Evidence map›Paper›PMID 38789490›Full record

ArticleScientific reports2024

COVID-19 mortality prediction in Hungarian ICU settings implementing random forest algorithm.

Ágoston Hamar, Daryan Mohammed, Alex Váradi, Róbert Herczeg, Norbert Balázsfalvi, Béla Fülesdi, István László, Lídia Gömöri, Péter Attila Gergely, Gabor Laszlo Kovacs and 2 more

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In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

12 authors.

Ágoston HamarDepartment of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary.
Daryan MohammedMolecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary.
Alex VáradiMolecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary.
Róbert HerczegMolecular Medicine Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary.
Norbert BalázsfalviDepartment of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary.
Béla FülesdiDepartment of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary.
István LászlóDepartment of Anaesthesiology and Intensive Care, University of Debrecen, Debrecen, Hungary.
Lídia GömöriDoctoral School of Neuroscience, University of Debrecen, Debrecen, Hungary.
Péter Attila GergelyInstitute of Forensic Medicine, University of Debrecen, Debrecen, Hungary.
Gabor Laszlo KovacsDepartment of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary.
Krisztián JáksóDepartment of Anaesthesiology and Intensive Care, Clinical Centre, University of Pécs, Pécs, Hungary.
Katalin GombosDepartment of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary. Gombos.katalin@pte.hu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emergence of newer SARS-CoV-2 variants of concern (VOCs) profoundly changed the ICU demography; this shift in the virus's genotype and its correlation to lethality in the ICUs is still not fully investigated. We aimed to survey ICU patients' clinical and laboratory parameters in correlation with SARS-CoV-2 variant genotypes to lethality. 503 COVID-19 ICU patients were included in our study beginning in January 2021 through November 2022 in Hungary. Furthermore, we implemented random forest (RF) as a potential predictor regarding SARS-CoV-2 lethality among 649 ICU patients in two ICU centers. Survival analysis and comparison of hypertension (HT), diabetes mellitus (DM), and vaccination effects were conducted. Logistic regression identified DM as a significant mortality risk factor (OR: 1.55, 95% CI 1.06-2.29, p = 0.025), while HT showed marginal significance. Additionally, vaccination demonstrated protection against mortality (p = 0.028). RF detected lethality with 81.42% accuracy (95% CI 73.01-88.11%, [AUC]: 91.6%), key predictors being PaO

Indexed as

COVID-19Intensive Care UnitsSARS-CoV-2AdultAgedAlgorithmsFemaleHumansHungaryHypertensionMaleMiddle AgedRandom ForestRisk FactorsCOVID-19Intensive care unitMachine learningMortality predictionRandom forestSARS-CoV-2

Identifiers

PMID38789490
PMCPMC11126653

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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.