Evidence map›Paper›PMID 36560149›Full record

ArticleSensors (Basel, Switzerland)2022

Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge.

Tero Koivisto, Olli Lahdenoja, Tero Hurnanen, Juho Koskinen, Kamal Jafarian, Tuija Vasankari, Samuli Jaakkola, Tuomas O Kiviniemi, K E Juhani Airaksinen

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. 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

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

7 citing papers in PubMed.

  1. Article
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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

9 authors.

Tero KoivistoDepartment of Computing, University of Turku, 20500 Turku, Finland.
Olli LahdenojaDepartment of Computing, University of Turku, 20500 Turku, Finland.
Tero HurnanenDepartment of Computing, University of Turku, 20500 Turku, Finland.
Juho KoskinenDepartment of Computing, University of Turku, 20500 Turku, Finland.
Kamal JafarianCardioSignal Inc., 20100 Turku, Finland.
Tuija VasankariHeart Center, Turku University Hospital, University of Turku, 20520 Turku, Finland.
Samuli JaakkolaHeart Center, Turku University Hospital, University of Turku, 20520 Turku, Finland.ORCID 0000-0001-5944-6814
Tuomas O KiviniemiHeart Center, Turku University Hospital, University of Turku, 20520 Turku, Finland.ORCID 0000-0002-0908-3741
K E Juhani AiraksinenHeart Center, Turku University Hospital, University of Turku, 20520 Turku, Finland.ORCID 0000-0002-0193-568X

Funding

Business Finland 1788/31/2019
6 · The paper itself

Abstract

Heart failure (HF) is a disease related to impaired performance of the heart and is a significant cause of mortality and treatment costs in the world. During its progression, HF causes worsening (decompensation) periods which generally require hospital care. In order to reduce the suffering of the patients and the treatment cost, avoiding unnecessary hospital visits is essential, as hospitalization can be prevented by medication. We have developed a data-collection device that includes a high-quality 3-axis accelerometer and 3-axis gyroscope and a single-lead ECG. This allows gathering ECG synchronized data utilizing seismo- and gyrocardiography (SCG, GCG, jointly mechanocardiography, MCG) and comparing the signals of HF patients in acute decompensation state (hospital admission) and compensated condition (hospital discharge). In the MECHANO-HF study, we gathered data from 20 patients, who each had admission and discharge measurements. In order to avoid overfitting, we used only features developed beforehand and selected features that were not outliers. As a result, we found three important signs indicating the worsening of the disease: an increase in signal RMS (root-mean-square) strength (across SCG and GCG), an increase in the strength of the third heart sound (S3), and a decrease in signal stability around the first heart sound (S1). The best individual feature (S3) alone was able to separate the recordings, giving 85.0% accuracy and 90.9% accuracy regarding all signals and signals with sinus rhythm only, respectively. These observations pave the way to implement solutions for patient self-screening of the HF using serial measurements.

Indexed as

Heart FailurePatient DischargeHeartHospitalizationHospitalsHumansaccelerometerECGgyrocardiographygyroscopeheart failuremechanocardiographyseismocardiographyself-screeningserial measurements

Identifiers

PMID36560149
PMCPMC9783454

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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.