Evidence map›Paper›PMID 38223728›Full record

ArticleHeliyon2024

Meta-analysis of the systemic immune-inflammatory index and in-hospital mortality of COVID-19 patients.

Hao Yuan, Jing Tian, Lu Wen

Abstract read
In one paragraph

Article in Heliyon, 2024. 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.

  1. COVID-19 and Diabetes: Clinical Symptoms, Acute Kidney Injury, Inflammatory Response, and Poor Prognosis Factors.Medical science monitor : international medical journal of experimental and clinical research · 2026
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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

3 authors.

Hao YuanDepartment of Respiratory and Critical Care Medicine, The Fourth Hospital of Changsha, Changsha, China.
Jing TianDepartment of Respiratory and Critical Care Medicine, The Fourth Hospital of Changsha, Changsha, China.
Lu WenDepartment of Hospital Infection Management, The Fourth Hospital of Changsha, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The potential significance of immunoinflammatory factors in the prognosis of individuals afflicted with coronavirus disease 2019 (COVID-19) is worthy of examination. The systemic immune-inflammatory index (SII), a recently developed immunoinflammatory metric based on the enumeration of neutrophils, platelets, and lymphocytes in blood samples, holds promise for elucidating this relationship. Consequently, in order to explore any possible correlation between the SII levels at admission and the in-hospital mortality of patients with COVID-19, we undertook a thorough systematic review and meta-analysis. Methods: In pursuit of accomplishing the aim of this meta-analysis, an extensive search was conducted to seek out pertinent observational studies featuring longitudinal follow-up across PubMed, Cochrane Library, Embase and the Web of Science databases. The I Results: Our analysis included sixteen studies, encompassing 10,007 hospitalized COVID-19 patients. Among them, 1801 patients (18.0 %) succumbed during hospitalization. The pooled results indicated that a high SII at admission was substantially linked to a higher risk of all-cause mortality (risk ratio [RR]: 2.41, 95 % confidence interval: 1.78 to 3.24, Conclusion: The results of our study indicate that a high SII upon admission could potentially function as a prognostic indicator for mortality during hospitalization in patients diagnosed with COVID-19, particularly in individuals categorized as low risk.

Indexed as

Coronavirus disease 2019Meta-analysisMortalityPrognosisSystemic immune-inflammatory index

Identifiers

PMID38223728
PMCPMC10784146

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.