Evidence map›Paper›PMID 36718462›Full record

ArticleInfection and drug resistance2023

Platelet-to-White Blood Cell Ratio as a Predictor of Mortality in Patients with Severe COVID-19 Pneumonia: A Retrospective Cohort Study.

Metus Thungthienthong, Veerapong Vattanavanit

Open access · goldAbstract read
In one paragraph

Article in Infection and drug resistance, 2023. 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
2.3field-weighted citation impact, top 12% 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

8 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Review
  8. 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

2 authors at 1 institution in 1 country.

Metus ThungthienthongDivision of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand.ORCID 0000-0002-0258-5386
Veerapong VattanavanitCritical Care Medicine Unit, Division of Internal Medicine, Faculty of Medicine, Prince of Songkla University, Hat Yai, Songkhla, Thailand.ORCID 0000-0001-7528-8887
Prince of Songkla University · TH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Complete blood count (CBC) parameters are widely used as predictors of Coronavirus disease 2019 (COVID-19) severity. However, the clinical significance of these markers in severe COVID-19 pneumonia remains unclear. This study aimed to investigate the role of CBC parameters in predicting mortality in patients with severe COVID-19 pneumonia. Patients and Methods: We conducted a retrospective study at a tertiary care center in southern Thailand. Between January 2020 and December 2021, adult patients who had been diagnosed with severe COVID-19 pneumonia were enrolled. Demographic and clinical data, including CBC data on admission, were analyzed and compared between survivors and non-survivors. Results: A total of 215 patients with severe COVID-19 pneumonia were enrolled. The in-hospital mortality was 29.3%. Non-survivors had a significantly lower platelet-to-white blood cell ratio (PWR) than survivors (15.8 vs 29.0, p < 0.001). PWR had the best accuracy in predicting in-hospital mortality, with an area under the curve (AUC) of the receiver operating characteristic curve of 0.801, followed by the CURB-65 of 0.789. Conclusion: PWR appears to be a simple independent predictor of mortality in patients with severe COVID-19 pneumonia.

Indexed as

COVID-19mortalityplateletpneumoniawhite blood cell

Identifiers

PMID36718462
PMCPMC9884061
OpenAlexW4317814800

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.