Evidence map›Paper›PMID 35775003›Full record

ArticleFrontiers in medicine2022

Relationship Between Platelet Count and In-hospital Mortality in Adult Patients With COVID-19: A Retrospective Cohort Study.

Qilin Yang, Jun Gao, Xiaomei Zeng, Junyu Chen, Deliang Wen

Abstract read
In one paragraph

Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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.

Qilin YangDepartment of Critical Care, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Jun GaoDepartment of Nephrology, Peking University International Hospital, Beijing, China.
Xiaomei ZengDepartment of Critical Care, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Junyu ChenDepartment of Critical Care, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Deliang WenDepartment of Critical Care, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The coronavirus disease 2019 (COVID-19) has become a global pandemic. Systemic inflammation in COVID-19 patients has been associated with poor clinical outcome. This study aims to determine the relationship between platelet count and in-hospital mortality. Methods: The original data of this study were from article development and validation of a predictive model of in-hospital mortality in COVID-19 patients. In this secondary analysis, we adopted multi-variable logistic regression analyses and smooth curve fitting to assess the independent association between platelet count and in-hospital mortality. We further applied a two-piecewise linear regression model to examine the nonlinear association between platelet count and in-hospital mortality. Results: Of the 2006 patients, the average age of the participants was 65.9 ± 16.5 years and 42.6% were women. We observed a U-shaped relationship between platelet count and in-hospital mortality. We found two different slopes, the correlations between platelet count and in-hospital mortality of COVID-19 patients were totally different below and above the inflection point which was around 370 × 10 Conclusions: A U-shaped association between platelet count and in-hospital mortality was found in the patients with COVID-19. The optimal of platelet count associated with the lowest risk of in-hospital mortality was around 370 × 10

Indexed as

coronavirus disease 2019ferritinin-hospital mortalityplatelet countsystemic inflammation

Identifiers

PMID35775003
PMCPMC9238274

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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.