Evidence map›Paper›PMID 41243757›Full record

ArticleClinical and translational science2025

Integrative Machine Learning and Bayesian Analysis Reveals Atrial Fibrillation as a Key Predictor of Severe COVID-19 Outcomes.

Young Sook Ku, Go Woon Lee, Hyundeok Seo, Jin Yeon Gil, Kyung Hyun Min, Jun Hyeob Kim, Jun Hyuk Park, Woorim Kim, Kyung Eun Lee

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical and translational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

9 authors.

Young Sook KuCollege of Pharmacy, Chungbuk National University, Cheongju, South Korea.
Go Woon LeeCollege of Pharmacy, Chungbuk National University, Cheongju, South Korea.
Hyundeok SeoCollege of Pharmacy, Kangwon National University, Chuncheon, South Korea.
Jin Yeon GilCollege of Pharmacy, Chungbuk National University, Cheongju, South Korea.
Kyung Hyun MinCollege of Pharmacy, Chungbuk National University, Cheongju, South Korea.
Jun Hyeob KimCollege of Pharmacy, Chungbuk National University, Cheongju, South Korea.
Jun Hyuk ParkCollege of Pharmacy, Chungbuk National University, Cheongju, South Korea.
Woorim KimCollege of Pharmacy, Kangwon National University, Chuncheon, South Korea.ORCID 0000-0001-7267-6745
Kyung Eun LeeCollege of Pharmacy, Chungbuk National University, Cheongju, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to identify predictors of critical outcomes, including mortality, in hospitalized COVID-19 patients treated with remdesivir, using statistical, machine learning, and Bayesian methods. A retrospective multicenter cohort of 1628 patients hospitalized between January 2021 and August 2022 was analyzed. Clinical data were collected from electronic medical records. Multivariable logistic regression, machine learning models (LightGBM, Elastic Net) with SHapley Additive exPlanations (SHAP), and Bayesian logistic regression were applied. Among the cohort, 14.5% experienced critical outcomes or death. Advanced age (≥ 65 years; aOR 3.950), atrial fibrillation (aOR 4.087), and kidney disease (aOR 1.939) were identified as significant predictors. Machine learning models achieved moderate predictive performance (AUROC: LightGBM 0.705, Elastic Net 0.698), with SHAP highlighting atrial fibrillation and age as key contributors. Bayesian analysis confirmed a strong association between atrial fibrillation and adverse outcomes (adjusted OR 5.121). Atrial fibrillation emerged as a consistent and strong predictor, underscoring its relevance in clinical risk assessment.

Indexed as

Atrial FibrillationCOVID-19COVID-19 Drug TreatmentMachine LearningAdenosine MonophosphateAgedAged, 80 and overAlanineAntiviral AgentsBayes TheoremFemaleHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentAdenosine MonophosphateAlanineAntiviral Agentsremdesiviratrial fibrillationBayesian analysisCOVID‐19machine learningrisk factor

Identifiers

PMID41243757
PMCPMC12620668

What Socratic 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 Socratic graph.