Evidence map›Paper›PMID 39497946›Full record

ArticleJACC. Advances2024

Performance of Ambulatory Electrocardiographic Data for Prediction of Stroke and Heart Failure Events.

Hannah T Schwennesen, Zhen Li, Bradley G Hammill, Amy G Clark, Sean Pokorney, Evangelos Hytopoulos, Mintu P Turakhia, Justin Cambra, Jonathan P Piccini

Abstract read
In one paragraph

Article in JACC. Advances, 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.

  1. Article
  2. 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

9 authors.

Hannah T SchwennesenCardiac Electrophysiology Section, Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.
Zhen LiDepartment of Population Health Sciences, Duke University, Durham, North Carolina, USA.
Bradley G HammillDepartment of Population Health Sciences, Duke University, Durham, North Carolina, USA.
Amy G ClarkDepartment of Population Health Sciences, Duke University, Durham, North Carolina, USA.
Sean PokorneyCardiac Electrophysiology Section, Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.
Evangelos HytopoulosiRhythm Technologies, San Francisco, California, USA.
Mintu P TurakhiaiRhythm Technologies, San Francisco, California, USA.
Justin CambraiRhythm Technologies, San Francisco, California, USA.
Jonathan P PicciniCardiac Electrophysiology Section, Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Despite clear associations between arrhythmia burden and cardiovascular risk, clinical risk scores that predict cardiovascular events do not incorporate individual-level arrhythmia characteristics from long-term continuous monitoring (LTCM). Objectives: This study evaluated the performance of risk models that use data from LTCM and patient claims for prediction of heart failure (HF) and ischemic stroke. Methods: We retrospectively analyzed features extracted from up to 14 days of LTCM electrocardiogram (ECG) data linked to patient-level claims data for 320,974 Medicare beneficiaries who underwent ZioXT ambulatory monitoring. We created predictive models for HF hospitalization, stroke hospitalization, and new-onset HF within 1 year using LASSO Cox regression for variable selection among ambulatory ECG variables and components of the CHA Results: A model that included components of the CHA Conclusions: The CHA

Indexed as

ambulatory ECG monitoringarrhythmiaCHA2DS2-VASchospitalizationrisk prediction

Identifiers

PMID39497946
PMCPMC11533076

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.