Literature DB >> 33466536

Implementation of a Savvy Mobile ECG Sensor for Heart Rhythm Disorder Screening at the Primary Healthcare Level: An Observational Prospective Study.

Staša Vodička1, Antonija Poplas Susič2, Erika Zelko3.   

Abstract

INTRODUCTION: The Jozef Stefan Institute developed a personal portable electrocardiogram (ECG) sensor Savvy that works with a smartphone, and this was used in our study. This study aimed to analyze the usefulness of telecardiology at the primary healthcare level using an ECG personal sensor.
METHODS: We included 400 patients with a history of suspected rhythm disturbance who visited their family physician at the Healthcare Center Ljubljana and Healthcare Center Murska Sobota from October 2016 to January 2018.
RESULTS: The study found that there was no statistically significant difference between the test and control groups in the number of present rhythm disorders and actions taken to treat patients with either observation or administration of a new drug. However, in the test group, there were significantly fewer patients being referred to a cardiologist than in the control group (p < 0.001). DISCUSSION: The use of an ECG sensor helps family physicians to distinguish between patients who need to be referred to a cardiologist and those who can be treated by them. This method is useful for both physicians and patients because it shortens the time taken to start treatment, can be used during pandemics such as COVID-19, and reduces unnecessary cost.

Entities:  

Keywords:  heart rhythm disorders; palpitations; personal mobile ECG sensor; primary healthcare; referrals

Year:  2021        PMID: 33466536      PMCID: PMC7824824          DOI: 10.3390/mi12010055

Source DB:  PubMed          Journal:  Micromachines (Basel)        ISSN: 2072-666X            Impact factor:   2.891


  30 in total

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7.  iPhone ECG screening by practice nurses and receptionists for atrial fibrillation in general practice: the GP-SEARCH qualitative pilot study.

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Authors:  Steven R Steinhubl; Jill Waalen; Alison M Edwards; Lauren M Ariniello; Rajesh R Mehta; Gail S Ebner; Chureen Carter; Katie Baca-Motes; Elise Felicione; Troy Sarich; Eric J Topol
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10.  Predicting atrial fibrillation in primary care using machine learning.

Authors:  Nathan R Hill; Daniel Ayoubkhani; Phil McEwan; Daniel M Sugrue; Usman Farooqui; Steven Lister; Matthew Lumley; Ameet Bakhai; Alexander T Cohen; Mark O'Neill; David Clifton; Jason Gordon
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  2 in total

1.  Differences in Treating Patients with Palpitations at the Primary Healthcare Level Using Telemedical Device Savvy before and during the COVID-19 Pandemic.

Authors:  Staša Vodička; Erika Zelko
Journal:  Micromachines (Basel)       Date:  2022-07-26       Impact factor: 3.523

2.  Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring.

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Journal:  Sensors (Basel)       Date:  2022-07-06       Impact factor: 3.847

  2 in total

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