strongest correlations (with 0.67, -0.41 and 0.52 correlation coefficient, respectively). Patients were split into a training and a validation cohort according to their centers. If one variable was above/below the following cut-offs, LDH>380, CRP>80 or lymphocyte count <0.8G/L, severe pneumonia extent on CT was detected with 100% sensitivity. Values above/below all three thresholds were denoted in 73% of patients with severe pneumonia extent. The combination of LDH<220 and CRP<22 was associated with mild pneumonia extent (<10%) with specificity of 100%. DISCUSSION: LDH showed the strongest correlation with the extent of Covid-19 pneumonia on CT. Combined with CRP±lymphocyte count, it helps predicting parenchymal extent of the pneumonia when CT scan is not available.

Indexed as

AgedAged, 80 and overBiomarkersCOVID-19C-Reactive ProteinFemaleFibrin Fibrinogen Degradation ProductsFranceHumansL-Lactate DehydrogenaseLymphocyte CountMaleMiddle AgedPneumonia, ViralRetrospective StudiesSARS-CoV-2BiomarkersC-Reactive ProteinFibrin Fibrinogen Degradation Productsfibrin fragment DL-Lactate DehydrogenaseCoronavirus 2019 diseaseCT-scanLeukocytes

Identifiers

PMID33166904
PMCPMC7644196

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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.