Dynamic risk stratification of worsening heart failure using a deep learning-enabled implanted ambulatory single-lead electrocardiogram.

James Philip Howard, Neethu Vasudevan, Shantanu Sarkar, Sean Landman, Jodi Koehler, Daniel Keene

Abstract read

In one paragraph

Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing 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

2 citing papers in PubMed.

Joint analysis of hemoglobin-to-RDW and creatinine-to-albumin ratios for mortality prediction in critical heart failure.iScience · 2026
Article
Prognostic Significance of AI-Enhanced ECG for Emergency Department Patients.Diagnostics (Basel, Switzerland) · 2025
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

6 authors.

James Philip HowardNational Heart and Lung Institute, Imperial College London, Du Cane Road, W12 0HS, London, UK.ORCID https://orcid.org/0000-0002-9989-6331

Neethu VasudevanResearch and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA.

Shantanu SarkarResearch and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA.

Sean LandmanResearch and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA.

Jodi KoehlerResearch and Technology, Cardiac Rhythm Management, Medtronic Inc., Minneapolis, MN, USA.

Daniel KeeneNational Heart and Lung Institute, Imperial College London, Du Cane Road, W12 0HS, London, UK.

Funding

British Heart Foundation FS/ICRF/22/26039

6 · The paper itself

Abstract

Aims: Implantable loop recorders (ILRs) provide continuous single-lead ambulatory electrocardiogram (aECG) monitoring. Whether these aECGs could be used to identify worsening heart failure (HF) is unknown. Methods and results: We linked ILR aECG from Medtronic device database to the left ventricular ejection fraction (LVEF) measurements in Optum Conclusion: An AI algorithm trained to detect LVEF ≤40% using ILR aECGs can also readily identify patients at increased risk of HF hospitalizations by monitoring changes in the probability of HF over 30 days.

Indexed as

Deep learningEjection fractionElectrocardiogramHeart failureNeural network

Identifiers

PMID39081943

DOI10.1093/ehjdh/ztae035

PMCPMC11284004

What OpenQuestion holds

Textmetadata

LicenceCC BY

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 OpenQuestion graph.