Evidence map›Paper›PMID 39133895›Full record

ArticleJornal brasileiro de nefrologia

Prediction of metabolic syndrome and its associated risk factors in patients with chronic kidney disease using machine learning techniques.

Jalila Andréa Sampaio Bittencourt, Carlos Magno Sousa Junior, Ewaldo Eder Carvalho Santana, Yuri Armin Crispim de Moraes, Erika Cristina Ribeiro de Lima Carneiro, Ariadna Jansen Campos Fontes, Lucas Almeida das Chagas, Naruna Aritana Costa Melo, Cindy Lima Pereira, Margareth Costa Penha and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Jornal brasileiro de nefrologia. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

14 authors.

Jalila Andréa Sampaio BittencourtUniversidade Federal do Maranhão, Departamento de Engenharia Eletrônica, Laboratório de Processamento da Informação Biológica, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0001-6294-8532
Carlos Magno Sousa JuniorUniversidade Federal do Maranhão, Departamento de Ciência da Computação, Laboratório de Aquisição e Processamento de Sinais, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0002-3384-2672
Ewaldo Eder Carvalho SantanaUniversidade Federal do Maranhão, Departamento de Ciência da Computação, Laboratório de Aquisição e Processamento de Sinais, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0002-8894-5353
Yuri Armin Crispim de MoraesUniversidade Federal do Maranhão, Departamento de Engenharia Eletrônica, Laboratório de Processamento da Informação Biológica, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0001-7308-8291
Erika Cristina Ribeiro de Lima CarneiroUniversidade Federal do Maranhão, Hospital Universitário, Centro de Prevenção de Doenças Renais, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0001-5785-2114
Ariadna Jansen Campos FontesUniversidade Federal do Maranhão, Centro de Ciências Biológicas e da Saúde, Laboratório de Imunofisiologia, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0002-8225-9926
Lucas Almeida das ChagasUniversidade Federal de São Paulo, Escola Paulista de Medicina, Departamento de Obstetrícia, São Paulo, SP, Brazil.ORCID http://orcid.org/0000-0002-5047-2300
Naruna Aritana Costa MeloUniversidade Federal do Maranhão, Laboratório de Ciências Biológicas, Laboratório de Genética e Biologia Molecular, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0002-1599-308X
Cindy Lima PereiraUniversidade Federal do Maranhão, Departamento de Engenharia Eletrônica, Laboratório de Processamento da Informação Biológica, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0002-6877-7321
Margareth Costa PenhaUniversidade Ceuma, Departamento de Biomedicina, Laboratório de Ciências Biomédicas, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0002-7677-1827
Nilviane PiresUniversidade Federal do Maranhão, Departamento de Engenharia Eletrônica, Laboratório de Processamento da Informação Biológica, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0003-0702-6783
Edward Araujo JúniorUniversidade Federal de São Paulo, Escola Paulista de Medicina, Departamento de Obstetrícia, São Paulo, SP, Brazil.ORCID http://orcid.org/0000-0002-6145-2532
Allan Kardec Duailibe Barros FilhoUniversidade Federal do Maranhão, Departamento de Engenharia Eletrônica, Laboratório de Processamento da Informação Biológica, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0002-1654-0955
Maria do Desterro Soares Brandão NascimentoUniversidade Federal do Maranhão, Departamento de Medicina, Programa de Pós-Graduação em Saúde do Adulto, São Luiz, MA, Brazil.ORCID http://orcid.org/0000-0003-2783-362X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionChronic kidney disease (CKD) and metabolic syndrome (MS) are recognized as public health problems which are related to overweight and cardiometabolic factors. The aim of this study was to develop a model to predict MS in people with CKD.

methodsThis was a prospective cross-sectional study of patients from a reference center in São Luís, MA, Brazil. The sample included adult volunteers classified according to the presence of mild or severe CKD. For MS tracking, the k-nearest neighbors (KNN) classifier algorithm was used with the following inputs: gender, smoking, neck circumference, and waist-to-hip ratio. Results were considered significant at p < 0.05.

resultsA total of 196 adult patients were evaluated with a mean age of 44.73 years, 71.9% female, 69.4% overweight, and 12.24% with CKD. Of the latter, 45.8% had MS, the majority had up to 3 altered metabolic components, and the group with CKD showed statistical significance in: waist circumference, systolic blood pressure, diastolic blood pressure, and fasting blood glucose. The KNN algorithm proved to be a good predictor for MS screening with 79% accuracy and sensitivity and 80% specificity (area under the ROC curve - AUC = 0.79).

conclusionThe KNN algorithm can be used as a low-cost screening method to evaluate the presence of MS in people with CKD.

Indexed as

Machine LearningMetabolic SyndromeRenal Insufficiency, ChronicAdultAlgorithmsBrazilCross-Sectional StudiesFemaleHumansMaleMiddle AgedProspective StudiesRisk Factors

Identifiers

PMID39133895
PMCPMC11318987

What OpenQuestion holds

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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.