Evidence map›Paper›PMID 39137408›Full record

ArticleJournal of medical Internet research2024

Using Topic Modeling to Understand Patients' and Caregivers' Perspectives About Left Ventricular Assist Device: Thematic Analysis.

Semyon Melnikov, Stav Klein, Moni Shahar, David Guy

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Semyon MelnikovNursing Department, Steyer School of Health Professions, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel.ORCID 0000-0002-2545-8435
Stav KleinThe Tel Aviv University Center for AI and Data Science, Tel Aviv University, Tel Aviv, Israel.ORCID 0009-0005-9170-9385
Moni ShaharThe Tel Aviv University Center for AI and Data Science, Tel Aviv University, Tel Aviv, Israel.ORCID 0000-0001-7512-9640
David GuySteyer School of Health Professions, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel.ORCID 0009-0000-9440-3983

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHeart failure (HF) is a significant global clinical and public health challenge, impacting 64.3 million individuals worldwide. To address the scarcity of donor organs, left ventricular assist device (LVAD) implantation has become a crucial intervention for managing end-stage HF, serving as a bridge to heart transplantation or as a destination therapy. Web-based health forums, such as MyLVAD.com, play a vital role as trusted sources of information for individuals with HF symptoms and their caregivers.

objectiveWe aim to uncover the latent topics within the posts shared by users on the MyLVAD.com website.

methodsUsing the latent Dirichlet allocation algorithm and a visualization tool, our objective was to uncover latent topics within the posts shared on the MyLVAD.com website. Through the application of topic modeling techniques, we analyzed 459 posts authored by recipients of LVAD and their family members from 2015 to 2023.

resultsThis study unveiled 5 prominent themes of concern among patients with LVAD and their family members. These themes included family support (39.5% weight value), encompassing subthemes such as family caregiving roles and emotional or practical support; clothing (23.9% weight value), with subthemes related to comfort, normalcy, and functionality; infection (18.2% weight value), covering driveline infections, prevention, and care; power (12% weight value), involving challenges associated with power dependency; and self-care maintenance, monitoring, and management (6.3% weight value), which included subthemes such as blood tests, monitoring, alarms, and device management.

conclusionsThese findings contribute to a better understanding of the experiences and needs of patients implanted with LVAD, providing valuable insights for health care professionals to offer tailored support and care. By using latent Dirichlet allocation to analyze posts from the MyLVAD.com forum, this study sheds light on key topics discussed by users, facilitating improved patient care and enhanced patient-provider communication.

Indexed as

CaregiversHeart-Assist DevicesHeart FailureHumanscardiovascularcardiovascular conditioncarecaregivercommunicationdevicedevices for hearthealth care forumheartheart diseaseheart failurelatent Dirichlet allocationleft ventricular assist deviceLVADmanagementmedical devicesmonitoringtooltopic modelingusersvisualization tool

Identifiers

PMID39137408
PMCPMC11350299

What OpenQuestion holds

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Registered trials

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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.