Evidence map›Paper›PMID 31442381›Full record

ArticleAmerican journal of physiology. Heart and circulatory physiology2019

Modeling arterial pulse waves in healthy aging: a database for in silico evaluation of hemodynamics and pulse wave indexes.

Peter H Charlton, Jorge Mariscal Harana, Samuel Vennin, Ye Li, Phil Chowienczyk, Jordi Alastruey

Abstract read
In one paragraph

Article in American journal of physiology. Heart and circulatory physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.

0numbers the graph read from it
0cells of the map it votes in
71citing 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

71 citing papers in PubMed.

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  7. [A multi-source information fusion-based method for the local pulse wave velocity estimation in carotid artery].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
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11 more citing papers are in PubMed but not listed here.

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.

Peter H CharltonDepartment of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.ORCID 0000-0003-3836-8655
Jorge Mariscal HaranaDepartment of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.ORCID 0000-0003-1507-8380
Samuel VenninDepartment of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.
Ye LiDepartment of Clinical Pharmacology, King's College London, King's Health Partners, London, United Kingdom.
Phil ChowienczykDepartment of Clinical Pharmacology, King's College London, King's Health Partners, London, United Kingdom.
Jordi AlastrueyDepartment of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom.ORCID 0000-0003-3742-5259

Funding

British Heart Foundation PG/15/104/31913British Heart Foundation PG/17/50/32903Wellcome Trust 203148/Z/16/Z
6 · The paper itself

Abstract

The arterial pulse wave (PW) is a rich source of information on cardiovascular (CV) health. It is widely measured by both consumer and clinical devices. However, the physical determinants of the PW are not yet fully understood, and the development of PW analysis algorithms is limited by a lack of PW data sets containing reference CV measurements. Our aim was to create a database of PWs simulated by a computer to span a range of CV conditions, representative of a sample of healthy adults. The typical CV properties of 25-75 yr olds were identified through a literature review. These were used as inputs to a computational model to simulate PWs for subjects of each age decade. Pressure, flow velocity, luminal area, and photoplethysmographic PWs were simulated at common measurement sites, and PW indexes were extracted. The database, containing PWs from 4,374 virtual subjects, was verified by comparing the simulated PWs and derived indexes with corresponding in vivo data. Good agreement was observed, with well-reproduced age-related changes in hemodynamic parameters and PW morphology. The utility of the database was demonstrated through case studies providing novel hemodynamic insights, in silico assessment of PW algorithms, and pilot data to inform the design of clinical PW algorithm assessments. In conclusion, the publicly available PW database is a valuable resource for understanding CV determinants of PWs and for the development and preclinical assessment of PW analysis algorithms. It is particularly useful because the exact CV properties that generated each PW are known.

Indexed as

Computer SimulationHealthy AgingHemodynamicsModels, CardiovascularPulse Wave AnalysisVascular StiffnessAdultAgedAge FactorsArteriesDatabases, FactualFemaleHumansMaleMiddle AgedNumerical Analysis, Computer-Assistedagingarteriesblood flowdatabase of virtual subjectspulse wave

Identifiers

PMID31442381
PMCPMC6879924

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.