Evidence map›Paper›PMID 33910141›Full record

ArticleClinical imaging2021

Automated quantitative thin slice volumetric low dose CT analysis predicts disease severity in COVID-19 patients.

Mircea Gabriel Stoleriu, Michael Gerckens, Florian Obereisenbuchner, Iva Zaimova, Justin Hetrodt, Sarah-Christin Mavi, Felicitas Schmidt, Anna Auguste Schoenlebe, Katharina Heinig-Menhard, Ina Koch and 8 more

Abstract read
In one paragraph

Article in Clinical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

18 authors.

Mircea Gabriel StoleriuCenter for Thoracic Surgery Munich, Ludwig-Maximilians-University Munich (LMU) and Asklepios Lung Clinic Munich-Gauting, Marchioninistr, 15, 81377 Munich and Robert-Koch-Allee 2, 82131 Gauting, Germany; Comprehensive Pneumology Center, Helmholtz Center Munich, Max-Lebsche-Platz 31, 81377 Munich, Germany(1). Electronic address: stoleriu@helmholtz-muenchen.de.
Michael GerckensComprehensive Pneumology Center, Helmholtz Center Munich, Max-Lebsche-Platz 31, 81377 Munich, Germany(1); Department of Internal Medicine V, Ludwig-Maximilians-University Munich (LMU), Marchioninistr, 15, 81377 Munich, Germany.
Florian ObereisenbuchnerDepartment of Pneumology, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Iva ZaimovaDepartment of Pneumology, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Justin HetrodtDepartment of Pneumology, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Sarah-Christin MaviDepartment of Pneumology, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Felicitas SchmidtDepartment of Intensive Care Medicine, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Anna Auguste SchoenlebeDepartment of Intensive Care Medicine, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Katharina Heinig-MenhardDepartment of Intensive Care Medicine, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Ina KochCenter for Thoracic Surgery Munich, Ludwig-Maximilians-University Munich (LMU) and Asklepios Lung Clinic Munich-Gauting, Marchioninistr, 15, 81377 Munich and Robert-Koch-Allee 2, 82131 Gauting, Germany; Comprehensive Pneumology Center, Helmholtz Center Munich, Max-Lebsche-Platz 31, 81377 Munich, Germany(1).
Rudolf A JörresInstitute and Outpatient Clinic for Occupational, Social and Environmental Medicine, Ludwig-Maximilians-University Munich (LMU), Ziemssenstraße 1, 80336 Munich, Germany.
Judith SpiroDepartment of Radiology, Ludwig-Maximilians-University Munich (LMU), Marchioninistr, 15, 81377 Munich, Germany.
Lorenz NowakDepartment of Intensive Care Medicine, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Rudolf HatzCenter for Thoracic Surgery Munich, Ludwig-Maximilians-University Munich (LMU) and Asklepios Lung Clinic Munich-Gauting, Marchioninistr, 15, 81377 Munich and Robert-Koch-Allee 2, 82131 Gauting, Germany; Comprehensive Pneumology Center, Helmholtz Center Munich, Max-Lebsche-Platz 31, 81377 Munich, Germany(1).
Jürgen BehrComprehensive Pneumology Center, Helmholtz Center Munich, Max-Lebsche-Platz 31, 81377 Munich, Germany(1); Department of Pneumology, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany; Department of Internal Medicine V, Ludwig-Maximilians-University Munich (LMU), Marchioninistr, 15, 81377 Munich, Germany.
Wolfgang GesierichComprehensive Pneumology Center, Helmholtz Center Munich, Max-Lebsche-Platz 31, 81377 Munich, Germany(1); Department of Pneumology, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Marion Heiß-NeumannDepartment of Pneumology, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.
Julien DinkelComprehensive Pneumology Center, Helmholtz Center Munich, Max-Lebsche-Platz 31, 81377 Munich, Germany(1); Department of Radiology, Ludwig-Maximilians-University Munich (LMU), Marchioninistr, 15, 81377 Munich, Germany; Department of Radiology, Asklepios Lung Clinic Munich-Gauting, Robert-Koch-Allee 2, 82131 Gauting, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study aimed to identify predictive (bio-)markers for COVID-19 severity derived from automated quantitative thin slice low dose volumetric CT analysis, clinical chemistry and lung function testing.

methodsSeventy-four COVID-19 patients admitted between March 16th and June 3rd 2020 to the Asklepios Lung Clinic Munich-Gauting, Germany, were included in the study. Patients were categorized in a non-severe group including patients hospitalized on general wards only and in a severe group including patients requiring intensive care treatment. Fully automated quantification of CT scans was performed via IMBIO CT Lung Texture analysis™ software. Predictive biomarkers were assessed with receiver-operator-curve and likelihood analysis.

resultsFifty-five patients (44% female) presented with non-severe COVID-19 and 19 patients (32% female) with severe disease. Five fatalities were reported in the severe group. Accurate automated CT analysis was possible with 61 CTs (82%). Disease severity was linked to lower residual normal lung (72.5% vs 87%, p = 0.003), increased ground glass opacities (GGO) (8% vs 5%, p = 0.031) and increased reticular pattern (8% vs 2%, p = 0.025). Disease severity was associated with advanced age (76 vs 59 years, p = 0.001) and elevated serum C-reactive protein (CRP, 92.2 vs 36.3 mg/L, p < 0.001), lactate dehydrogenase (LDH, 485 vs 268 IU/L, p < 0.001) and oxygen supplementation (p < 0.001) upon admission. Predictive risk factors for the development of severe COVID-19 were oxygen supplementation, LDH >313 IU/L, CRP >71 mg/L, <70% normal lung texture, >12.5% GGO and >4.5% reticular pattern.

conclusionAutomated low dose CT analysis upon admission might be a useful tool to predict COVID-19 severity in patients.

Indexed as

COVID-19Cone-Beam Computed TomographyFemaleHumansLungMaleMiddle AgedRetrospective StudiesSARS-CoV-2Severity of Illness IndexTomography, X-Ray ComputedCOVID-19 pneumoniaCT analysisLow dose volumetric CT

Identifiers

PMID33910141
PMCPMC8058052

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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.