Evidence map›Paper›PMID 39436486›Full record

ReviewHeart failure reviews2025

Noninvasive biometric monitoring technologies for patients with heart failure.

Jose Arriola-Montenegro, Pornthira Mutirangura, Hassan Akram, Adamantios Tsangaris, Despoina Koukousaki, Michael Tschida, Joel Money, Marinos Kosmopoulos, Mikako Harata, Andrew Hughes and 2 more

Abstract readReview
PubMed Publisher
In one paragraph

Review in Heart failure reviews, 2025. 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

12 authors.

Jose Arriola-MontenegroDepartment of Medicine, University of Minnesota, Minneapolis, MN, USA.
Pornthira MutiranguraDepartment of Medicine, University of Minnesota, Minneapolis, MN, USA.
Hassan AkramDepartment of Medicine, University of Minnesota, Minneapolis, MN, USA.
Adamantios TsangarisDepartment of Medicine, Division of Cardiology, University of Minnesota, Minneapolis, MN, 55127, USA.
Despoina KoukousakiDepartment of Medicine, Division of Cardiology, University of Minnesota, Minneapolis, MN, 55127, USA.
Michael TschidaUniversity of Minnesota, Minneapolis, MN, USA.
Joel MoneyDepartment of Medicine, Division of Cardiology, University of Minnesota, Minneapolis, MN, 55127, USA.
Marinos KosmopoulosDepartment of Medicine, University of Minnesota, Minneapolis, MN, USA.
Mikako HarataDepartment of Medicine, University of Minnesota, Minneapolis, MN, USA.
Andrew HughesDepartment of Medicine, Division of Cardiology, University of Minnesota, Minneapolis, MN, 55127, USA.
Andras TothDepartment of Medical Imaging, University of Pecs, Pecs, Hungary.
Tamas AlexyDepartment of Medicine, Division of Cardiology, University of Minnesota, Minneapolis, MN, 55127, USA. alexy001@umn.edu.ORCID 0000-0001-8381-6299

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Heart failure remains one of the leading causes of mortality and hospitalizations in the US that not only impacts quality of life but also poses a significant public health burden. The majority of affected patients are admitted with signs and symptoms of congestion. Despite the initial enthusiasm, traditional remote monitoring strategies focusing primarily on weight gain failed to improve clinical outcomes. Implantable pulmonary artery pressure sensors provide earlier and actionable data, but most patients would favor forgoing an invasive procedure in favor of an alternative, non-invasive monitoring platform. Several devices utilizing different combinations of multiparameter monitoring to reliably detect congestion have recently been developed and are undergoing testing in the clinical setting. Combining these sensors with the power of artificial intelligence and machine learning has the potential to revolutionize remote patient monitoring and early congestion detection and to facilitate timely interventions by the care team to prevent hospitalization. This manuscript provides an objective review of novel, noninvasive, multiparameter remote monitoring platforms that may be tailored to individual heart failure phenotypes, aiming to improve quality of life and survival.

Indexed as

BiometryHeart FailureHumansMonitoring, PhysiologicQuality of LifeTelemedicineHeart failureMachine learningNoninvasive biometric monitoring technologies

Identifiers

PMID39436486

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.