Evidence map›Paper›PMID 36547437›Full record

ArticleJournal of cardiovascular development and disease2022

Blood Pressure Prediction Using Ensemble Rules during Isometric Sustained Weight Test.

Ramón Carrazana-Escalona, Adán Andreu-Heredia, María Moreno-Padilla, Gustavo A Reyes Del Paso, Miguel E Sánchez-Hechavarría, Gustavo Muñoz-Bustos

Open access · goldAbstract read
In one paragraph

Article in Journal of cardiovascular development and disease, 2022. 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
0.1field-weighted citation impact, top 62% of its field
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, 1 citations in OpenAlex.

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

6 authors at 4 institutions in 3 countries.

Ramón Carrazana-EscalonaDepartment of Basic Science, School of Medicine, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile.
Adán Andreu-HerediaDepartamento de Ciencias Básicas Biomédicas, Facultad de Medicina 1, Universidad de Ciencias Médicas de Santiago de Cuba, Santiago de Cuba 90100, Cuba.
María Moreno-PadillaDepartamento de Psicología, Universidad de Jaén, 23009 Jaén, Spain.
Gustavo A Reyes Del PasoDepartamento de Psicología, Universidad de Jaén, 23009 Jaén, Spain.ORCID 0000-0002-2188-8673
Miguel E Sánchez-HechavarríaDepartamento de Ciencias Clínicas y Preclínicas, Facultad de Medicina, Universidad Católica de la Santísima Concepción, Concepción 4090541, Chile.ORCID 0000-0001-9461-203X
Gustavo Muñoz-BustosFacultad de Ciencias de la Salud, Sede Concepción, Universidad de Las Américas, Concepción 4100000, Chile.ORCID 0000-0002-5351-3016
Universidad Católica de la Santísima Concepción · CLUniversidad de Jaén · ESUniversidad de Ciencias Médicas de la Habana · CUUniversity of Concepción · CL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPredicting beat-to-beat blood pressure has several clinical applications. While most machine learning models focus on accuracy, it is necessary to build models that explain the relationships of hemodynamical parameters with blood pressure without sacrificing accuracy, especially during exercise.

objectiveThe aim of this study is to use the RuleFit model to measure the importance, interactions, and relationships among several parameters extracted from photoplethysmography (PPG) and electrocardiography (ECG) signals during a dynamic weight-bearing test (WBT) and to assess the accuracy and interpretability of the model results.

methodsRuleFit was applied to hemodynamical ECG and PPG parameters during rest and WBT in six healthy young subjects. The WBT involves holding a 500 g weight in the left hand for 2 min. Blood pressure is taken in the opposite arm before and during exercise thereof.

resultsThe root mean square error of the model residuals was 4.72 and 2.68 mmHg for systolic blood pressure and diastolic blood pressure, respectively, during rest and 4.59 and 4.01 mmHg, respectively, during the WBT. Furthermore, the blood pressure measurements appeared to be nonlinear, and interaction effects were observed. Moreover, blood pressure predictions based on PPG parameters showed a strong correlation with individual characteristics and responses to exercise.

conclusionThe RuleFit model is an excellent tool to study interactions among variables for predicting blood pressure. Compared to other models, the RuleFit model showed superior performance. RuleFit can be used for predicting and interpreting relationships among predictors extracted from PPG and ECG signals.

Indexed as

blood pressureblood pressure predictionlinear regressionRuleFit model

Identifiers

PMID36547437
PMCPMC9781478
OpenAlexW4311723413

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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.