Blood Biomarkers Predict Cardiac Workload Using Machine Learning.
Introduction. Rate pressure product (the product of heart rate and systolic blood pressure) is a measure of cardiac workload. Resting rate pressure product (rRPP) varies from one individual to the next, but its biochemical/cellular phenotype remains unknown. This study determined the degree to which...
| Publicado en: | BioMed Research International pp. 1 - 6 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/1/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150614507&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150614507 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/1/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 150614507 150614507 150614507 10.1155/2021/6172815 150614507 ppf: 1 ppct: 5 formats: fmt: @attributes: type: P tig: atl: Blood Biomarkers Predict Cardiac Workload Using Machine Learning. aug: au: Shou, Lan Huang, Wendy Wenyu Barszczyk, Andrew Wu, Si Jia Han, Helen Waese-Perlman, Alex Chen, Lulu Wei, Jing Luo, Hong Lee, Kang affil: The Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, 58 Haishu Rd., Hangzhou, Zhejiang, China, 311121 sug: subj: Biological Markers Blood Cardiac Output Machine Learning Utilization Coronary Circulation Heart Rate Physiology Human Algorithms Linear Regression Pearson's Correlation Coefficient Descriptive Statistics Confidence Intervals Models, Statistical Blood Glucose Proteins Blood Neutrophils Phenotype ab: Introduction. Rate pressure product (the product of heart rate and systolic blood pressure) is a measure of cardiac workload. Resting rate pressure product (rRPP) varies from one individual to the next, but its biochemical/cellular phenotype remains unknown. This study determined the degree to which an individual's biochemical/cellular profile as characterized by a standard blood panel is predictive of rRPP, as well the importance of each blood biomarker in this prediction. Methods. We included data from 55,730 participants in this study with complete rRPP measurements and concurrently collected blood panel information from the Health Management Centre at the Affiliated Hospital of Hangzhou Normal University. We used the XGBoost machine learning algorithm to train a tree-based model and then assessed its accuracy on an independent portion of the dataset and then compared its performance against a standard linear regression technique. We further determined the predictive importance of each feature in the blood panel. Results. We found a fair positive correlation (Pearson r) of 0.377 (95% CI: 0.375-0.378) between observed rRPP and rRPP predicted from blood biomarkers. By comparison, the performance for standard linear regression was 0.352 (95% CI: 0.351-0.354). The top three predictors in this model were glucose concentration, total protein concentration, and neutrophil count. Discussion/Conclusion. Blood biomarkers predict resting RPP when modeled in combination with one another; such models are valuable for studying the complex interrelations between resting cardiac workload and one's biochemical/cellular phenotype. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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