Cargando…

Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?

Background: The guidelines recommend intensive blood pressure control. Randomized trials have focused on the relevance of the systolic blood pressure (SBP) lowering, leaving the safety of the diastolic blood pressure (DBP) reduction unresolved. There are data available which show that low DBP should...

Descripción completa

Detalles Bibliográficos
Autores principales: Siński, Maciej, Berka, Petr, Lewandowski, Jacek, Sobieraj, Piotr, Piechocki, Kacper, Paleczny, Bartłomiej, Siennicka, Agnieszka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785044/
https://www.ncbi.nlm.nih.gov/pubmed/36556072
http://dx.doi.org/10.3390/jcm11247454
_version_ 1784857957684477952
author Siński, Maciej
Berka, Petr
Lewandowski, Jacek
Sobieraj, Piotr
Piechocki, Kacper
Paleczny, Bartłomiej
Siennicka, Agnieszka
author_facet Siński, Maciej
Berka, Petr
Lewandowski, Jacek
Sobieraj, Piotr
Piechocki, Kacper
Paleczny, Bartłomiej
Siennicka, Agnieszka
author_sort Siński, Maciej
collection PubMed
description Background: The guidelines recommend intensive blood pressure control. Randomized trials have focused on the relevance of the systolic blood pressure (SBP) lowering, leaving the safety of the diastolic blood pressure (DBP) reduction unresolved. There are data available which show that low DBP should not stop clinicians from achieving SBP targets; however, registries and analyses of randomized trials present conflicting results. The purpose of the study was to apply machine learning (ML) algorithms to determine, whether DBP is an important risk factor to predict stroke, heart failure (HF), myocardial infarction (MI), and primary outcome in the SPRINT trial database. Methods: ML experiments were performed using decision tree, random forest, k-nearest neighbor, naive Bayesian, multi-layer perceptron, and logistic regression algorithms, including and excluding DBP as the risk factor in an unselected and selected (DBP < 70 mmHg) study population. Results: Including DBP as the risk factor did not change the performance of the machine learning models evaluated using accuracy, AUC, mean, and weighted F-measure, and was not required to make proper predictions of stroke, MI, HF, and primary outcome. Conclusions: Analyses of the SPRINT trial data using ML algorithms imply that DBP should not be treated as an independent risk factor when intensifying blood pressure control.
format Online
Article
Text
id pubmed-9785044
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-97850442022-12-24 Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control? Siński, Maciej Berka, Petr Lewandowski, Jacek Sobieraj, Piotr Piechocki, Kacper Paleczny, Bartłomiej Siennicka, Agnieszka J Clin Med Article Background: The guidelines recommend intensive blood pressure control. Randomized trials have focused on the relevance of the systolic blood pressure (SBP) lowering, leaving the safety of the diastolic blood pressure (DBP) reduction unresolved. There are data available which show that low DBP should not stop clinicians from achieving SBP targets; however, registries and analyses of randomized trials present conflicting results. The purpose of the study was to apply machine learning (ML) algorithms to determine, whether DBP is an important risk factor to predict stroke, heart failure (HF), myocardial infarction (MI), and primary outcome in the SPRINT trial database. Methods: ML experiments were performed using decision tree, random forest, k-nearest neighbor, naive Bayesian, multi-layer perceptron, and logistic regression algorithms, including and excluding DBP as the risk factor in an unselected and selected (DBP < 70 mmHg) study population. Results: Including DBP as the risk factor did not change the performance of the machine learning models evaluated using accuracy, AUC, mean, and weighted F-measure, and was not required to make proper predictions of stroke, MI, HF, and primary outcome. Conclusions: Analyses of the SPRINT trial data using ML algorithms imply that DBP should not be treated as an independent risk factor when intensifying blood pressure control. MDPI 2022-12-15 /pmc/articles/PMC9785044/ /pubmed/36556072 http://dx.doi.org/10.3390/jcm11247454 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Siński, Maciej
Berka, Petr
Lewandowski, Jacek
Sobieraj, Piotr
Piechocki, Kacper
Paleczny, Bartłomiej
Siennicka, Agnieszka
Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?
title Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?
title_full Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?
title_fullStr Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?
title_full_unstemmed Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?
title_short Answering Clinical Questions Using Machine Learning: Should We Look at Diastolic Blood Pressure When Tailoring Blood Pressure Control?
title_sort answering clinical questions using machine learning: should we look at diastolic blood pressure when tailoring blood pressure control?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9785044/
https://www.ncbi.nlm.nih.gov/pubmed/36556072
http://dx.doi.org/10.3390/jcm11247454
work_keys_str_mv AT sinskimaciej answeringclinicalquestionsusingmachinelearningshouldwelookatdiastolicbloodpressurewhentailoringbloodpressurecontrol
AT berkapetr answeringclinicalquestionsusingmachinelearningshouldwelookatdiastolicbloodpressurewhentailoringbloodpressurecontrol
AT lewandowskijacek answeringclinicalquestionsusingmachinelearningshouldwelookatdiastolicbloodpressurewhentailoringbloodpressurecontrol
AT sobierajpiotr answeringclinicalquestionsusingmachinelearningshouldwelookatdiastolicbloodpressurewhentailoringbloodpressurecontrol
AT piechockikacper answeringclinicalquestionsusingmachinelearningshouldwelookatdiastolicbloodpressurewhentailoringbloodpressurecontrol
AT palecznybartłomiej answeringclinicalquestionsusingmachinelearningshouldwelookatdiastolicbloodpressurewhentailoringbloodpressurecontrol
AT siennickaagnieszka answeringclinicalquestionsusingmachinelearningshouldwelookatdiastolicbloodpressurewhentailoringbloodpressurecontrol