Evidence map›Paper›PMID 35346297›Full record

ArticleJournal of cardiothoracic surgery2022

Can really plasma interleukin-6 predict postoperative delirium among patients undergoing open repair surgery of acute type A aortic dissection?

Wen-He Yang, Fu-Shan Xue, Cheng-Wen Li

Open access · goldAbstract readLetter
In one paragraph

Article in Journal of cardiothoracic surgery, 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
–field-weighted citation impact, top 97% 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, 0 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

3 authors at 1 institution in 1 country.

Wen-He YangDepartment of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, NO. 95 Yong-An Road, Xi-Cheng, Beijing, 100050, People's Republic of China.
Fu-Shan XueDepartment of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, NO. 95 Yong-An Road, Xi-Cheng, Beijing, 100050, People's Republic of China. xuefushan@aliyun.com.ORCID http://orcid.org/0000-0002-1028-6036
Cheng-Wen LiDepartment of Anesthesiology, Beijing Friendship Hospital, Capital Medical University, NO. 95 Yong-An Road, Xi-Cheng, Beijing, 100050, People's Republic of China.
Capital Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The letter to the editor was written in response to "Plasma interleukin-6 is a potential predictive biomarker for postoperative delirium (POD) among acute type a aortic dissection patients treated with open surgical repair", which is recently published by Lv et al. (J Cardiothorac Surg 16(1):146, 2021). In this article, Lv et al. conclude that plasma IL-6 is a potential biomarker for prediction of POD. However, we note several issues in this study that would have made interpretation of their results questionable. Our main concerns include the use of a short POD assessment time, no providing the data of analgesics and sedatives used in the ICU, application of incorrect statistical methods when assessing predictive ability of plasma IL-6 for the development of POD, and incorrect interpretation for the area under the receiver operating characteristic curve. We believe that addressing these issues will improve the transparency of this study and help the interpretation of findings.

Indexed as

Aortic DissectionDeliriumBiomarkersHumansInterleukin-6ROC CurveBiomarkersInterleukin-6Acute aortic dissectionBiomarkerInterleukin-6Postoperative deliriumPrediction

Identifiers

PMID35346297
PMCPMC8962087
OpenAlexW4220768847

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.