Evidence map›Paper›PMID 42173944›Full record

Observational studyScientific reports2026

Machine-learning-based cardiovascular mortality prediction using a cumulative PM

Agata M Wijata, Gregory Y H Lip, Anna Kurasz, Michał Święczkowski, Piotr Jemielita, Paweł Kralisz, Sławomir Dobrzycki, Jakub Nalepa, Łukasz Kuźma

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

Observational study in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Agata M WijataFaculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland.
Gregory Y H LipDepartment of Cardiology, Lipidology and Internal Medicine with Intensive Coronary Care Unit, Medical University of Bialystok, Bialystok, Poland.ORCID http://orcid.org/0000-0002-7566-1626
Anna KuraszDepartment of Invasive Cardiology and Internal Medicine, Medical University of Białystok, 1 Kilińskiego St, 15-089, Białystok, Poland.
Michał ŚwięczkowskiDepartment of Invasive Cardiology and Internal Medicine, Medical University of Białystok, 1 Kilińskiego St, 15-089, Białystok, Poland. mswieczkowski1@gmail.com.
Piotr JemielitaDepartment of Invasive Cardiology and Internal Medicine, Medical University of Białystok, 1 Kilińskiego St, 15-089, Białystok, Poland.
Paweł KraliszDepartment of Invasive Cardiology and Internal Medicine, Medical University of Białystok, 1 Kilińskiego St, 15-089, Białystok, Poland.
Sławomir DobrzyckiDepartment of Invasive Cardiology and Internal Medicine, Medical University of Białystok, 1 Kilińskiego St, 15-089, Białystok, Poland.
Jakub NalepaDepartment of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
Łukasz KuźmaLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, UK.

Funding

Narodowe Centrum Nauki UMO-2021/41/B/NZ7/03716
6 · The paper itself

Abstract

Classical risk factors for cardiovascular disease (CVD) are well established. Although the association between air pollution (AP) and CVD is also well recognized, its integration into cardiovascular mortality risk prediction models remains limited, particularly in the context of machine learning approaches. We conducted a study within the prospective EP-PARTICLES cohort to quantify the improvement in CVD mortality risk prediction achieved by integrating environmental factors into prediction models using the 'ePM-years Index' which reflects the proportion of cumulative long-term PM

Indexed as

Air PollutionCardiovascular DiseasesEnvironmental ExposureMachine LearningParticulate MatterAgedAir PollutantsFemaleHumansMaleMiddle AgedPrediction AlgorithmsPredictive Learning ModelsRetrospective StudiesRisk AssessmentRisk FactorsAir PollutantsParticulate MatterAir pollutionCardiovascular diseasesEnvironmental factorsePM-years IndexMachine learningPredictive modelsRisk factors

Identifiers

PMID42173944
PMCPMC13332242

What OpenQuestion 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 OpenQuestion graph.