Evidence map›Paper›PMID 34900628›Full record

ArticleJournal of translational internal medicine2021

Joint Predictive Value of cTnI and NT-proBNP on Mortality in Patients with Coronavirus Disease 2019: A Retrospective Research in Wuhan, China.

Haoyu Weng, Fan Yang, Long Zhang, Han Jin, Shengcong Liu, Fangfang Fan, Zhihao Liu, Xizi Zheng, Hongyu Yang, Yuxi Li and 4 more

Open access · hybridAbstract read
In one paragraph

Article in Journal of translational internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 42% 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Effectiveness of Liu-zi-jue exercise on coronavirus disease 2019 in the patients: a randomized controlled trial.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2022
    Trial
  3. Article
  4. 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

14 authors at 3 institutions in 2 countries.

Haoyu WengDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Fan YangDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Long ZhangDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Han JinDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Shengcong LiuDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Fangfang FanDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Zhihao LiuDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Xizi ZhengDepartment of Nephrology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Hongyu YangDepartment of Nephrology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Yuxi LiDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Tieci YiDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Haichao LiDepartment of Respiratory and Critical Care Medicine, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Yan ZhangDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Jianping LiDepartment of Cardiology, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China.
Peking University · CNPeking University First Hospital · CNMinistry of Education · RO

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesThe pandemic of coronavirus disease 2019 (COVID-19) remains to be the biggest public threat all over the world. Because of the rapid deterioration in some patients, markers that could predict poor clinical outcomes are urgently required. This study was to evaluate the predictive values of cardiac injury parameters, including cardiac troponin I (cTnI) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, on mortality in COVID-19 patients.

methodsCOVID-19 patients in Zhongfaxincheng branch of Tongji Hospital (Wuhan, China) from February 8-28, 2020, were enrolled in this study. We followed up the patients for 30 days after admission.

resultsA total of 134 patients were included in the study. Multivariate Cox regression showed that 1) patients with elevated cTnI levels had a higher risk of death (hazard ratio [HR] 7.33, 95% confidence interval [CI] 2.56-21.00) than patients with normal cTnI levels; 2) patients with elevated NT-proBNP levels had a higher risk of death (HR 27.88, 95% CI 3.55-218.78) than patients with normal NT-proBNP levels; 3) patients with both elevated cTnI and NT-proBNP levels had a significantly higher risk of death (HR 53.87, 95% CI 6.31-459.91,

conclusionsIn COVID-19 patients, cTnI and NT-proBNP levels could be monitored to identify patients at a high risk of death in their later course of disease.

Indexed as

cardiac injurycoronavirus disease 2019cTnINT-proBNPpredictive

Identifiers

PMID34900628
PMCPMC8629414
OpenAlexW3204705903

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.