Evidence map›Paper›PMID 39619634›Full record

ArticleGlobal heart2024

Analysis of the Predictors of Mortality from Ischemic Heart Diseases in the Southern Region of Brazil: A Geographic Machine-Learning-Based Study.

Amanda de Carvalho Dutra, Lincoln Luis Silva, Isadora Martins Borba, Amanda Gubert Alves Dos Santos, Diogo Pinetti Marquezoni, Matheus Henrique Arruda Beltrame, Rogério do Lago Franco, Ualid Saleh Hatoum, Juliana Harumi Miyoshi, Gustavo Cezar Wagner Leandro and 4 more

Abstract readMulticenter Study
In one paragraph

Article in Global heart, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

The trial behind it

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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

14 authors.

Amanda de Carvalho DutraGraduation Program in Health Sciences, State University of Maringa, Parana, Brazil.ORCID 0000-0002-2372-7275
Lincoln Luis SilvaGraduation Program in Health Sciences, State University of Maringa, Parana, Brazil.ORCID 0000-0001-8445-0743
Isadora Martins BorbaDepartment of Medicine, State University of Maringa, Parana, Brazil.ORCID 0000-0003-4637-3286
Amanda Gubert Alves Dos SantosDepartment of Clinical Analysis and Biomedicine, State University of Maringa, Parana, Brazil.ORCID 0000-0001-7330-2905
Diogo Pinetti MarquezoniSão Paulo State University, São Paulo, Brazil.ORCID 0000-0001-6460-8486
Matheus Henrique Arruda BeltrameDepartment of Medicine, State University of Maringa, Parana, Brazil.ORCID 0000-0001-8685-264X
Rogério do Lago FrancoGraduation Program in Health Sciences, State University of Maringa, Parana, Brazil.ORCID 0000-0002-5926-6941
Ualid Saleh HatoumGraduation Program in Health Sciences, State University of Maringa, Parana, Brazil.ORCID 0000-0002-3375-2116
Juliana Harumi MiyoshiGraduation Program in pharmaceutical sciences, State University of Maringa, Parana, Brazil.ORCID 0000-0003-1234-9608
Gustavo Cezar Wagner LeandroGraduation Program in Health Sciences, State University of Maringa, Parana, Brazil.ORCID 0000-0002-5014-2387
Marcos Rogério BitencourtGraduation Program in Health Sciences, State University of Maringa, Parana, Brazil.ORCID 0000-0001-8116-2404
Oscar Kenji NiheiEducation, Letters and Health Center, State University of the West of Paraná, Parana, Brazil.ORCID 0000-0002-9156-7787
João Ricardo Nickenig VissociDuke Global Health Institute, Duke University, Durham, North Carolina, United States.ORCID 0000-0001-7276-0402
Luciano de AndradeGraduation Program in Health Sciences, State University of Maringa, Parana, Brazil.ORCID 0000-0003-2077-1518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Mortality due to ischemic heart disease (IHD) is heterogeneously distributed globally, and identifying the sites most affected by it is essential in developing strategies to mitigate the impact of the disease, despite the complexity resulting from the great diversity of variables involved. Objective: To analyze the predictability of IHD mortality using machine learning (ML) techniques in combination with geospatial analysis in southern Brazil. Methods: Ecological study using secondary and retrospective data on mortality due to ischemic heart disease (IHD) obtained from the Mortality Information Systems (SIM-DATASUS) de 2018 a 2022, covering 1,191 municipalities in the states of Paraná (399), Santa Catarina (295), and Rio Grande do Sul (497). Ordinary Least Squares Regression (OLS), Geographically Weighted Regression (GWR), Random Forest (RF), and Geographically Weighted Random Forest (GWRF) analyses were performed to verify the model with the best performance capable of identifying the most affected sites by the disease based on a set of predictors composed by variables of procedures and access to health. Results: In the analyzed period, there were 59,093 deaths, 65% of which were men, 82.7% were white, and 72.8% occurred between 60 and 70 years of age. Ischemic heart disease presented the highest mortality rates in the northwest and north regions of the state of Paraná, and in the central-east, southwest and southeast regions of Rio Grande do Sul, the latter state accounting for 41% of total deaths. The GWRF presented the best performance with R Conclusion: The GWRF identified spatial heterogeneity in the variation of geographic predictors, contrasting the limitation of linear regression models. The findings showed patterns of vulnerability in southern Brazil, suggesting the formulation of health policies to improve access to diagnostic and therapeutic resources, with the potential to reduce IHD mortality.

Indexed as

Machine LearningMyocardial IschemiaAdultAgedBrazilFemaleHumansMaleMiddle AgedRetrospective StudiesRisk FactorsSurvival RateEpidemiologyMyocardial IschemiaSpatial AnalysisSupervised Machine Learning

Identifiers

PMID39619634
PMCPMC11606396

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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.