Evidence map›Paper›PMID 38633836›Full record

ArticleAPL bioengineering2024

Implementation of an epicardial implantable MEMS sensor for continuous and real-time postoperative assessment of left ventricular activity in adult minipigs over a short- and long-term period.

C Zinno, F Agnesi, G D'Alesio, A Dushpanova, L Brogi, D Camboni, F Bernini, D Terlizzi, V Casieri, K Gabisonia and 9 more

Abstract read
In one paragraph

Article in APL bioengineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

19 authors.

C ZinnoThe BioRobotics Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.ORCID https://orcid.org/0000-0001-8873-8875
F AgnesiThe BioRobotics Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.ORCID https://orcid.org/0000-0003-1780-8084
G D'AlesioThe BioRobotics Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.
L BrogiBio@SNS, Scuola Normale Superiore, Pisa, Italy.ORCID https://orcid.org/0000-0003-3474-9266
D CamboniThe BioRobotics Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.ORCID https://orcid.org/0000-0002-6853-475X
F BerniniBioMedLab, Interdisciplinary Research Center "Health Science," Scuola Superiore Sant'Anna, Pisa, Italy.
D TerlizziFondazione Toscana "G. Monasterio," Pisa, Italy.ORCID https://orcid.org/0000-0002-4917-869X
V CasieriUnit of Translational Critical Care Medicine, Laboratory of Basic and Applied Medical Sciences, Interdisciplinary Research Center "Health Science," Scuola Superiore Sant'Anna, Pisa, Italy.
K GabisoniaBioMedLab, Interdisciplinary Research Center "Health Science," Scuola Superiore Sant'Anna, Pisa, Italy.ORCID https://orcid.org/0000-0002-4981-3673
L AlibrandiUnit of Translational Critical Care Medicine, Laboratory of Basic and Applied Medical Sciences, Interdisciplinary Research Center "Health Science," Scuola Superiore Sant'Anna, Pisa, Italy.ORCID https://orcid.org/0009-0002-6703-8129
C GrigoratosFondazione Toscana "G. Monasterio," Pisa, Italy.
J MagomajewDepartment of Chemistry, Fraunhofer Institute for Microengineering and Microsystems, 55129 Mainz, Germany.ORCID https://orcid.org/0009-0006-7478-6458
G D Aquaro
S SchmittDepartment of Chemistry, Fraunhofer Institute for Microengineering and Microsystems, 55129 Mainz, Germany.ORCID https://orcid.org/0000-0003-1484-6050
P DetempleDepartment of Chemistry, Fraunhofer Institute for Microengineering and Microsystems, 55129 Mainz, Germany.ORCID https://orcid.org/0009-0007-2407-1259
C M OddoThe BioRobotics Institute, Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy.ORCID https://orcid.org/0000-0002-1489-5701
V Lionetti

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The sensing of left ventricular (LV) activity is fundamental in the diagnosis and monitoring of cardiovascular health in high-risk patients after cardiac surgery to achieve better short- and long-term outcome. Conventional approaches rely on noninvasive measurements even if, in the latest years, invasive microelectromechanical systems (MEMS) sensors have emerged as a valuable approach for precise and continuous monitoring of cardiac activity. The main challenges in designing cardiac MEMS sensors are represented by miniaturization, biocompatibility, and long-term stability. Here, we present a MEMS piezoresistive cardiac sensor capable of continuous monitoring of LV activity over time following epicardial implantation with a pericardial patch graft in adult minipigs. In acute and chronic scenarios, the sensor was able to compute heart rate with a root mean square error lower than 2 BPM. Early after up to 1 month of implantation, the device was able to record the heart activity during the most important phases of the cardiac cycle (systole and diastole peaks). The sensor signal waveform, in addition, closely reflected the typical waveforms of pressure signal obtained via intraventricular catheters, offering a safer alternative to heart catheterization. Furthermore, histological analysis of the LV implantation site following sensor retrieval revealed no evidence of myocardial fibrosis. Our results suggest that the epicardial LV implantation of an MEMS sensor is a suitable and reliable approach for direct continuous monitoring of cardiac activity. This work envisions the use of this sensor as a cardiac sensing device in closed-loop applications for patients undergoing heart surgery.

Identifiers

PMID38633836
PMCPMC11023704

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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.