Evidence map›Paper›PMID 35979974›Full record

ArticleTurkish journal of anaesthesiology and reanimation2022

Correlation of Clinical Course with Computed Tomography Findings and Biochemical Parameters at the Time of Admission in COVID-19 Patients.

Ezel Yaltırık Bilgin, Erkan Bilgin, Hatice Fidan, Yıldıray Çelenk, Tuğba Tok

Open access · goldAbstract read
In one paragraph

Article in Turkish journal of anaesthesiology and reanimation, 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
0.1field-weighted citation impact, top 58% 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

0 citing papers in PubMed, 1 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

5 authors at 2 institutions in 2 countries.

Ezel Yaltırık BilginDepartment of Radiology, Ereğli State Hospital, Zonguldak, Turkey.
Erkan BilginDepartment of Radiology, Ereğli State Hospital, Zonguldak, Turkey.
Hatice FidanDepartment of Anaesthesia and Intensive Care, Ereğli State Hospital, Zonguldak, Turkey.
Yıldıray ÇelenkDepartment of Emergency Medicine, Ereğli State Hospital, Zonguldak, Turkey.
Tuğba TokDepartment of Infectious Disease, Ereğli State Hospital, Zonguldak, Turkey.
State Hospital · GBBülent Ecevit University · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThe primary objective of our study was to evaluate the predictive performance of serum inflammatory markers and the semiquantitative computed tomography severity scoring system on diagnosing the Covid 2019 disease and its course.

methodsOur study is a single-center retrospective cohort study. The data of 213 adults who were confirmed to have coronavirus disease 2019 by polymerase chain reaction tests in the period between April 2020 and August 2020 were evaluated. One hundred eighty four of these patients whose C-reactive protein, d-dimer, and ferritin levels, lymphocyte counts, and thoracic computed tomography images were obtained at the time of admission were included in the study. The semi-quantitative computed tomography severity score was calculated for all patients.

resultsThe median age of the 184 patients included in the study was 51.5 (19-91) years. The incidence of intensive care need and mortality was 10.3% (n=19) and 5.4% (n=10), respectively. The intensive care need and mortality rate was significantly correlated with higher thoracic computed tomography involvement scores at admission. There was a statistically significant and positive correlation between the computed tomography scores and the C-reactive protein, d-dimer, and ferritin levels. Older age (>65 years-old) and thoracic computed tomography scores of 11 and higher were independent factors correlated with need for intensive care.

conclusionSerum inflammatory markers and semi-quantitative computed tomography severity scoring system were predictive in diagnosing the Covid 2019 disease and its course.

Identifiers

PMID35979974
PMCPMC9533072
OpenAlexW3194798551

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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.