Evidence map›Paper›PMID 34159195›Full record

ArticleBioMed research international2021

Blood Biomarkers Predict Cardiac Workload Using Machine Learning.

Lan Shou, Wendy Wenyu Huang, Andrew Barszczyk, Si Jia Wu, Helen Han, Alex Waese-Perlman, Lulu Chen, Jing Wei, Hong Luo, Kang Lee

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BioMed research international, 2021. 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

10 authors.

Lan ShouThe Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, 58 Haishu Rd., Hangzhou, Zhejiang, China 311121.
Wendy Wenyu HuangApplied Psychology and Human Development, University of Toronto, 252 Bloor St. West, Toronto, Ontario, Canada M5S 1V6.
Andrew BarszczykDepartment of Physiology, University of Toronto, Medical Sciences Building, Rm. 3306. 1 King's College, Toronto, Ontario, Canada M5S 1A8.ORCID https://orcid.org/0000-0001-5341-9655
Si Jia WuApplied Psychology and Human Development, University of Toronto, 252 Bloor St. West, Toronto, Ontario, Canada M5S 1V6.
Helen HanApplied Psychology and Human Development, University of Toronto, 252 Bloor St. West, Toronto, Ontario, Canada M5S 1V6.
Alex Waese-PerlmanApplied Psychology and Human Development, University of Toronto, 252 Bloor St. West, Toronto, Ontario, Canada M5S 1V6.
Lulu ChenThe Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, 58 Haishu Rd., Hangzhou, Zhejiang, China 311121.
Jing WeiThe Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, 58 Haishu Rd., Hangzhou, Zhejiang, China 311121.
Hong LuoThe Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, 58 Haishu Rd., Hangzhou, Zhejiang, China 311121.ORCID https://orcid.org/0000-0001-5348-9119
Kang LeeApplied Psychology and Human Development, University of Toronto, 252 Bloor St. West, Toronto, Ontario, Canada M5S 1V6.ORCID https://orcid.org/0000-0001-5695-2615

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionRate 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.

methodsWe 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.

resultsWe found a fair positive correlation (Pearson

conclusionBlood 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.

Indexed as

Blood PressureHeart RateMachine LearningAdultAlgorithmsBiomarkersCardiovascular SystemChinaFemaleHeartHumansLinear ModelsMaleMiddle AgedNeutrophilsPhenotypeBiomarkers

Identifiers

PMID34159195
PMCPMC8187049

What OpenQuestion holds

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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.