Evidence map›Paper›PMID 29850117›Full record

ArticleJournal of thoracic disease2018

Novel risk factors for the healthcare associated infections (HAIs) in patients with Stanford type A aortic dissection (TAAD).

Wen-Sen Chen, Bu-Qing Ni, Song-Qin Li, Yong-Feng Shao, Wei-Hong Zhang

Open access · hybridAbstract read
In one paragraph

Article in Journal of thoracic disease, 2018. 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
1.3field-weighted citation impact, top 19% 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

3 citing papers in PubMed, 11 citations in OpenAlex.

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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 1 country.

Wen-Sen ChenInfection Management Office, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Bu-Qing NiInfection Management Office, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Song-Qin LiInfection Management Office, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Yong-Feng ShaoInfection Management Office, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Wei-Hong ZhangInfection Management Office, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Nanjing Medical University · CNJiangsu Province Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRisk factors for healthcare associated infections (HAIs) following surgical repair of acute type A aortic dissection (TAAD) has not been well defined.

methodsThis was a retrospective study of patients treated between Jan 2013 and May 2016 at the large tertiary teaching hospital in Jiangsu, China. Logistic regression analysis was performed to investigate the association patients with acute TAAD who underwent ascending aortic and arch replacement under deep hypothermic circulatory arrest (DHCA) and healthcare associated infections during hospitalization.

resultsOf the final 210 patients with aortic dissection (AD) admitted to our hospital, 100 patients had TAAD (100/210, 47.62%), which were then allocated to the HAIs group (n=36) and Non-HAIs group (n=64). We found that DCHA >29 min [odds ratio (OR) =2.60, 95% confidential interval (CI), 1.01-6.80, P=0.048], preoperative PLT <171×10

conclusionsTAAD patients with longer DHCA time, lower preoperative PLT, higher serum D-dimer would significantly increase the risks after surgical repair of arch replacement.

Indexed as

Aortic dissection (AD)healthcare associated infection (HAI)risk factors

Identifiers

PMID29850117
PMCPMC5949482
OpenAlexW2795917869

What OpenQuestion holds

Textmetadata
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.