Evidence map›Paper›PMID 25151397›Full record

ArticleMedical & biological engineering & computing2014

Patient-specific generation of the Purkinje network driven by clinical measurements of a normal propagation.

Christian Vergara, Simone Palamara, Domenico Catanzariti, Fabio Nobile, Elena Faggiano, Cesarino Pangrazzi, Maurizio Centonze, Massimiliano Maines, Alfio Quarteroni, Giuseppe Vergara

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Article in Medical & biological engineering & computing, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Christian VergaraDipartimento di Ingegneria, Università di Bergamo, Viale Marconi 5, 24044, Dalmine, BG, Italy, christian.vergara@unibg.it.
Simone Palamara
Domenico Catanzariti
Fabio Nobile
Elena Faggiano
Cesarino Pangrazzi
Maurizio Centonze
Massimiliano Maines
Alfio Quarteroni
Giuseppe Vergara

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The propagation of the electrical signal in the Purkinje network is the starting point for the activation of the ventricular muscular cells leading to the contraction of the ventricle. In the computational models, describing the electrical activity of the ventricle is therefore important to account for the Purkinje fibers. Until now, the inclusion of such fibers has been obtained either by using surrogates such as space-dependent conduction properties or by generating a network based on an a priori anatomical knowledge. The aim of this work was to propose a new method for the generation of the Purkinje network using clinical measures of the activation times on the endocardium related to a normal electrical propagation, allowing to generate a patient-specific network. The measures were acquired by means of the EnSite NavX system. This system allows to measure for each point of the ventricular endocardium the time at which the activation front, that spreads through the ventricle, has reached the subjacent muscle. We compared the accuracy of the proposed method with the one of other strategies proposed so far in the literature for three subjects with a normal electrical propagation. The results showed that with our method we were able to reduce the absolute errors, intended as the difference between the measured and the computed data, by a factor in the range 9-25 %, with respect to the best of the other strategies. This highlighted the reliability of the proposed method and the importance of including a patient-specific Purkinje network in computational models.

Indexed as

Action PotentialsComputer SimulationHumansModels, CardiovascularNeuromuscular JunctionPurkinje FibersTime Factors

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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.